Package 'PanomiR' reference manual

Title:	Detection of miRNAs that regulate interacting groups of pathways
Description:	PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data.
Authors:	Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya Sytchev [aut], Winston Hide [aut]
Maintainer:	Pourya Naderi <[email protected]>
License:	MIT + file LICENSE
Version:	1.11.0
Built:	2024-10-30 09:21:48 UTC
Source:	https://github.com/bioc/PanomiR

function to align a list of sets and a reference universe

Description

function to align a list of sets and a reference universe

Usage

alignToUniverse(pathwaySets, universe)
alignToUniverse(pathwaySets, universe)

Arguments

`pathwaySets`	a list of sets
`universe`	all set elements must be a subset of universe

Value

a list of sets, aligned to universe

Plots clusters of pathways with associated directionality.

Description

Plots clusters of pathways with associated directionality.

Usage

clusterPlot(
  subNet,
  subplot = FALSE,
  topClusters = 2,
  prefix = "",
  outDir = ".",
  plotSave = TRUE
)
clusterPlot(
  subNet,
  subplot = FALSE,
  topClusters = 2,
  prefix = "",
  outDir = ".",
  plotSave = TRUE
)

Arguments

`subNet`	pathways network (edge list of pathways)
`subplot`	if TRUE, store individual clusters plots and connected plots in Figures directory of plots
`topClusters`	plot figures for top x clusters
`prefix`	add prefix to plots
`outDir`	output directory
`plotSave`	saves the plot if set true. Otherwise display

Value

a set of plots for DE-PCXN and subclusters

Examples

data(miniTestsPanomiR)
clusterPlot(miniTestsPanomiR$miniPathClusts$DE_PCXN, plotSave = FALSE)
data(miniTestsPanomiR)
clusterPlot(miniTestsPanomiR$miniPathClusts$DE_PCXN, plotSave = FALSE)

Differential Expression Analysis For Pathways

Description

Performs differential expression analysis for pathways using LIMMA package with gene counts

Usage

differentialPathwayAnalysis(
  geneCounts,
  pathways,
  covariates,
  condition,
  adjustCovars = NULL,
  covariateCorrection = FALSE,
  quantileNorm = FALSE,
  outDir = ".",
  saveOutName = NULL,
  id = "ENSEMBL",
  deGenes = NULL,
  minPathSize = 10,
  method = "x2",
  trim = 0.025,
  geneCountsLog = TRUE,
  contrastConds = NA
)
differentialPathwayAnalysis(
  geneCounts,
  pathways,
  covariates,
  condition,
  adjustCovars = NULL,
  covariateCorrection = FALSE,
  quantileNorm = FALSE,
  outDir = ".",
  saveOutName = NULL,
  id = "ENSEMBL",
  deGenes = NULL,
  minPathSize = 10,
  method = "x2",
  trim = 0.025,
  geneCountsLog = TRUE,
  contrastConds = NA
)

Arguments

`geneCounts`	Gene counts, rows refer to genes and columns to samples.
`pathways`	Pathways table, containing pathway names and genes with id specified.
`covariates`	Covariates/metadata file; rows matches the columns of geneCounts.
`condition`	Condition to be examined (tumor vs normal etc); must exist in covariates column.
`adjustCovars`	Adjustment covariates like batch; if NULL, no adjustments performed.
`covariateCorrection`	If TRUE, performs covariates detection and correction; requires adjustCovars; (limma).
`quantileNorm`	If TRUE, performs quantile normalization on pathway summary statistics; from preprocess package.
`outDir`	Output directory.
`saveOutName`	If not NULL, saves output as RDS using save name, if NULL, does not save output.
`id`	ID matching genes to pathways; rownames of geneCounts.
`deGenes`	If not NULL, add t-scores to pathways summary statistics; filter by genes t-scores.
`minPathSize`	Minimum pathway size.
`method`	Define method to use for pathway summary statistics; specifications in documentations.
`trim`	Filter pathways with mean less than trim threshold in pathway summary statistics.
`geneCountsLog`	If TRUE, log(geneCounts).
`contrastConds`	Provide a contrast expression to be used in Limma comparison. This is necessary if you have more than two levels in the condition covariate.

