swish {fishpond} | R Documentation |
swish: SAMseq With Inferential Samples Helps
swish(y, x, cov = NULL, pair = NULL, interaction = FALSE, nperms = 30, estPi0 = FALSE, qvaluePkg = "qvalue", pc = 5, nRandomPairs = 30, fast = 1, quiet = FALSE)
y |
a SummarizedExperiment containing the inferential replicate matrices of median-ratio-scaled TPM as assays 'infRep1', 'infRep2', etc. |
x |
the name of the condition variable. A factor with two levels for a two group analysis (possible to adjust for covariate or matched samples, see next two arguments) |
cov |
the name of the covariate for adjustment.
If provided a stratified Wilcoxon in performed.
Cannot be used with |
pair |
the name of the pair variable, which should be the
number of the pair. Can be an integer or factor.
If specified, a signed rank test is used
to build the statistic. All samples across |
interaction |
logical, whether to perform a test of an interaction
between |
nperms |
the number of permutations |
estPi0 |
logical, whether to estimate pi0 |
qvaluePkg |
character, which package to use for q-value estimation,
|
pc |
pseudocount for finite estimation of |
nRandomPairs |
the number of random pseudo-pairs (only used with
|
fast |
an integer, toggles different methods based on speed
( |
quiet |
display no messages |
a SummarizedExperiment with metadata columns added:
the statistic (either a centered Wilcoxon Mann-Whitney
or a signed rank statistic, aggregated over inferential replicates),
a log2 fold change (the median over inferential replicates,
and averaged over pairs or groups (if groups, weighted by sample size),
the local FDR and q-value, as estimated by the samr
package.
The citation for swish
method is:
Anqi Zhu, Avi Srivastava, Joseph G Ibrahim, Rob Patro, Michael I Love "Nonparametric expression analysis using inferential replicate counts" Nucleic Acids Research (2019). https://doi.org/10.1093/nar/gkz622
The swish
method builds upon the SAMseq
method,
and extends it by incorporating inferential uncertainty, as well
as providing methods for additional experimental designs (see vignette).
For reference, the publication describing the SAMseq
method is:
Jun Li and Robert Tibshirani "Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data" Stat Methods Med Res (2013). https://doi.org/10.1177/0962280211428386
library(SummarizedExperiment) set.seed(1) y <- makeSimSwishData() y <- scaleInfReps(y) y <- labelKeep(y) y <- swish(y, x="condition") # histogram of the swish statistics hist(mcols(y)$stat, breaks=40, col="grey") cols = rep(c("blue","purple","red"),each=2) for (i in 1:6) { arrows(mcols(y)$stat[i], 20, mcols(y)$stat[i], 10, col=cols[i], length=.1, lwd=2) } # plot inferential replicates plotInfReps(y, 1, "condition") plotInfReps(y, 3, "condition") plotInfReps(y, 5, "condition")