| Title: | Platform for integrative analysis of omics data |
|---|---|
| Description: | Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. |
| Authors: | Leif Varemo Wigge <[email protected]> and Intawat Nookaew <[email protected]> |
| Maintainer: | Leif Varemo Wigge <[email protected]> |
| License: | GPL (>=2) |
| Version: | 2.23.0 |
| Built: | 2024-11-30 04:20:48 UTC |
| Source: | https://github.com/bioc/piano |
Run gene set analysis with various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses.
The Piano package consists of two parts. The major part revolves around gene
set analysis (GSA), and the central function for this is
runGSA. There are some downstream functions (e.g.
GSAsummaryTable and geneSetSummary) that handle
the results from the GSA. By running runGSA multiple times with
different settings it is possible to compute consensus gene set scores.
Another set of functions (e.g. consensusScores and
consensusHeatmap) take a list of result objects given by
runGSA for this step. The second part of the Piano package contains a
set of functions devoted for an easy-to-use approach on microarray analysis
(wrapped around the affy and limma
packages), which are constructed to integrate nicely with the downstream GSA
part. The starting function in this case is loadMAdata.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
runGSA and loadMAdata
Based on multiple result objects from the runGSA function,
this function computes the consensus scores, based on rank aggregation, for
each directionality class and produces a heatmap plot of the results.
consensusHeatmap( resList, method = "median", cutoff = 5, adjusted = FALSE, plot = TRUE, ncharLabel = 25, cellnote = "consensusScore", columnnames = "full", colorkey = TRUE, colorgrad = NULL, cex = NULL )consensusHeatmap( resList, method = "median", cutoff = 5, adjusted = FALSE, plot = TRUE, ncharLabel = 25, cellnote = "consensusScore", columnnames = "full", colorkey = TRUE, colorgrad = NULL, cex = NULL )
resList |
a list where each element is an object of class
|
method |
a character string selecting the method, either |
cutoff |
the maximum consensus score of a gene set, in any of the directionality classes, to be included in the heatmap. |
adjusted |
a logical, whether to use adjusted p-values or not. Note
that if |
plot |
whether or not to draw the heatmap. Setting |
ncharLabel |
the number of characters to include in the row labels. |
cellnote |
a character string selecting the information to be printed
inside each cell of the heatmap. Either |
columnnames |
either |
colorkey |
a logical (default |
colorgrad |
a character vector giving the color names to use in the heatmap. |
cex |
a numeric, to control the text size. |
This function computes the consensus gene set scores for each directionality
class based on the results (gene set p-values) listed in resList,
using the consensusScores function. For each class, only the
GSAres objects in resList that contain p-values for that class
are used as a basis for the rank aggregation. Hence, if not all classes are
covered by at least 2 GSAres objects in the list, the
consensusHeatmap function will not work. The results are displayed in
a heatmap showing the consensus scores.
A list, returned invisibly, containing the matrix of consensus scores as represented in the heatmap as well as the matrix of corresponding median p-values and the matrix of number of genes in each gene set (inlcuding the subset of up and down regulated genes for the mixed directional classes).
Leif Varemo [email protected] and Intawat Nookaew [email protected]
# Load some example GSA results: data(gsa_results) # Consensus heatmap: dev.new(width=10,height=10) consensusHeatmap(resList=gsa_results) # Store the output: dev.new(width=10,height=10) ch <- consensusHeatmap(resList=gsa_results) # Access the median p-values for gene set s1: ch$pMat["s1",]# Load some example GSA results: data(gsa_results) # Consensus heatmap: dev.new(width=10,height=10) consensusHeatmap(resList=gsa_results) # Store the output: dev.new(width=10,height=10) ch <- consensusHeatmap(resList=gsa_results) # Access the median p-values for gene set s1: ch$pMat["s1",]
Calculates the consensus scores for the gene sets using multiple gene set
analysis methods (with runGSA()). Optionally also produces a boxplot
to visualize the results.
consensusScores( resList, class, direction, n = 50, adjusted = FALSE, method = "median", plot = TRUE, cexLabel = 0.8, cexLegend = 1, showLegend = TRUE, rowNames = "names", logScale = FALSE, main )consensusScores( resList, class, direction, n = 50, adjusted = FALSE, method = "median", plot = TRUE, cexLabel = 0.8, cexLegend = 1, showLegend = TRUE, rowNames = "names", logScale = FALSE, main )
resList |
a list where each element is an object of class
|
class |
a character string determining the p-values of which
directionality class that should be used as significance information for the
plot. Can be one of |
direction |
a character string giving the direction of regulation, can
be either |
n |
consensus rank cutoff. All gene sets with consensus rank (see
details below) |
adjusted |
a logical, whether to use adjusted p-values or not. Note
that if |
method |
a character string selecting the method, either "mean", "median", "max", "Borda" or "Copeland". |
plot |
a logical, whether or not to draw the boxplot. |
cexLabel |
the x- and y-axis label sizes. |
cexLegend |
the legend text size. |
showLegend |
a logical, whether or not to show the legend and the indivual method ranks as points in the plot. |
rowNames |
a character string determining which rownames to use, set to
either |
logScale |
a logical, whether or not to use log-scale for the x-axis. |
main |
a character vector giving an alternative title of the plot. |
Based on the results given by the elements of resList, preferably
representing similar runs with runGSA but with different
methods, this function ranks the gene sets for each GSAres object,
based on the selected directionality class. Next, the median rank for each
gene set is taken as a score for top-ranking gene sets. The highest scoring
gene-sets (with consensus rank, i.e.
rank(rankScore,ties.method="min"), smaller or equal to n) are
selected and depicted in a boxplot, showing the distribution of individual
ranks (shown as colored points), as well as the median rank (shown as a red
line). As an alternative of using the median rank as consensus score, it is
possible to choose the mean or using the Borda or Copeland method, through
the method argument. A more conservative approach can also be taken
using the maximum rank as a consensus score, prioritizing gene-sets that are
consistently ranked high across all GSA runs.
All elements of resList have to be objects containing results for the
same number of gene-sets. The ranking procedure handles ties by giving them
their minimum rank.
