Title: | Tools for Working With Synteny Objects |
---|---|
Description: | Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects. |
Authors: | Nicholas Cooley [aut, cre] , Aidan Lakshman [aut, ctb] , Adelle Fernando [ctb], Erik Wright [aut] |
Maintainer: | Nicholas Cooley <[email protected]> |
License: | GPL-3 |
Version: | 1.17.0 |
Built: | 2024-06-30 02:41:46 UTC |
Source: | https://github.com/bioc/SynExtend |
Wrapper to run BLAST queries using the commandline BLAST tool directly from R.
Can operate on an XStringSet
or a FASTA
file.
This function requires the BLAST+ commandline tools, which can be downloaded here.
BlastSeqs(seqs, BlastDB, blastType=c('blastn', 'blastp', 'tblastn', 'blastx', 'tblastx'), extraArgs='', verbose=TRUE)
BlastSeqs(seqs, BlastDB, blastType=c('blastn', 'blastp', 'tblastn', 'blastx', 'tblastx'), extraArgs='', verbose=TRUE)
seqs |
Sequence(s) to run BLAST query on. This can be either an
|
BlastDB |
Path to FASTA file in a pre-built BLAST Database. These can be built using either |
blastType |
Type of BLAST query to run. See 'Details' for more information on available types. |
extraArgs |
Additional arguments to be passed to the BLAST query executed on the command line. This should be a single character string. |
verbose |
Should output be displayed? |
BLAST implements multiple types of search. Available types are the following:
blastn
: Nucleotide sequences against database of nucleotide sequences
blastp
: Protein sequences against database of protein sequences
tblastn
: Protein sequences against translated database of nucleotide sequences
blastx
: Translated nucleotide sequences against database of protein sequences
tblastx
: Translated nucleotide sequences against translated database of nucleotide sequences
Different BLAST queries require different inputs. The function will throw an error if the input data does not match expected input for the requested query type.
Input sequences for blastn
,
blastx
, and tblastx
should be nucleotide data.
Input sequences for
blastp
and tblastn
should be amino acid data.
Database for blastn
, tblastn
, tblastx
should be nucleotide data.
Database for blastp
and blastx
should be amino acid data.
Returns a data frame (data.frame) of results of the BLAST query.
Aidan Lakshman [email protected]
#
#
PairSummaries
object.
Attempt to expand blocks of paired features in a PairSummaries
object.
BlockExpansion(Pairs, GapTolerance = 4L, DropSingletons = FALSE, Criteria = "PID", Floor = 0.5, NewPairsOnly = TRUE, DBPATH, Verbose = FALSE)
BlockExpansion(Pairs, GapTolerance = 4L, DropSingletons = FALSE, Criteria = "PID", Floor = 0.5, NewPairsOnly = TRUE, DBPATH, Verbose = FALSE)
Pairs |
An object of class |
GapTolerance |
Integer value indicating the |
DropSingletons |
Ignore solo pairs when planning expansion routes. Set to |
Criteria |
Either “PID” or “Score”, indicating which metric to use to keep or reject pairs. |
Floor |
Lower PID limit for keeping a pair that was evaluated during expansion. |
NewPairsOnly |
Logical indicating whether or not to return only the pairs that were kept from all expansion attempts, or to return a |
DBPATH |
A file or connection pointing to the |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
BlockExpansion
uses a naive expansion algorithm to attempt to fill in gaps in blocks of paired features and to attempt to expand blocks of paired features.
An object of class PairSummaries
.
Nicholas Cooley [email protected]
PairSummaries
, NucleotideOverlap
, link{SubSetPairs}
, FindSynteny
# this function will be deprecated soon, # please see the new ExpandDiagonal() function. library(RSQLite) DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Pairs <- PairSummaries(SyntenyLinks = Endosymbionts_LinkedFeatures, PIDs = TRUE, Score = TRUE, DBPATH = DBPATH, Verbose = TRUE) data("Endosymbionts_Pairs01", package = "SynExtend") Pairs02 <- BlockExpansion(Pairs = Pairs, NewPairsOnly = FALSE, DBPATH = DBPATH, Verbose = TRUE)
# this function will be deprecated soon, # please see the new ExpandDiagonal() function. library(RSQLite) DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Pairs <- PairSummaries(SyntenyLinks = Endosymbionts_LinkedFeatures, PIDs = TRUE, Score = TRUE, DBPATH = DBPATH, Verbose = TRUE) data("Endosymbionts_Pairs01", package = "SynExtend") Pairs02 <- BlockExpansion(Pairs = Pairs, NewPairsOnly = FALSE, DBPATH = DBPATH, Verbose = TRUE)
Take in a PairSummaries
object and reject predicted pairs that conflict with syntenic blocks either locally or globally.
BlockReconciliation(Pairs, ConservativeRejection = TRUE, Precedent = "Size", PIDThreshold = NULL, SCOREThreshold = NULL, Verbose = FALSE)
BlockReconciliation(Pairs, ConservativeRejection = TRUE, Precedent = "Size", PIDThreshold = NULL, SCOREThreshold = NULL, Verbose = FALSE)
Pairs |
A |
ConservativeRejection |
A logical defaulting to |
Precedent |
A character vector of length 1, defaulting to “Size”. Selector for whether function attempts to reconcile with block size as precedent, or mean block PID as precedent. Currently “Metric” will select mean block PID to set block precedent. Blocks of size 1 cannot reject other blocks. The default behavior causes the rejection of any set of predicted pairs that conflict with a larger block of predicted pairs. Switching to “Metric” changes this behavior to any block of size 2 or greater will reject any predicted pair that both conflicts with the current block, and is part of a block with a lower mean PID. |
PIDThreshold |
Defaults to |
SCOREThreshold |
Defaults to |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
If a given PairSummaries
object contains predicted pairs that conflict, i.e. imply paralogy, or an “incorrect” and a “correct” ortholog prediction, these predictions will be reconciled. The function scrolls through pairs based on the size of the syntenic block that they are part of, from largest to smallest. When ConservativeRejection
is TRUE
only predicted pairs that exist within the syntenic block “space” will be removed, this option leaves room for conflicting predictions to remain if they are non-local to each other, or are on different indices. When ConservativeRejection
is FALSE
any pair that conflicts with a larger syntenic block will be rejected. This option forces only 1-1 feature pairings, for features are part of any syntenic block. Predicted pairs that represent a syntenic block size of 1 feature will not reject other pairs. PIDThreshold
and SCOREThreshold
can be used to retain pairs that would otherwise be rejected based on available assessments of their pairwise alignment.
A data.frame of class “data.frame” and “PairSummaries” of paired genes that are connected by syntenic hits. Contains columns describing the k-mers that link the pair. Columns “p1” and “p2” give the location ids of the the genes in the pair in the form “DatabaseIdentifier_ContigIdentifier_GeneIdentifier”. “ExactMatch” provides an integer representing the exact number of nucleotides contained in the linking k-mers. “TotalKmers” provides an integer describing the number of distinct k-mers linking the pair. “MaxKmer” provides an integer describing the largest k-mer that links the pair. A column titled “Consensus” provides a value between zero and 1 indicating whether the kmers that link a pair of features are in the same position in each feature, with 1 indicating they are in exactly the same position and 0 indicating they are in as different a position as is possible. The “Adjacent” column provides an integer value ranging between 0 and 2 denoting whether a feature pair's direct neighbors are also paired. Gap filled pairs neither have neighbors, or are included as neighbors. The “TetDist” column provides the euclidean distance between oligonucleotide - of size 4 - frequences between predicted pairs. “PIDType” provides a character vector with values of “NT” where either of the pair indicates it is not a translatable sequence or “AA” where both sequences are translatable. If users choose to perform pairwise alignments there will be a “PID” column providing a numeric describing the percent identity between the two sequences. If users choose to predict PIDs using their own, or a provided model, a “PredictedPID” column will be provided.
Nicholas Cooley [email protected]
FindSynteny
, Synteny-class
, PairSummaries
# this function will be deprecated soon... ## Not run: data("Endosymbionts_Pairs02", package = "SynExtend") Pairs03 <- BlockReconciliation(Pairs = Endosymbionts_Pairs02, ConservativeRejection = FALSE, Verbose = TRUE) ## End(Not run)
# this function will be deprecated soon... ## Not run: data("Endosymbionts_Pairs02", package = "SynExtend") Pairs03 <- BlockReconciliation(Pairs = Endosymbionts_Pairs02, ConservativeRejection = FALSE, Verbose = TRUE) ## End(Not run)
EvoWeaver has best performance with an ensemble method combining individual evidence streams. This data file provides pretrained models for ease of use. These models are trained on genes from Streptomyces species.
These models are used internally if the user does not provide their own model, and aren't explicitly designed to be accessed by the user.
See the examples for how to train your own ensemble model.
data("BuiltInEnsembles")
data("BuiltInEnsembles")
The data contain a list of objects of class glm
.
## Training own ensemble method to avoid ## using built-ins exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes[1:50]) datavals <- predict(ew, NoPrediction=TRUE) # Make sure the actual values correspond to the right pairs! # This example just picks random numbers # Do not do this for your own models actual_values <- sample(c(0,1), nrow(datavals), replace=TRUE) datavals[,'y'] <- actual_values myModel <- glm(y~., datavals[,-c(1,2)], family='binomial') predictionPW <- EvoWeaver(exData$Genes[51:60]) predict(predictionPW, PretrainedModel=myModel)
## Training own ensemble method to avoid ## using built-ins exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes[1:50]) datavals <- predict(ew, NoPrediction=TRUE) # Make sure the actual values correspond to the right pairs! # This example just picks random numbers # Do not do this for your own models actual_values <- sample(c(0,1), nrow(datavals), replace=TRUE) datavals[,'y'] <- actual_values myModel <- glm(y~., datavals[,-c(1,2)], family='binomial') predictionPW <- EvoWeaver(exData$Genes[51:60]) predict(predictionPW, PretrainedModel=myModel)
Simulated values of Clustering Information Distance for random trees with 4 to 200 shared leaves.
data("CIDist_NullDist")
data("CIDist_NullDist")
Each column of the matrix corresponds to the distribution of distances between random trees with the given number of leaves. This begins at CI_DISTANCE_INTERNAL[,1]
corresponding to 4 leaves, and ends at CI_DISTANCE_INTERNAL[,197]
corresponding to 200 leaves. Distances begin at 4 leaves since there is only one unrooted tree with 1, 2, or 3 leaves (so the distance between any given tree with less than 4 leaves is always 0).
Each row of the matrix corresponds to statistics for the given simulation set. The first row gives the minimum value, the next 9 give quantiles in c(1%, 5%, 10%, 25%, 50%, 75%, 90%, 95%, 99%)
, and the last three rows give the max
, mean
, and sd
(resp.).
A matrix CI_DISTANCE_INTERNAL
with 197 columns and 13 rows.
Datafiles obtained from the TreeDistData package, published as part of Smith (2020).
Smith, Martin R. Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 2020. 36(20):5007-5013.
data(CIDist_NullDist)
data(CIDist_NullDist)
A relatively simple k-means clustering approach to drop predicted pairs that belong to clusters with a PID centroid below a specified user threshold.
ClusterByK(SynExtendObject, UserConfidence = list("PID" = 0.3), ClusterScalar = 4, MaxClusters = 15L, ColSelect = c("p1featurelength", "p2featurelength", "TotalMatch", "Consensus", "PID", "Score"), ColNorm = "Score", ShowPlot = FALSE, Verbose = FALSE)
ClusterByK(SynExtendObject, UserConfidence = list("PID" = 0.3), ClusterScalar = 4, MaxClusters = 15L, ColSelect = c("p1featurelength", "p2featurelength", "TotalMatch", "Consensus", "PID", "Score"), ColNorm = "Score", ShowPlot = FALSE, Verbose = FALSE)
SynExtendObject |
An object of class |
UserConfidence |
A named list of length 1 where the name identifies a column of the |
ClusterScalar |
A numeric value used to scale selection of how many clusters are used in kmeans clustering. A transformed total within-cluster sum of squares value is fit to a right hyperbola, and a scaled half-max value is used to select cluster number. “ClusterScalar” is multiplied by the half-max to adjust cluster number selection. |
MaxClusters |
Integer value indicating the largest number of clusters to test in a series of k-means clustering tests. |
ColSelect |
A character vector of column names indicating which columns to use for k-means clustering. When “p1featurelength”, “p2featurelength”, and “TotalMatch” are included together, they are morphed into a value representing the match size proportional to the longer of the two sequences. |
ColNorm |
A character vector of column names indicating columns the user would like to unit normalize. By default only set to “Score”. |
ShowPlot |
Logical indicating whether or not to plot the CDFs for the PIDs of all k-means clusters for the determined cluster number. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
ClusterByK
uses a naive k-means routine to select for predicted pairs that belong to clusters whose centroids are greater than or equal to the user specified column-value pair. This means that the confidence is not a minimum, and that pairs with values below the user confidence can be retained. The sum of within cluster sum of squares is used to approximate “knee” selection with the “ClusterScalar” value. With a “ClusterScalar” value of 1 the half-max of a right-hyperbola fitted to the sum of within-cluster sum of squares is used to pick the cluster number for evaluation, “ClusterScalar” is multiplied by the half-max to tune cluster number selection.
ClusterByK
returns the original object with an appended column and new attributes. The new column “ClusterID” is an integer value indicating which k-means cluster a candidate pair belongs to, while the attribute “Retain” is a named logical vector where the names correspond to ClusterIDs, and the logical value indicates whether the cluster center was above the user suppled column-value pair.
This function is intended to be used at the genome-to-genome comparison level, and not say, at the level of an all-vs-all comparison of many genomes. It will work well in all-vs-all cases, but it is not optimized for that scale yet.
An object of class PairSummaries
.
Nicholas Cooley [email protected]
SummarizePairs
, NucleotideOverlap
, FindSynteny
, ExpandDiagonal
data("Endosymbionts_Pairs01", package = "SynExtend") Pairs02 <- ClusterByK(SynExtendObject = Endosymbionts_Pairs01)
data("Endosymbionts_Pairs01", package = "SynExtend") Pairs02 <- ClusterByK(SynExtendObject = Endosymbionts_Pairs01)
Apply function FUN to each node of a dendrogram recursively. When y <- dendrapply(x, fn), then y is a dendrogram of the same graph structure as x and for each node, y.node[j] <- FUN( x.node[j], ...) (where y.node[j] is an (invalid!) notation for the j-th node of y). Also provides flexibility in the order in which nodes are evaluated.
NOTE: This man page is for the dendrapply
function defined in the SynExtend package. See ?stats::dendrapply
for the default method (defined in the stats package).
dendrapply(X, FUN, ..., how = c("pre.order", "post.order"))
dendrapply(X, FUN, ..., how = c("pre.order", "post.order"))
X |
An object of class |
FUN |
An R function to be applied to each dendrogram node, typically working on its |
... |
potential further arguments passed to |
how |
one of |
"pre.order"
preserves the functionality of the previous dendrapply
. For each node n
, FUN
is applied first to n
, then to n[[1]]
(and any children it may have), then n[[2]]
and its children, etc. Notably, each node is evaluted prior to any of its children.
"post.order"
allows for calculations that depend on the children of a given node. For each node n
, FUN
is applied first to all children of n
, then is applied to n
itself. Notably, each node is evaluated after all of its children.
Usually a dendrogram of the same (graph) structure as X
. For that, the function must be conceptually of the form FUN <- function(X) { attributes(X) <- .....; X }
, i.e., returning the node with some attributes added or changed.
If the function provided does not return the node, the result is a nested list of the same structure as X
, or as close as can be achieved with the return values. If the function should only be applied to the leaves of X
, consider using rapply
instead.
dendrapply
identifies leaf nodes as nodes such that attr(node, 'leaf') == TRUE
, and internal nodes as nodes such that attr(node, 'leaf') %in% c(NULL, FALSE)
. If you modify or remove this attribute, dendrapply
may perform unexpectedly.
The prior implementation of dendrapply
was recursive and inefficient for dendrograms with many non-leaves. This version is no longer recursive, and thus should no longer cause issues stemming from insufficient C stack size (as mentioned in the 'Warning' in dendrogram
).
