Generates single sample gene set scores across a datasets by many methods

It is common to assess the activity of a gene set in a given sample. There are many ways to do that, and this method is analogous to the multiGSEA() function in that it enables the user to run a multitude of single-sample-gene-set-scoring algorithms over a target expression matrix using a GeneSetDb() object.

scoreSingleSamples(
  gdb,
  y,
  methods = "ewm",
  as.matrix = FALSE,
  drop.sd = 1e-04,
  verbose = FALSE,
  recenter = FALSE,
  rescale = FALSE,
  ...,
  as.dt = FALSE
)

Arguments

gdb

A GeneSetDb
y

An expression matrix to score genesets against
methods

A character vector that enumerates the scoring methods you want to run over the samples. Please reference the "Single Sample Scoring Methods" section for more information.
as.matrix

Return results as a list of matrices instead of a melted data.frame? Defaults to FALSE.
drop.sd

Genes with a standard deviation across columns in y that is less than this value will be dropped.
verbose

make some noise? Defaults to FALSE.
recenter, rescale

If TRUE, the scores computed by each method are centered and scaled using the scale function. These variables correspond to the center and scale parameters in the scale function. Defaults to FALSE.
...

these parameters are passed down into the the individual single sample scoring funcitons to customize them further.
as.dt

If FALSE (default), the data.frame like thing that this funciton returns will be set to a data.frame. Set this to TRUE to keep this object as a data.table

Value

A long data.frame with sample_id,method,score values per row. If as.matrix=TRUE, a matrix with as many rows as geneSets(gdb) and as many columns as ncol(x)

Details

Please refer to the "Generating Single Sample Gene Set Scores" of the multiGSEA vignette for further exposition.

Single Sample Scoring Methods

The following methods are currenly provided.

  • "ewm": The eigenWeightedMean() calculates the fraction each gene contributes to a pre-specified principal component. These contributions act as weights over each gene, which are then used in a simple weighted mean calculation over all the genes in the geneset per sample. This is similar, in spirit, to the svd/gsdecon method (ie. method = "gsd"``) You can use this method to perform an "eigenweighted" zscore by setting unscaleanduncentertoFALSE. "ewz": with unscaleanduncenterset toFALSE`.

  • "gsd": This method was first introduced by Jason Hackney in doi:10.1038/ng.3520. Please refer to the gsdScore() function for more information.

  • "ssgsea": Using ssGSEA as implemented in the GSVA package.

  • "zscore": The features in the expression matrix are rowwise z transformed. The gene set level score is then calculated by adding up the zscores for the genes in the gene set, then dividing that number by either the the size (or its sqaure root (default)) of the gene set.

  • "mean": Simply take the mean of the values from the expression matrix that are in the gene set. Right or wrong, sometimes you just want the mean without transforming the data.

  • "gsva": The gsva method of GSVA package.

  • "plage": Using "plage" as implemented in the GSVA package

Examples

library(reshape2)

#> Warning: package ‘reshape2’ was built under R version 4.0.2
gdb <- exampleGeneSetDb()
vm <- exampleExpressionSet()
scores <- scoreSingleSamples(
  gdb, vm, methods = c("ewm", "ssgsea", "gsva", "ewz", "zscore"),
  center = TRUE, scale = TRUE, ssgsea.norm = TRUE)

#> Warning: The current version of scoreSingleSamples produces *minor* differences in GSVA-based single sample gene set scores.
#> https://github.com/lianos/multiGSEA/issues/10
#> Warning: The current version of scoreSingleSamples produces *minor* differences in GSVA-based single sample gene set scores.
#> https://github.com/lianos/multiGSEA/issues/10

sw <- reshape2::dcast(scores, name + sample_id ~ method, value.var='score')

corplot(sw[, c("ewm", "ssgsea", "gsva", "ewz", "zscore")],
        title = "Single Sample Score Comparison")


# I think the eigenWeighteZscore is just ewm with un(center|scale) = FALSE

zs <- scoreSingleSamples(
  gdb, vm, methods = c('ewm', 'ewz', 'zscore'), summary = "mean",
  center = TRUE, scale = TRUE, uncenter = FALSE, unscale = FALSE)
zw <- reshape2::dcast(zs, name + sample_id ~ method, value.var='score')
corplot(zw[, c("ewm", "ewz", "zscore")], title = "EW zscores")