A list containing a matrix of the ranks for the top n gene
sets, given by each run, as well as the corresponding matrix of p-values,
given by each run.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
# Load some example GSA results: data(gsa_results) # Consensus scores for the top 50 gene sets (in the non-directional class): cs <- consensusScores(resList=gsa_results,class="non") # Access the ranks given to gene set s7 by each individual method: cs$rankMat["s7",]# Load some example GSA results: data(gsa_results) # Consensus scores for the top 50 gene sets (in the non-directional class): cs <- consensusScores(resList=gsa_results,class="non") # Access the ranks given to gene set s7 by each individual method: cs$rankMat["s7",]
Identifies differentially expressed genes by using the linear model approach of limma. Optionally produces a Venn diagram, heatmap, Polar plot and volcano plot.
diffExp( arrayData, contrasts, chromosomeMapping, fitMethod = "ls", adjustMethod = "fdr", significance = 0.001, plot = TRUE, heatmapCutoff = 1e-10, volcanoFC = 2, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black"), save = FALSE, verbose = TRUE )diffExp( arrayData, contrasts, chromosomeMapping, fitMethod = "ls", adjustMethod = "fdr", significance = 0.001, plot = TRUE, heatmapCutoff = 1e-10, volcanoFC = 2, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black"), save = FALSE, verbose = TRUE )
arrayData |
an object of class |
contrasts |
a character vector giving the contrasts to be tested for
differential expression. Use |
chromosomeMapping |
character string giving the name of the chromosome
mapping file, or an object of class |
fitMethod |
character string giving the fitting method used by
|
adjustMethod |
character string giving the method to use for adjustment
of multiple testing. Can be |
significance |
number giving the significance cutoff level for the Venn
diagram and the horizontal line drawn in the volcano plot. Defaults to
|
plot |
should plots be produced? Set either to |
heatmapCutoff |
number giving the significance cutoff level for the
heatmap. Defaults to |
volcanoFC |
number giving the x-coordinates of the vertical lines drawn
in the volcano plot. Defaults to |
colors |
character vector of colors to be used by the Venn diagram and Polar plot. |
save |
should the figures and p-values be saved? Defaults to
|
verbose |
verbose? Defaults to |
This function uses limma to calculate p-values measuring
differential expression in the given contrasts. The
uniqueFactors given by extractFactors can be used to
define a contrast vector, where each element should be a character string on
the form "uniqueFactorA - uniqueFactorB", note the space surrounding
the -. (See the example below and for extractFactors.)
If appropriate annotation is missing for the ArrayData object the
user can suppply this as chromosomeMapping. This should be either a
data.frame or a tab delimited text file and include the columns
chromosome with the chromosome name and chromosome location
containing the starting position of each gene. A - sign can be used
to denote the antisense strand but this will be disregarded while plotting.
The rownames should be probe IDs or, if using a text file, the first
column with a column header should contain the probe IDs.
Note that the fitMethod="robust" may need longer time to run.
A Venn diagram can be drawn for up to five contrasts (diffExp() will
use vennDiagram).
The heatmap shows normalized expression values of the genes that pass the
heatmapCutoff in at least one contrast.
A volcano plot is produced for each contrast showing magnitude of change versus significance.
The Polar plot sorts the genes according to chromosomal location, for each chromosome starting with unknown positions followed by increasing number in the chromosome location column. Genes which do not map to any chromosome are listed as U for unknown. The radial lines in the Polar plot are -log10 scaled p-values, so that a longer line means a smaller p-value. This gives an overview of the magnitude of differential expression for each contrast.
Typical usages are:
# Identify significantly changed genes in
'm1' and 'm2' compared to 'wt': diffExp(arrayData, contrasts=c("m1 - wt",
"m2 - wt"))
A list with elements:
pValues |
|
foldChanges |
|
resTable |
a |
vennMembers |
|
Leif Varemo [email protected] and Intawat Nookaew [email protected]
Smyth, G. K. (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397–420.
piano, loadMAdata,
extractFactors, polarPlot, runGSA,
limma, venn, heatmap.2
# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Perform differential expression analysis: pfc <- diffExp(myArrayData, contrasts=c("aerobic_Clim - anaerobic_Clim", "aerobic_Nlim - anaerobic_Nlim")) # Order the genes according to p-values, for aerobic_Clim vs anaerobic_Clim: o <- order(pfc$resTable$'aerobic_Clim - anaerobic_Clim'$P.Value) # Display statistics for the top 10 significant genes: pfc$resTable$'aerobic_Clim - anaerobic_Clim'[o[1:10],]# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Perform differential expression analysis: pfc <- diffExp(myArrayData, contrasts=c("aerobic_Clim - anaerobic_Clim", "aerobic_Nlim - anaerobic_Nlim")) # Order the genes according to p-values, for aerobic_Clim vs anaerobic_Clim: o <- order(pfc$resTable$'aerobic_Clim - anaerobic_Clim'$P.Value) # Display statistics for the top 10 significant genes: pfc$resTable$'aerobic_Clim - anaerobic_Clim'[o[1:10],]
Explore GSA results interactively in a web browser using shiny.
exploreGSAres(gsaRes, browser = TRUE, geneAnnot = NULL, genesets)exploreGSAres(gsaRes, browser = TRUE, geneAnnot = NULL, genesets)
gsaRes |
an object of class |
browser |
a logical, whether or not to open the Shiny app in a browser
window. Set to |
geneAnnot |
a |
genesets |
a character vector or list (named or un-named) of character vectors containing subsets of gene-set names that can be selected and displayed in the network plot. |
Additional gene-level information, e.g. alternative names or description,
can be supplied via the geneAnnot argument. This information will
show up in the gene table and the gene summary tabs.
Does not return any object.
Leif Varemo [email protected]
piano, runGSA, GSAheatmap, networkPlot2
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Explore results: ## Not run: exploreGSAres(gsares)# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Explore results: ## Not run: exploreGSAres(gsares)
ArrayData factorsExtracts the factors, given by an ArrayData object, that can be used
by diffExp
extractFactors(arrayData)extractFactors(arrayData)
arrayData |
an |
A list with elements:
factors |
Assigns one factor to each array |
uniqueFactors |
The unique factors that can be used to form contrasts |
Leif Varemo [email protected] and Intawat Nookaew [email protected]
# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") #Extract the factors that can be used in the call to diffExp: extractFactors(myArrayData)# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") #Extract the factors that can be used in the call to diffExp: extractFactors(myArrayData)
Returns a summary of the statistics and gene members of a given gene set in
a GSAres object.
geneSetSummary(gsaRes, geneSet)geneSetSummary(gsaRes, geneSet)
gsaRes |
an object of class |
geneSet |
a character string giving the name of a gene-set. |
This function can be used to access information on specific gene sets of
interest. The same results are available for all gene sets using
GSAsummaryTable.