Value

List containing differentially expressed pathways as DEP and pathway summary statistics as pathwaySummaryStats.

Examples


data("path_gene_table")
data("miniTestsPanomiR")

differentialPathwayAnalysis(geneCounts = miniTestsPanomiR$mini_LIHC_Exp,
pathways =  path_gene_table,
covariates = miniTestsPanomiR$mini_LIHC_Cov,
condition = 'shortLetterCode')

data("path_gene_table")
data("miniTestsPanomiR")

differentialPathwayAnalysis(geneCounts = miniTestsPanomiR$mini_LIHC_Exp,
pathways =  path_gene_table,
covariates = miniTestsPanomiR$mini_LIHC_Cov,
condition = 'shortLetterCode')

Pairwise enrichment analysis between two given lists of sets

Description

Pairwise enrichment analysis between two given lists of sets

Usage

enrichAllPairs(mirSets, pathwaySets, pathsRef, numCores)
enrichAllPairs(mirSets, pathwaySets, pathsRef, numCores)

Arguments

`mirSets`	a list of targets of miRNAs
`pathwaySets`	a list of pathways
`pathsRef`	universe of genes.
`numCores`	number of cores to calculate the results.

Value

enrichment analysis results

Obtain Design Matrix

Description

Modified from covariates pipeline of Menachem Former. Imported from https://github.com/th1vairam/CovariateAnalysis

Usage

getDesignMatrix(covariatesDataFrame, intercept = TRUE, reLevels = list())
getDesignMatrix(covariatesDataFrame, intercept = TRUE, reLevels = list())

Arguments

`covariatesDataFrame`	Dataframe of covariates.
`intercept`	intercept in the linear model.
`reLevels`	TBA.

Value

List containing a design matrix.

Examples

data(iris)
getDesignMatrix(iris)
data(iris)
getDesignMatrix(iris)

function to get a DE table

Description

function to get a DE table

Usage

getDiffExpTable(expMat, designMat, contrastsName)
getDiffExpTable(expMat, designMat, contrastsName)

Arguments

`expMat`	an expression matrix
`designMat`	a design Matrix
`contrastsName`	the contrast to perform

Value

a table of differential expression

function to get residuals with respect to a set of covariates

Description

function to get residuals with respect to a set of covariates

Usage

getResidual(covariates, adjustCovars, pathSumStats)
getResidual(covariates, adjustCovars, pathSumStats)

Arguments

`covariates`	a covariate dataframe.
`adjustCovars`	covariates to adjust for
`pathSumStats`	an expression matrix

Value

a matrix of adjusted expression

Example genesets from MSigDB

Description

Example genesets from MSigDB

Usage

data(gscExample)
data(gscExample)

Format

A GeneSet Collection object containing two genesets.

Source

http://www.gsea-msigdb.org/gsea/index.jsp

Examples

data(gscExample)
data(gscExample)

Outputs a table with col x (miRNA), probability of observing k (depending on methodology) against a random distribution with jack-knifing of the pathway cluster (removing a pathway at a time)

Description

Outputs a table with col x (miRNA), probability of observing k (depending on methodology) against a random distribution with jack-knifing of the pathway cluster (removing a pathway at a time)

Usage

jackKnifeBase(
  selector,
  pathways,
  enrichNull,
  fn,
  jackKnifeData,
  m,
  numCores = 1
)
jackKnifeBase(
  selector,
  pathways,
  enrichNull,
  fn,
  jackKnifeData,
  m,
  numCores = 1
)

Arguments

`selector`	Table with x(miRNA) in pathway cluster and observed k (depending on methodology).
`pathways`	Pathways in pathway cluster.
`enrichNull`	Enrichment dataset with x (miRNA), y (pathway) and pval (probability of observing x in pathway cluster).
`fn`	Methodology function.
`jackKnifeData`	Random distribution data with jack-knifing (i.e. one less pathway)
`m`	method name
`numCores`	number of cores

Value

Outputs a new selector table with col x, pval_jk

Function imported from https://github.com/th1vairam/CovariateAnalysis Modified from http://stackoverflow.com/questions/13088770/ Function to find linearly dependednt columns of a matrix

Description

Function imported from https://github.com/th1vairam/CovariateAnalysis Modified from http://stackoverflow.com/questions/13088770/ Function to find linearly dependednt columns of a matrix

Usage

linColumnFinder(mat)
linColumnFinder(mat)

Arguments

mat

an input design matrix.

