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Basic Pipeline for scRNAseq Data Analysis: Trajectory Analysis

Somi Kim, Eunseo Paek, Donggon Cha 2021/07/06

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

library(monocle)
library(ggplot2)

load("/BiO/home/edu4/data/RData/tcell_sce.RData")
load("/BiO/home/edu4/data/RData/tcell_hvgs.RData")

Including Plots

You can also embed plots, for example:

counts <- sce_t@assays@data$logcounts

pd.df = as.data.frame(sce_t@colData)
pd <- new("AnnotatedDataFrame", data=pd.df)

fd.df = data.frame(row.names = rownames(counts),
                   gene_short_name = rownames(counts))
fd <- new("AnnotatedDataFrame", data=fd.df)

cds <- newCellDataSet(as.matrix(counts),
                      phenoData = pd,
                      featureData = fd,
                      expressionFamily = negbinomial.size())
cds <- setOrderingFilter(cds, hvg.t)

cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds)
cds <- reduceDimension(cds)

cds <- orderCells(cds, reverse=TRUE)
plot_cell_trajectory(cds, color_by = "Pseudotime")

plot_genes_branched_pseudotime(cds[c("CD4", "CD8A"),],
                               branch_point = 2,
                               color_by = "Pseudotime",
                               ncol = 1)