A list with the elements name, containing the gene-set name,
geneLevelStats, containing the gene-level statistics of the member
genes, directions, containing the directions of the member genes, and
stats, a table of the gene set statistics and p-values.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
piano, runGSA,
GSAsummaryTable
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Get info on a specific gene set: geneSetSummary(gsares,"s1")# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Get info on a specific gene set: geneSetSummary(gsares,"s1")
This data set is completely randomly generated and contains p-values for
2000 genes, fold-changes for those genes and a gene set collection giving
the connection between genes and 50 gene sets. Only intended to be used as
example data for runGSA.
A list containing 3 elements: gsa_input$pvals and gsa_input$directions are numeric vectors, gsa_input$gsc is a two-column matrix with gene names in the first column and gene set names in the second.
This data set contains gene set analysis results, as returned by the
runGSA function, that is used as example data for downstream
functions. The input data to runGSA was randomly generated and is
accessible through data(gsa_input).
A list where each element is an object of class GSAres, as returned
by runGSA.
This function selects the top scoring (most significant) gene sets for each directionality class and produces a heatmap plot of the results.
GSAheatmap( gsaRes, cutoff = 5, adjusted = FALSE, ncharLabel = 25, cellnote = "pvalue", columnnames = "full", colorkey = TRUE, colorgrad = NULL, cex = NULL )GSAheatmap( gsaRes, cutoff = 5, adjusted = FALSE, ncharLabel = 25, cellnote = "pvalue", columnnames = "full", colorkey = TRUE, colorgrad = NULL, cex = NULL )
gsaRes |
an object of class |
cutoff |
an integer n, so that the top n gene sets (plus possible ties) in each directionality class will be included in the heatmap. |
adjusted |
a logical, whether to use adjusted p-values or not. Note
that if |
ncharLabel |
the number of characters to include in the row labels. |
cellnote |
a character string selecting the information to be printed
inside each cell of the heatmap. Either |
columnnames |
either |
colorkey |
a logical (default |
colorgrad |
a character vector giving the color names to use in the heatmap. |
cex |
a numeric, to control the text size. |
This function selects the top significant gene sets in each directionality
class and draws a heatmap of the results. It provides a quick summary
alternative to the GSAsummaryTable function or the
networkPlot.
A list, returned invisibly, containing the matrix of p-values (adjusted or non-adjusted depending on the settings) as represented in the heatmap as well as the matrix of corresponding ranks and the matrix of number of genes in each gene set (inlcuding the subset of up and down regulated genes for the mixed directional classes).
Leif Varemo [email protected] and Intawat Nookaew [email protected]
piano, runGSA,
GSAsummaryTable, networkPlot2, exploreGSAres
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Make heatmap: dev.new(width=10,height=10) GSAheatmap(gsares)# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Make heatmap: dev.new(width=10,height=10) GSAheatmap(gsares)
Displays or saves a summary table of the results from runGSA.
GSAsummaryTable(gsaRes, save = FALSE, file = NULL)GSAsummaryTable(gsaRes, save = FALSE, file = NULL)
gsaRes |
an object of class |
save |
a logical, whether or not to save the table. |
file |
a character string giving the file name to save to. |
The table is by default saved as an .xls file, if file is unused.
The summary table as a data.frame (returned invisibly if
save=TRUE).
Leif Varemo [email protected] and Intawat Nookaew [email protected]
piano, runGSA,
networkPlot, GSAheatmap
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Summary table: GSAsummaryTable(gsares)# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Summary table: GSAsummaryTable(gsares)
Load a gene set collection, to be used in runGSA, in GMT, SBML
or SIF format, or optionally from a data.frame.
loadGSC(file, type = "auto", addInfo)loadGSC(file, type = "auto", addInfo)
file |
a character string, giving the name of the file containing the gene set collection. Optionally an object that can be coerced into a two-column data.frame, the first column containing genes and the second gene sets, representing all "gene"-to-"gene set" connections. |
type |
a character string giving the file type. Can be either of
|
addInfo |
an optional data.frame with two columns, the first containging the gene set names and the second containing additional information for each gene set. Some additional info may load automatically from the different file types. |
This function is used to create a gene-set collection object to be used with
runGSA.
The "gmt" files available from the Molecular Signatures Database
(http://www.broadinstitute.org/gsea/msigdb/) can be loaded using
loadGSC. This website is a valuable resource and contains several
different collections of gene sets.
By using the functionality of e.g. the biomaRt package, a gene-set
collection with custom gene names (matching the statistics used in
runGSA) can easily be compiled into a two-column data.frame
(column order: genes, gene sets) and loaded with type="data.frame".
If a sif-file is used it is assumed that the first column contains gene sets and the third column contains genes.
A genome-scale metabolic model in SBML format can be used to define gene
sets. In this case, metabolites will be the gene sets, containing all the
genes that code for enzymes catalyzing reactions in which the metabolite
takes part in. In order to load an SBML-file it is required that libSBML and
rsbml is installed. Note that the SBML loading is an experimental
feature and is highly dependent on the version and format of the SBML file
and requires it to contain gene associations for the reactions. By examining
the returned GSC object it is easy to see if the correct gene sets
were loaded.