Value

a list of independent columns

Examples

data("iris")
designMat <- getDesignMatrix(iris)
linColumnFinder(designMat$design)
data("iris")
designMat <- getDesignMatrix(iris)
linColumnFinder(designMat$design)

Outputs a table with pathways and their respective clusters

Description

Outputs a table with pathways and their respective clusters

Usage

mappingPathwaysClusters(
  pcxn,
  dePathways,
  clusteringFunction = NULL,
  edgeFDR = 0.05,
  correlationCutOff = 0.316,
  pathwayFDR = 0.05,
  topPathways = 200,
  plotOut = TRUE,
  subplot = TRUE,
  topClusters = 2,
  prefix = "",
  outDir = ".",
  saveNameCSV = NULL,
  weighted = FALSE
)
mappingPathwaysClusters(
  pcxn,
  dePathways,
  clusteringFunction = NULL,
  edgeFDR = 0.05,
  correlationCutOff = 0.316,
  pathwayFDR = 0.05,
  topPathways = 200,
  plotOut = TRUE,
  subplot = TRUE,
  topClusters = 2,
  prefix = "",
  outDir = ".",
  saveNameCSV = NULL,
  weighted = FALSE
)

Arguments

`pcxn`	pathways network (edge list of pathways)
`dePathways`	differential expressed pathways, obtained from DifferentialPathwayAnalysis
`clusteringFunction`	clustering algorithm
`edgeFDR`	FDR threshold for pathway-pathway adjusted p-values; filter edges with adjusted p-values less than given threshold
`correlationCutOff`	cut-off threshold for pathway-pathway correlation; filter pathways with correlation less than given threshold
`pathwayFDR`	FDR threshold for DE pathways adjusted p-values; filter pathways with adjusted p-values less than given threshold
`topPathways`	use only top x paths; if NULL, use all paths
`plotOut`	if TRUE, store graph plot in Figures directory of plots
`subplot`	if TRUE, store individual clusters plots and connected plots in Figures directory of plots
`topClusters`	plot figures for top x clusters
`prefix`	add prefix to plots
`outDir`	output directory
`saveNameCSV`	if not NULL, saves output as csv using save name
`weighted`	True if you wish to include correlation weights in clustering

Value

a list where the first item is a table with each row containing a pathway and its respective cluster. The second item is an igraph object.

Examples

data("miniTestsPanomiR")

mappingPathwaysClusters(pcxn = miniTestsPanomiR$miniPCXN,
                         dePathways = miniTestsPanomiR$miniDEP,
                         topPathways = 200,
                         outDir=".",
                         plot = FALSE,
                         subplot = FALSE,
                         prefix='',
                         clusteringFunction = "cluster_louvain",
                         correlationCutOff = 0.1)

data("miniTestsPanomiR")

mappingPathwaysClusters(pcxn = miniTestsPanomiR$miniPCXN,
                         dePathways = miniTestsPanomiR$miniDEP,
                         topPathways = 200,
                         outDir=".",
                         plot = FALSE,
                         subplot = FALSE,
                         prefix='',
                         clusteringFunction = "cluster_louvain",
                         correlationCutOff = 0.1)

Outputs a table with col x, miRNA, probability of observing k against a random distribution of the cover of methodology

Description

Outputs a table with col x, miRNA, probability of observing k against a random distribution of the cover of methodology

Usage

methodProbBase(samplingData, selector, m, nPaths = 100, coverFn = NULL)
methodProbBase(samplingData, selector, m, nPaths = 100, coverFn = NULL)

Arguments

`samplingData`	Random distribution data.
`selector`	Table with x(miRNA) in pathway cluster and observed k (depending on methodology).
`m`	Method name.
`nPaths`	Number of pathways used to generate the samplingData at each iteration. Default is set at 100.
`coverFn`	Cover of methodology function.

