--- title: "RNA-seq" author: Steven Roberts 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 --- ```{r setup, include=FALSE} library(knitr) library(tidyverse) library(kableExtra) library(DESeq2) library(pheatmap) library(RColorBrewer) library(data.table) library(DT) library(Biostrings) knitr::opts_chunk$set( echo = TRUE, # Display code chunks eval = FALSE, # Evaluate code chunks warning = FALSE, # Hide warnings message = FALSE, # Hide messages fig.width = 6, # Set plot width in inches fig.height = 4, # Set plot height in inches fig.align = "center" # Align plots to the center ) ``` # Kallisto Alignment ## Getting sequencing reads ```{r pull, engine='bash'} cd ../data wget --recursive --no-parent --no-directories \ --accept '*001.fastq.gz' \ https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/nopp/ ``` ```{r fastqc, engine='bash'} cd ../data /home/shared/FastQC/fastqc *fastq.gz -o ../output ``` ```{bash} eval "$(/opt/anaconda/anaconda3/bin/conda shell.bash hook)" conda activate which multiqc cd ../output multiqc . ``` ## Obtain the reference ```{r pullref, engine='bash'} cd ../data curl -O https://gannet.fish.washington.edu/seashell/bu-github/nb-2023/Cgigas/data/rna.fna ``` ## Index Reference ```{r kallisto, engine='bash'} #!/bin/bash # run kallisto to index the transcriptome /home/shared/kallisto/kallisto \ # index the transcriptome index -i \ # specify the output file name ../data/cgigas_roslin_rna.index \ # specify the input file name ../data/rna.fna ``` ## Align reads ```{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 ``` ## Merge quant data ```{r matrix, engine='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 ``` # DESeq2 ```{r, eval=TRUE} countmatrix <- read.delim("../output/kallisto_01.isoform.counts.matrix", header = TRUE, sep = '\t') rownames(countmatrix) <- countmatrix$X countmatrix <- countmatrix[,-1] head(countmatrix) ``` ```{r,eval=TRUE} countmatrix <- round(countmatrix, 0) str(countmatrix) ``` ```{r} dim(countmatrix) dim(deseq2.colData) ``` ```{r} length(colnames(data)) ``` ```{r, eval=TRUE} deseq2.colData <- data.frame(condition=factor(c(rep("control", 12), rep("desicated", 12))), type=factor(rep("single-read", 24))) rownames(deseq2.colData) <- colnames(countmatrix) deseq2.dds <- DESeqDataSetFromMatrix(countData = countmatrix, colData = deseq2.colData, design = ~ condition) ``` ```{r, eval=TRUE, cache=TRUE} deseq2.dds <- DESeq(deseq2.dds) deseq2.res <- results(deseq2.dds) deseq2.res <- deseq2.res[order(rownames(deseq2.res)), ] ``` ```{r PCA, eval=TRUE} vsd <- vst(deseq2.dds, blind = FALSE) plotPCA(vsd, intgroup = "condition") ``` ```{r heatmap, eval=TRUE} # Select top 50 differentially expressed genes res <- results(deseq2.dds) res_ordered <- res[order(res$padj), ] top_genes <- row.names(res_ordered)[1:50] # Extract counts and normalize counts <- counts(deseq2.dds, normalized = TRUE) counts_top <- counts[top_genes, ] # Log-transform counts log_counts_top <- log2(counts_top + 1) # Generate heatmap pheatmap(log_counts_top, scale = "row") ``` ```{r, eval=TRUE} head(deseq2.res) ``` ```{r, eval=TRUE} # 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, ]) ``` ```{r, eval=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") # 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 newplot, eval=TRUE} # Prepare the data for plotting res_df <- as.data.frame(deseq2.res) res_df$gene <- row.names(res_df) # Create volcano plot volcano_plot <- ggplot(res_df, aes(x = log2FoldChange, y = -log10(padj), color = padj < 0.05)) + geom_point(alpha = 0.6, size = 1.5) + scale_color_manual(values = c("grey", "red")) + labs(title = "Volcano Plot", x = "Log2 Fold Change", y = "-Log10 Adjusted P-value", color = "Significantly\nDifferentially Expressed") + theme_minimal() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.position = "top") print(volcano_plot) ``` ```{r, eval=TRUE} write.table(tmp.sig, "../output/DEGlist.tab", sep = '\t', row.names = T) ``` ```{r, eval=TRUE} deglist <- read.csv("../output/DEGlist.tab", sep = '\t', header = TRUE) deglist$RowName <- rownames(deglist) deglist2 <- deglist[, c("RowName", "pvalue")] # Optionally, reorder the columns ``` ```{r, eval=TRUE} datatable(deglist) ``` # Annotation? ```{r} cg_sp <- read.csv("https://raw.githubusercontent.com/sr320/nb-2022/main/C_gigas/analyses/CgR-blastp-sp.tab", header = FALSE, sep="\t") %>% distinct(V1, .keep_all = TRUE) ``` ```{r} loc <- read.csv("https://raw.githubusercontent.com/sr320/nb-2022/main/C_gigas/analyses/LOC_Acc.tab", sep = " ", header = FALSE) ``` ```{r} comb <- left_join(loc, cg_sp, by = c("V2" = "V1")) %>% left_join(deglist, by = c("V1" = "RowName")) ``` # Gene Enrichment Analysis ```{r} gene_deg_status <- res_df %>% mutate(degstaus = ifelse(padj < 0.05, 1, 0)) ``` ```{r} # Read the FASTA file fasta_data <- readDNAStringSet("../data/rna.fna") # Calculate gene lengths gene_lengths <- width(fasta_data) # Extract gene names/IDs from sequence IDs gene_names <- sapply(names(fasta_data), function(x) strsplit(x, " ")[[1]][1]) # Create a data frame with gene IDs and lengths gene_lengths_df <- data.frame(geneID = gene_names, length = gene_lengths) ``` ## Need GO Mappings ```{r} pwf <- nullp(gene_data, bias.data = gene_lengths) GO_analysis <- goseq(pwf, gene2cat = "your_organism_GO_mapping") ```