A list like object of class GSC containing two elements. The
first is gsc, a list of the gene sets, each element a character
vector of genes. The second element is addInfo, a data.frame
containing the optional additional information.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
# Randomly generated gene sets: g <- sort(paste("g",floor(runif(100)*500+1),sep="")) g <- c(g,sort(paste("g",floor(runif(900)*1000+1),sep=""))) g <- c(g,sort(paste("g",floor(runif(1000)*2000+1),sep=""))) s <- paste("s",floor(rbeta(2000,0.9,1.7)*50+1),sep="") # Make data.frame: gsc <- cbind(g,s) # Load gene set collection from data.frame: gsc <- loadGSC(gsc)# Randomly generated gene sets: g <- sort(paste("g",floor(runif(100)*500+1),sep="")) g <- c(g,sort(paste("g",floor(runif(900)*1000+1),sep=""))) g <- c(g,sort(paste("g",floor(runif(1000)*2000+1),sep=""))) s <- paste("s",floor(rbeta(2000,0.9,1.7)*50+1),sep="") # Make data.frame: gsc <- cbind(g,s) # Load gene set collection from data.frame: gsc <- loadGSC(gsc)
Loads, preprocesses and annotates microarray data to be further used by downstream functions in the piano package.
loadMAdata( datadir = getwd(), setup = "setup.txt", dataNorm, platform = "NULL", annotation, normalization = "plier", filter = TRUE, verbose = TRUE, ... )loadMAdata( datadir = getwd(), setup = "setup.txt", dataNorm, platform = "NULL", annotation, normalization = "plier", filter = TRUE, verbose = TRUE, ... )
datadir |
character string giving the directory in which to look for
the data. Defaults to |
setup |
character string giving the name of the file containing the
experimental setup, or an object of class |
dataNorm |
character string giving the name of the normalized data, or
an object of class |
platform |
character string giving the name of the platform, can be
either |
annotation |
character string giving the name of the annotation file,
or an object of class |
normalization |
character string giving the normalization method, can
be either |
filter |
should the data be filtered? If |
verbose |
verbose? Defaults to |
... |
additional arguments to be passed to |
This function requires at least two inputs: (1) data, either CEL files in
the directory specified by datadir or normalized data specified by
dataNorm, and (2) experimental setup specified by setup.
The setup shold be either a tab delimited text file with column headers or a
data.frame. The first column should contain the names of the CEL
files or the column names used for the normalized data, please be sure to
use names valid as column names, e.g. avoid names starting with numbers.
Additional columns should assign attributes in some category to each array.
(For an example run the example below and look at the object
myArrayData$setup.)
The piano package is customized for yeast 2.0 arrays and annotation
will work automatically, if the cdfName of the arrays equals Yeast_2.
If using normalized yeast 2.0 data as input, the user needs to set the
argument platform="yeast2" to tell the function to use yeast
annotation. If other platforms than yeast 2.0 is used, set
platform=NULL (default) and supply appropriate annotation by the
argument annotation. Note that the cdfName will override
platform, so it can still be set to NULL for yeast 2.0 CEL
files. Note also that annotation overrides platform, so if the
user wants to use an alternative annotation for yeast, this can be done
simply by specifying this in annotation.
The annotation should have the column headers Gene name,
Chromosome and Chromosome location. The Gene name is
used in the heatmap in diffExp and the Chromosome and
Chromosome location is used by the polarPlot. The rownames (or
first column if using a text file) should contain the probe IDs. If
using a text file the first column should have the header probeID or
similar. The filtering step discards all probes not listed in the
annotation.
Normalization is performed on all CEL file data using one of the Affymetrix
methods: PLIER ("plier") as implemented by
justPlier, RMA (Robust Multi-Array Average)
("rma") expression measure as implemented by
rma or MAS 5.0 expression measure "mas5" as
implemented by mas5.
It is possible to pass additional arguments to
ReadAffy, e.g. cdfname as this
might be required for some types of CEL files.
An ArrayData object (which is essentially a list) with
the following elements:
dataRaw |
raw data as an AffyBatch object |
dataNorm |
|
setup |
|
annotation |
|
Depending on input arguments the ArrayData object may not include
dataRaw and/or annotation.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
Gautier, L., Cope, L., Bolstad, B. M., and Irizarry, R. A. affy - analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20, 3, 307-315 (2004).
piano, runQC, diffExp,
ReadAffy,
expresso,
justPlier, yeast2.db
# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Print to look at details: myArrayData# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Print to look at details: myArrayData
Draws a network with gene sets as nodes and the thickness of the edges correlating to the number of shared genes. The gene set significance is visualized as color intensities. Gives an overview of the influence of overlap on significant gene sets.
networkPlot( gsaRes, class, direction, adjusted = FALSE, significance = 0.001, geneSets = NULL, overlap = 1, lay = 1, label = "names", cexLabel = 0.9, ncharLabel = 25, cexLegend = 1, nodeSize = c(10, 40), edgeWidth = c(1, 15), edgeColor = NULL, scoreColors = NULL, main )networkPlot( gsaRes, class, direction, adjusted = FALSE, significance = 0.001, geneSets = NULL, overlap = 1, lay = 1, label = "names", cexLabel = 0.9, ncharLabel = 25, cexLegend = 1, nodeSize = c(10, 40), edgeWidth = c(1, 15), edgeColor = NULL, scoreColors = NULL, main )
gsaRes |
an object of class |
class |
a character string determining the p-values of which
directionality class that should be used as significance information for the
plot. Can be one of |
direction |
a character string giving the direction of regulation, can
be either |
adjusted |
a logical, if adjusted p-values should be used, or not. Note
that if |
significance |
the significance cut-off that determines which gene sets are included in the plot. Defaults to 0.001. |
geneSets |
a character vector of gene set names, to be included in the
plot. Defaults to |
overlap |
a positive numerical. Determines the smallest number of sharing genes between two gene-sets that is needed in order to draw a line/edge between the gene-sets. Defaults to 1. |
lay |
a numerical between 1-5, or a layout function (see
|
label |
a character string, either |
cexLabel |
the text size of the node labels. |
ncharLabel |
the number of characters to include in the node labels. |
cexLegend |
the text size of the legend. |
nodeSize |
a numerical vector of length 2 giving the maximum and minimum node sizes. The node size represents the size of the gene set, and all values will be scaled to the given interval. |
edgeWidth |
a numerical vector of length 2 giving the maximum and minimum edge widths. The edge width represents the number of shared genes between two gene sets, and all values will be scaled to the given interval. |
edgeColor |
a character vector giving the colors to use for increasing edge width. Can also be set to a single color. Defaults to a gray-scale. |
scoreColors |
a character vector giving the colors from which the
gradient used for node coloring will be created. In the case of
|
main |
an optional character vector setting the title of the plot. |
In the case of class="distinct" and direction="both", the
distinct directional p-values (pDistinctDirUp and
pDistinctDirDn, see runGSA) will be used in
combination. Using the geneSets and lay arguments, multiple
comparative plots (i.e. with the same layout) can be drawn, based for
instance on the output gene set list from other network plots with different
directionality classes.
