analyzeGenesetTopology: Analyze Gene List Topology

Description Usage Arguments Details Value Examples

View source: R/analyzeGeneListTopology.R

Description

Analyzes the topology of a gene list using gene correlation data and dimension-reduction techniques.

Usage

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analyzeGenesetTopology(genesOfInterest, Species = c("hsapiens",
  "mmusculus"), Sample_Type = c("normal", "cancer"), Tissue = "all",
  crossComparisonType = c("PCA", "variantGenes", "coCorrelativeGenes",
  "pathwayEnrich"), pathwayType = c("simple"),
  setComparisonCutoff = "Auto", pathwayEnrichment = FALSE,
  pValueCutoff = 0.05, numTopGenesToPlot = "Auto",
  alternativeTSNE = TRUE, numClusters = "Auto",
  outputPrefix = "CorrelationAnalyzeR_Output", returnDataOnly = TRUE,
  pool = NULL, makePool = FALSE)

Arguments

genesOfInterest

A vector of genes to analyze or the name of an official MSIGDB term.

Species

Species to obtain gene names for. Either 'hsapiens' or 'mmusculus'

Sample_Type

Type of RNA Seq samples used to create correlation data. Either "all", "normal", or "cancer". Can be a single value for all genes, or a vector corresponding to genesOfInterest.

Tissue

Which tissue type should gene correlations be derived from? Default = "all". Can be a single value for all genes, or a vector corresponding to genesOfInterest. Run getTissueTypes() to see available tissues.

crossComparisonType

The type of topology tests to run. (see details)

pathwayType

Which pathway annotations should be considered? Options listed in correlationAnalyzeR::GSEA_categories. See details of getTERM2GENE for more info.

setComparisonCutoff

Only relevant for co-correlation analysis – the number of genes which must aggree for a gene to be considered co-correlative within the input gene list.

pathwayEnrichment

Logic. If TRUE, pathway enrichment will be performed on variant genes – if 'variantGenes' selected – and/or on co-correlative genes – if "coCorrelativeGenes" selected.

pValueCutoff

Numeric. The p value cutoff applied when running all pathway enrichment tests.

numTopGenesToPlot

When creating a heatmap of the top co-correlative or top variant genes, how many genes should be plotted on the y axis? Default: "Auto"

alternativeTSNE

Logical. If TRUE, then a TSNE will be run as an alternative to PCA for visualizing large input gene lists. This is highly recommended as 100+ member gene lists cannot be visualized otherwise.

numClusters

The number of clusters to create with hclust or TSNE analysis. Default: "Auto"

outputPrefix

Prefix for saved files. Should include directory info.

returnDataOnly

if TRUE will return only a list of analysis results. Default: TRUE

pool

an object created by pool::dbPool to accessing SQL database. It will be created if not supplied.

makePool

Logical. Should a pool be created if one is not supplied? Default: FALSE.

Details

Cross Comparison Types: - variantGenes: These are the genes which best explain variation between genes within the input list. These genes can divide a list into functional groups. - coCorrelativeGenes: These are the genes which best explain similarities between all genes in the input list. These genes can explain what biological processes unify the input genes. - PCA: This is a dimensionality reduction technique for exploring the topology of a gene list. The PCA analyses here employes hclust to divide the gene list into functional clusters. If the input list is > 100 genes, RTsne will be used for visualization. - pathwayEnrich: Cluster profiler's enricher function will be run on the input gene list.

Value

A list of correlations for input genes, and the results of chosen analysis + visualizations.

Examples

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genesOfInterest <- c("CDK12", "AURKB", "SFPQ", "NFKB1", "BRCC3", "BRCA2", "PARP1",
                     "DHX9", "SON", "AURKA", "SETX", "BRCA1", "ATMIN")
correlationAnalyzeR::analyzeGenesetTopology(genesOfInterest = genesOfInterest,
                                 Species = "hsapiens",
                                 Sample_Type = "cancer", returnDataOnly = TRUE,
                                 Tissue = "brain",
                                 crossComparisonType = c("variantGenes", "PCA"))

millerh1/correlationAnalyzeR documentation built on Dec. 10, 2019, 1:31 a.m.