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RNA-seq CPM and DEGs

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 Configure 		 NeuN-  Nucleus accumbens  Not applicable  RNA-seq CPM  Average RNA-seq CPM in NAcc (NeuN-) samples   Data format 
    
Assembly: Human Feb. 2009 (GRCh37/hg19)

Introduction

This document describes the RNA-seq genome browser tracks for the project ‘Neuronal Brain Region-Specific DNA Methylation and Chromatin Accessibility are Associated with Neuropsychiatric Disease Heritability’. For an overview of the project, please see the README.

Mapping and quality control of RNA-seq reads

We trimmed the first 3 bp of read1, which were derived from template switching oligos and not the cDNA of interest, using seqtk (v1.2-r94) with the following parameters: seqtk trimfq -b 3 ${READ1}. We then quasi-mapped these trimmed reads to a FASTA file of protein-coding and lncRNA genes from GENCODE v19 and performed transcript-level quantification using Salmon (v0.7.2).

Identifying differentially expressed genes (DEGs)

We used tximport (v1.2.0) to compute normalized gene-level counts from the transcript-level abundance estimates (scaling these using the average transcript length over samples and the library size). Only autosomal genes with at least 1 cpm in at least 4 libraries (the size of the smallest group of samples) were retained for downstream analysis (24,161 / 33,351 genes). We normalized these counts using TMM18 then used edgeR (v3.16.5) and limma (v3.30.7) to transform these counts to log2-cpm, estimate the mean-variance relationship, and compute appropriate observation-level weights ready for linear modelling.

In our design matrix, we blocked on donor (donor1, …, donor6) and included a term for each group (e.g., BA9_neg for NeuN- cells from BA9, BA9_pos for NeuN+ cells from BA9, etc.). We ran surrogate variable analysis21 using the sva (v3.22.0) R/Bioconductor package and identified 5 surrogate variables, some of which correlated with the date on which these samples were flow-sorted. We ultimately decided to include all 5 surrogate variables in the linear model. Using the empirical Bayes shrinkage method implemented in limma, we tested for differential expression of genes in three comparisons:

  1. NAcc vs. BA9 in NeuN+ cells
  2. NAcc vs. BA9 in NeuN- cells
  3. NeuN+ cells vs. NeuN- cells

For a gene to be called a differentially expressed gene (DEG), it had to have a Benjamini-Hochberg adjusted P-value < 0.05 with no minimum log2 fold change cutoff.

Credits

Questions should be directed to Peter Hickey.