Description Usage Arguments Value Functions Examples
View source: R/DownsampleMatrix.R
Passes the output of generateSimulatedData() to differential expression tests, picking either ttests 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 fdradjusted pvalues for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.
subDiffExttest(): A vector of fdradjusted pvalues for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.
subDiffExANOVA(): A vector of fdradjusted pvalues for all genes. Nonviable results (such as for genes with 0 counts in a simulated dataset) are coerced to 1.
subDiffEx
: Get PCA components for a SCtkE object
subDiffExttest
: Runs ttests 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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