Aidan Lakshman [email protected]
as.dendrogram
, lapply
for applying a function to each component of a list.
rapply
is particularly useful for applying a function to the leaves of a dendrogram, and almost always be used when the function does not need to be applied to interior nodes due to significantly better performance.
require(graphics) ## a smallish simple dendrogram dhc <- as.dendrogram(hc <- hclust(dist(USArrests), "ave")) (dhc21 <- dhc[[2]][[1]]) ## too simple: dendrapply(dhc21, function(n) utils::str(attributes(n))) ## toy example to set colored leaf labels : local({ colLab <<- function(n) { if(is.leaf(n)) { a <- attributes(n) i <<- i+1 attr(n, "nodePar") <- c(a$nodePar, list(lab.col = mycols[i], lab.font = i%%3)) } n } mycols <- grDevices::rainbow(attr(dhc21,"members")) i <- 0 }) dL <- dendrapply(dhc21, colLab) op <- par(mfrow = 2:1) plot(dhc21) plot(dL) ## --> colored labels! par(op) ## Illustrating difference between pre.order and post.order dend <- as.dendrogram(hclust(dist(seq_len(4L)))) f <- function(x){ if(!is.null(attr(x, 'leaf'))){ v <- as.character(attr(x, 'label')) } else { v <- paste0(attr(x[[1]], 'newattr'), attr(x[[2]], 'newattr')) } attr(x, 'newattr') <- v x } # trying with default, note character(0) entries preorder_try <- dendrapply(dend, f) dendrapply(preorder_try, \(x){ print(attr(x, 'newattr')); x }) ## trying with postorder, note that children nodes will already ## have been populated, so no character(0) entries postorder_try <- dendrapply(dend, f, how='post.order') dendrapply(postorder_try, \(x){ print(attr(x, 'newattr')); x })
require(graphics) ## a smallish simple dendrogram dhc <- as.dendrogram(hc <- hclust(dist(USArrests), "ave")) (dhc21 <- dhc[[2]][[1]]) ## too simple: dendrapply(dhc21, function(n) utils::str(attributes(n))) ## toy example to set colored leaf labels : local({ colLab <<- function(n) { if(is.leaf(n)) { a <- attributes(n) i <<- i+1 attr(n, "nodePar") <- c(a$nodePar, list(lab.col = mycols[i], lab.font = i%%3)) } n } mycols <- grDevices::rainbow(attr(dhc21,"members")) i <- 0 }) dL <- dendrapply(dhc21, colLab) op <- par(mfrow = 2:1) plot(dhc21) plot(dL) ## --> colored labels! par(op) ## Illustrating difference between pre.order and post.order dend <- as.dendrogram(hclust(dist(seq_len(4L)))) f <- function(x){ if(!is.null(attr(x, 'leaf'))){ v <- as.character(attr(x, 'label')) } else { v <- paste0(attr(x[[1]], 'newattr'), attr(x[[2]], 'newattr')) } attr(x, 'newattr') <- v x } # trying with default, note character(0) entries preorder_try <- dendrapply(dend, f) dendrapply(preorder_try, \(x){ print(attr(x, 'newattr')); x }) ## trying with postorder, note that children nodes will already ## have been populated, so no character(0) entries postorder_try <- dendrapply(dend, f, how='post.order') dendrapply(postorder_try, \(x){ print(attr(x, 'newattr')); x })
PairSummaries
objects.
Takes in a PairSummaries
object and return a list of identifiers organized into single linkage clusters.
DisjointSet(Pairs, Verbose = FALSE)
DisjointSet(Pairs, Verbose = FALSE)
Pairs |
A |
Verbose |
Logical indicating whether to print progress bars and messages. Defaults to |
Takes in a PairSummaries
object and return a list of identifiers organized into single linkage clusters.
Returns a list of character vectors representing IDs of sequence features, typically genes.
Nicholas Cooley [email protected]
FindSynteny
, Synteny-class
, PairSummaries
, FindSets
data("Endosymbionts_Pairs03", package = "SynExtend") Sets <- DisjointSet(Pairs = Endosymbionts_Pairs03, Verbose = TRUE)
data("Endosymbionts_Pairs03", package = "SynExtend") Sets <- DisjointSet(Pairs = Endosymbionts_Pairs03, Verbose = TRUE)
Calculates if a presence/absence pattern is random, Brownian, or neither with respect to a given phylogeny.
DPhyloStatistic(dend, PAProfile, NumIter = 1000L)
DPhyloStatistic(dend, PAProfile, NumIter = 1000L)
dend |
An object of class |
PAProfile |
A vector representing presence/absence of binary traits. See Details for more information. |
NumIter |
Number of iterations to simulate for random permutation analysis. |
This function implements the D-Statistic for binary traits on a phylogeny, as introduced in Fritz and Purvis (2009). The statstic is the following ratio:
Here is the D value for the input data,
is the value under simulated Brownian evolution, and
is the value under random permutation of the input data. The D value measures the sum of sister clade differences in a phylogeny weighted by branch lengths. A score close to 1 indicates phylogenetically random distribution, and a score close to 0 indicates the trait likely evolved under Brownian motion. Scores can fall outside this range; these scores are only intended as benchmark points on the scale. See the original paper cited in References for more information.
The input PAProfile
supports a number of formatting options:
Character vector, where each element is a label of the dendrogram. Presence in the character vector indicates presence of the trait in the corresponding label.
Integer vector of length equivalent to the number of leaves, comprised of 0s and 1s. 0 indicates absence in the corresponding leaf, and 1 indicates presence.
Logical vector of length equivalent to number of leaves. FALSE
indicates absence in the corresponding leaf, and TRUE
indicates presence.
See Examples for a demonstration of each case.
Returns a numerical value. Values close to 0 indicate random distribution, and values close to 1 indicate a Brownian distribution.
Aidan Lakshman [email protected]
Fritz S.A. and Purvis A. Selectivity in Mammalian Extinction Risk and Threat Types: a New Measure of Phylogenetic Signal Strength in Binary Traits. Conservation Biology, 2010. 24(4):1042-1051.
########################################################## ### Replicating results from Table 1 in original paper ### ########################################################## # creates a dendrogram with 16 leaves and branch lengths all 1 distMat <- suppressWarnings(matrix(1:17, nrow=16, ncol=16)) testDend <- as.dendrogram(hclust(as.dist(distMat))) testDend <- dendrapply(testDend, \(x){ attr(x, 'height') <- attr(x, 'height') / 2 return(x) }) attr(testDend[[1]], 'height') <- attr(testDend[[2]], 'height') <- 3 attr(testDend, 'height') <- 4 plot(testDend) set.seed(123) # extremely clumped (should be close to -2.4) DPhyloStatistic(testDend, as.character(1:8)) # clumped Brownian (should be close to 0) DPhyloStatistic(testDend, as.character(c(1,2,5,6,10,12,13,14))) # random (should be close to 1.0) DPhyloStatistic(testDend, as.character(c(1,4:6,10,13,14,16))) # overdispersed (should be close to 1.9) DPhyloStatistic(testDend, as.character(seq(2,16,by=2))) ########################################### ### Different ways to create PAProfiles ### ########################################### allLabs <- as.character(labels(testDend)) # All these ways create a PAProfile with # presence in members 1:4 # and absence in members 5:16 # numeric vector: c(rep(1,4), rep(0, length(allLabs)-4)) # logical vector: c(rep(TRUE,4), rep(FALSE, length(allLabs)-4)) # character vector: allLabs[1:4]
########################################################## ### Replicating results from Table 1 in original paper ### ########################################################## # creates a dendrogram with 16 leaves and branch lengths all 1 distMat <- suppressWarnings(matrix(1:17, nrow=16, ncol=16)) testDend <- as.dendrogram(hclust(as.dist(distMat))) testDend <- dendrapply(testDend, \(x){ attr(x, 'height') <- attr(x, 'height') / 2 return(x) }) attr(testDend[[1]], 'height') <- attr(testDend[[2]], 'height') <- 3 attr(testDend, 'height') <- 4 plot(testDend) set.seed(123) # extremely clumped (should be close to -2.4) DPhyloStatistic(testDend, as.character(1:8)) # clumped Brownian (should be close to 0) DPhyloStatistic(testDend, as.character(c(1,2,5,6,10,12,13,14))) # random (should be close to 1.0) DPhyloStatistic(testDend, as.character(c(1,4:6,10,13,14,16))) # overdispersed (should be close to 1.9) DPhyloStatistic(testDend, as.character(seq(2,16,by=2))) ########################################### ### Different ways to create PAProfiles ### ########################################### allLabs <- as.character(labels(testDend)) # All these ways create a PAProfile with # presence in members 1:4 # and absence in members 5:16 # numeric vector: c(rep(1,4), rep(0, length(allLabs)-4)) # logical vector: c(rep(TRUE,4), rep(FALSE, length(allLabs)-4)) # character vector: allLabs[1:4]
A named list of DataFrame
s.
data("Endosymbionts_GeneCalls")
data("Endosymbionts_GeneCalls")
Example genecalls.
A named list.
data(Endosymbionts_GeneCalls)
data(Endosymbionts_GeneCalls)
An object of class LinkedPairs
.
data("Endosymbionts_LinkedFeatures")
data("Endosymbionts_LinkedFeatures")
An object of class LinkedPairs
.
An object of class LinkedPairs
.
data(Endosymbionts_LinkedFeatures)
data(Endosymbionts_LinkedFeatures)
An object of class PairSummaries
.
data("Endosymbionts_Pairs01")
data("Endosymbionts_Pairs01")
An object of class PairSummaries
.
An object of class PairSummaries
.
data(Endosymbionts_Pairs01)
data(Endosymbionts_Pairs01)
An object of class PairSummaries
where blocks have been expanded.
data("Endosymbionts_Pairs02")
data("Endosymbionts_Pairs02")
An object of class PairSummaries
.
An object of class PairSummaries
.
data(Endosymbionts_Pairs02)
data(Endosymbionts_Pairs02)
An object of class PairSummaries
where blocks have been expanded and competitors have been rejected.
data("Endosymbionts_Pairs03")
data("Endosymbionts_Pairs03")
An object of class PairSummaries
.
An object of class PairSummaries
.
data(Endosymbionts_Pairs03)
data(Endosymbionts_Pairs03)
A named list of disjoint sets representing hypothetical COGs.
data("Endosymbionts_Sets")
data("Endosymbionts_Sets")
A named list of disjoint sets representing hypothetical COGs.
A named list of disjoint sets representing hypothetical COGs.
data(Endosymbionts_Sets)
data(Endosymbionts_Sets)
An object of class Synteny
.
data("Endosymbionts_Synteny")
data("Endosymbionts_Synteny")
An object of class Synteny
.
An object of class Synteny
.
data(Endosymbionts_Synteny)
data(Endosymbionts_Synteny)
Estimate the total disk consumption for ExoLabel
.
EstimateExoLabel(num_v, avg_degree=1, num_edges=num_v*avg_degree, node_name_length=8L)
EstimateExoLabel(num_v, avg_degree=1, num_edges=num_v*avg_degree, node_name_length=8L)
num_v |
Approximate number of total unique nodes in the network. |
avg_degree |
Average degree of each node in the network. |
num_edges |
Approximate total number of edges in the network. |
node_name_length |
Approximate average length of each node name, in characters. |
This function provides a rough estimate of the total disk space required to run ExoLabel
for a given input network. avg_degree
and num_edges
need not both be specified. The function prints out the estimated size of the original edgelist files, the estimated disk space to be consumed by ExoLabel
, and the approximate ratio of disk space relative to the original file.
node_name_length
specifies the average length of the node names–since the names themselves must be stored on disk, this contributes to the overall size. For relatively short node names (1-16 characters) this has a negligible impact on overall disk consumption.
Returns a vector of length three, showing the estimated total edgelist file size, estimated disk consumption, and ratio of the two. All sizes are shown in bytes.
Estimating the average node label size is challenging, and unfortunately it does have a relatively large effect on the estimated edgelist file size. This function should be used for rough estimations of sizing, not absolute values. Errors in estimation of rough node name size will have a larger impact on edgelist file estimation than on the ExoLabel disk usage, so users can have higher confidence in estimated ExoLabel consumption.
Aidan Lakshman <[email protected]>
# 100,000 nodes, average degree 2 EstimateExoLabel(num_v=100000, avg_degree=2) # 10,000 nodes, 50,000 edges EstimateExoLabel(num_v=10000, num_edges=50000)
# 100,000 nodes, average degree 2 EstimateExoLabel(num_v=100000, avg_degree=2) # 10,000 nodes, 50,000 edges EstimateExoLabel(num_v=10000, num_edges=50000)
Take in a Synteny
object and return predicted rearrangement events.
EstimRearrScen(SyntenyObject, NumRuns = -1, Mean = FALSE, MinBlockLength = -1, Verbose = TRUE)
EstimRearrScen(SyntenyObject, NumRuns = -1, Mean = FALSE, MinBlockLength = -1, Verbose = TRUE)
SyntenyObject |
|
NumRuns |
Numeric; Number of times to simulate scenarios. The default value of -1 (and all non-positive
values) runs each analysis for |
Mean |
Logical; If TRUE, returns the mean number of inversions and transpositions found. If FALSE, returns the scenario corresponding to the minimum total number of operations across all runs. This parameter only affects the number of inversions and transpositions reported; the specific scenario returned is one of the runs that resulted in a minimum value. |
MinBlockLength |
Numeric; Minimum size of syntenic blocks to use for analysis. The default value accepts all blocks. Set to a larger value to ignore sections of short mutations that could be the result of SNPs or other small-scale mutations. |
Verbose |
Logical; indicates whether or not to display a progress bar and print the time difference upon completion. |
EstimRearrScen
is an implementation of the Double Cut and Join
(DCJ) method for analyzing large scale mutation events.
The DCJ model is commonly used to model genome rearrangement operations. Given a genome, we can create a connected graph encoding the order of conserved genomic regions. Each syntenic region is split into two nodes, with one encoding the beginning and one encoding the end (beginning and end defined relative to the direction of transcription). Each node is then connected to the two nodes it is adjacent to in the genome.
For example, given a genome with 3 syntenic regions such that
is transcribed in the opposite direction relative to
, our graph would consist
of nodes and edges
.
Given two genomes, we derive syntenic regions between the two samples and then construct two of these graph structures. A DCJ operation is one that cuts two connections of a common color and creates two new edges. The goal of the DCJ model is to rearrange the graph of the first genome into the second genome using DCJ operations. The DCJ distance is defined as the minimum number of DCJ operations to transform one graph into another.
It can be easily shown that inversions can be performed with a single DCJ operation, and block interchanges/order rearrangements can be performed with a sequence of two DCJ operations. DCJ distance defines a metric space, and prior work has demonstrated algorithms for fast computation of the DCJ distance.
However, DCJ distance inherently incentivizes inversions over block interchanges due to the former requiring half as many DCJ operations. This is a strong assumption, and there is no evidence to support gene order rearrangements occuring half as often as gene inversions.
This implementation incentivizes minimum number of total events rather than total number of DCJs. As the search space is large and multiple sequences of events can be equally parsimonious, this algorithm computes multiple scenarios with random sequences of operations to try to find the minimum amount of events. Users can choose to receive the best found solution or the mean number of events from all solutions.
An NxN matrix of lists with the same shape as the input Synteny object. This is
wrapped into a GenRearr
object for pretty printing.
The diagonal corresponds to total sequence length of the corresponding genome.
In the upper triangle, entry
[i,j]
corresponds to the percent hits between genome i
and genome j
.
In the lower triangle, entry [i,j]
contains a List object with 5 properties:
$Inversions
and $Transpositions
contain the (Mean/min) number of estimated inversions
and transpositions (resp.) between genome i
and genome j
.
$pct_hits
contains percent hits between the genomes.
$Scenario
shows the sequence of events corresponding to the minimum
rearrangement scenario found. See below for details.
$Key
provides a mapping between syntenic blocks and genome positions.
See below for details.
The print.GenRearr
method prints this data out as a matrix, with the diagonal
showing the number of chromosomes and the lower triangle displaying xI,yT
, where
x,y
the number of inversions and transpositions (resp.) between the
corresponding entries.
The $Scenario
entry describes a sequences of steps to rearrange one genome
into another, as found by this algorithm. The goal of the DCJ model is to rearrange
the second genome into the first. Thus, with N
syntenic regions total, we can
arbitrarily choose the syntenic blocks in genome 1 to be ordered 1,2,...,N
,
and then have genome 2 numbers relative to that.
