getTable: getTable
In mahmoudibrahim/genesorteR: Feature Ranking in Clustered Single Cell Data

Description Usage Arguments Details Value Author(s) Examples

View source: R/getTable.R

Summarize sortGenes and getPValues results in one data frame.

getTable(
  gs,
  pp,
  fc_cutoff = 0,
  adjpval_cutoff = 0.05,
  islog = TRUE,
  pseudocount = 1
)

`gs`	The output of `sortGenes`.
`pp`	The output of `getPValues`.
`fc_cutoff`	Default is 0, which means only genes that have an average fold-change higher than 0 for a given cluster are reported. Positive and negative numbers are allowed. Set to FALSE to switch off filtering on the average fold-change value.
`adjpval_cutoff`	A numeric adjusted p-value cutoff value. Default is 0.05.
`islog`	A logical value. TRUE (default) means the expression matrix supplied previously to `sortGenes` is in log space.
`pseudocount`	A numeric value. A pseudocount to add to the expression matrix before taking the log if `islog` is FALSE.

getTable combines the results of sortGenes and getPValues in one table and also calculates the average fold-change of expression, allowing to select a fold-change cutoff and a p-value cutoff to determine variable genes.

getTable returns a data frame containing the gene name, the average log fold change, the adjusted p-value and the specificity score for all variable genes in each cluster. The data frame is sorted by the specificity score. Note that a gene may appear multiple times for multiple clusters.

Mahmoud M Ibrahim <mmibrahim@pm.me>

data(kidneyTabulaMuris)
gs = sortGenes(kidneyTabulaMuris$exp, kidneyTabulaMuris$cellType)
pp = getPValues(gs)
tab = getTable(gs, pp)

#A quick diagnostic plot (fold change correlates with specificity score)
plot(tab$Average.Log.Fold.Change, tab$Specificity.Score, col = as.factor(tab$Cluster), pch = 20)

#all variable genes
unique(tab$Gene.Name)

#To get all genes without any cutoffs, set adjpval_cutoff to >1 and fc_cutoff to FALSE
tab = getTable(gs, pp, fc_cutoff = FALSE, adjpval_cutoff = 1.1)