Returns a list with two components: geneSets containing the
names and numbers of the gene sets in the plot, and layout,
containing the saved layout of the plot, which can be passed back to the
lay argument in order to draw a subsequent plot with the same layout.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
piano, runGSA, GSAheatmap,
networkPlot2, exploreGSAres,
layout
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Network plot: networkPlot(gsares,class="non",significance=0.01) # Use circular layout and save the layout: nw <- networkPlot(gsares,class="non",significance=0.01,lay=5) # Use the saved layout to overlay the distinct-directional p-values for easy comparison. # Note that the gene sets are now not selected based on a significance cutoff, but from a list: networkPlot(gsares,class="distinct",direction="both",lay=nw$layout,geneSets=nw$geneSets)# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Network plot: networkPlot(gsares,class="non",significance=0.01) # Use circular layout and save the layout: nw <- networkPlot(gsares,class="non",significance=0.01,lay=5) # Use the saved layout to overlay the distinct-directional p-values for easy comparison. # Note that the gene sets are now not selected based on a significance cutoff, but from a list: networkPlot(gsares,class="distinct",direction="both",lay=nw$layout,geneSets=nw$geneSets)
Draws a network with gene sets as nodes and the thickness of the edges
correlating to the number of shared genes. The gene set significance is
visualized as color intensities. Gives an overview of the influence of
overlap on significant gene sets. Uses package visNetwork for plotting.
networkPlot2( gsaRes, class, direction, adjusted = TRUE, significance = 0.001, geneSets = NULL, lay = "visNetwork", physics = TRUE, overlap = 0.1, label = "names", labelSize = 22, ncharLabel = 25, nodeSize = c(10, 40), edgeWidth = c(1, 15), edgeColor = NULL, scoreColors = NULL, naColor = "yellow", main, submain, seed = 1, maxAllowedNodes = Inf, shiny = FALSE )networkPlot2( gsaRes, class, direction, adjusted = TRUE, significance = 0.001, geneSets = NULL, lay = "visNetwork", physics = TRUE, overlap = 0.1, label = "names", labelSize = 22, ncharLabel = 25, nodeSize = c(10, 40), edgeWidth = c(1, 15), edgeColor = NULL, scoreColors = NULL, naColor = "yellow", main, submain, seed = 1, maxAllowedNodes = Inf, shiny = FALSE )
gsaRes |
an object of class |
class |
a character string determining the p-values of which
directionality class that should be used as significance information for the
plot. Can be one of |
direction |
a character string giving the direction of regulation, can
be either |
adjusted |
a logical, if adjusted p-values should be used, or not. Note
that if |
significance |
the significance cut-off that determines which gene sets are included in the plot. Defaults to 0.001. |
geneSets |
a character vector of gene set names, to be included in the
plot. Defaults to |
lay |
One of |
physics |
logical, whether or not to use physics simulation. |
overlap |
a positive numerical. Determines the smallest number or fraction of sharing genes between two gene-sets that is needed in order to draw a line/edge between the gene-sets. If >= 1, the argument is interpreted as number of genes. If between 0 and 1, the argument is interprested as the fraction of genes of the smalles gene-set in a given pair. Defaults to 0.1. |
label |
a character string, either |
labelSize |
the text size of the node labels. |
ncharLabel |
the number of characters to include in the node labels. |
nodeSize |
a numerical vector of length 2 giving the maximum and minimum node sizes. The node size represents the size of the gene set, and all values will be scaled to the given interval. |
edgeWidth |
a numerical vector of length 2 giving the maximum and minimum edge widths. The edge width represents the number of shared genes between two gene sets, and all values will be scaled to the given interval. |
edgeColor |
a character vector giving the colors to use for increasing edge width. Can also be set to a single color. Defaults to a gray-scale. |
scoreColors |
a character vector giving the colors from which the
gradient used for node coloring will be created. In the case of
|
naColor |
the color for gene-sets when selected p-value is NA |
main |
an optional character vector setting the title of the plot. |
submain |
an optional character vector setting the subtitle of the plot. |
seed |
random seed for reproducible layouts |
maxAllowedNodes |
if the set parameters results in a network with more than
|
shiny |
Only for internal use. Set to FALSE by default. |
In the case of class="distinct" and direction="both", the
distinct directional p-values (pDistinctDirUp and
pDistinctDirDn, see runGSA) will be used in
combination.
Returns an object of class visNetwork that can be further manipulated,
see examples.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
piano, runGSA, GSAheatmap,
exploreGSAres
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Network plot: networkPlot2(gsares, class="non", significance=0.1) # Display number to gene-set name mapping: res <- networkPlot2(gsares, class="non", significance=0.1) res$x$nodes[,c("id","geneSetNames")] # Examples of reusing res later: # Draw same again: require(visNetwork) visNetwork(res$x$nodes,res$x$edges) # os simly just: res # Draw only essential, rest is default: visNetwork(res$x$nodes[,c("id","label")],res$x$edges[,c("from","to")]) # Add custom options: visNetwork(res$x$nodes[,c("id","label")],res$x$edges[,c("from","to")]) %>% visIgraphLayout("layout_in_circle") # Other example: res %>% visNodes(shadow=FALSE) # See package visNetwork for more examples# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500) # Network plot: networkPlot2(gsares, class="non", significance=0.1) # Display number to gene-set name mapping: res <- networkPlot2(gsares, class="non", significance=0.1) res$x$nodes[,c("id","geneSetNames")] # Examples of reusing res later: # Draw same again: require(visNetwork) visNetwork(res$x$nodes,res$x$edges) # os simly just: res # Draw only essential, rest is default: visNetwork(res$x$nodes[,c("id","label")],res$x$edges[,c("from","to")]) # Add custom options: visNetwork(res$x$nodes[,c("id","label")],res$x$edges[,c("from","to")]) %>% visIgraphLayout("layout_in_circle") # Other example: res %>% visNodes(shadow=FALSE) # See package visNetwork for more examples
Produces a Polar plot, mapping p-values to chromosome location. This
function is used by diffExp.
polarPlot( pValues, chromosomeMapping, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black"), save = FALSE, verbose = TRUE )polarPlot( pValues, chromosomeMapping, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black"), save = FALSE, verbose = TRUE )
pValues |
a |
chromosomeMapping |
character string giving the name of the chromosome
mapping file, or an object of class |
colors |
character vector of colors to be used by the Polar plot. |
save |
should the figures be saved? Defaults to |
verbose |
verbose? Defaults to |
This function is mainly used by diffExp but can also be used
separately by the user.