Value

Outputs a new selector table with col x, pval and cover.

Readouts and datasets for minimal reproducible examples of the PanomiR.

Description

The item miniEnrich is a reduced representation of the TargetScan For full table use miRNAPathwayEnrichment function in the package along with msigdb_c2 and targetScan_03 datasets.

Usage

data(miniTestsPanomiR)
data(miniTestsPanomiR)

Format

A list of 5:

mini_LIHC_Exp: a reduced expression dataset from TCGA LIHC data
mini_LIHC_Cov: a reduced covariates dataset from TCGA LIHC data
miniEnrich: a reduced table of miRNA-pathway enrichment, TargetScan.
miniDEP: Differentially activated pathways from reduced TCGA LIHC
miniPCXN: reduced representation of PCXN network
miniPathClusts: miniDEP mapped to miniPCXN

Details

These datasets include reduced representation of TCGA LIHC data for reproducing the pipeline. doi: 10.1016/j.cell.2017.05.046

A reduced representation of PCxN is provided. For full dataset and method please refer to pcxn.org or https://doi.org/10.1371/journal.pcbi.1006042

Examples

data(miniTestsPanomiR)
data(miniTestsPanomiR)

Enrichment Probability Of miRNAs

Description

Outputs enrichment probability of miRNAs based on pathway clusters.

Usage

miRNAPathwayEnrichment(
  mirSets,
  pathwaySets,
  geneSelection = NULL,
  mirSelection = NULL,
  fromID = "ENSEMBL",
  toID = "ENTREZID",
  minPathSize = 9,
  numCores = 1,
  outDir = ".",
  saveOutName = NULL
)
miRNAPathwayEnrichment(
  mirSets,
  pathwaySets,
  geneSelection = NULL,
  mirSelection = NULL,
  fromID = "ENSEMBL",
  toID = "ENTREZID",
  minPathSize = 9,
  numCores = 1,
  outDir = ".",
  saveOutName = NULL
)

Arguments

`mirSets`	Table of miRNAs and a list of their interactions with genes in ENTREZ ID.
`pathwaySets`	Table of pathways and a list of their interactions with genes in ENTREZ ID.
`geneSelection`	Table of genes with dtype; if not NULL, select only genes from a given table.
`mirSelection`	Table of miRNA names; if not NULL, select only miRNAs from given table.
`fromID`	ID of genes in geneSelection.
`toID`	ID of genes used in pcxn and pathways set.
`minPathSize`	Filter out pathways with sets less than given value.
`numCores`	Number of CPU cores to use, must be at least one.
`outDir`	Output directory.
`saveOutName`	If not NULL, saves output as RDS using save name.

Value

Table of enrichment, each row contains mirna-pathway and its enrichment p-values.

Examples

data(msigdb_c2)
data(targetScan_03)
miRNAPathwayEnrichment(targetScan_03[1:20],msigdb_c2[1:20])
data(msigdb_c2)
data(targetScan_03)
miRNAPathwayEnrichment(targetScan_03[1:20],msigdb_c2[1:20])

Canonical pathways from Molecular Signatures Database, MsigDb V6.2

Description

Canonical pathways from Molecular Signatures Database, MsigDb V6.2

Usage

data(msigdb_c2)
data(msigdb_c2)

Format

A list of 1143 pathways

Source

http://www.gsea-msigdb.org/gsea/index.jsp

Examples

data(msigdb_c2)
data(msigdb_c2)

A table of gene-pathway association. based on the pathways of MSigDB.

Description

A table of gene-pathway association. based on the pathways of MSigDB.

Usage

data(path_gene_table)
data(path_gene_table)

Format

A matrix with 3 columns and 76926 rows:

Pathway: An MSigDB annotated pathway
ENTREZID: The ENTREZID of a gene belonging to the pathway
ENSEMBL: The ENSEMBL of a gene belonging to the pathway

Examples

data(path_gene_table)
data(path_gene_table)

Pathway-Gene Associations

Description

Generates a table of pathways and genes associations.

