Contents

LAST UPDATE AT

   [1] "Tue Apr 17 07:55:43 2018"

1 Required packages and other preparations

library("scran")
library("readxl")
library("BiocStyle")
library("knitr")
library("MASS")
library("RColorBrewer")
library("stringr")
library("pheatmap")
library("matrixStats")
library("purrr")
library("readr")
library("factoextra")
library("magrittr")
library("entropy")
library("forcats")
library("readxl")
library("DESeq2")
library("broom")
library("locfit")
library("recount")
library("psych")
library("vsn")
library("matrixStats")
library("pheatmap")
library("tidyverse")
library("Rtsne")
library("devtools")
library("ggthemes")
library("scar")

theme_set(theme_solarized(base_size = 18))

data_dir <- file.path("data/")

2 mTEC single cell-RNA-Seq data

We first import the mTEC data that we will describe in more detail below.

load(file.path(data_dir, "mtec_counts.RData"))
load(file.path(data_dir, "mtec_cell_anno.RData"))
load(file.path(data_dir, "mtec_gene_anno.RData"))
load(file.path(data_dir, "tras.RData"))

We summarize single-cell transcriptomes in the form of count matrices in which each row represents a gene and each column represents one cell. The matrix is filled with the number of sequenced fragments whose genomic alignment overlaps with the genomic coordinates of the genes. It looks like this:

mtec_counts