analyzeSingleGenes: Analyze Single Genes
In millerh1/correlationAnalyzeR: Generate Novel Insights from Gene Correlation Data

Description Usage Arguments Value Examples

Obtains correlations and corGSEA results for each gene of interest

analyzeSingleGenes(genesOfInterest, Species = c("hsapiens", "mmusculus"),
  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, makePool = FALSE)

`genesOfInterest`	A vector of genes to analyze.
`Species`	Species to obtain gene names for. Either 'hsapiens' or 'mmusculus'
`GSEA_Type`	Which GSEA annotations should be considered? Options 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.
`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.
`crossCompareMode`	Instead of normal single gene analysis, will generate correlations for single gene across all tissue-disease groups. GSEA will not be run. Will only consider user input for returnDataOnly, whichCompareGroups, outputPrefix, Species, and genesOfInterest.
`nperm`	Number of permutations to run in GSEA. Default is 2000
`TERM2GENE`	Mapping of geneset IDs to gene names. If NULL, it will be generated automatically. Only applicable if GSEA is to be run.
`whichCompareGroups`	For crossCompareMode, select "all", "normal", or "cancer" to analyze correlations from the corresponding groups.
`outputPrefix`	Prefix for saved files. Should include directory info.
`sampler`	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.
`topPlots`	Logical. If TRUE, myGSEA() will build gsea plots for top correlated genesets.
`returnDataOnly`	if TRUE will return only a dataframe of correlation values and will not generate any folders or files.
`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.

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

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

genesOfInterest <- c("Brca1")
correlationAnalyzeR::analyzeSingleGenes(genesOfInterest = genesOfInterest,
                              Species = "mmusculus",
                              GSEA_Type = "simple", returnDataOnly = TRUE,
                              crossCompareMode = TRUE,
                              whichCompareGroups = "normal")