Usage

pathwayGeneTab(
  pathAdress = NA,
  pathwayList = NA,
  fromType = "ENTREZID",
  toType = "ENSEMBL",
  outDir = NA
)
pathwayGeneTab(
  pathAdress = NA,
  pathwayList = NA,
  fromType = "ENTREZID",
  toType = "ENSEMBL",
  outDir = NA
)

Arguments

`pathAdress`	Address to an RDS file containing list of pathways where each element is a list of genes similar to GMT format.
`pathwayList`	If you wish to use a list of pathways instead of a file use this argument instead. The list must contain no NA values.
`fromType`	gene annotation type used in your input data.
`toType`	gene annotation type to be produced in the output.
`outDir`	Address to save an RDS for a table of pathway-gene association

Value

pathExpTab Table of pathway-gene association.

Examples

pathway1 <- c("125", "3099", "126")
pathway2 <- c("5232", "5230", "5162")
pathList <- list("Path1" = pathway1, "Path2" = pathway2)
res <- pathwayGeneTab(pathwayList = pathList)

data(msigdb_c2)
pathwayGeneTab(pathwayList = msigdb_c2[1:2])
pathway1 <- c("125", "3099", "126")
pathway2 <- c("5232", "5230", "5162")
pathList <- list("Path1" = pathway1, "Path2" = pathway2)
res <- pathwayGeneTab(pathwayList = pathList)

data(msigdb_c2)
pathwayGeneTab(pathwayList = msigdb_c2[1:2])

Pathway Summary Statistics

Description

Generates a table of pathway activity profiles per sample

Usage

pathwaySummary(
  exprsMat,
  pathwayRef,
  id = "ENSEMBL",
  zNormalize = FALSE,
  method = FALSE,
  deGenes = NULL,
  trim = 0,
  tScores = NULL
)
pathwaySummary(
  exprsMat,
  pathwayRef,
  id = "ENSEMBL",
  zNormalize = FALSE,
  method = FALSE,
  deGenes = NULL,
  trim = 0,
  tScores = NULL
)

Arguments

`exprsMat`	Gene expression matrix with row names as genes and samples as columns.
`pathwayRef`	Table of pathway-gene associations. Created from `pathwayGeneTab` function.
`id`	Gene annotation type in the row name of gene expression data.
`zNormalize`	Normalization of pathway summary score.
`method`	Choice of how to summarize gene ranks into pathway statistics.
`deGenes`	List of differentially expressed genes along with t-scores. Only necessary if working on Top 50% summary method.
`trim`	Percentage of top and bottom ranked genes to be excluded from pathway summary statistics.
`tScores`	Argument for-top-50-percent-genes method.

Value

pathExp Table of pathway activity profiles per sample.

Examples

pathTab <- tibble::tribble(
 ~Pathway, ~ENTREZID,  ~ENSEMBL,
 "Path1",   "125",      "ENSG00000196616",
 "Path1",   "3099",     "ENSG00000159399",
 "Path2",   "5230",     "ENSG00000102144",
 "Path2",   "5162",     "ENSG00000168291"
 )
exprsMat <- matrix(2 * (seq_len(12)), 4, 3)
rownames(exprsMat) <- pathTab$ENSEMBL
colnames(exprsMat) <- LETTERS[seq_len(3)]
pathwaySummary(exprsMat, pathTab, method = "x2")
pathTab <- tibble::tribble(
 ~Pathway, ~ENTREZID,  ~ENSEMBL,
 "Path1",   "125",      "ENSG00000196616",
 "Path1",   "3099",     "ENSG00000159399",
 "Path2",   "5230",     "ENSG00000102144",
 "Path2",   "5162",     "ENSG00000168291"
 )
exprsMat <- matrix(2 * (seq_len(12)), 4, 3)
rownames(exprsMat) <- pathTab$ENSEMBL
colnames(exprsMat) <- LETTERS[seq_len(3)]
pathwaySummary(exprsMat, pathTab, method = "x2")