As an example, suppose genome 1 has elements A B E(r) G
and genome 2 has elements
E B(r) A(r) G
, with X(r) denoting block X has reversed direction of transcription.
We can then arbitrarily assign blocks to numbers such that genome 1 is (1 2 3 4)
and genome 2 is (3 -2 -1 4)
, where a negative indicates reversed direction of transcription
relative to the corresponding syntenic block in genome 1.
Each entry in $Scenario
details an operation, the result after that operation,
and the number of blocks involved in the operation. If we reversed the middle two
entries of genome 2, the entry in $Scenario
would be:
inversion: 3 1 2 4 { 2 }
Here we inverted the whole block (-2 -1)
into (1 2)
. We could then
finish the rearrangement by performing a transposition to move block 3 between
2 and 4. The entries of $Scenario
in this case would be the following:
Original: 3 -2 -1 4
inversion: 3 1 2 4 { 2 }
block interchange: 1 2 3 4 { 3 }
Step 1 is the original state of genome 2, step 2 inverts 2 elements to arrive at
(3 1 2 4)
, and then step 3 moves one element to arrive at (1 2 3 4)
.
It is important to note that the numbered genomic regions in $Scenario
are not genes,
they are blocks of conserved syntenic regions between the genomes. These blocks may not match
up with the original blocks from the Synteny object, since some are combined during
pre-processing to expedite calculations.
$Key
is a mapping between these numbered regions and the original genomic regions.
This is a 5 column matrix with the following columns (in order):
start1
: Nucleotide position for the first nucleotide in of the syntenic region
on genome 1.
start2
: Same as start1
, but for genome 2
length
: Length of block, in nucleotides
rel_direction_on_2
: 1 if the blocks have the same transcriptonal direction on both
genomes, and 0 if the direction is reversed in genome 2
index1
: Label of the genetic region used in $Scenario
output
Aidan Lakshman ([email protected])
Friedberg, R., Darling, A. E., & Yancopoulos, S. (2008). Genome rearrangement by the double cut and join operation. Bioinformatics, 385-416.
db <- system.file("extdata", "Influenza.sqlite", package="DECIPHER") synteny <- FindSynteny(db) synteny rearrs <- EstimRearrScen(synteny) rearrs # view whole object rearrs[[2,1]] # view details on Genomes 1 and 2
db <- system.file("extdata", "Influenza.sqlite", package="DECIPHER") synteny <- FindSynteny(db) synteny rearrs <- EstimRearrScen(synteny) rearrs # view whole object rearrs[[2,1]] # view details on Genomes 1 and 2
EvoWeaver is an S3 class with methods for predicting functional association using
protein or gene data. EvoWeaver implements multiple algorithms for analyzing coevolutionary signal between genes, which are combined into overall predictions on functional association. For details on predictions, see predict.EvoWeaver
.
EvoWeaver(ListOfData, MySpeciesTree=NULL, NoWarn=FALSE) ## S3 method for class 'EvoWeaver' SpeciesTree(ew, Verbose=TRUE, Processors=1L)
EvoWeaver(ListOfData, MySpeciesTree=NULL, NoWarn=FALSE) ## S3 method for class 'EvoWeaver' SpeciesTree(ew, Verbose=TRUE, Processors=1L)
ListOfData |
A list of gene data, where each entry corresponds to information on a particular gene. List must contain either dendrograms or vectors, and cannot contain a mixture. If list is composed of dendrograms, each dendrogram is a gene tree for the corresponding entry. If list is composed of vectors, vectors should be numeric or character vectors denoting the genomes containing that gene. |
MySpeciesTree |
An object of class |
NoWarn |
Several algorithms depend on having certain data. When a |
ew |
An object of class |
Verbose |
Should output be displayed when calculating species tree? |
Processors |
Number of processors to use. Set to |
EvoWeaver expects input data to be a list. All entries must be one of the following:
ListOfData[[i]] = c('ID#1', 'ID#2', ..., 'ID#k')
(a) ListOfData[[i]] = c('i1_d1_p1', 'i2_d2_p2', ..., 'ik_dk_pk')
(b) ListOfData[[i]] = c('i1_d1_s1_p1', 'i2_d2_s2_p2', ..., 'ik_dk_sk_pk')
ListOfData[[i]] = dendrogram(...)
In (1), each ID#i corresponds to the unique identifier for genome #i. For entry #j in the list, the presence of 'ID#i' means genome #i has an ortholog for gene/protein #j.
Case (2a) is the same as (1), just with the formatting of names slightly different. Each entry is of the form i_d_p
, where i
is the unique identifier for the genome, d
is which chromosome the ortholog is located, and p
is what position the ortholog appears in on that chromosome. p
must be a numeric
, while the other entries can be any value.
Case (2b) is a variation on (2a), adding in an identifier s
. This value must be 0
or 1
, corresponding to whether the gene is on the forward or reverse strand. Whether 0
denotes forward or reverse is inconsequential as long as the scheme is consistent.
Case (3) expects gene trees for each gene, with labeled leaves corresponding to each source genome. If ListOfData
is in this format, taking labels(ListOfData[[i]])
should produce a character vector that matches the format of one of the previous cases.
See the Examples section for illustrative examples.
Whenever possible, provide a full set of dendrogram
objects with leaf labels in form (2b). This will allow the most algorithms to run. What follows is a more detailed description of which inputs allow which algorithms.
EvoWeaver requires input of scenario (3) to use distance matrix methods, and requires input of scenario (2) (or (3) with leaves labeled according to (2)) for gene organization analyses. Transcriptional direction analysis requires input of scenario (2b).
Sequence-level methods require dendrograms with sequence information included as the state
attribute in each leaf node.
Note that ALL entries must belong to the same category–a combination of character vectors and dendrograms is not allowed.
Prediction of a functional association network is done using predict(EvoWeaverObject)
. See predict.EvoWeaver
for more information.
The SpeciesTree
function takes in an object of class EvoWeaver
and returns a species tree. If the object was not initialized with a species tree, it calculates one using SuperTree
. The species tree for a EvoWeaver object can be set with attr(ew, 'speciesTree') <- ...
.
Returns a EvoWeaver object.
Aidan Lakshman [email protected]
predict.EvoWeaver
,
ExampleStreptomycesData
,
BuiltInEnsembles
,
SuperTree
# I'm using gene to mean either a gene or protein ## Imagine we have the following 4 genomes: ## (each letter denotes a distinct gene) ## Genome 1: a b c d ## Genome 2: d c e ## Genome 3: b a e ## Genome 4: a e ## We have 5 total genes: (a,b,c,d,e) ## a is present in genomes 1, 3, 4 ## b is present in genomes 1, 3 ## c is present in genomes 1, 2 ## d is present in genomes 1, 2 ## e is present in genomes 2, 3, 4 ## Constructing a EvoWeaver object according to (1): l <- list() l[['a']] <- c('1', '3', '4') l[['b']] <- c('1', '3') l[['c']] <- c('1', '2') l[['d']] <- c('1', '2') l[['e']] <- c('2', '3', '4') ## Each value of the list corresponds to a gene ## The associated vector shows which genomes have that gene pwCase1 <- EvoWeaver(l) ## Constructing a EvoWeaver object according to (2): ## Here we need to add in the chromosome and the position ## As we only have one chromosome, ## we can just set that to 1 for all. ## Position can be identified with knowledge, or with ## FindGenes(...) from DECIPHER. ## In this toy case, genomes are small so it's simple. l <- list() l[['a']] <- c('1_1_1', '3_1_2', '4_1_1') l[['b']] <- c('1_1_2', '3_1_1') l[['c']] <- c('1_1_3', '2_1_2') l[['d']] <- c('1_1_4', '2_1_1') l[['e']] <- c('2_1_3', '3_1_3', '4_1_2') pwCase2a <- EvoWeaver(l) ## If we want transcriptional information, we need an ## value corresponding to the strand of each gene ## Notice that the genome identifer need not be numeric, ## but the strand identifer must be 0 or 1 l <- list() l[['a']] <- c('a_1_0_1', 'c_1_1_2', 'd_1_0_1') l[['b']] <- c('a_1_1_2', 'c_1_1_1') l[['c']] <- c('a_1_1_3', 'b_1_0_2') l[['d']] <- c('a_1_0_4', 'b_1_0_1') l[['e']] <- c('b_1_0_3', 'c_1_0_3', 'd_1_0_2') ## For Case 3, we just need dendrogram objects for each # l[['a']] <- dendrogram(...) # l[['b']] <- dendrogram(...) # l[['c']] <- dendrogram(...) # l[['d']] <- dendrogram(...) # l[['e']] <- dendrogram(...) ## Leaf labels for these will be the same as the ## entries in Case 1.
# I'm using gene to mean either a gene or protein ## Imagine we have the following 4 genomes: ## (each letter denotes a distinct gene) ## Genome 1: a b c d ## Genome 2: d c e ## Genome 3: b a e ## Genome 4: a e ## We have 5 total genes: (a,b,c,d,e) ## a is present in genomes 1, 3, 4 ## b is present in genomes 1, 3 ## c is present in genomes 1, 2 ## d is present in genomes 1, 2 ## e is present in genomes 2, 3, 4 ## Constructing a EvoWeaver object according to (1): l <- list() l[['a']] <- c('1', '3', '4') l[['b']] <- c('1', '3') l[['c']] <- c('1', '2') l[['d']] <- c('1', '2') l[['e']] <- c('2', '3', '4') ## Each value of the list corresponds to a gene ## The associated vector shows which genomes have that gene pwCase1 <- EvoWeaver(l) ## Constructing a EvoWeaver object according to (2): ## Here we need to add in the chromosome and the position ## As we only have one chromosome, ## we can just set that to 1 for all. ## Position can be identified with knowledge, or with ## FindGenes(...) from DECIPHER. ## In this toy case, genomes are small so it's simple. l <- list() l[['a']] <- c('1_1_1', '3_1_2', '4_1_1') l[['b']] <- c('1_1_2', '3_1_1') l[['c']] <- c('1_1_3', '2_1_2') l[['d']] <- c('1_1_4', '2_1_1') l[['e']] <- c('2_1_3', '3_1_3', '4_1_2') pwCase2a <- EvoWeaver(l) ## If we want transcriptional information, we need an ## value corresponding to the strand of each gene ## Notice that the genome identifer need not be numeric, ## but the strand identifer must be 0 or 1 l <- list() l[['a']] <- c('a_1_0_1', 'c_1_1_2', 'd_1_0_1') l[['b']] <- c('a_1_1_2', 'c_1_1_1') l[['c']] <- c('a_1_1_3', 'b_1_0_2') l[['d']] <- c('a_1_0_4', 'b_1_0_1') l[['e']] <- c('b_1_0_3', 'c_1_0_3', 'd_1_0_2') ## For Case 3, we just need dendrogram objects for each # l[['a']] <- dendrogram(...) # l[['b']] <- dendrogram(...) # l[['c']] <- dendrogram(...) # l[['d']] <- dendrogram(...) # l[['e']] <- dendrogram(...) ## Leaf labels for these will be the same as the ## entries in Case 1.
EvoWeaver
incorporates four classes of prediction, each with multiple
methods and algorithms. Co-localization (Coloc) methods examine
conservation of relative location and transcriptional direction of genetic
regions within the genome.
predict.EvoWeaver
currently supports three Coloc methods:
'Coloc'
'ColocMoran'
'TranscripMI'
All distance matrix methods require a EvoWeaver
object initialized
with gene locations using the a three or four number code. See EvoWeaver
for more information on input data types.
The built-in Coloc
examines relative location of genes within genomes
as evidence of interaction. For a given pair of genes, the score is given by
, where
the set of genomes and
the
difference in index between the two genes in genome
. Using gene index
instead of number of base pairs avoids bias introduced by gene and genome length.
ColocMoran
measures the extent to which gene distances are preserved across a phylogeny. This function uses the same initial scoring scheme as Coloc
, but can handle paralogs. The raw scores are passed into MoransI
to calculate spatial autocorrelation. "Space" is taken as , where
is the Cophenetic distance matrix calculated from the species tree of the inputs. As such, this method requires a species tree as input, which can be calculated from a set of gene trees using
SuperTree
.
TranscripMI
uses mutual information of the transcriptional direction of each pair of genes. Conservation of relative transcriptional direction between gene pairs has been shown to imply functional association in prior work. This algorithm requires that the EvoWeaver
object is initialized with a four number code, with the third number either 0
or 1
, denoting whether the gene is on the forward or reverse strand. The mutual information is calculated as:
Here ,
is the direction of the gene with lower index,
is the direction of the gene with higher index, and
is the probability of
. Note that this is a weighted MI as introduced by Beckley and Wright (2021). The mutual information is augmented by the addition of a single pseudocount to each value, and normalized by the joint entropy of
. P-values are calculated using Fisher's Exact Test on the contingency table.
None.
Aidan Lakshman [email protected]
Beckley, Andrew and E. S. Wright. Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results. The Lancet Microbe, 2021. 2(10): 545-554.
Korbel, J. O., et al., Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs. Nature Biotechnology, 2004. 22(7): 911-917.
Moran, P. A. P., Notes on Continuous Stochastic Phenomena. Biometrika, 1950. 37(1): 17-23.
EvoWeaver Phylogenetic Profiling Predictors
EvoWeaver Phylogenetic Structure Predictors
EvoWeaver Sequence-Level Predictors
EvoWeaver
incorporates four classes of prediction, each with multiple
methods and algorithms. Phylogenetic Profiling (PP) methods examine conservation
of gain/loss events within orthology groups using phylogenetic profiles
constructed from presence/absence patterns.
predict.EvoWeaver
currently supports seven PP methods:
'Jaccard'
'Hamming'
'MutualInformation'
'PAPV'
'CorrGL'
'ProfDCA'
'Behdenna'
'GainLoss'
Most PP methods are compatible with a EvoWeaver
object initialized
with any input type. See EvoWeaver
for more information on input data types.
All of these methods use presence/absence (PA) profiles, which are binary vectors such that 1 implies the corresponding genome has that particular gene, and 0 implies the genome does not have that particular gene.
Methods Hamming
and Jaccard
use Hamming and Jaccard distance
(respectively) of PA profiles to determine overall score.
MutualInformation
uses mutual information of PA profiels to determine
score, employing a weighting scheme such that and
give
positive information, and
and
give negative information.
PAPV
calculates a p-value for PA profiles using Fisher's Exact Test. The returned score is provided as 1-p_value
so that larger scores indicate more significance, and smaller scores indicate less significance. This rescaling is consistent with the other similarity metrics in EvoWeaver
. This can be used with Jaccard
, Hamming
, or MutualInformation
to weight raw scores by statistical significance.
ProfDCA
uses the direct coupling analysis algorithm introduced by
Weigt et al. (2005) to determine direct information between PA profiles.
This approach has been validated on PA profiles in Fukunaga and Iwasaki (2022),
though the implementation in EvoWeaver
forsakes the persistent contrasive divergence method in favor of the the algorithm from
Lokhov et al. (2018) for increased speed and exact solutions. Note that this algorithm is still extremely slow relative to the other methods despite the aforementioned runtime improvements.
Behdenna
implements the method detailed in Behdenna et al. (2016) to
find statistically significant interactions using co-occurence of gain/loss
events mapped to ancestral states on a species tree. This method requires
a species tree as input. If the EvoWeaver
object is initialized with dendrogram
objects, SuperTree
will be used to infer a species tree.
GainLoss
uses a similar method to Behdenna
. This method uses Fitch Parsimony to infer where events were gained or lost on a species tree, and then looks for distance between these gain/loss events. Unlike Behdenna
, this method takes into account the types of events (ex. gain/gain and loss/loss are treated differently than gain/loss). This method requires
a species tree as input. If the EvoWeaver
object is initialized with dendrogram
objects, SuperTree
will be used to infer a species tree.
CorrGL
infers where events were gained or lost on a species tree as in method
GainLoss
, then uses a Pearson's correlation coefficient weighted by p-value to infer similarity.
None.
Aidan Lakshman [email protected]
Behdenna, A., et al., Testing for Independence between Evolutionary Processes. Systematic Biology, 2016. 65(5): p. 812-823.
Date, S.V. and E.M. Marcotte, Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nature Biotechnology, 2003. 21(9): p. 1055-1062.
Fukunaga, T. and W. Iwasaki, Inverse Potts model improves accuracy of phylogenetic profiling. Bioinformatics, 2022.
