Description Usage Arguments Value Functions Examples
View source: R/DownsampleMatrix.R
Passes the output of generateSimulatedData() to differential expression tests, picking either t-tests or ANOVA for data with only two conditions or multiple conditions, respectively.
1 2 3 4 5 | subDiffEx(tempData)
subDiffExttest(countMatrix, class.labels, test.type = "t.equalvar")
subDiffExANOVA(countMatrix, condition)
|
tempData |
Matrix. The output of generateSimulatedData(), where the first row contains condition labels. |
countMatrix |
Matrix. A simulated counts matrix, sans labels. |
class.labels |
Factor. The condition labels for the simulated cells. Will be coerced into 1's and 0's. |
test.type |
Type of test to perform. The default is t.equalvar. |
condition |
Factor. The condition labels for the simulated cells. |
subDiffEx(): A vector of fdr-adjusted p-values for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.
subDiffExttest(): A vector of fdr-adjusted p-values for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.
subDiffExANOVA(): A vector of fdr-adjusted p-values for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.
subDiffEx
:
subDiffExttest
: Runs t-tests on all genes in a simulated dataset with 2
conditions, and adjusts for FDR.
subDiffExANOVA
: Runs ANOVA on all genes in a simulated dataset with
more than 2 conditions, and adjusts for FDR.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | data("mouseBrainSubsetSCE")
res <- generateSimulatedData(
totalReads = 1000, cells=10,
originalData = assay(mouseBrainSubsetSCE, "counts"),
realLabels = colData(mouseBrainSubsetSCE)[, "level1class"])
tempSigDiff <- subDiffEx(res)
data("mouseBrainSubsetSCE")
#sort first 100 expressed genes
ord <- rownames(mouseBrainSubsetSCE)[
order(rowSums(assay(mouseBrainSubsetSCE, "counts")),
decreasing = TRUE)][1:100]
#subset to those first 100 genes
subset <- mouseBrainSubsetSCE[ord, ]
res <- generateSimulatedData(totalReads = 1000, cells=10,
originalData = assay(subset, "counts"),
realLabels = colData(subset)[, "level1class"])
realLabels <- res[1, ]
output <- res[-1, ]
fdr <- subDiffExttest(output, realLabels)
data("mouseBrainSubsetSCE")
#sort first 100 expressed genes
ord <- rownames(mouseBrainSubsetSCE)[
order(rowSums(assay(mouseBrainSubsetSCE, "counts")),
decreasing = TRUE)][1:100]
# subset to those first 100 genes
subset <- mouseBrainSubsetSCE[ord, ]
res <- generateSimulatedData(totalReads = 1000, cells=10,
originalData = assay(subset, "counts"),
realLabels = colData(subset)[, "level2class"])
realLabels <- res[1, ]
output <- res[-1, ]
fdr <- subDiffExANOVA(output, realLabels)
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