Creates a network out of pcxn table

Description

Creates a network out of pcxn table

Usage

pcxnToNet(pcxn, edgeFDR, correlationCutOff, weighted)
pcxnToNet(pcxn, edgeFDR, correlationCutOff, weighted)

Arguments

`pcxn`	pathways network edge list of pathways
`edgeFDR`	FDR threshold for pathway-pathway adjusted p-values; filter edges with adjusted p-values less than given threshold
`correlationCutOff`	cut-off threshold for pathway-pathway correlation; filter pathways with correlation less than given threshold
`weighted`	True if you wish to include correlation weights in clustering

Value

enrichment analysis results

Prioritize miRNA

Description

Outputs a table of miRNA ordered with respective p-values derived from method for prioritization

Usage

prioritizeMicroRNA(
  enriches0,
  pathClust,
  method = "AggInv",
  methodThresh = NULL,
  enrichmentFDR = 0.25,
  topClust = 2,
  sampRate = 1000,
  outDir = ".",
  dataDir = ".",
  saveSampling = TRUE,
  runJackKnife = TRUE,
  saveJackKnife = FALSE,
  numCores = 1,
  saveCSV = TRUE,
  prefix = "",
  autoSeed = TRUE
)
prioritizeMicroRNA(
  enriches0,
  pathClust,
  method = "AggInv",
  methodThresh = NULL,
  enrichmentFDR = 0.25,
  topClust = 2,
  sampRate = 1000,
  outDir = ".",
  dataDir = ".",
  saveSampling = TRUE,
  runJackKnife = TRUE,
  saveJackKnife = FALSE,
  numCores = 1,
  saveCSV = TRUE,
  prefix = "",
  autoSeed = TRUE
)

Arguments

`enriches0`	miRNA-pathway enrichment dataset obtained from miRNAPathwayEnrichment.
`pathClust`	Pathway clusters, obtained from MappingPathwaysClusters.
`method`	Vector of methods pCut, AggInv, AggLog, sumz, sumlog.
`methodThresh`	Vector of methods threshold for each method in method, if NULL use default thresh values in method.
`enrichmentFDR`	FDR cut-off calculating miRNA-pathway hits in the input cluster based on significant enrichment readouts.
`topClust`	Top x clusters to perform miRNA prioritization on.
`sampRate`	Sampling rate for CLT.
`outDir`	Output directory.
`dataDir`	Data directory.
`saveSampling`	If TRUE, saves sampling data as RDS for each cluster in topClust in dataDir.
`runJackKnife`	If TRUE, jacknifing will be performed.
`saveJackKnife`	If TRUE, saves jack-knifed sampling data as RDS for each cluster in topClust in dataDir.
`numCores`	Number of CPU cores to use, must be at least one.
`saveCSV`	If TRUE, saves CSV file for each cluster in topClust in outDir.
`prefix`	Prefix for all saved data.
`autoSeed`	random permutations are generated based on predetermined seeds. TRUE will give identical results in different runs.

Value

Table of miRNA and p-values, each row contains a miRNA and its associated p-values from the methods.

Examples

data("miniTestsPanomiR")

prioritizeMicroRNA(enriches0 = miniTestsPanomiR$miniEnrich,
   pathClust = miniTestsPanomiR$miniPathClusts$Clustering,
   topClust = 1,
   sampRate = 50,
   method = c("aggInv"),
   saveSampling = FALSE,
   runJackKnife = FALSE,
   numCores = 1,
   saveCSV = FALSE)
data("miniTestsPanomiR")

prioritizeMicroRNA(enriches0 = miniTestsPanomiR$miniEnrich,
   pathClust = miniTestsPanomiR$miniPathClusts$Clustering,
   topClust = 1,
   sampRate = 50,
   method = c("aggInv"),
   saveSampling = FALSE,
   runJackKnife = FALSE,
   numCores = 1,
   saveCSV = FALSE)

Publication-ready miRNA-Pathway Enrichment table

Description

This function summarizes the outputs

Usage

reportEnrichment(enrichmentTable)
reportEnrichment(enrichmentTable)