Lokhov, A.Y., et al., Optimal structure and parameter learning of Ising models. Science advances, 2018. 4(3): p. e1700791.
Pellegrini, M., et al., Assigning protein function by comparative genome analysis: Protein phylogenetic profiles. Proceedings of the National Academy of Sciences, 1999. 96(8) p. 4285-4288
Weigt, M., et al., Identification of direct residue contacts in protein-protein interaction by message passing. Proceedings of the National Academy of Sciences, 2009. 106(1): p. 67-72.
EvoWeaver Phylogenetic Structure Predictors
EvoWeaver Gene Organization Predictors
EvoWeaver Sequence-Level Predictors
EvoWeaver
incorporates four classes of prediction, each with multiple
methods and algorithms. Phylogenetic Structure (PS) methods examine conservation
of overall evolutionary rates within orthology groups using distance matrices
constructed from each gene tree.
predict.EvoWeaver
currently supports three PS methods:
'MirrorTree'
'ContextTree'
'TreeDistance'
All distance matrix methods require a EvoWeaver
object initialized
with dendrogram
objects. See EvoWeaver
for more information on input data types.
The MirrorTree
method was introduced by Pazos et al. (2001). This method
builds distance matrices using a nucleotide substitution model, and then
calculates coevolution between gene families using the Pearson correlation
coefficient of the upper triangle of the two corresponding matrices.
Experimental analysis has shown data in the upper triangle is heavily
redundant and rapidly overwhelms available system memory. Previous work
has incorporated dimensionality reduction such as SVD to reduce the dimensionality
of the data, but this prevents parallelization of the data and doesn't solve memory
issues (since SVD takes as input the entire matrix with columns corresponding to upper triangle values). EvoWeaver
instead uses a seeded random projection
following Achlioptas (2001) to reduce the dimensionality of the data in a
reproducible and parallel-compatible way. We also utilize Spearman's ,
which outperforms Pearson's
following dimensionality reduction.
Subsequent work by Pazos et al. (2005) and Sato et al. (2005, 2006) found
multiple ways to improve predictions from the initial MirrorTree
method.
These methods incorporate additional phylogenetic context, and are thus
called ContextTree
methods. These improvements include correcting
for overall evolutionary rate using a species tree and/or using projection vectors.
The built-in ContextTree
method implements a species tree correction, and weights the resulting score by the normalized Hamming distance of the presence/absence profiles. This can correct for gene trees with low overlap that achieve spuriously high scores via random projection. Additional correction measures are implemented in the MTCorrection
argument.
The TreeDistance
method uses phylogenetic tree distance to quantify differences between gene trees. This method implements a number of metrics and groups them together to improve overall runtime. The default tree distance method is normalized Robinson-Foulds distance due to its lower computational complexity. Other methods can be specified using the TreeMethods
argument, which expects a character vector containing one or more of the following:
"RF"
: Robinson-Foulds Distance
"Nye"
: Nye Similarity
"all"
: All of the above methods
See the links above for more information and references. All of these metrics are accessible using the PhyloDistance
method. Method "JRF"
defaults to a k
value of 4, but this can be specified further if necessary using the JRFk
input parameter. Higher values of k
approach the value of Robinson-Foulds
distance, but these have a negligible impact on performance so use of the default parameter is encouraged for simplicity. Multiple metrics can be specified.
None.
Aidan Lakshman [email protected]
Achlioptas, Dimitris. Database-friendly random projections. Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 2001. p. 274-281.
Pazos, F. and A. Valencia, Similarity of phylogenetic trees as indicator of protein–protein interaction. Protein Engineering, Design and Selection, 2001. 14(9): p. 609-614.
Pazos, F., et al., Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome. J Mol Biol, 2005. 352(4): p. 1002-15.
Sato, T., et al., The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics, 2005. 21(17): p. 3482-9.
Sato, T., et al., Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions. Bioinformatics, 2006. 22(20): p. 2488-92.
EvoWeaver Phylogenetic Profiling Predictors
EvoWeaver Gene Organization Predictors
EvoWeaver Sequence-Level Predictors
EvoWeaver
incorporates four classes of prediction, each with multiple
methods and algorithms. Sequence-Level (SL) methods examine conservation of patterns in sequence data, commonly exhibited due to physical interactions
between proteins.
predict.EvoWeaver
currently supports three SL methods:
'ResidueMI'
'NVDT'
'Ancestral'
All residue methods require a EvoWeaver
object initialized
with dendrogram
objects and ancestral states. See EvoWeaver
for more information on input data types.
The ResidueMI
method looks at mutual information between sites in a multiple sequence alignment (MSA). This approach extends prior work in Martin et al. (2005). Each site from the first gene group is paired with the site from the second gene group that maximizes their mutual information.
The NVDT
method uses the natural vector encoding method introduced in Zhao et al. (2022). This encodes each gene sequences as a 92-dimensional vector, with the following entries:
Here is the raw total count of nucleotide
(or di/trinucleotide). For single nucleotides, we also calculate
, the mean location of nucleotide
, and
, the second moment of the location of nucleotide
. The overall natural vector for a COG is calculated as the normalized mean vector from the natural vectors of all component gene sequences. Interaction scores are computed using Pearson's R between each COG's natural vector. These di/trinucleotide counts are by default excluded, but can be included using the
extended=TRUE
argument. Using the extended counts has shown minimal increased accuracy at the cost of slower runtime in benchmarking.
The Ancestral
method calculates coevolution by looking at correlation of residue mutations near the leaves of each respective gene tree.
None.
Aidan Lakshman [email protected]
Martin, L. C., Gloor, G. B., Dunn, S. D. & Wahl, L. M, Using information theory to search for co-evolving residues in proteins. Bioinformatics, 2005. 21(4116-4124).
Zhao, N., et al., Protein-protein interaction and non-interaction predictions using gene sequence natural vector. Nature Communications Biology, 2022. 5(652).
EvoWeaver Phylogenetic Profiling Predictors
EvoWeaver Phylogenetic Structure Predictors
EvoWeaver Gene Organization Predictors
EvoWeb objects are outputted from predict.EvoWeaver
.
This class wraps the simMat
object with some other diagnostic
information intended to help interpret the output of EvoWeaver
predictions..
predict.EvoWeaver
returns a EvoWeb
object, which bundles some methods
to make formatting and printing of results slightly nicer. This currently
only implements a plot
function, but future functionality is in the works.
An object of class "EvoWeb"
, which inherits from "simMat"
.
Aidan Lakshman [email protected]
############## ## Prediction with built-in model and data ############### exData <- get(data("ExampleStreptomycesData")) # Subset isn't necessary but is faster for a working example ew <- EvoWeaver(exData$Genes[1:10]) evoweb <- predict(ew, Method='Jaccard') # print out results as an adjacency matrix print(evoweb) # print out results as a pairwise data.frame as.data.frame(evoweb)
############## ## Prediction with built-in model and data ############### exData <- get(data("ExampleStreptomycesData")) # Subset isn't necessary but is faster for a working example ew <- EvoWeaver(exData$Genes[1:10]) evoweb <- predict(ew, Method='Jaccard') # print out results as an adjacency matrix print(evoweb) # print out results as a pairwise data.frame as.data.frame(evoweb)
Data from Streptomyces species to test EvoWeaver
functionality.
data("ExampleStreptomycesData")
data("ExampleStreptomycesData")
This dataset contains a number of Clusters of Orthologous Genes (COGs) and a species
tree for use with EvoWeaver. This dataset showcases an example of using EvoWeaver with
a list of vectors. Entries in each vector are formatted correctly for use with
co-localization prediction. Each COG i
contains entries of the form
a_b_c
, indicating that the gene was found in genome a
on chromosome
b
, and was at the c
'th location. The original dataset is comprised
of 301 unique genomes.
The data contain two elements, Genes
and Tree
. Genes
is
a list of presence/absence vectors in the input required for EvoWeaver
.
Tree
is a species tree used for additional input.
exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes) # Subset isn't necessary but is faster for a working example predict(ew, Subset=1:10, MySpeciesTree=exData$Tree)
exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes) # Subset isn't necessary but is faster for a working example predict(ew, Subset=1:10, MySpeciesTree=exData$Tree)
Runs Fast Label Propagation using disk space for constant memory complexity.
ExoLabel(edgelistfiles, outfile=tempfile(), mode=c("undirected", "directed"), add_self_loops=FALSE, ignore_weights=FALSE, normalize_weights=FALSE, iterations=0L, return_table=FALSE, consensus_cluster=FALSE, verbose=interactive(), sep='\t', tempfiledir=tempdir(), cleanup_files=TRUE)
ExoLabel(edgelistfiles, outfile=tempfile(), mode=c("undirected", "directed"), add_self_loops=FALSE, ignore_weights=FALSE, normalize_weights=FALSE, iterations=0L, return_table=FALSE, consensus_cluster=FALSE, verbose=interactive(), sep='\t', tempfiledir=tempdir(), cleanup_files=TRUE)
edgelistfiles |
Character vector of files to be processed. Each entry should be a machine-interpretable path to an edgelist file. See Details for expected format. |
outfile |
File to write final clusters to. Optional, defaults to a temporary file. |
mode |
String specifying whether edges should be interpreted as undirected (default) or directed. Can be "undirected", "directed", or an unambiguous abbreviation. |
add_self_loops |
Should self loops be added to the network? If |
ignore_weights |
Should weights be ignored? If |
normalize_weights |
Should weights be normalized? If |
iterations |
Number of iterations to run fast label propagation algorithm for. Set to a value of 0 or less for infinite iterations. |
return_table |
Should result of clustering be returned as a file, or a |
consensus_cluster |
Should consensus clustering be used? If |
verbose |
Should status messages (output, progress, etc.) be displayed while running? |
sep |
Character that separates entries on a line in each file in |
tempfiledir |
Character vector corresponding to the location where temporary files used during execution should be stored. Defaults to R's |
cleanup_files |
Should intermediary files be deleted when the process completes? Note that |
Very large graphs require too much RAM for processing on some machines. In a graph containing billions of nodes and edges, loading the entire structure into RAM is rarely feasible. This implementation uses disk space for storing representations of each graph. While this is slower than computing on RAM, it allows this algorithm to scale to graphs of enormous size while only taking a small amount of RAM. Local benchmarking resulting in a plateau of approximately 100MB of RAM consumption for arbitrarily sized networks. If your graph is small enough to fit into RAM, consider using the LP_igraph()
function instead.
This function expects a set of edgelist files, provided as a vector of filepaths. Each entry in the file is expected to be in the following:
VERTEX1<sep>VERTEX2<sep>WEIGHT<linesep>
This line defines a single edge between vertices VERTEX1
and VERTEX2
with weight WEIGHT
. VERTEX1
and VERTEX2
are strings corresponding to vertex names, WEIGHT
is a numeric value that can be interpreted as a double
. The separators <sep>
and <linesep>
correspond to the arguments sep
and linesep
, respectively. The default arguments work for standard .tsv
formatting, i.e., a file of three columns of tab-separated values.
If ignore_weight=TRUE
, the file can be formatted as:
VERTEX1<sep>VERTEX2<linesep>
Note that the v1 v2 w
format is still accepted for ignore_weight=FALSE
, but the specified weights will be ignored.
Consensus clustering can be enabled by setting consensus_cluster=TRUE
. Consensus clustering runs this algorithm on the graph multiple times, transforming weight values according to a sigmoid function. By default, this runs nine times for sigmoids with scale 0.5 and shape c(0,0.2,0.4,0.6,0.8,1.0,1.33,1.67,2.0)
, collapsing weights below 0.1 to zero. The resulting clusters form a network such that the edge weight between any two nodes connected in the initial graph is the proportion of clusters they shared over clustering runs. This network is used for a final label propagation run, which identifies the consensus clusters. Users can specify a numeric vector as input to consensus_cluster
, which will override the default shape parameters and number of iterations.
If return_table=TRUE
, returns a data.frame
object with two columns. The first column contains the name of each vertex, and the second column contains the cluster it was assigned to.
If return_table=FALSE
, returns a character vector of length 1. This vector contains the path to the file where clusters were written to. The file is formatted as a .tsv
, with each line containing two tab separated columns (vertex name, assigned cluster)
While this algorithm can scale to very large graphs, it does have some internal limitations. First, nodes must be comprised of no more than 254 characters. If this limitation is restrictive, please feel free to contact me. Alternatively, you can increase the size yourself by changing the definition of MAX_NODE_NAME_SIZE
in src/outmem_graph.c
. This limitation is provided to decrease memory overhead and improve runtime, but arbitrary values are possible.
Second, nodes are indexed using 64-bit unsigned integers, with 0 reserved for other values. This means that the maximum possible number of nodes available is 2^64-2, which is about 18.5 quintillion.
Third, this algorithm uses disk space to store large objects. As such, please ensure you have sufficient disk space for the graph you intend to process. I've tried to put safeguards in the code itself, but funky stuff can happen when the OS runs out of space.
The disk space required by this algorithm is equivalent to , where
V
the number of unique vertices, L
the average length of each vertex label, and E
the number of edges. We can instead represent this purely in terms of vertices by setting D
to the average degree of each node. The total disk space required is thus . If you'd like to estimate the disk consumption of this algorithm, use the
EstimateExoLabel
function.
Aidan Lakshman <[email protected]>
Traag, V.A., Subelj, L. Large network community detection by fast label propagation. Sci Rep 13, 2701 (2023). https://doi.org/10.1038/s41598-023-29610-z
num_verts <- 20L num_edges <- 20L all_verts <- sample(letters, num_verts) all_edges <- vapply(seq_len(num_edges), \(i) paste(c(sample(all_verts, 2L), as.character(round(runif(1),3))), collapse='\t'), character(1L)) edgefile <- tempfile() if(file.exists(edgefile)) file.remove(edgefile) writeLines(all_edges, edgefile) res <- ExoLabel(edgefile, return_table=TRUE) print(res)
num_verts <- 20L num_edges <- 20L all_verts <- sample(letters, num_verts) all_edges <- vapply(seq_len(num_edges), \(i) paste(c(sample(all_verts, 2L), as.character(round(runif(1),3))), collapse='\t'), character(1L)) edgefile <- tempfile() if(file.exists(edgefile)) file.remove(edgefile) writeLines(all_edges, edgefile) res <- ExoLabel(edgefile, return_table=TRUE) print(res)
PairSummaries
object.
Attempt to expand blocks of paired features in a PairSummaries
object.
ExpandDiagonal(SynExtendObject, FeatureSeqs, DataBase, InheritConfidence = TRUE, GapTolerance = 100L, DropSingletons = FALSE, UserConfidence = list("PID" = 0.3), Verbose = FALSE)
ExpandDiagonal(SynExtendObject, FeatureSeqs, DataBase, InheritConfidence = TRUE, GapTolerance = 100L, DropSingletons = FALSE, UserConfidence = list("PID" = 0.3), Verbose = FALSE)
SynExtendObject |
An object of class |
FeatureSeqs |
An object of class |
DataBase |
A character string pointing to a SQLite database, or a connection to a |
InheritConfidence |
A logical indicating whether or not to inheret the user specified column-value pairs assigned to the input object. |
GapTolerance |
Integer value indicating the |
DropSingletons |
Ignore solo pairs when planning expansion routes. Set to |
UserConfidence |
A named list of length 1 where the name identifies a column of the |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
ExpandDiagonal
uses a naive expansion algorithm to attempt to fill in gaps in blocks of paired features and to attempt to expand blocks of paired features.
An object of class PairSummaries
.
Nicholas Cooley [email protected]
PairSummaries
, NucleotideOverlap
, link{SubSetPairs}
, FindSynteny
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Endosymbiont_Seqs <- PrepareSeqs(SynExtendObject = Endosymbionts_LinkedFeatures, DataBase = DBPATH, Verbose = TRUE) data("Endosymbionts_Pairs02", package = "SynExtend") Pairs03 <- ExpandDiagonal(SynExtendObject = Endosymbionts_Pairs02, DataBase = DBPATH, FeatureSeqs = Endosymbiont_Seqs, Verbose = TRUE)
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Endosymbiont_Seqs <- PrepareSeqs(SynExtendObject = Endosymbionts_LinkedFeatures, DataBase = DBPATH, Verbose = TRUE) data("Endosymbionts_Pairs02", package = "SynExtend") Pairs03 <- ExpandDiagonal(SynExtendObject = Endosymbionts_Pairs02, DataBase = DBPATH, FeatureSeqs = Endosymbiont_Seqs, Verbose = TRUE)
DNAStringSets
s.
