--- title: "RNASeq Pipline, but make it pretty" author: Sarah Yerrace date: "`r format(Sys.time(), '%d %B, %Y')`" output: html_document: theme: readable highlight: zenburn toc: true toc_float: true number_sections: true code_folding: show code_download: true --- # Set Up Here are all the packages you will need for this code ```{r setup, include=TRUE, message=FALSE, warning=FALSE} BiocManager::install("DESeq2") library(knitr) library(tidyverse) library(kableExtra) library(DESeq2) library(pheatmap) library(RColorBrewer) library(data.table) library(DT) library(Biostrings) knitr::opts_chunk$set( echo = TRUE, # Display code chunks eval = FALSE, # Evaluate code chunks warning = FALSE, # Hide warnings message = FALSE, # Hide messages fig.width = 6, # Set plot width in inches fig.height = 4, # Set plot height in inches fig.align = "center" # Align plots to the center ) ``` # Alignment using Kallisto ## Getting sequences File Location https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/nopp/ This chunk uses wget to download files from the file location that matches a given pattern. In this instance, '*001.fastq.gz' dictates the file type and the last part of the file name. ```{r pull, engine='bash'} cd ../data wget --recursive --no-parent --no-directories \ --accept '*001.fastq.gz' \ https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/nopp/ ``` ## Obtain the reference We need to get a reference to which we can compare our sequence data ```{r pullref, engine='bash'} cd ../data curl -O https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/rna.fna ``` ## Index Reference This code is indexing the file rna.fna while also renaming it as cgigas_roslin_rna.index ```{r kallisto, engine='bash'} /home/shared/kallisto/kallisto \ index -i \ ../data/cgigas_roslin_rna.index \ ../data/rna.fna ``` ## Align reads This code alligns the sequences ```{r kallisto-align, engine='bash'} mkdir ../output/kallisto_01 find ../data/*_L001_R1_001.fastq.gz \ | xargs basename -s _L001_R1_001.fastq.gz | xargs -I{} /home/shared/kallisto/kallisto \ quant -i ../data/cgigas_roslin_rna.index \ -o ../output/kallisto_01/{} \ -t 40 \ --single -l 100 -s 10 ../data/{}_L001_R1_001.fastq.gz ``` This command runs the abundance_estimates_to_matrix.pl script from the Trinity RNA-seq assembly software package to create a gene expression matrix from kallisto output files. ```{bash} perl /home/shared/trinityrnaseq-v2.12.0/util/abundance_estimates_to_matrix.pl \ --est_method kallisto \ --gene_trans_map none \ --out_prefix ../output/kallisto_01 \ --name_sample_by_basedir \ ../output/kallisto_01/D54_S145/abundance.tsv \ ../output/kallisto_01/D56_S136/abundance.tsv \ ../output/kallisto_01/D58_S144/abundance.tsv \ ../output/kallisto_01/M45_S140/abundance.tsv \ ../output/kallisto_01/M48_S137/abundance.tsv \ ../output/kallisto_01/M89_S138/abundance.tsv \ ../output/kallisto_01/D55_S146/abundance.tsv \ ../output/kallisto_01/D57_S143/abundance.tsv \ ../output/kallisto_01/D59_S142/abundance.tsv \ ../output/kallisto_01/M46_S141/abundance.tsv \ ../output/kallisto_01/M49_S139/abundance.tsv \ ../output/kallisto_01/M90_S147/abundance.tsv \ ../output/kallisto_01/N48_S194/abundance.tsv \ ../output/kallisto_01/N50_S187/abundance.tsv \ ../output/kallisto_01/N52_S184/abundance.tsv \ ../output/kallisto_01/N54_S193/abundance.tsv \ ../output/kallisto_01/N56_S192/abundance.tsv \ ../output/kallisto_01/N58_S195/abundance.tsv \ ../output/kallisto_01/N49_S185/abundance.tsv \ ../output/kallisto_01/N51_S186/abundance.tsv \ ../output/kallisto_01/N53_S188/abundance.tsv \ ../output/kallisto_01/N55_S190/abundance.tsv \ ../output/kallisto_01/N57_S191/abundance.tsv \ ../output/kallisto_01/N59_S189/abundance.tsv ``` ### Read in Count matrix Reads in a count matrix of isoform counts generated by Kallisto, with row names set to the gene/transcript IDs and the first column removed. ```{r, eval=TRUE} countmatrix <- read.delim("../output/kallisto_01.isoform.counts.matrix", header = TRUE, sep = '\t') rownames(countmatrix) <- countmatrix$X countmatrix <- countmatrix[,-1] head(countmatrix) ``` ### Round integers to whole numbers Rounds the counts to whole numbers for further analysis ```{r, eval=TRUE} countmatrix <- round(countmatrix, 0) str(countmatrix) ``` # Get DEGs based on Desication This code performs differential expression analysis to identify differentially expressed genes (DEGs) between a control condition and a desiccated condition. ```{r, eval=TRUE} deseq2.colData <- data.frame(condition=factor(c(rep("control", 12), rep("desicated", 12))), type=factor(rep("single-read", 24))) rownames(deseq2.colData) <- colnames(data) deseq2.dds <- DESeqDataSetFromMatrix(countData = countmatrix, colData = deseq2.colData, design = ~ condition) ``` ```{r, eval=TRUE, cache=TRUE} deseq2.dds <- DESeq(deseq2.dds) deseq2.res <- results(deseq2.dds) deseq2.res <- deseq2.res[order(rownames(deseq2.res)), ] ``` ```{r} head(deseq2.res) ``` ```{r} # Count number of hits with adjusted p-value less then 0.05 dim(deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ]) ``` # Making a plot This code makes a plot where statistally significant values will be highlighted red ```{r plot, eval=TRUE, results='markdown', include=TRUE} tmp <- deseq2.res # The main plot plot(tmp$baseMean, tmp$log2FoldChange, pch=20, cex=0.45, ylim=c(-3, 3), log="x", col="darkgray", main="DEG Dessication (pval <= 0.05)", xlab="mean of normalized counts", ylab="Log2 Fold Change") #This changes the x label, y label, and main title # Getting the significant points and plotting them again so they're a different color tmp.sig <- deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ] points(tmp.sig$baseMean, tmp.sig$log2FoldChange, pch=20, cex=0.45, col="red") # 2 FC lines abline(h=c(-1,1), col="blue") ``` ```{r} write.table(tmp.sig, "../output/DEGlist.tab", row.names = T) ```