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reubenthomas 2020-11-11 10:53:34 -08:00
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single-cell-analysis/Session_3/NormalizationAndDifferentialExpression.Rmd
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@ -115,6 +115,16 @@ data <- subset(x=data, subset=nFeature_RNA > 200 & nCount_RNA > quantnCountRNA &
print(sprintf("After filtering outliers: %d cells and %d genes", ncol(data), nrow(data)))
data <- SCTransform(data, method="qpoisson", vars.to.regress = NULL)
data <- RunPCA(data, verbose = FALSE)
data <- RunTSNE(data, dims = 1:30, verbose = FALSE)
data <- FindNeighbors(data, dims = 1:30, verbose = FALSE)
data <- FindClusters(data, verbose = FALSE)
DimPlot(data, label = TRUE, reduction = "tsne")
DimPlot(data, label = TRUE, reduction = "tsne")
# For raw count data, we would typically do LogNormalization:
data <- NormalizeData(object=data, normalization.method="LogNormalize", scale.factor=10000)
# Again, these are the defaults, generate 2000 features using the "vst" feature selection method