Return organized DNAStringSet
s based on three currently supported object combinations. First return a single DNAStringSet
of feature sequences from a DFrame of genecalls and a DNAStingSet
of the source assembly. Second return a list of DNAStringSet
s of predicted pairs from a PairSummaries
object and a character string of the location of a DECIPHER
SQLite database. Third return a list of DNAStringSet
s of predicted single linkage communities from a PairSummaries
object, a character string of the location of a DECIPHER
SQLite database, and a list of identifiers generated by DisjointSet
.
ExtractBy(x, y, z, Verbose = FALSE)
ExtractBy(x, y, z, Verbose = FALSE)
x |
A |
y |
A character vector of length 1 indicating the location of a |
z |
Optional; a list of identifiers generated by |
Verbose |
Logical indicating whether to print progress bars and messages. Defaults to |
All sequences are forced into the same direction based on the Strand
column supplied by either the gene calls DFrame
specified by x
, or the GeneCalls
attribute of the PairSummaries
object specified by y
.
Return a DNAStringSet
, or list of DNAStringSet
s arranged depending upon the objects supplied. See description.
Nicholas Cooley [email protected]
FindSynteny
, Synteny-class
, PairSummaries
, DisjointSet
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_Pairs03", package = "SynExtend") data("Endosymbionts_Sets", package = "SynExtend") # extract the first 10 disjoint sets Sets <- ExtractBy(x = Endosymbionts_Pairs03, y = DBPATH, z = Endosymbionts_Sets[1:10], Verbose = TRUE) # extract just the pairs Sets <- ExtractBy(x = Endosymbionts_Pairs03, y = DBPATH, Verbose = TRUE)
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_Pairs03", package = "SynExtend") data("Endosymbionts_Sets", package = "SynExtend") # extract the first 10 disjoint sets Sets <- ExtractBy(x = Endosymbionts_Pairs03, y = DBPATH, z = Endosymbionts_Sets[1:10], Verbose = TRUE) # extract just the pairs Sets <- ExtractBy(x = Endosymbionts_Pairs03, y = DBPATH, Verbose = TRUE)
Get sequencing data from the SRA.
FastQFromSRR(SRR, ARGS = list("--gzip" = NULL, "--skip-technical" = NULL, "--readids" = NULL, "--read-filter" = "pass", "--dumpbase" = NULL, "--split-3" = NULL, "--clip" = NULL), KEEPFILES = FALSE)
FastQFromSRR(SRR, ARGS = list("--gzip" = NULL, "--skip-technical" = NULL, "--readids" = NULL, "--read-filter" = "pass", "--dumpbase" = NULL, "--split-3" = NULL, "--clip" = NULL), KEEPFILES = FALSE)
SRR |
A character vector of length 1 representing an SRA Run Accession, such as one that would be passed to the |
ARGS |
A list representing key and value sets used to construct the call to |
KEEPFILES |
Logical indicating whether or not keep the downloaded fastq files outside of the R session. If |
FastQFromSRR
is a barebones wrapper for fastq-dump
, it is set up for convenience purposes only and does not add any additional functionality. Requires a functioning installation of the SRAtoolkit.
A list of QualityScaledDNAStringSets
. The composition of this list will be determined by fastq-dump
's splitting arguments.
Nicholas Cooley [email protected]
x <- "ERR10466327" y <- FastQFromSRR(SRR = x)
x <- "ERR10466327" y <- FastQFromSRR(SRR = x)
Take in a pair of vectors representing the columns of an undirected pairs list and return the single linkage clusters.
FindSets(p1, p2, Verbose = FALSE)
FindSets(p1, p2, Verbose = FALSE)
p1 |
Column 1 of a pairs matrix or list. |
p2 |
Column 2 of a pairs matrix or list. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
FindSets
uses a version of the union-find algorithm to collect single linkage clusters from a pairs list. Currently meant to be used inside a wrapper function, but left exposed for user convenience.
A two column matrix with the first column being input nodes, and the second the node representing a single linkage cluster.
Nicholas Cooley [email protected]
set.seed(1986) m <- cbind(as.integer(sample(30, size = 25, replace = TRUE)), as.integer(sample(35, size = 25, replace = TRUE))) Levs <- unique(c(m[, 1], m[, 2])) m <- cbind("1" = as.integer(factor(x = m[, 1L], levels = Levs)), "2" = as.integer(factor(x = m[, 2L], levels = Levs))) z <- FindSets(p1 = m[, 1], p2 = m[, 2])
set.seed(1986) m <- cbind(as.integer(sample(30, size = 25, replace = TRUE)), as.integer(sample(35, size = 25, replace = TRUE))) Levs <- unique(c(m[, 1], m[, 2])) m <- cbind("1" = as.integer(factor(x = m[, 1L], levels = Levs)), "2" = as.integer(factor(x = m[, 2L], levels = Levs))) z <- FindSets(p1 = m[, 1], p2 = m[, 2])
Ancestral states for binary traits can be inferred from presence/absence patterns at the tips of a dendrogram using Fitch Parsimony. This function works for an arbitrary number of states on bifurcating dendrogram
objects.
FitchParsimony(dend, num_traits, traits_list, initial_state=rep(0L,num_traits), fill_ambiguous=TRUE)
FitchParsimony(dend, num_traits, traits_list, initial_state=rep(0L,num_traits), fill_ambiguous=TRUE)
dend |
An object of class |
num_traits |
The number of traits to inferred, as an integer. |
traits_list |
A list of character vectors, where the |
initial_state |
The state assumed for the root node. Set to |
fill_ambiguous |
If |
Fitch Parismony allows for fast inference of ancestral states of binary traits. The algorithm proceeds in three steps.
First, traits are inferred upwards based on child nodes. If the child nodes have the same state (1/1
or 0/0
), then the parent node is also set to that state. If the states are different, the parent node is set to 2
, denoting an ambiguous entry. If one child is ambiguous and the other is not, the parent is set to the non-ambiguous entry.
Second, traits are inferred downward to attempt to fill in ambiguous entries. If a node is not ambiguous but its child is, the child's state is set to the parent state. If specified, the root node's state is set to initial_state
prior to this step.
Third, traits that remain ambiguous are optionally filled in (only if fill_ambiguous
is set to TRUE
). This proceeds by randomly setting ambiguous traits to either 1
or 0
.
The result is stored in the FitchState
attribute within each node.
A dendrogram with attribute FitchState
set for each node, where this attribute is a binary vector of length num_traits
.
It's FitchParsimony because this implementation is entirely in R, as opposed to internal SynExtend
methods that utilize a slightly faster C-based implementation that is not user-exposed.
Aidan Lakshman [email protected]
Fitch, Walter M. Toward defining the course of evolution: minimum change for a specific tree topology. Systematic Biology, 1971. 20(4): p. 406-416.
d <- as.dendrogram(hclust(dist(USArrests), "ave")) labs <- labels(d) # Defining some presence absence patterns set.seed(123L) pa_1 <- sample(labs, 15L) pa_2 <- sample(labs, 20L) # inferring ancestral states fpd <- FitchParsimony(d, 2L, list(pa_1, pa_2)) # Checking a state attr(fpd[[1L]], 'FitchState') # Visualizing the results for the first pattern # Tips show P/A patterns, edges show gain/loss (green/red) fpd <- dendrapply(fpd, \(x){ ai <- 1L s <- attr(x, 'FitchState') l <- list() if(is.leaf(x)){ # coloring tips based presence/absence l$col <- ifelse(s[ai]==1L, 'green', 'red') l$pch <- 19 attr(x, 'nodePar') <- l } else { # coloring edges based on gain/loss for(i in seq_along(x)){ sc <- attr(x[[i]], 'FitchState') if(s[ai] != sc[ai]){ l$col <- ifelse(s[ai] == 1L, 'red', 'green') } else { l$col <- 'black' } attr(x[[i]], 'edgePar') <- l } } x }, how='post.order') plot(fpd, leaflab='none')
d <- as.dendrogram(hclust(dist(USArrests), "ave")) labs <- labels(d) # Defining some presence absence patterns set.seed(123L) pa_1 <- sample(labs, 15L) pa_2 <- sample(labs, 20L) # inferring ancestral states fpd <- FitchParsimony(d, 2L, list(pa_1, pa_2)) # Checking a state attr(fpd[[1L]], 'FitchState') # Visualizing the results for the first pattern # Tips show P/A patterns, edges show gain/loss (green/red) fpd <- dendrapply(fpd, \(x){ ai <- 1L s <- attr(x, 'FitchState') l <- list() if(is.leaf(x)){ # coloring tips based presence/absence l$col <- ifelse(s[ai]==1L, 'green', 'red') l$pch <- 19 attr(x, 'nodePar') <- l } else { # coloring edges based on gain/loss for(i in seq_along(x)){ sc <- attr(x[[i]], 'FitchState') if(s[ai] != sc[ai]){ l$col <- ifelse(s[ai] == 1L, 'red', 'green') } else { l$col <- 'black' } attr(x[[i]], 'edgePar') <- l } } x }, how='post.order') plot(fpd, leaflab='none')
Though the function PairSummaries provides an argument allowing users to ask for alignments, given the time consuming nature of that process on large data, models are provided for predicting PIDs of pairs based on k-mer statistics without performing alignments.
data("Generic")
data("Generic")
A model for predicting the PID of a pair of sequences based on the k-mers that were used to link the pair.
The format is an object of class “glm”.
data(Generic)
data(Generic)
Generate a DataFrame of gene calls from a gff3 file
gffToDataFrame(GFF, AdditionalAttrs = NULL, AdditionalTypes = NULL, RawTableOnly = FALSE, Verbose = FALSE)
gffToDataFrame(GFF, AdditionalAttrs = NULL, AdditionalTypes = NULL, RawTableOnly = FALSE, Verbose = FALSE)
GFF |
A url or filepath specifying a gff3 file to import |
AdditionalAttrs |
A vector of character strings to designate the attributes to pull. Default Attributes include: “ID”, “Parent”, “Name”, “gbkey”, “gene”, “product”, “protein_id”, “gene_biotype”, “transl_table”, and “Note”. |
AdditionalTypes |
A vector of character strings to query from the the “Types” column. Default types are limited to “Gene” and “Pseudogene”, but any possible entry for “Type” in a gff3 format can be added, such as “rRNA”, or “CRISPR_REPEAT”. |
RawTableOnly |
Logical specifying whether to return the raw imported GFF without complex parsing. Remains as a holdover from function construction and debugging. For simple gff3 import see |
Verbose |
Logical specifying whether to print a progress bar and time difference. |
Import a gff file into a rectangular parsable object.
A DataFrame with relevant information extracted from a GFF.
Nicholas Cooley [email protected]
ImportedGFF <- gffToDataFrame(GFF = system.file("extdata", "GCF_021065005.1_ASM2106500v1_genomic.gff.gz", package = "SynExtend"), Verbose = TRUE)
ImportedGFF <- gffToDataFrame(GFF = system.file("extdata", "GCF_021065005.1_ASM2106500v1_genomic.gff.gz", package = "SynExtend"), Verbose = TRUE)
Syntenic blocks describe where order is shared between two sequences. These blocks are made up of exact match hits. These hits can be overlayed on the locations of sequence features to clearly illustrate where exact sequence similarity is shared between pairs of sequence features.
## S3 method for class 'LinkedPairs' print(x, quote = FALSE, right = TRUE, ...)
## S3 method for class 'LinkedPairs' print(x, quote = FALSE, right = TRUE, ...)
x |
An object of class |
quote |
Logical indicating whether to print the output surrounded by quotes. |
right |
Logical specifying whether to right align strings. |
... |
Other arguments for |
Objects of class LinkedPairs
are stored as square matrices of list elements with dimnames
derived from the dimnames
of the object of class ”Synteny” from which it was created. The diagonal of the matrix is only filled if OutputFormat
”Comprehensive” is selected in NucleotideOverlap
, in which case it will be filled with the gene locations supplied to GeneCalls
. The upper triangle is always filled, and contains location information in nucleotide space for all syntenic hits that link features between sequences in the form of an integer matrix with named columns. ”QueryGene” and ”SubjectGene” correspond to the integer rownames of the supplied gene calls. ”QueryIndex” and ”SubjectIndex” correspond to ”Index1” and ”Index2” columns of the source synteny object position. Remaining columns describe the exact positioning and size of extracted hits. The lower triangle is not filled if OutputFormat
”Sparse” is selected and contains relative displacement positions for the 'left-most' and 'right-most' hit involved in linking the particular features indicated in the related line up the corresponding position in the upper triangle.
The object serves only as a simple package for input data to the PairSummaries
function, and as such may not be entirely user friendly. However it has been left exposed to the user should they find this data interesting.
An object of class ”LinkedPairs”.
Nicholas Cooley [email protected]
Wrapper to create BLAST databases for subsequent queries using the commandline BLAST tool directly from R.
Can operate on an XStringSet
or a FASTA
file.
This function requires the BLAST+ commandline tools, which can be downloaded here.
MakeBlastDb(seqs, dbtype=c('prot', 'nucl'), dbname=NULL, dbpath=NULL, extraArgs='', createDirectory=FALSE, verbose=TRUE)
MakeBlastDb(seqs, dbtype=c('prot', 'nucl'), dbname=NULL, dbpath=NULL, extraArgs='', createDirectory=FALSE, verbose=TRUE)
seqs |
Sequence(s) to create a BLAST database from. This can be either an
|
dbtype |
Either |
dbname |
Name of the resulting database. If not provided, defaults to
a random string prefixed by |
dbpath |
Path where database should be created. If not provided,
defaults to |
extraArgs |
Additional arguments to be passed to the query executed on the command line. This should be a single character string. |
createDirectory |
Should a directory be created for the database if it doesn't exist? If |
verbose |
Should output be displayed? |
This offers a quick way to create BLAST databases from R. This function essentially wraps the makeblastdb
commandline function. All arguments supported by makeblastdb
are supported in the extraArgs
argument.
Returns a length 2 named character vector specifying the name of the BLAST database and the path to it.
Aidan Lakshman [email protected]
#
#
Calculates Moran's I to measure spatial autocorrelation for a set of signals dispersed in space.
MoransI(values, weights, alternative='two.sided')
MoransI(values, weights, alternative='two.sided')
values |
Numeric vector containing signals for each point in space. |
weights |
Distances between each point in space. This should be a numeric object of class |
alternative |
For hypothesis testing against the null of no spatial correlation, how should a p-value be calculated? Should be one of |
Moran's I is a measure of how much the spatial arrangement of a set of datapoints correlates with the value of each datapoint. The index takes a value in the range , with values close to 1 indicating high correlation between location and value (points have increasingly similar values as they increase in proximity), values close to -1 indicating anticorrelation(points have increasingly different values as they increase in proximity), and values close to 0 indicating no correlation.
The value itself is calculated as:
Here, is the number of points,
is the distance between points
and
,
(the sum of all the weights),
is the value of point
, and
is the sample mean of the values.
Moran's I has a closed form calculation for variance and expected value, which are calcalated within this function. The full form of the variance is fairly complex, but all the equations are available for reference here.
A p-value is estimated using the expected value and variance using a null hypothesis of no spatial autocorrelation, and the alternative hypothesis specified in the alternative
argument. Note that if fewer than four datapoints are supplied, the variance of Moran's I is infinite. The function will return a standard deviation of Inf
and a p-value of 1 in this case.
A list
object containing the following named values:
observed
: The value of Moran's I (numeric in the range ).
expected
: The expected value of Moran's I for the input data.
sd
: The standard deviation of Moran's I for the input data.
p.value
: The p-value for the input data, calculated with the alternative hypothesis as specified in alternative
.
Aidan Lakshman [email protected]
Moran, P. A. P., Notes on Continuous Stochastic Phenomena. Biometrika, 1950. 37(1): 17-23.
Gittleman, J. L. and M. Kot., Adaptation: Statistics and a Null Model for Estimating Phylogenetic Effects. Systematic Zoology, 1990. 39:227-241.