Arguments

enrichmentTable

Outputs from [miRNAPathwayEnrichment()] function

Value

A summarized miRNA-Pathway enrichment table

Examples

data(msigdb_c2)
data(targetScan_03)
eTab <- miRNAPathwayEnrichment(targetScan_03[1:20],msigdb_c2[1:20])

repTab <- reportEnrichment(eTab)
data(msigdb_c2)
data(targetScan_03)
eTab <- miRNAPathwayEnrichment(targetScan_03[1:20],msigdb_c2[1:20])

repTab <- reportEnrichment(eTab)

Outputs a table of sampling data(rows are miRNA and cols are samples)

Description

Outputs a table of sampling data(rows are miRNA and cols are samples)

Usage

samplingDataBase(
  enrichNull,
  selector,
  sampRate,
  fn,
  nPaths,
  samplingDataFile,
  jackKnife = FALSE,
  saveSampling,
  numCores = 1,
  autoSeed = TRUE
)
samplingDataBase(
  enrichNull,
  selector,
  sampRate,
  fn,
  nPaths,
  samplingDataFile,
  jackKnife = FALSE,
  saveSampling,
  numCores = 1,
  autoSeed = TRUE
)

Arguments

`enrichNull`	Enrichment dataset with x (miRNA), y (pathway) and pval (probability of observing x in pathway cluster).
`selector`	Table with x(miRNA) in pathway cluster.
`sampRate`	Sampling rate.
`fn`	Methodology function.
`nPaths`	Number of pathways in pathway cluster.
`samplingDataFile`	If file exists, load. Else, perform random sampling
`jackKnife`	If TRUE, conduct sampling with one less pathway, used for jack knifing
`saveSampling`	If TRUE, data is saved.
`numCores`	number of cores used
`autoSeed`	random permutations are generated based on predetermined seeds. TRUE will give identical results in different runs.

Value

Outputs of sampling data.

Pathway-Gene Associations from GeneSet collections

Description

This function enables to utilize MSigDB packages and GSEABase objects to incorporate customized genesets into PanomiR.

Usage

tableFromGSC(gsCollection, fromType = "ENTREZID", toType = "ENSEMBL")
tableFromGSC(gsCollection, fromType = "ENTREZID", toType = "ENSEMBL")

Arguments

`gsCollection`	An GSEABase gene set collection object
`fromType`	gene annotation type used in your input data
`toType`	gene annotation type to be produced in the output

Value

A table of pathway-gene associations

Examples

data(gscExample)
tableFromGSC(gscExample)
data(gscExample)
tableFromGSC(gscExample)

A processed list of miRNA target gene sets from the TargetScan dataset. Each list item is a list of genes targeted by the respective miRNA family

Description

The interactions are filtered to only human interactions.

Usage

data(targetScan_03)
data(targetScan_03)

Format

A list of 439 items

Details

The interactions are filtered to have a Cumulative weighted context++ score of < -0.3

Source

http://www.targetscan.org/vert_72/

Examples

data(targetScan_03)
data(targetScan_03)

Package 'PanomiR'

Help Index

function to align a list of sets and a reference universe

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Plots clusters of pathways with associated directionality.

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Differential Expression Analysis For Pathways

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Pairwise enrichment analysis between two given lists of sets

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Obtain Design Matrix

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function to get a DE table

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function to get residuals with respect to a set of covariates

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Example genesets from MSigDB

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Outputs a table with col x (miRNA), probability of observing k (depending on methodology) against a random distribution with jack-knifing of the pathway cluster (removing a pathway at a time)

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Function imported from https://github.com/th1vairam/CovariateAnalysis Modified from http://stackoverflow.com/questions/13088770/ Function to find linearly dependednt columns of a matrix

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Outputs a table with pathways and their respective clusters

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Outputs a table with col x, miRNA, probability of observing k against a random distribution of the cover of methodology

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Readouts and datasets for minimal reproducible examples of the PanomiR.

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Enrichment Probability Of miRNAs

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Examples