From ae7b2f0480eb3a7d8635b247995a7f3c0c21393d Mon Sep 17 00:00:00 2001 From: reubenthomas Date: Wed, 11 Nov 2020 10:53:34 -0800 Subject: [PATCH] adding all files --- .../NormalizationAndDifferentialExpression.Rmd | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/single-cell-analysis/Session_3/NormalizationAndDifferentialExpression.Rmd b/single-cell-analysis/Session_3/NormalizationAndDifferentialExpression.Rmd index 1a6160c..714dfdf 100644 --- a/single-cell-analysis/Session_3/NormalizationAndDifferentialExpression.Rmd +++ b/single-cell-analysis/Session_3/NormalizationAndDifferentialExpression.Rmd @@ -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