# Make a distance matrix for a set of 50 points # These are just random numbers in the range [0.1,2] NUM_POINTS <- 50 distmat <- as.dist(matrix(runif(NUM_POINTS**2, 0.1, 2), ncol=NUM_POINTS)) # Generate some random values for each of the points vals <- runif(NUM_POINTS, 0, 3) # Calculate Moran's I MoransI(vals, distmat, alternative='two.sided') # effect size should be pretty small # and p-value close to 0.5 # since this is basically random data
# Make a distance matrix for a set of 50 points # These are just random numbers in the range [0.1,2] NUM_POINTS <- 50 distmat <- as.dist(matrix(runif(NUM_POINTS**2, 0.1, 2), ncol=NUM_POINTS)) # Generate some random values for each of the points vals <- runif(NUM_POINTS, 0, 3) # Calculate Moran's I MoransI(vals, distmat, alternative='two.sided') # effect size should be pretty small # and p-value close to 0.5 # since this is basically random data
A function for concisely tabulating where genomic features are connected by syntenic hits.
NucleotideOverlap(SyntenyObject, GeneCalls, LimitIndex = FALSE, AcceptContigNames = TRUE, Verbose = FALSE)
NucleotideOverlap(SyntenyObject, GeneCalls, LimitIndex = FALSE, AcceptContigNames = TRUE, Verbose = FALSE)
SyntenyObject |
An object of class “Synteny” built from the |
GeneCalls |
A named list of objects of class “DFrame” built from |
LimitIndex |
Logical indicating whether to limit which indices in a synteny object to query. |
AcceptContigNames |
Match names of contigs between gene calls object and synteny object. Where relevant, the first white space and everything following are removed from contig names. If “TRUE”, NucleotideOverlap assumes that the contigs at each position in the synteny object and “GeneCalls” object are in the same order. Is automatically set to TRUE when “GeneCalls” are of class “GRanges”. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
Builds a matrix of lists that contain information about linked pairs of genomic features.
An object of class “LinkedPairs”. “LinkedPairs” is fundamentally just a list in the form of a matrix. The lower triangle of the matrix is populated with matrices that contain all kmer hits from the “Synteny” object that link features from the “GeneCalls” object. The upper triangle is populated by matrices of the summaries of those hits by feature. The diagonal is populated by named vectors of the lengths of the contigs, much like in the “Synteny” object. The “LinkedPairs” object also contains a “GeneCalls” attribute that contains the user supplied features in a slightly more trimmed down form. This allows users to only need to supply gene calls once and not again in the “PairSummaries” function.
Nicholas Cooley [email protected]
data("Endosymbionts_GeneCalls", package = "SynExtend") data("Endosymbionts_Synteny", package = "SynExtend") Links <- NucleotideOverlap(SyntenyObject = Endosymbionts_Synteny, GeneCalls = Endosymbionts_GeneCalls, LimitIndex = FALSE, Verbose = TRUE)
data("Endosymbionts_GeneCalls", package = "SynExtend") data("Endosymbionts_Synteny", package = "SynExtend") Links <- NucleotideOverlap(SyntenyObject = Endosymbionts_Synteny, GeneCalls = Endosymbionts_GeneCalls, LimitIndex = FALSE, Verbose = TRUE)
Takes in a “LinkedPairs” object and gene calls, and returns a data.frame of paired features.
PairSummaries(SyntenyLinks, DBPATH, PIDs = FALSE, Score = FALSE, IgnoreDefaultStringSet = FALSE, Verbose = FALSE, Model = "Generic", DefaultTranslationTable = "11", AcceptContigNames = TRUE, OffSetsAllowed = NULL, Storage = 1, ...)
PairSummaries(SyntenyLinks, DBPATH, PIDs = FALSE, Score = FALSE, IgnoreDefaultStringSet = FALSE, Verbose = FALSE, Model = "Generic", DefaultTranslationTable = "11", AcceptContigNames = TRUE, OffSetsAllowed = NULL, Storage = 1, ...)
SyntenyLinks |
A |
DBPATH |
A SQLite connection object or a character string specifying the path to the database file constructed from DECIPHER's |
PIDs |
Logical indicating whether to provide a PID for each pair. If |
Score |
Logical indicating whether to provide a length normalized score with DECIPHER's |
IgnoreDefaultStringSet |
Logical indicating alignment type preferences. If |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
Model |
A character string specifying a model to use to predict PIDs without performing an alignment. By default this argument is “Generic” specifying a generic PID prediction model based on PIDs computed from a randomly selected set of genomes. Currently no other models are included. Users may also supply their own model of type “glm” if they so desire in the form of an RData file. This model will need to take in some, or of the columns of statistics per pair that PairSummaries supplies. |
DefaultTranslationTable |
A character used to set the default translation table for |
AcceptContigNames |
Match names of contigs between gene calls object and synteny object. Where relevant, the first white space and everything following are removed from contig names. If |
OffSetsAllowed |
Defaults to |
Storage |
Numeric indicating the approximate size a user wishes to allow for holding |
... |
Arguments to be passed to |
The LinkedPairs
object generated by NucleotideOverlap
is a container for raw data that describes possible orthologous relationships, however ultimate assignment of orthology is up to user discretion. PairSummaries
generates a clear table with relevant statistics for a user to work with as they choose. The option to align all pairs, though onerous can allow users to apply a hard threshold to predictions by PID, while built in models can allow more expedient thresholding from predicted PIDs.
A data.frame of class “data.frame” and “PairSummaries” of paired genes that are connected by syntenic hits. Contains columns describing the k-mers that link the pair. Columns “p1” and “p2” give the location ids of the the genes in the pair in the form “DatabaseIdentifier_ContigIdentifier_GeneIdentifier”. “ExactMatch” provides an integer representing the exact number of nucleotides contained in the linking k-mers. “TotalKmers” provides an integer describing the number of distinct k-mers linking the pair. “MaxKmer” provides an integer describing the largest k-mer that links the pair. A column titled “Consensus” provides a value between zero and 1 indicating whether the kmers that link a pair of features are in the same position in each feature, with 1 indicating they are in exactly the same position and 0 indicating they are in as different a position as is possible. The “Adjacent” column provides an integer value ranging between 0 and 2 denoting whether a feature pair's direct neighbors are also paired. Gap filled pairs neither have neighbors, or are included as neighbors. The “TetDist” column provides the euclidean distance between oligonucleotide - of size 4 - frequences between predicted pairs. “PIDType” provides a character vector with values of “NT” where either of the pair indicates it is not a translatable sequence or “AA” where both sequences are translatable. If users choose to perform pairwise alignments there will be a “PID” column providing a numeric describing the percent identity between the two sequences. If users choose to predict PIDs using their own, or a provided model, a “PredictedPID” column will be provided.
Nicholas Cooley [email protected]
FindSynteny
, Synteny-class
, NucleotideOverlap
# this function will be deprecated soon, # please see the new SummarizePairs() function. DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Pairs <- PairSummaries(SyntenyLinks = Endosymbionts_LinkedFeatures, PIDs = FALSE, DBPATH = DBPATH, Verbose = TRUE)
# this function will be deprecated soon, # please see the new SummarizePairs() function. DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Pairs <- PairSummaries(SyntenyLinks = Endosymbionts_LinkedFeatures, PIDs = FALSE, DBPATH = DBPATH, Verbose = TRUE)
Calculates distance between two unrooted phylogenies using a variety of metrics.
PhyloDistance(dend1, dend2, Method=c("CI", "RF", "KF", "JRF"), RawScore=FALSE, JRFExp=2)
PhyloDistance(dend1, dend2, Method=c("CI", "RF", "KF", "JRF"), RawScore=FALSE, JRFExp=2)
dend1 |
An object of class |
dend2 |
An object of class |
Method |
Method to use for calculating tree distances. The following values are supported: |
RawScore |
If |
JRFExp |
|
This function implements a variety of tree distances, specified by the value of Method
. The following values are supported, along with links to documentation pages for each function:
"RF"
: Robinson-Foulds Distance
"JRF"
: Jaccard-Robinson-Foulds Distance, equivalent to the Nye Distance Metric when JRFVal=1
Information on each of these algorithms, how scores are calculated, and references to literature can be found at the above links. Method "CI"
is selected by default due to recent work showing this method as the most robust tree distance metric under general conditions.
Returns a normalized distance, with 0 indicating identical trees and 1 indicating
maximal difference. If the trees have no leaves in common, the function will return 1
.
If RawScore=TRUE
, returns a vector of the components used to calculate the distance. This is typically a length 3 vector, but specific details can be found on the description for each algorithm.
Note that this function requires the input dendrograms to be labeled alike (ex.
leaf labeled abc
in dend1
represents the same species as
leaf labeled abc
in dend2
).
Labels can easily be modified using dendrapply
.
Aidan Lakshman [email protected]
Clustering Information Distance
Jaccard-Robinson-Foulds Distance
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # Robinson-Foulds Distance PhyloDistance(tree1, tree2, Method="RF") # Clustering Information Distance PhyloDistance(tree1, tree2, Method="CI") # Kuhner-Felsenstein Distance PhyloDistance(tree1, tree2, Method="KF") # Nye Distance Metric PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1) # Jaccard-Robinson-Foulds Distance PhyloDistance(tree1, tree2, Method="JRF", JRFExp=2)
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # Robinson-Foulds Distance PhyloDistance(tree1, tree2, Method="RF") # Clustering Information Distance PhyloDistance(tree1, tree2, Method="CI") # Kuhner-Felsenstein Distance PhyloDistance(tree1, tree2, Method="KF") # Nye Distance Metric PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1) # Jaccard-Robinson-Foulds Distance PhyloDistance(tree1, tree2, Method="JRF", JRFExp=2)
Calculate distance between two unrooted phylogenies using mutual clustering information of branch partitions.
This function is called as part of PhyloDistance
and calculates tree distance using the clustering information
approach first described in Smith (2020). This function iteratively pairs
internal tree branches of a phylogeny based on their similarity, then scores
overall similarity as the sum of these measures. The similarity score is then
converted to a distance by normalizing by the average entropy of the two trees.
This metric has been demonstrated to outperform numerous other metrics in capabilities;
see the original publication cited in References for more information.
Users may wish to use the actual similarity values rather than a distance metric;
the option to specify RawScore=TRUE
is provided for this case. Distance
is calculated as , where
,
is the entropy of the
'th tree, and
is the similarity score between them. As shown in
the original publication, this satisfies the necessary requirements to be considered
a distance metric. Setting
RawScore=TRUE
will instead return a vector with
, where
is an approximation for the two sided p-value of the result based on random simulations from Smith (2020).
Returns a normalized distance, with 0 indicating identical trees and 1 indicating maximal difference. Note that branch lengths are not considered, so two trees with different branch lengths may return a distance of 0.
If RawScore=TRUE
, returns a named length 4 vector with the first entry the similarity
score, subsequent entries the entropy values for each tree, and the last entry the approximate p-value for the result based on simulations.
If the trees have no leaves in common, the function will return 1
if
RawScore=FALSE
, and c(0, NA, NA, NA)
if TRUE
.
Note that this function requires the input dendrograms to be labeled alike (ex.
leaf labeled abc
in dend1
represents the same species as
leaf labeled abc
in dend2
).
Labels can easily be modified using dendrapply
.
Aidan Lakshman [email protected]
Smith, Martin R. Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 2020. 36(20):5007-5013.
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # get RF distance PhyloDistance(tree1, tree2, Method="CI") # get similarity score with individual entropies PhyloDistance(tree1, tree2, Method="CI", RawScore=TRUE)
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # get RF distance PhyloDistance(tree1, tree2, Method="CI") # get similarity score with individual entropies PhyloDistance(tree1, tree2, Method="CI", RawScore=TRUE)
Calculate JRF distance between two unrooted phylogenies.
This function is called as part of PhyloDistance
and calculates the Jaccard-Robinson-Foulds distance between two unrooted phylogenies. Each dendrogram is first pruned to only internal branches implying a partition in the shared leaf set; trivial partitions (where one leaf set contains 1 or 0 leaves) are ignored.
The total score is calculated by pairing branches and scoring their similarity. For a set of two branches that partition the leaves into
and
(resp.), the distance between the branches is calculated as:
where are chosen to maximize the score of the pairing, and
the value of
ExpVal
. The sum of these scores for all branches produces the overall distance between the two trees, which is then normalized by the number of branches in each tree.
There are a few special cases to this distance. If ExpVal=1
, the distance is equivalent to the metric introduced in Nye et al. (2006). As ExpVal
approaches infinity, the value becomes close to the (non-Generalized) Robinson Foulds Distance.
Returns a normalized distance, with 0 indicating identical trees and 1 indicating maximal difference.
If RawScore=TRUE
, returns a named length 3 vector with the first entry the summed distance score over the branch pairings, and the subsequent entries the number of partitions for each tree.
If the trees have no leaves in common, the function will return 1
if
RawScore=FALSE
, and c(0, NA, NA)
if TRUE
.
Note that this function requires the input dendrograms to be labeled alike (ex.
leaf labeled abc
in dend1
represents the same species as
leaf labeled abc
in dend2
).
Labels can easily be modified using dendrapply
.
Aidan Lakshman [email protected]
Nye, T. M. W., Liò, P., & Gilks, W. R. A novel algorithm and web-based tool for comparing two alternative phylogenetic trees. Bioinformatics, 2006. 22(1): 117–119.
Böcker, S., Canzar, S., & Klau, G. W.. The generalized Robinson-Foulds metric. Algorithms in Bioinformatics, 2013. 8126: 156–169.
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # Nye Metric PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1) # Jaccard-RobinsonFoulds PhyloDistance(tree1, tree2, Method="JRF", JRFExp=2) # Good approximation to RF Dist (note RFDist is much faster for this) PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1000) PhyloDistance(tree1, tree2, Method="RF")
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # Nye Metric PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1) # Jaccard-RobinsonFoulds PhyloDistance(tree1, tree2, Method="JRF", JRFExp=2) # Good approximation to RF Dist (note RFDist is much faster for this) PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1000) PhyloDistance(tree1, tree2, Method="RF")
Calculate KF distance between two unrooted phylogenies.
This function is called as part of PhyloDistance
and calculates Kuhner-Felsenstein distance between two unrooted phylogenies. Each dendrogram is first pruned to only internal branches implying a partition in the shared leaf set; trivial partitions (where one leaf set contains 1 or 0 leaves) are ignored. The total score is calculated as the sum of squared differences between lengths of branches implying equivalent partitions. If a particular branch is unique to a given tree, it is treated as having length 0 in the other tree. The final score is normalized by the sum of squared lengths of all internal branches of both trees, resulting in a final distance that ranges from 0 to 1.
Returns a normalized distance, with 0 indicating identical trees and 1 indicating maximal difference.
If the trees have no leaves in common, the function will return 1
.
Note that this function requires the input dendrograms to be labeled alike (ex.
leaf labeled abc
in dend1
represents the same species as
leaf labeled abc
in dend2
).
Labels can easily be modified using dendrapply
.
Aidan Lakshman [email protected]
Robinson, D.F. and Foulds, L.R. Comparison of phylogenetic trees. Mathematical Biosciences, 1987. 53(1–2): 131–147.
Kuhner, M. K. and Felsenstein, J. Simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution, 1994. 11: 459–468.
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # get KF distance PhyloDistance(tree1, tree2, Method="KF")
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # get KF distance PhyloDistance(tree1, tree2, Method="KF")
Calculate RF distance between two unrooted phylogenies.
This function is called as part of PhyloDistance
and calculates Robinson-Foulds distance between two unrooted phylogenies. Each dendrogram is first pruned to only internal branches implying a partition in the shared leaf set; trivial partitions (where one leaf set contains 1 or 0 leaves) are ignored. The total score is calculated as the number of unique partitions divided by the total number of partitions in both trees. Setting RawScore=TRUE
will instead return a vector with
, corresponding to the shared partitions and partitions in the first and second trees (respectively).
This algorithm incorporates some optimizations from Pattengale et al. (2007) to improve computation time of the original fast RF algorithm detailed in Day (1985).
Returns a normalized distance, with 0 indicating identical trees and 1 indicating maximal difference. Note that branch lengths are not considered, so two trees with different branch lengths may return a distance of 0.
If RawScore=TRUE
, returns a named length 3 vector with the first entry the number of unique partitions, and the subsequent entries the number of partitions for each tree.
If the trees have no leaves in common, the function will return 1
if
RawScore=FALSE
, and c(0, NA, NA)
if TRUE
.
Note that this function requires the input dendrograms to be labeled alike (ex.
leaf labeled abc
in dend1
represents the same species as
leaf labeled abc
in dend2
).
Labels can easily be modified using dendrapply
.
