getBiomarker {singleCellTK} | R Documentation |
Selects the 500 most variable genes in the SCE, performs PCA or t-SNE based on them and stores the values in the reducedDims slot of the SCE object.
getBiomarker(inSCE, gene, binary = "Binary", useAssay = "counts") getPCA(inSCE, useAssay = "logcounts", reducedDimName = "PCA") getTSNE(inSCE, useAssay = "logcounts", reducedDimName = "TSNE") plotBiomarker(inSCE, gene, binary = "Binary", visual = "PCA", shape = "No Shape", x = "PC1", y = "PC2", useAssay = "counts", reducedDimName = "PCA") plotPCA(inSCE, colorBy = "No Color", shape = "No Shape", pcX = "PC1", pcY = "PC2", reducedDimName = "PCA", runPCA = FALSE, useAssay = "logcounts") plotTSNE(inSCE, colorBy = "No Color", shape = "No Shape", reducedDimName = "TSNE", runTSNE = FALSE, useAssay = "logcounts")
inSCE |
Input SCtkExperiment object. Required |
gene |
gene list |
binary |
"Binary" for binary expression or "Continuous" for a gradient. Default: "Binary" |
useAssay |
Indicate which assay to use for PCA. Default is "counts" |
reducedDimName |
Store the PCA data with this name. The default is PCA. The toolkit will store data with the pattern <ASSAY>_<ALGORITHM>. |
visual |
Type of visualization (PCA or tSNE). Default: "PCA" |
shape |
Shape of the points |
x |
x coordinate for PCA |
y |
y coordinate for PCA |
colorBy |
The variable to color clusters by |
pcX |
User choice for the first principal component |
pcY |
User choice for the second principal component |
runPCA |
Run PCA if the reducedDimName does not exist. the Default is FALSE. |
runTSNE |
Run t-SNE if the reducedDimName does not exist. the Default is FALSE. |
getBiomarker(): A data.frame of expression values
getPCA(): A SCtkE object with the specified reduecedDim and pcaVariances updated
getTSNE(): A SCtkE object with the specified reduecedDim and pcaVariances updated
plotBiomarker(): A Biomarker plot
plotPCA(): A PCA plot
plotTSNE(): A t-SNE plot
getBiomarker
: Given a list of genes and a SCtkExperiment object, return
the binary or continuous expression of the genes.
getPCA
: Get PCA components for a SCtkE object
getTSNE
: Get t-SNE components for a SCtkE object
plotBiomarker
: Given a set of genes, return a ggplot of expression
values.
plotPCA
: plot PCA results
plotTSNE
: plot t-SNE results
getBiomarker(mouseBrainSubsetSCE, gene="C1qa") data("mouseBrainSubsetSCE") #add a CPM assay assay(mouseBrainSubsetSCE, "cpm") <- apply(assay(mouseBrainSubsetSCE, "counts"), 2, function(x) { x / (sum(x) / 1000000) }) mouseBrainSubsetSCE <- getPCA(mouseBrainSubsetSCE, useAssay = "cpm", reducedDimName = "PCA_cpm") reducedDims(mouseBrainSubsetSCE) data("mouseBrainSubsetSCE") #add a CPM assay assay(mouseBrainSubsetSCE, "cpm") <- apply( assay(mouseBrainSubsetSCE, "counts"), 2, function(x) { x / (sum(x) / 1000000) }) mouseBrainSubsetSCE <- getTSNE(mouseBrainSubsetSCE, useAssay = "cpm", reducedDimName = "TSNE_cpm") reducedDims(mouseBrainSubsetSCE) data("mouseBrainSubsetSCE") plotBiomarker(mouseBrainSubsetSCE, gene="C1qa", shape="level1class") data("mouseBrainSubsetSCE") plotPCA(mouseBrainSubsetSCE, colorBy = "level1class", reducedDimName = "PCA_counts") data("mouseBrainSubsetSCE") plotTSNE(mouseBrainSubsetSCE, colorBy = "level1class", reducedDimName = "TSNE_counts")