CalcDEvsRest | R Documentation |
Performs differential gene expression tests for each cluster in an sCVdata object, comparing the cells in the cluster to the remaining cells in the data using the gene expression matrix of input data object. Alternatively, this function can be skipped, and existing DE test results can be assigned directly to the sCVdata object.
CalcDEvsRest(sCVd, inD)
## S4 method for signature 'sCVdata'
CalcDEvsRest(sCVd, inD)
sCVd |
An sCVdata object. |
inD |
The input dataset. An object of class |
This function performs Wilcoxon rank sum tests comparing gene expression
between each cluster and all other cells in the input data. Gene expression
ratio in log space (logGER
) is reported for all genes in the
comparison. Genes are tested if they are detected in the cluster at a higher
proportion than Param(sCVd,"DRthresh")
, and both unadjusted p-values
and false discovery rates are reported for all genes tested. To help track
its progress, this function uses progress bars from pbapply
. To
disable these, set pboptions(type="none")
. To
re-enable, set pboptions(type="timer")
.
If using existing DE test results, assign results of one vs. all tests for
every cluster in sCVdata to the DEvsRest
slot of the
sCVdata
object. See example and slot documentation.
A named list of data frames, one entry for each level in
Clusters(sCVd)
(with corresponding name). Each entry is data frame
containing gene differential expression stats when comparing the cells of
that cluster to all other cells in the input data. Rows represent genes,
and variables include logGER
(an effect size measure: gene
expression ratio in log space, often referred to as logFC) and FDR
(significance measure: false discovery rate). Also included are
Wstat
and pVal
, the test statistic and the p-value of the
Wilcoxon rank sum test.
sCVdata
: Calculate one vs. all DE tests for sCVdata
CalcSCV
for wrapper function to calculate all
statistics for an sCVdata object, and fx_calcESvsRest
and
fx_calcDEvsRest
for the internal functions performing the
calculations. Wilcox test is now powered by wilcoxauc
for super speed.
## Not run:
## Example using CalcDEvsRest ##
DEvsRest(your_sCV_obj) <- CalcDEvsRest(sCVd=your_sCV_obj,
inD=your_scRNAseq_data_object)
## Example using MAST results from Seurat to replace CalcDEvsRest ##
MAST_oneVSall <- FindAllMarkers(your_seurat_obj,
logfc.threshold=0,
min.pct=0.1,
test.use="MAST",
latent.vars="nUMI")
# ^ FindAllMarkers and CalcDEvsRest do equivalent comparisons
names(MAST_oneVSall)[names(MAST_oneVSall) == "avg_logFC"] <- "logGER"
# ^ Effect size variable must be named 'logGER'
names(MAST_oneVSall)[names(MAST_oneVSall) == "p_val_adj"] <- "FDR"
# ^ Significance variable must be named 'FDR'
MAST_oneVSall_list <- sapply(levels(MAST_oneVSall$cluster),
function(X) {
temp <- MAST_oneVSall[MAST_oneVSall$cluster == X,]
rownames(temp) <- temp$gene
# ^ Rownames must be gene names.
return(temp)
},simplify=F)
# ^ Dataframe converted to list of dataframes per cluster
DEvsRest(your_sCV_obj) <- MAST_oneVSall_list
# ^ Slot MAST results into sCVdata object
## End(Not run)
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