View source: R/SparrowResult-methods.R
geneSetsStats | R Documentation |
This function calculates the number of genes that move up/down for the given contrasts, as well as mean and trimmed mean of the logFC and t-statistics. Note that the statistics calculated and returned here are purely a function of the statistics generated at the gene-level stage of the analysis.
geneSetsStats(
x,
feature.min.logFC = 1,
feature.max.padj = 0.1,
trim = 0.1,
reannotate.significance = FALSE,
as.dt = FALSE
)
x |
A |
feature.min.logFC |
used with |
feature.max.padj |
used with |
trim |
The amount to trim when calculated trimmed |
reannotate.significance |
this is internally by the package, and should
left as |
as.dt |
If |
A data.table with statistics at the gene set level across the
prescribed contrast run on x
. These statistics are independent
of any particular GSEA method, but rather summarize aggregate shifts
of the gene sets individual features. The columns included in the output
are summarized below:
n.sig
: The number of individual features whose abs(logFC)
and padj
thersholds satisfy the criteria of the feature.min.logFC
and
feature.max.padj
parameters of the original seas()
call
n.neutral
: The number of individual features whose abs(logFC) and padj
thersholds do not satisfy the feature.*
criteria named above.
n.up, n.down
: The number of individual features with logFC > 0
or
logFC < 0
, respectively, irrespective of the feature.*
thresholds
referenced above.
n.sig.up, n.sig.down
: The number of individual features that pass the
feature.*
thresholds and have logFC > 0 or logFC < 0, respectively.
mean.logFC, mean.logFC.trim
: The mean (or trimmed mean) of the individual
logFC estimates for the features in the gene set. The amount of trim is
specified in the trim
parameter of the seas()
call.
mean.t, mean.t.trim
: The mean (or trimmed mean) of the individual
t-statistics for the features in the gene sets. These are NA
if the input
expression object was a DGEList
.
vm <- exampleExpressionSet(do.voom=TRUE)
gdb <- exampleGeneSetDb()
mg <- seas(vm, gdb, design = vm$design, contrast = 'tumor')
head(geneSetsStats(mg))
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