analyzeSingleGenes: Analyze Single Genes

In Bishop-Laboratory/correlationAnalyzeR: Generate Novel Insights from Gene Correlation Data

Analyze Single Genes

Description

Obtains correlations and corGSEA results for each gene of interest.

Usage

analyzeSingleGenes(
  genesOfInterest,
  GSEA_Type = c("simple"),
  Sample_Type = "normal",
  Tissue = "all",
  crossCompareMode = FALSE,
  nperm = 2000,
  TERM2GENE = NULL,
  whichCompareGroups = c("all", "normal", "cancer"),
  outputPrefix = "CorrelationAnalyzeR_Output",
  sampler = FALSE,
  runGSEA = TRUE,
  topPlots = TRUE,
  returnDataOnly = TRUE,
  pool = NULL,
  corrMat = NULL,
  corrMat_label = "User-Supplied",
  makePool = FALSE
)

Arguments

genesOfInterest	A vector of genes to analyze.
GSEA_Type	Character vector listing the gene set databases to use. Options are listed in correlationAnalyzeR::pathwayCategories – See details of ?getTERM2GENE for more info.
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. Default: "normal"
Tissue	Which tissue type should gene correlations be derived from? Can be a single value for all genes, or a vector corresponding to genesOfInterest. Run getTissueTypes() to see available tissues. Default: "all"
crossCompareMode	Instead of normal single gene analysis, analyzeSingleGenes() will generate correlations for a single gene across all tissue-disease groups. GSEA will not be run. analyzeSingleGenes() will only consider user input for returnDataOnly, whichCompareGroups, outputPrefix, and genesOfInterest.
nperm	Number of permutations to run in GSEA. Default: 2000
TERM2GENE	Mapping of geneset IDs to gene names. If not supplied, it will be generated automatically. Only applicable if GSEA is to be run. TERM2GENE objects can be generated manually using the getTERM2GENE() function.
whichCompareGroups	For crossCompareMode, select "all", "normal", or "cancer" to analyze correlations from the corresponding groups. Default: "all".
outputPrefix	Prefix for saved files – the directory name to store output files in. This is ignored unless returnDataOnly is FALSE. Default: "CorrelationAnalyzeR_Output"
sampler	Logical. If TRUE, will only return 100,000 random genesets from either simple or complex TERM2GENEs. Useful for reducing GSEA computational burden.
runGSEA	If TRUE will run GSEA using gene correlation values. Default: TRUE.
topPlots	Logical. If TRUE, myGSEA() will build gsea plots for top correlated genesets. Default: TRUE.
returnDataOnly	if TRUE will return result list object and will not generate any folders or files. Default: TRUE.
pool	an object created by pool::dbPool to accessing SQL database. It will be created if not supplied as long as makePool is TRUE.
corrMat	A custom correlation matrix generated by generateCorrelations() to use instead of pre-supplied databases. If supplied, "Tissue" and "Sample_Type" are ignored.
corrMat_label	If corrMat is provided, this label will be used for plotting. Default: "User-Supplied".
makePool	Logical. Should a database pool be created if one is not supplied? Default: FALSE.

Details

analyzeSingleGenes() performs most of the core tasks for analyzing gene function via co-expression correlations. Please view the vignette for more detail about this function, including the structure of the ouput data list.

Value

A named list of correlation values, corGSEA results, and visualizations for each gene of interest.

Examples

genesOfInterest <- c("ATM", "SLC7A11")
res <- correlationAnalyzeR::analyzeSingleGenes(genesOfInterest = genesOfInterest,
                              returnDataOnly = TRUE,
                              GSEA_Type = "simple",
                              Sample_Type = c("normal", "cancer"),
                              Tissue = c("respiratory", "pancreas"))

genesOfInterest <- c("BRCA1")
res <- correlationAnalyzeR::analyzeSingleGenes(genesOfInterest = genesOfInterest,
                              GSEA_Type = "simple", returnDataOnly = TRUE,
                              crossCompareMode = TRUE,
                              whichCompareGroups = "normal")

Bishop-Laboratory/correlationAnalyzeR documentation built on June 28, 2022, 8:31 p.m.