The argument chromosomeMapping should be either a data.frame
or a tab delimited text file and include the columns chromosome with
the chromosome name and chromosome location containing the starting
position of each gene. A - sign can be used to denote the antisense
strand but this will be disregarded while plotting. The rownames should be
probe IDs or, if using a text file, the first column with a column
header should contain the probe IDs. If relying on an
ArrayData object (called arrayData) and containing an
annotation field, the chromosomeMapping can be set to
arrayData$annotation[,c(2,3)] (see the example below).
The Polar plot sorts the genes according to chromosomal location, for each chromosome starting with unknown positions followed by increasing number in the chromosome location column. Genes which do not map to any chromosome are listed as U for unknown. The radial lines in the Polar plot are -log10 scaled p-values, so that a longer line means a smaller p-value. This gives an overview of the magnitude of differential expression for each contrast.
Does not return any object.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Perform differential expression analysis: pfc <- diffExp(myArrayData, plot=FALSE, contrasts=c("aerobic_Clim - anaerobic_Clim", "aerobic_Nlim - anaerobic_Nlim")) # Get chromosome mapping from myArrayData: chrMap <- myArrayData$annotation[,c(2,3)] # Get p-values from pfc pval <- pfc$pValues # Draw the polar plot: polarPlot(pval, chromosomeMapping=chrMap)# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Perform differential expression analysis: pfc <- diffExp(myArrayData, plot=FALSE, contrasts=c("aerobic_Clim - anaerobic_Clim", "aerobic_Nlim - anaerobic_Nlim")) # Get chromosome mapping from myArrayData: chrMap <- myArrayData$annotation[,c(2,3)] # Get p-values from pfc pval <- pfc$pValues # Draw the polar plot: polarPlot(pval, chromosomeMapping=chrMap)
Performs gene set analysis (GSA) based on a given number of gene-level statistics and a gene set collection, using a variety of available methods, returning the gene set statistics and p-values of different directionality classes.
runGSA( geneLevelStats, directions = NULL, geneSetStat = "mean", signifMethod = "geneSampling", adjMethod = "fdr", gsc, gsSizeLim = c(1, Inf), permStats = NULL, permDirections = NULL, nPerm = 10000, gseaParam = 1, ncpus = 1, verbose = TRUE )runGSA( geneLevelStats, directions = NULL, geneSetStat = "mean", signifMethod = "geneSampling", adjMethod = "fdr", gsc, gsSizeLim = c(1, Inf), permStats = NULL, permDirections = NULL, nPerm = 10000, gseaParam = 1, ncpus = 1, verbose = TRUE )
geneLevelStats |
a vector or a one-column data.frame or matrix, containing the gene level statistics. Gene level statistics can be e.g. p-values, t-values or F-values. |
directions |
a vector or a one-column data.frame or matrix, containing fold-change like values for the related gene-level statistics. This is mainly used if statistics are p-values or F-values, but not required. The values should be positive or negative, but only the sign information will be used, so the actual value will not matter. |
geneSetStat |
the statistical GSA method to use. Can be one of
|
signifMethod |
the method for significance assessment of gene sets,
i.e. p-value calculation. Can be one of |
adjMethod |
the method for adjusting for multiple testing. Can be any
of the methods supported by |
gsc |
a gene set collection given as an object of class |
gsSizeLim |
a vector of length two, giving the minimum and maximum gene
set size (number of member genes) to be kept for the analysis. Defaults to
|
permStats |
a matrix with permutated gene-level statistics (columns)
for each gene (rows). This should be calculated by the user by randomizing
the sample labels in the original data, and recalculating the gene level
statistics for each comparison a large number of times, thus generating a
vector (rows in the matrix) of background statistics for each gene. This
argument is required and only used if
|
permDirections |
similar to |
nPerm |
the number of permutations to use for gene sampling, i.e. if
|
gseaParam |
the exponent parameter of the GSEA and FGSEA approach. This defaults to 1, as recommended by the GSEA authors. |
ncpus |
the number of cpus to use. If larger than 1, the gene
permutation part will be run in parallel and thus decrease runtime. Requires
R package snowfall to be installed. Should be set so that
|
verbose |
a logical. Whether or not to display progress messages during the analysis. |
The rownames of geneLevelStats and directions should be
identical and match the names of the members of the gene sets in gsc.
If geneSetStat is set to "fisher", "stouffer",
"reporter" or "tailStrength" only p-values are allowed as
geneLevelStats. If geneSetStat is set to "maxmean",
"gsea", "fgsea" or "page" only t-like
geneLevelStats are allowed (e.g. t-values, fold-changes).
For geneSetStat set to "fisher", "stouffer",
"reporter", "wilcoxon" or "page", the gene set p-values
can be calculated from a theoretical null-distribution, in this case, set
signifMethod="nullDist". For all methods
signifMethod="geneSampling" or
signifMethod="samplePermutation" can be used, except for
"fgsea" where only signifMethod="geneSampling" is allowed. If
signifMethod="geneSampling" gene sampling is used, meaning that the
gene labels are randomized nPerm times and the gene set statistics
are recalculated so that a background distribution for each original gene
set is acquired. The gene set p-values are calculated based on this
background distribution. Similarly if
signifMethod="samplePermutation" sample permutation is used. In this
case the argument permStats (and optionally permDirections)
has to be supplied.