Aidan Lakshman [email protected]
Robinson, D.F. and Foulds, L.R. Comparison of phylogenetic trees. Mathematical Biosciences, 1987. 53(1–2): 131–147.
Day, William H.E. Optimal algorithms for comparing trees with labeled leaves. Journal of classification, 1985. 2(1): 7-28.
Pattengale, N.D., Gottlieb, E.J., and Moret, B.M. Efficiently computing the Robinson-Foulds metric. Journal of computational biology, 2007. 14(6): 724-735.
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # get RF distance PhyloDistance(tree1, tree2, Method="RF") # get number of unique splits per tree PhyloDistance(tree1, tree2, Method="RF", RawScore=TRUE)
# making some toy dendrograms set.seed(123) dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8)) tree1 <- as.dendrogram(hclust(dm1)) tree2 <- as.dendrogram(hclust(dm2)) # get RF distance PhyloDistance(tree1, tree2, Method="RF") # get number of unique splits per tree PhyloDistance(tree1, tree2, Method="RF", RawScore=TRUE)
EvoWeb objects are outputted from predict.EvoWeaver
.
This function plots the predictions in the object using a force-directed embedding of connections in the adjacency matrix.
This function is still a work in progress.
## S3 method for class 'EvoWeb' plot(x, NumSims=10, Gravity=0.05, Coulomb=0.1, Connection=5, MoveRate=0.25, Cutoff=0.2, ColorPalette=topo.colors, Verbose=TRUE, ...)
## S3 method for class 'EvoWeb' plot(x, NumSims=10, Gravity=0.05, Coulomb=0.1, Connection=5, MoveRate=0.25, Cutoff=0.2, ColorPalette=topo.colors, Verbose=TRUE, ...)
x |
A EvoWeb object. See |
NumSims |
Number of iterations to run the model for. |
Gravity |
Strength of Gravity force. See 'Details'. |
Coulomb |
Strength of Coulomb force. See 'Details'. |
Connection |
Strength of Connective force. See 'Details'. |
MoveRate |
Controls how far each point moves in each iteration. |
Cutoff |
Cutoff value; if |
ColorPalette |
Color palette for graphing. Valid inputs are any palette available in
|
Verbose |
Logical indicating whether to print progress bars and messages. Defaults to |
... |
Additional parameters for consistency with generic. |
This function plots the EvoWeb
object using a force-directed embedding.
This embedding has three force components:
Gravity Force: Attractive force pulling nodes towards (0,0)
Coulomb Force: Repulsive force pushing close nodes away from each other
Connective Force: Tries to push node connections to equal corresponding values in the adjacency matrix
The parameters in the function are sufficient to get an embedding, though users are welcome to try to tune them for a better visualization. This function is meant to aid with visualization of the adjacency matrix, not for concrete analyses of clusters.
The function included in this release is early stage. Next release cycle will update this function with an updated version of this algorithm to improve plotting, visualization, and runtime.
No return value; creates a plot in the graphics window.
Aidan Lakshman [email protected]
exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes) # Subset isn't necessary but is faster for a working example # Same w/ method='Jaccard' evoweb <- predict(ew, Method='Jaccard', Subset=1:50) plot(evoweb)
exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes) # Subset isn't necessary but is faster for a working example # Same w/ method='Jaccard' evoweb <- predict(ew, Method='Jaccard', Subset=1:50) plot(evoweb)
This S3 method predicts pairwise functional associations between gene groups encoded in a EvoWeaver
object.
This returns an object of type EvoWeb
, which is essentially an adjacency
matrix with some extra S3 methods to make printing cleaner.
## S3 method for class 'EvoWeaver' predict(object, Method='Ensemble', Subset=NULL, Processors=1L, MySpeciesTree=SpeciesTree(object), PretrainedModel=NULL, NoPrediction=FALSE, ReturnRawData=FALSE, Verbose=TRUE, ...)
## S3 method for class 'EvoWeaver' predict(object, Method='Ensemble', Subset=NULL, Processors=1L, MySpeciesTree=SpeciesTree(object), PretrainedModel=NULL, NoPrediction=FALSE, ReturnRawData=FALSE, Verbose=TRUE, ...)
object |
A EvoWeaver object |
Method |
Method(s) to use for prediction. This can be a character vector with multiple entries for predicting using multiple methods. See 'Details' for more information. |
Subset |
Subset of data to predict on. This can either be a vector or a If a vector, prediction proceeds for all possible pairs of elements specified in the vector
(either by name, for character vector, or by index, for numeric vector). For example,
If a matrix, subset is interpreted as a matrix of pairs, where each row of the matrix specifies a pair to evaluate. These can also be specifed by name (character) or by index (numeric).
|
Processors |
Number of cores to use for methods that support multithreaded execution.
Setting to |
MySpeciesTree |
Phylogenetic tree of all genomes in the dataset. Required for |
PretrainedModel |
A pretrained model for use with ensemble predictions. If unspecified when
Has no effect if |
NoPrediction |
For If If |
ReturnRawData |
Internal parameter used for ensemble predictions. Should not be set by the user. |
Verbose |
Logical indicating whether to print progress bars and messages. Defaults to |
... |
Additional parameters for other predictors and consistency with generic. |
predict.EvoWeaver
wraps several methods to create an easy interface for multiple prediction types. Method='Ensemble'
is the default value, but each of the component analyses can also be accessed. The following is a list of all algorithms implemented in EvoWeaver (* denotes algorithms used in the EvoWeaver publication):
'Ensemble'
: Ensemble prediction combining individual coevolutionary predictors. See Note
below.
* 'Jaccard'
: Jaccard distance of Presence/Absence (P/A) profiles
'Hamming'
: Hamming distance of P/A profiles
* 'MutualInformation'
: MI of P/A profiles
* 'PAPV'
: 1-p_value
of P/A profiles
'ProfDCA'
: Direct Coupling Analysis of P/A profiles
'Behdenna'
: Analysis of Gain/Loss events following Behdenna et al. (2016)
* 'CorrGL'
: Correlation of ancestral Gain/Loss events
* 'GainLoss'
: Score-based method based on distance between inferred ancestral Gain/Loss events
* 'MirrorTree'
: MirrorTree using Random Projection for dimensionality reduction
* 'ContextTree'
: MirrorTree with Random Projection correcting for species tree and P/A conservation
* 'Coloc'
: Co-localization analysis
* 'ColocMoran'
: Co-localization analysis using Moran's I for phylogenetic correction and significance
* 'TranscripMI'
: Mutual Information of Transcriptional Direction
* 'NVDT'
: Correlation of distribution of sequence level residues following Zhao et al. (2022)
* 'ResidueMI'
: Mutual information of sites in multiple sequence alignment
The best performing individual predictors are c('CorrGL', 'GainLoss', 'MirrorTree', 'Jaccard')
. Users interesting in running quick analyses should use c('CorrGL', 'GainLoss', 'Jaccard')
.
Additional information and references for each prediction algorithm can be found at the following pages:
This returns a EvoWeb
object, an S3 class that makes formatting and printing of
results slightly nicer. See EvoWeb
for more information.
Different methods require different types of input. The constructor
EvoWeaver
will notify the user which methods are
runnable with the given data. Method Ensemble
automatically selects the
methods that can be run with the given input data.
See EvoWeaver
for more information on input data types.
Returns a EvoWeb object. See EvoWeb
for more info.
The current ensemble method included with EvoWeaver is out of date. EvoWeaver's publication used a random forest model from the randomForest
package for prediction. The next release of EvoWeaver will include multiple new built-in ensemble methods, but in the interim users are recommended to rely on randomForest
or neuralnet
. Planned algorithms are random forests and feed-forward neural networks. Feel free to contact me regarding other models you would like to see added.
If NumCores
is set to NULL
, EvoWeaver will use one less core than is detected, or one core if detectCores()
cannot detect the number of available cores. This is because of a recurring issue
on my machine where the R session takes all available cores and is then locked
out of forking processes, with the only solution to restart the entire R session.
This may be an issue specific to ARM Macs, but out of an abundance of caution
I've made the default setting to be slightly slower but guarantee completion
rather than risk bricking a machine.
Aidan Lakshman [email protected]
EvoWeaver Phylogenetic Profiling Predictors
EvoWeaver Phylogenetic Structure Predictors
EvoWeaver Gene Organization Predictors
EvoWeaver Sequence-Level Predictors
############## ## Prediction with built-in model and data ############### exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes[1:50]) # Subset isn't necessary but is faster for a working example evoweb1 <- predict(ew, Subset=1:10, MySpeciesTree=exData$Tree) # print out results as an adjacency matrix evoweb1 ############### ## Training own ensemble model ############### datavals <- predict(ew, NoPrediction=TRUE) actual_values <- sample(c(0,1), nrow(datavals), replace=TRUE) # This example just picks random numbers # ***Do not do this for your own models*** # Make sure the actual values correspond to the right pairs! datavals[,'y'] <- actual_values myModel <- glm(y~., datavals[,-c(1,2)], family='binomial') testEvoWeaverObject <- EvoWeaver(exData$Genes[51:60]) evoweb2 <- predict(testEvoWeaverObject, PretrainedModel=myModel) # Print result as a matrix of pairwise scores evoweb2
############## ## Prediction with built-in model and data ############### exData <- get(data("ExampleStreptomycesData")) ew <- EvoWeaver(exData$Genes[1:50]) # Subset isn't necessary but is faster for a working example evoweb1 <- predict(ew, Subset=1:10, MySpeciesTree=exData$Tree) # print out results as an adjacency matrix evoweb1 ############### ## Training own ensemble model ############### datavals <- predict(ew, NoPrediction=TRUE) actual_values <- sample(c(0,1), nrow(datavals), replace=TRUE) # This example just picks random numbers # ***Do not do this for your own models*** # Make sure the actual values correspond to the right pairs! datavals[,'y'] <- actual_values myModel <- glm(y~., datavals[,-c(1,2)], family='binomial') testEvoWeaverObject <- EvoWeaver(exData$Genes[51:60]) evoweb2 <- predict(testEvoWeaverObject, PretrainedModel=myModel) # Print result as a matrix of pairwise scores evoweb2
Given a SynExtend
object with a GeneCalls
attribute, and a DECIPHER
database, return all gene sequences and their translations.
PrepareSeqs(SynExtendObject, DataBase, DefaultTranslationTable = "11", Identifiers = NULL, Storage = 1, Verbose = FALSE)
PrepareSeqs(SynExtendObject, DataBase, DefaultTranslationTable = "11", Identifiers = NULL, Storage = 1, Verbose = FALSE)
SynExtendObject |
An object of class |
DataBase |
A character string pointing to a SQLite database, or a connection to a |
DefaultTranslationTable |
A character vector of length 1 identifying the translation table to use if one is not supplied in the |
Identifiers |
By default |
Storage |
A soft memory limit for how much space to allow when building the resulting object. Translated to Gb. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
PrepareSeqs
returns the sequences of genes and their translations where appropriate.
An object of class FeatureSeqs
.
Nicholas Cooley [email protected]
SummarizePairs
, NucleotideOverlap
, FindSynteny
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") CurrentSeqs <- PrepareSeqs(SynExtendObject = Endosymbionts_LinkedFeatures, DataBase = DBPATH, Verbose = TRUE)
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") CurrentSeqs <- PrepareSeqs(SynExtendObject = Endosymbionts_LinkedFeatures, DataBase = DBPATH, Verbose = TRUE)
A relatively simple k-means clustering approach to drop predicted pairs that belong to clusters with a PID centroid below a specified user threshold.
SelectByK(Pairs, UserConfidence = 0.5, ClusterScalar = 1, MaxClusters = 15L, ReturnAllCommunities = FALSE, Verbose = FALSE, ShowPlot = FALSE, RetainHighest = TRUE)
SelectByK(Pairs, UserConfidence = 0.5, ClusterScalar = 1, MaxClusters = 15L, ReturnAllCommunities = FALSE, Verbose = FALSE, ShowPlot = FALSE, RetainHighest = TRUE)
Pairs |
An object of class |
UserConfidence |
A numeric value greater than 0 and less than 1 that represents a minimum PID centroid that users believe represents a |
ClusterScalar |
A numeric value used to scale selection of how many clusters are used in kmeans clustering. Total within-cluster sum of squares are fit to a right hyperbola, and the half-max is used to select cluster number. “ClusterScalar” is multiplied by the half-max to adjust cluster number selection. |
MaxClusters |
Integer value indicating the largest number of clusters to test in a series of k-means clustering tests. |
ReturnAllCommunities |
A logical value, if “TRUE”, function returns of a list where the second position is a list of “PairSummaries” tables for each k-means cluster. By default is “FALSE”, returning only a “PairSummaries” object of the retained predicted pairs. |
ShowPlot |
Logical indicating whether or not to plot the CDFs for the PIDs of all k-means clusters for the determined cluster number. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
RetainHighest |
Logical indicating whether to retain the cluster with the highest PID centroid in the case where the PID is below the specified user confidence. |
SelectByK
uses a naive k-means routine to select for predicted pairs that belong to clusters whose centroids are greater than or equal to the user specified PID confidence. This means that the confidence is not a minimum, and that pairs with PIDs below the user confidence can be retained. The sum of within cluster sum of squares is used to approximate “knee” selection with the user supplied “ClusterScalar” value. By default, with a “ClusterScalar” value of 1 the half-max of a right-hyperbola fitted to the sum of within-cluster sum of squares is used to pick the cluster number for evaluation, “ClusterScalar” is multiplied by the half-max to tune cluster number selection.
This function is intended to be used at the genome-to-genome comparison level, and not say, at the level of an all-vs-all comparison of many genomes.
An object of class PairSummaries
.
Nicholas Cooley [email protected]
PairSummaries
, NucleotideOverlap
, link{SubSetPairs}
, FindSynteny
# this function will be deprecated soon, # please see the new ClusterByK() function. DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Pairs <- PairSummaries(SyntenyLinks = Endosymbionts_LinkedFeatures, PIDs = TRUE, Score = TRUE, DBPATH = DBPATH, Verbose = TRUE) Pairs02 <- SelectByK(Pairs = Pairs)
# this function will be deprecated soon, # please see the new ClusterByK() function. DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Pairs <- PairSummaries(SyntenyLinks = Endosymbionts_LinkedFeatures, PIDs = TRUE, Score = TRUE, DBPATH = DBPATH, Verbose = TRUE) Pairs02 <- SelectByK(Pairs = Pairs)
Takes in a DNAStringSet
or AAStringSet
representing a pairwise alignment and a subsitution matrix such as those present in PFASUM
, and return a numeric value representing sequence similarity as defined by the substitution matrix.
SequenceSimilarity(Seqs, SubMat, penalizeGapLetter = TRUE, includeTerminalGaps = TRUE, allowNegative = TRUE)
SequenceSimilarity(Seqs, SubMat, penalizeGapLetter = TRUE, includeTerminalGaps = TRUE, allowNegative = TRUE)
Seqs |
A |
SubMat |
A named matrix representing a substitution matrix. If left “NULL” and “Seqs” is a |
penalizeGapLetter |
A logical indicating whether or not to penalize Gap-Letter matches. Defaults to “TRUE”. |
includeTerminalGaps |
A logical indicating whether or not to penalize terminal matches. Defaults to “TRUE”. |
allowNegative |
A logical indicating whether or not allow negative scores. Defaults to “TRUE”. If “FALSE” scores that are returned as less than zero are converted to zero. |
Takes in a DNAStringSet
or AAStringSet
representing a pairwise alignment and a subsitution matrix such as those present in PFASUM
, and return a numeric value representing sequence similarity as defined by the substitution matrix.
Returns a single numeric.
Erik Wright [email protected] Nicholas Cooley [email protected]
AlignSeqs
, AlignProfiles
, AlignTranslation
, DistanceMatrix
db <- system.file("extdata", "Bacteria_175seqs.sqlite", package = "DECIPHER") dna <- SearchDB(db, remove = "all") alignedDNA <- AlignSeqs(dna[1:2]) DNAPlaceholder <- diag(15) dimnames(DNAPlaceholder) <- list(DNA_ALPHABET[1:15], DNA_ALPHABET[1:15]) SequenceSimilarity(Seqs = alignedDNA, SubMat = DNAPlaceholder, includeTerminalGaps = TRUE, penalizeGapLetter = TRUE, allowNegative = TRUE)
db <- system.file("extdata", "Bacteria_175seqs.sqlite", package = "DECIPHER") dna <- SearchDB(db, remove = "all") alignedDNA <- AlignSeqs(dna[1:2]) DNAPlaceholder <- diag(15) dimnames(DNAPlaceholder) <- list(DNA_ALPHABET[1:15], DNA_ALPHABET[1:15]) SequenceSimilarity(Seqs = alignedDNA, SubMat = DNAPlaceholder, includeTerminalGaps = TRUE, penalizeGapLetter = TRUE, allowNegative = TRUE)
The simMat
object is an internally utilized class that provides similar
functionality to the dist
object, but with matrix-like accessors.