The runGSA function returns p-values for each gene set. Depending on
the choice of methods and gene statistics up to three classes of p-values
can be calculated, describing different aspects of regulation
directionality. The three directionality classes are Distinct-directional,
Mixed-directional and Non-directional. The non-directional p-values
(pNonDirectional) are calculated based on absolute values of the gene
statistics (or p-values without sign information), meaning that gene sets
containing a high portion of significant genes, independent of direction,
will turn up significant. That is, gene-sets with a low
pNonDirectional should be interpreted to be significantly affected by
gene regulation, but there can be a mix of both up and down regulation
involved. The mixed-directional p-values (pMixedDirUp and
pMixedDirDn) are calculated using the subset of the gene statistics
that are up-regulated and down-regulated, respectively. This means that a
gene set with a low pMixedDirUp will have a component of
significantly up-regulated genes, disregardful of the extent of
down-regulated genes, and the reverse for pMixedDirDn. This also
means that one can get gene sets that are both significantly affected by
down-regulation and significantly affected by up-regulation at the same
time. Note that sample permutation cannot be used to calculate
pMixedDirUp and pMixedDirDn since the subset sizes will
differ. Finally, the distinct-directional p-values (pDistinctDirup
and pDistinctDirDn) are calculated from statistics with sign
information (e.g. t-statistics). In this case, if a gene set contains both
up- and down-regulated genes, they will cancel out each other. A gene-set
with a low pDistinctDirUp will be significantly affected by
up-regulation, but not a mix of up- and down-regulation (as in the case of
the mixed-directional and non-directional p-values). In order to be able to
calculate distinct-directional gene set p-values while using p-values as
gene-level statistics, the gene-level p-values are transformed as follows:
The up-regulated portion of the p-values are divided by 2 (scaled to range
between 0-0.5) and the down-regulated portion of p-values are set to 1-p/2
(scaled to range between 1-0.5). This means that a significantly
down-regulated gene will get a p-value close to 1. These new p-values are
used as input to the gene-set analysis procedure to get
pDistinctDirUp. Similarly, the opposite is done, so that the
up-regulated portion is scaled between 1-0.5 and the down-regulated between
0-0.5 to get the pDistinctDirDn.
A list-like object of class GSAres containing the following
elements:
geneStatType |
The interpretated type of gene-level statistics |
geneSetStat |
The method for gene set statistic calculation |
signifMethod |
The method for significance estimation |
adjMethod |
The method of adjustment for multiple testing |
info |
A list object with detailed info number of genes and gene sets |
gsSizeLim |
The selected gene set size limits |
gsStatName |
The name of the gene set statistic type |
nPerm |
The number of permutations |
gseaParam |
The GSEA parameter |
geneLevelStats |
The input gene-level statistics |
directions |
The input directions |
gsc |
The input gene set collection |
nGenesTot |
The total number of genes in each gene set |
nGenesUp |
The number of up-regulated genes in each gene set |
nGenesDn |
The number of down-regulated genes in each gene set |
statDistinctDir |
Gene set statistics of the distinct-directional class |
statDistinctDirUp |
Gene set statistics of the distinct-directional class |
statDistinctDirDn |
Gene set statistics of the distinct-directional class |
statNonDirectional |
Gene set statistics of the non-directional class |
statMixedDirUp |
Gene set statistics of the mixed-directional class |
statMixedDirDn |
Gene set statistics of the mixed-directional class |
pDistinctDirUp |
Gene set p-values of the distinct-directional class |
pDistinctDirDn |
Gene set p-values of the distinct-directional class |
pNonDirectional |
Gene set p-values of the non-directional class |
pMixedDirUp |
Gene set p-values of the mixed-directional class |
pMixedDirDn |
Gene set p-values of the mixed-directional class |
pAdjDistinctDirUp |
Adjusted gene set p-values of the distinct-directional class |
pAdjDistinctDirDn |
Adjusted gene set p-values of the distinct-directional class |
pAdjNonDirectional |
Adjusted gene set p-values of the non-directional class |
pAdjMixedDirUp |
Adjusted gene set p-values of the mixed-directional class |
pAdjMixedDirDn |
Adjusted gene set p-values of the mixed-directional class |
runtime |
The execution time in seconds |
Leif Varemo [email protected] and Intawat Nookaew [email protected]
Fisher, R. Statistical methods for research workers. Oliver and Boyd, Edinburgh, (1932).
Stouffer, S., Suchman, E., Devinney, L., Star, S., and Williams Jr, R. The American soldier: adjustment during army life. Princeton University Press, Oxford, England, (1949).
Patil, K. and Nielsen, J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proceedings of the National Academy of Sciences of the United States of America 102(8), 2685 (2005).
Oliveira, A., Patil, K., and Nielsen, J. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. BMC Systems Biology 2(1), 17 (2008).
Kim, S. and Volsky, D. Page: parametric analysis of gene set enrichment. BMC bioinformatics 6(1), 144 (2005).
Taylor, J. and Tibshirani, R. A tail strength measure for assessing the overall univariate significance in a dataset. Biostatistics 7(2), 167-181 (2006).
Mootha, V., Lindgren, C., Eriksson, K., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., et al. Pgc-1-alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature genetics 34(3), 267-273 (2003).
Subramanian, A., Tamayo, P., Mootha, V., Mukherjee, S., Ebert, B., Gillette, M., Paulovich, A., Pomeroy, S., Golub, T., Lander, E., et al. Gene set enrichment analysis: a knowledgebased approach for interpreting genom-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102(43), 15545-15550 (2005).
Efron, B. and Tibshirani, R. On testing the significance of sets of genes. The Annals of Applied Statistics 1, 107-129 (2007).
piano, loadGSC,
GSAsummaryTable, geneSetSummary,
networkPlot2, exploreGSAres, samr,
limma, GSA, fgsea
# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500)# Load example input data to GSA: data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Run gene set analysis: gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, gsc=gsc, nPerm=500)
Performs gene set analysis (GSA) based on a list of significant genes and a gene set collection, using Fisher's exact test, returning the gene set p-values.
runGSAhyper( genes, pvalues, pcutoff, universe, gsc, gsSizeLim = c(1, Inf), adjMethod = "fdr" )runGSAhyper( genes, pvalues, pcutoff, universe, gsc, gsSizeLim = c(1, Inf), adjMethod = "fdr" )
genes |
a vector of all genes in your experiment, or a small list of significant genes. |
pvalues |
a vector (or object to be coerced into one) of pvalues for genes or a binary vector with 0 for significant genes. Defaults to rep(0,length(genes)), i.e. genes is a vector of genes of interest. |
pcutoff |
p-value cutoff for significant genes. Defaults to 0 if pvalues are binary. If p-values are spread in [0,1] defaults to 0.05. |
universe |
a vector of genes that represent the universe. Defaults to genes if pvalues are not all 0. If pvalues are all 0, defaults to all unique genes in gsc. |
gsc |
a gene set collection given as an object of class |
gsSizeLim |
a vector of length two, giving the minimum and maximum gene
set size (number of member genes) to be kept for the analysis. Defaults to
|
adjMethod |
the method for adjusting for multiple testing. Can be any
of the methods supported by |
The statistical test performed is a one-tailed Fisher's exact test on the contingency table with columns "In gene set" and "Not in gene set" and rows "Significant" and "Non-significant" (this is equivalent to a hypergeometric test).