Like dist
, this object stores values as a vector, reducing memory by
making use of assumed symmetry. simMat
currently only supports numeric
data types.
## Create a blank sym object simMat(VALUE, nelem, NAMES=NULL, DIAG=FALSE) ## S3 method for class 'vector' as.simMat(x, NAMES=NULL, DIAG=TRUE, ...) ## S3 method for class 'matrix' as.simMat(x, ...) ## S3 method for class 'simMat' print(x, ...) ## S3 method for class 'simMat' as.matrix(x, ...) ## S3 method for class 'simMat' as.data.frame(x, ...) ## S3 method for class 'simMat' Diag(x, ...) ## S3 replacement method for class 'simMat' Diag(x) <- value
## Create a blank sym object simMat(VALUE, nelem, NAMES=NULL, DIAG=FALSE) ## S3 method for class 'vector' as.simMat(x, NAMES=NULL, DIAG=TRUE, ...) ## S3 method for class 'matrix' as.simMat(x, ...) ## S3 method for class 'simMat' print(x, ...) ## S3 method for class 'simMat' as.matrix(x, ...) ## S3 method for class 'simMat' as.data.frame(x, ...) ## S3 method for class 'simMat' Diag(x, ...) ## S3 replacement method for class 'simMat' Diag(x) <- value
VALUE |
Numeric (or |
nelem |
Integer; number of elements represented in the matrix. This corresponds to the
number of rows and columns of the object, so setting |
NAMES |
Character (Optional); names for each row/column. If provided, this should be
a character vector of length equal to |
DIAG |
Logical; Is the diagonal included in the data? If |
x |
Various; for |
value |
Numeric; value(s) to replace diagonal with. |
... |
Additional parameters provided for consistency with generic. |
The simMat
object has a very similar format to dist
objects, but
with a few notable changes:
simMat
objects have streamlined print
and show
methods to make displaying large matrices better. print
accepts an additional argument n
corresponding to the maximum number of rows/columns to print before truncating.
simMat
objects support matrix-style get/set operations like s[1,]
or s[1,3:5]
simMat
objects allow any values on the diagonal, rather than just
zeros as in dist
objects.
simMat
objects support conversion to matrices and data.frame
objects
simMat
objects implement get/set Diag()
methods. Note usage of capitalized Diag
; this is to avoid conflicts and weirdness with using base diag
.
See the examples for details on using these features.
The number of elements printed when calling print
or show
on a
simMat
object is determined by the "SynExtend.simMat"
option.
simMat
and as.simMat
return an object of class "simMat"
. Internally,
the object stores the upper triangle of the matrix similar to how dist
stores objects.
The object has the following attributes (besides "class"
equal to "simMat"
):
nrow |
the number of rows in the matrix implied by the vector |
NAMES |
the names of the rows/columns |
as.matrix(s)
returns the equivalent matrix to a "simMat"
object.
as.data.frame(s)
returns a data.frame
object corresponding to
pairwise similarities.
Aidan Lakshman [email protected]
## Creating a blank simMat object initialized to zeros s <- simMat(0, nelem=20) s ## Print out 5 rows instead of 10 print(s, n=5) ## Create a simMat object with 5 entries from a vector dimn <- 5 vec <- 1:(dimn*(dimn-1) / 2) s1 <- as.simMat(vec, DIAG=FALSE) s1 ## Here we include the diagonal vec <- 1:(dimn*(dimn+1) / 2) s2 <- as.simMat(vec, DIAG=TRUE) s2 ## Subsetting s2[1,] s2[1,3:4] # all entries except first row s2[-1,] # all combos not including 1 s2[-1,-1] ## Replace values (automatically recycled) s2[1,] <- 10 s2 ## Get/set diagonal Diag(s1) Diag(s1) <- 5 s1
## Creating a blank simMat object initialized to zeros s <- simMat(0, nelem=20) s ## Print out 5 rows instead of 10 print(s, n=5) ## Create a simMat object with 5 entries from a vector dimn <- 5 vec <- 1:(dimn*(dimn-1) / 2) s1 <- as.simMat(vec, DIAG=FALSE) s1 ## Here we include the diagonal vec <- 1:(dimn*(dimn+1) / 2) s2 <- as.simMat(vec, DIAG=TRUE) s2 ## Subsetting s2[1,] s2[1,3:4] # all entries except first row s2[-1,] # all combos not including 1 s2[-1,-1] ## Replace values (automatically recycled) s2[1,] <- 10 s2 ## Get/set diagonal Diag(s1) Diag(s1) <- 5 s1
Subsets dendrogram objects based on leaf labels. Subsetting can either be by leaves to keep, or leaves to remove.
NOTE: This man page is specifically for subset.dendogram
, see ?base::subset
for the generic subset
function defined for vectors, matrices, and data frames.
## S3 method for class 'dendrogram' subset(x, subset, invert=FALSE, ...)
## S3 method for class 'dendrogram' subset(x, subset, invert=FALSE, ...)
x |
An object of class |
subset |
A vector of labels to keep (see |
invert |
If set to |
... |
Additional arguments for consistency with generic. |
An object of class 'dendrogram'
corresponding to the subsetted tree.
If none of the labels specified in the subset
argument appear in the tree (or if all do when invert=TRUE
), a warning is thrown and an empty object of class 'dendrogram'
is returned.
Aidan Lakshman [email protected]
d <- as.dendrogram(hclust(dist(USArrests), "ave")) # Show original dendrogram plot(d) # Subset to first 10 labels d1 <- subset(d, labels(d)[1:10]) plot(d1) # Subset d1 to all except the first 2 labels d2 <- subset(d1, labels(d1)[1:2], invert=TRUE) plot(d2)
d <- as.dendrogram(hclust(dist(USArrests), "ave")) # Show original dendrogram plot(d) # Subset to first 10 labels d1 <- subset(d, labels(d)[1:10]) plot(d1) # Subset d1 to all except the first 2 labels d2 <- subset(d1, labels(d1)[1:2], invert=TRUE) plot(d2)
For a given object of class “PairSummaries”, pairs based on either competing predictions, user thresholds on prediction statistics, or both.
SubSetPairs(CurrentPairs, UserThresholds, RejectCompetitors = TRUE, RejectionCriteria = "PID", WinnersOnly = TRUE, Verbose = FALSE)
SubSetPairs(CurrentPairs, UserThresholds, RejectCompetitors = TRUE, RejectionCriteria = "PID", WinnersOnly = TRUE, Verbose = FALSE)
CurrentPairs |
An object of class “PairSummaries”. Can also take in a generic “data.frame”, as long as the feature naming scheme is the same as that followed by all |
UserThresholds |
A named vector where values indicate a threshold for statistics to be above, and names designate which statistic to threshold on. |
RejectCompetitors |
A logical that defaults to “TRUE”. Allowing users to choose to remove competing predictions. When set to “FALSE”, no competitor rejection is performed. When “TRUE” all competing pairs with the exception of the best pair as determined by “RejectionCriteria” are rejected. Can additionally be set to a numeric or integer, in which case only competing predictions below that value are dropped. |
RejectionCriteria |
A character indicating which column value competitor rejection should reference. Defaults to “PID”. |
WinnersOnly |
A logical indicating whether or not to return just the pairs that are selected. Defaults to “TRUE” to return a subset object of class “PairSummaries”. When “FALSE”, function returns a list of two “PairSummaries” objects, one of the selected pairs, and the second of the rejected pairs. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
SubSetPairs
uses a naive competitor rejection algorithm to remove predicted pairs when nodes are predicted to be paired to multiple nodes within the same index.
An object of class “PairSummaries”, or a list of two “PairSummaries” objects.
Nicholas Cooley [email protected]
PairSummaries
NucleotideOverlap
# expected to be deprecated soon... data("Endosymbionts_Pairs03", package = "SynExtend") # remove competitors under default conditions Pairs2 <- SubSetPairs(CurrentPairs = Endosymbionts_Pairs03, Verbose = TRUE) THRESH <- c(0.5, 21) names(THRESH) <- c("Consensus", "TotalMatch") # remove pairs only based on user defined thresholds Pairs3 <- SubSetPairs(CurrentPairs = Endosymbionts_Pairs03, UserThresholds = THRESH, RejectCompetitors = FALSE, Verbose = TRUE)
# expected to be deprecated soon... data("Endosymbionts_Pairs03", package = "SynExtend") # remove competitors under default conditions Pairs2 <- SubSetPairs(CurrentPairs = Endosymbionts_Pairs03, Verbose = TRUE) THRESH <- c(0.5, 21) names(THRESH) <- c("Consensus", "TotalMatch") # remove pairs only based on user defined thresholds Pairs3 <- SubSetPairs(CurrentPairs = Endosymbionts_Pairs03, UserThresholds = THRESH, RejectCompetitors = FALSE, Verbose = TRUE)
Given the correct set of SynExtend
objects and a DECIPHER
database, return a data.frame of summarized genomic feature pairs. SummarizePairs
will collect all the linked genomic features in the supplied LinkedPairs-class
object and return descriptions of the alignments of those features.
SummarizePairs(SynExtendObject, FeatureSeqs, DataBase, AlignmentFun = "AlignProfiles", RetainAnchors = FALSE, DefaultTranslationTable = "11", KmerSize = 5, IgnoreDefaultStringSet = FALSE, Verbose = FALSE, ShowPlot = FALSE, Processors = 1, ...)
SummarizePairs(SynExtendObject, FeatureSeqs, DataBase, AlignmentFun = "AlignProfiles", RetainAnchors = FALSE, DefaultTranslationTable = "11", KmerSize = 5, IgnoreDefaultStringSet = FALSE, Verbose = FALSE, ShowPlot = FALSE, Processors = 1, ...)
SynExtendObject |
An object of class |
FeatureSeqs |
An object of class |
DataBase |
A character string pointing to a SQLite database, or a connection to a |
AlignmentFun |
A character string specifying a |
RetainAnchors |
An argument that only affects |
DefaultTranslationTable |
A character vector of length 1 identifying the translation table to use if one is not supplied in the |
KmerSize |
An integer specifying what Kmer size to collect Kmer distance between sequences at. |
IgnoreDefaultStringSet |
A soft memory limit for how much space to allow when building the resulting object. Translated to Gb. |
Verbose |
Logical indicating whether or not to display a progress bar and print the time difference upon completion. |
ShowPlot |
Logical indicating whether or not to provide a plot of features collected by the function. |
Processors |
An integer value indicating how many processors to supply to |
... |
Additional arguments to pass to interior functions. Currently not implemented. |
SummarizePairs
collects features describing each linked feature pair. These include an alignment PID, an alignment Score, a Kmer distance, a concensus score for the linking hits –or whether or not linking hits are in similar places in each feature– and a few other features.
An object of class PairSummaries
.
Nicholas Cooley [email protected]
PrepareSeqs
, NucleotideOverlap
, FindSynteny
, LinkedPairs-class
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Endosymbiont_Seqs <- PrepareSeqs(SynExtendObject = Endosymbionts_LinkedFeatures, DataBase = DBPATH, Verbose = TRUE) SummarizedPairs <- SummarizePairs(SynExtendObject = Endosymbionts_LinkedFeatures, FeatureSeqs = Endosymbiont_Seqs, DataBase = DBPATH)
DBPATH <- system.file("extdata", "Endosymbionts_v02.sqlite", package = "SynExtend") data("Endosymbionts_LinkedFeatures", package = "SynExtend") Endosymbiont_Seqs <- PrepareSeqs(SynExtendObject = Endosymbionts_LinkedFeatures, DataBase = DBPATH, Verbose = TRUE) SummarizedPairs <- SummarizePairs(SynExtendObject = Endosymbionts_LinkedFeatures, FeatureSeqs = Endosymbiont_Seqs, DataBase = DBPATH)
Given a set of unrooted gene trees, creates a species tree. This function works for rooted gene trees, but may not accurately root the resulting tree.
SuperTree(myDendList, NAMEFUN=NULL, Verbose=TRUE, Processors=1)
SuperTree(myDendList, NAMEFUN=NULL, Verbose=TRUE, Processors=1)
myDendList |
List of |
NAMEFUN |
Optional input specifying a function to apply to each leaf to convert gene tree leaf
labels into species names. This function should take as input a character vector
and return a character vector of the same size. By default equals |
Verbose |
Should output be displayed? |
Processors |
Number of processors to use for calculating the final species tree. |
This implementation follows the ASTRID algorithm for estimating a species tree from a set of unrooted gene trees. Input gene trees are not required to have identical species sets, as the algorithm can handle missing entries in gene trees. The algorithm essentially works by averaging the Cophenetic distance matrices of all gene trees, then constructing a neighbor-joining tree from the resulting distance matrix. See the original paper linked in the references section for more information.
If two species never appear together in a gene tree, their distance cannot be estimated in the algorithm and will thus be missing. SuperTree
handles this by imputing the value using the distances available with data-interpolating empirical orthogonal functions (DINEOF). This approach has relatively high accuracy even up to high levels of missingness. Eigenvector calculation speed is improved using a Lanczos algorithm for matrix compression.
SuperTree
allows an optional argument called NAMEFUN
to apply a
renaming step to leaf labels. Gene trees as constructed by other functions in
SynExtend
(ex. DisjointSet
) often include other information
aside from species name when labeling genes, but SuperTree
requires that
leaf nodes of the gene tree are labeled with just an identifier corresponding to
which species/genome each leaf is from. Duplicate values are allowed. See the examples
section for more details on what this looks like and how to handle it.
A dendrogram
object corresponding to the species tree constructed
from input gene trees.
Aidan Lakshman [email protected]
Vachaspati, P., Warnow, T. ASTRID: Accurate Species TRees from Internode Distances. BMC Genomics, 2015. 16 (Suppl 10): S3.
Taylor, M.H., Losch, M., Wenzel, M. and Schröter, J. On the sensitivity of field reconstruction and prediction using empirical orthogonal functions derived from gappy data. Journal of Climate, 2013. 26(22): 9194-9205.
# Loads a list of dendrograms # each is a gene tree from Streptomyces genomes data("SuperTreeEx", package="SynExtend") # Notice that the labels of the tree are in #_#_# format # See the man page for SuperTreeEx for more info labs <- labels(exData[[1]]) labs # The first number corresponds to the species, # so we need to trim the rest in each leaf label namefun <- function(x) gsub("([0-9A-Za-z]*)_.*", "\\1", x) namefun(labs) # trims to just first number # This function replaces gene identifiers with species identifiers # we pass it to NAMEFUN # Note NAMEFUN should take in a character vector and return a character vector tree <- SuperTree(exData, NAMEFUN=namefun)
# Loads a list of dendrograms # each is a gene tree from Streptomyces genomes data("SuperTreeEx", package="SynExtend") # Notice that the labels of the tree are in #_#_# format # See the man page for SuperTreeEx for more info labs <- labels(exData[[1]]) labs # The first number corresponds to the species, # so we need to trim the rest in each leaf label namefun <- function(x) gsub("([0-9A-Za-z]*)_.*", "\\1", x) namefun(labs) # trims to just first number # This function replaces gene identifiers with species identifiers # we pass it to NAMEFUN # Note NAMEFUN should take in a character vector and return a character vector tree <- SuperTree(exData, NAMEFUN=namefun)
A set of 20 dendrograms for use in
SuperTree
examples.
data("SuperTreeEx")
data("SuperTreeEx")
A list with 20 elements, where each is a object of type dendrogram
corresponding to a gene tree constructed from a set of 301 Streptomyces
genomes. Each leaf node is labeled in the form A_B_C
, where A
is
a number identifying the genome, B
is a number identifying the contig,
and C
is a number identifying the gene. Altogether, each label uniquely
identifies a gene.
data(SuperTreeEx, package="SynExtend")
data(SuperTreeEx, package="SynExtend")