Command run for gene set i:
fisher.test(res$contingencyTable[[i]], alternative="greater"),
the res$contingencyTable object is available from the object returned
from runGSAhyper.
The main difference between runGSA and runGSAhyper is
that runGSA uses the gene-level statistics (numerical values for each
gene) to calculate the gene set p-values, whereas runGSAhyper only
uses the group membership of each gene (in/not in gene set,
significant/non-significant). This means that for runGSAhyper a
p-value cut-off for determining significant genes has to be chosen by the
user and after this, all significant genes will be seen as equally
significant (i.e. the actual p-values are not used). The advantage with
runGSAhyper is that you can use it to find enriched gene sets when
you only have a list of interesting genes, without any statistics.
A list-like object containing the following elements:
pvalues |
a vector of gene set p-values |
p.adj |
a vector of gene set p-values, adjusted for multiple testing |
resTab |
a full result table |
contingencyTable |
a list of the contingency tables used for each gene set |
gsc |
the input gene set collection |
Leif Varemo [email protected] and Intawat Nookaew [email protected]
piano, loadGSC, runGSA,
fisher.test, phyper, networkPlot
# Load example input data (dummy p-values and gene set collection): data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Randomly select 100 genes of interest (as an example): genes <- sample(unique(gsa_input$gsc[,1]),100) # Run gene set analysis using Fisher's exact test: res <- runGSAhyper(genes, gsc=gsc) # If you have p-values for the genes and want to make a cutoff for significance: genes <- names(gsa_input$pvals) # All gene names p <- gsa_input$pvals # p-values for all genes res <- runGSAhyper(genes, p, pcutoff=0.001, gsc=gsc) # If the 20 first genes are the interesting/significant ones they can be selected # with a binary vector: significant <- c(rep(0,20),rep(1,length(genes)-20)) res <- runGSAhyper(genes, significant, gsc=gsc)# Load example input data (dummy p-values and gene set collection): data("gsa_input") # Load gene set collection: gsc <- loadGSC(gsa_input$gsc) # Randomly select 100 genes of interest (as an example): genes <- sample(unique(gsa_input$gsc[,1]),100) # Run gene set analysis using Fisher's exact test: res <- runGSAhyper(genes, gsc=gsc) # If you have p-values for the genes and want to make a cutoff for significance: genes <- names(gsa_input$pvals) # All gene names p <- gsa_input$pvals # p-values for all genes res <- runGSAhyper(genes, p, pcutoff=0.001, gsc=gsc) # If the 20 first genes are the interesting/significant ones they can be selected # with a binary vector: significant <- c(rep(0,20),rep(1,length(genes)-20)) res <- runGSAhyper(genes, significant, gsc=gsc)
Performs a set of quality control methods and produces the results as figures.
runQC( arrayData, rnaDeg = TRUE, nuseRle = TRUE, hist = TRUE, boxplot = TRUE, pca = TRUE, colorFactor = 1, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black", "white"), save = FALSE, verbose = TRUE )runQC( arrayData, rnaDeg = TRUE, nuseRle = TRUE, hist = TRUE, boxplot = TRUE, pca = TRUE, colorFactor = 1, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black", "white"), save = FALSE, verbose = TRUE )
arrayData |
an object of class |
rnaDeg |
should RNA degradation be detected? Defaults to |
nuseRle |
should Normalized Unscaled Standard Errors (NUSE) and
Relative Log Expressions (RLE) be calculated? Defaults to |
hist |
produce histograms of expression values? Defaults to
|
boxplot |
produce boxplots of expression values? Defaults to
|
pca |
should PCA be run? Defaults to |
colorFactor |
a number specifying which column of the setup (given by
the |
colors |
a character vector of colors to be used in the PCA plot. |
save |
should the figures be saved? Defaults to |
verbose |
verbose? Defaults to |
This function is essentially a wrapper for various available quality control
functions for AffyBatch objects and normalized microarray data. RNA
degradation (rnaDeg=TRUE) and NUSE & RLE (nuseRle=TRUE)
require raw data (a dataRaw element in the ArrayData object).
The PCA uses prcomp on centralized normalized data.
Typical usages are:
# Run all quality controls: runQC(arrayData)
Does not return any object.
Leif Varemo [email protected] and Intawat Nookaew [email protected]
Brettschneider J, Collin F, Bolstad BM, and Speed TP. Quality assessment for short oligonucleotide arrays. Technometrics. (2007) In press
piano, loadMAdata,
diffExp, AffyRNAdeg,
fitPLM,
AffyBatch, prcomp
# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Run PCA only: runQC(myArrayData,rnaDeg=FALSE, nuseRle=FALSE, hist=FALSE, boxplot=FALSE)# Get path to example data and setup files: dataPath <- system.file("extdata", package="piano") # Load normalized data: myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2") # Run PCA only: runQC(myArrayData,rnaDeg=FALSE, nuseRle=FALSE, hist=FALSE, boxplot=FALSE)
Given a single object or a list of objects of class GSAres, extract the information needed for visualization in the external python function Kiwi and write it to files that can be used as input.
writeFilesForKiwi(gsaRes, label = "", overwrite = FALSE)writeFilesForKiwi(gsaRes, label = "", overwrite = FALSE)
gsaRes |
either an object of class |
label |
a character string that will be appended to the names of the resulting files. |
overwrite |
a logical, whether or not to overwrite existing files with identical names. |
This function takes the result from a gene set analysis as returned by the
runGSA function and writes three files that can be directly
used as input to Kiwi. Kiwi is a external function i python that can be used
for network-based visualization of the GSA results (http://sysbio.se/kiwi).
Three files are written in the current directory. GSC.txt contains the gene-gene set associations, i.e. the gene set collection. GLS.txt contains the gene-level statistics. GSS.txt contains the gene set statistics.
Leif Varemo [email protected]
# Load some example GSA results: data(gsa_results) # Write the files: writeFilesForKiwi(gsa_results,"exp1")# Load some example GSA results: data(gsa_results) # Write the files: writeFilesForKiwi(gsa_results,"exp1")