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Learning And Exploring The Workflow of RNA-Seq Analysis - A Note To Myself | Everyday Is A School Day Learned RNA-seq workflow using C. difficile data from a published study 🧬. Processed raw reads through fastp → kallisto → DESeq2 pipeline. Results matched the original paper's findings, with clear dif...

Learned RNA-seq workflow using C. diff data from a study 🧬. Processed raw reads thru fastp → kallisto → DESeq2 pipeline. Results matched the original paper’s findings, with clear differential expression between mucus and control conditions 📊.

#idsky #microsky #rstats #deseq2 #bioconductor

Besides learning #BASH, I explored #R packages such as #tidyverse, #ggplot2, #DESeq2, #GEOquery, #org.Hs.eg.db, #clusterProfiler, #EnhancedVolcano, #celldex, #pheatmap, and #pathview, which open doors to further exploration of #RNASeq data. #Bioinformatics #RProgramming #Linux

Dispersion Estimates Plot

MA Plot

3) Dispersion Estimates plot showing a smooth fitted line implying a good model fit showing existing variability for gene expression counts across the samples.

4) MA Plot having a funnel shaped appearance showing differentially expressed genes with significant fold changes

#DESeq2 #Plots

PCA Plot showing PC1: 96% variance and PC2: 1% variance

Sample to sample distance plot

Here are some Plots from DESeq2:

1) PCA Plot showing a distinction between the Melatonin & Control samples revealing significant expression changes

2) A Sample-to-Sample Distance Heatmap showing treatment groups tight clustering & distinct differences between Melatonin & Control samples

#DESeq2

DESeq2 Dispersion Estimates

Dispersion model fit successfully! 🎯

This plot illustrates how gene-wise dispersion estimates are modeled across mean expression levels, ensuring reliable statistical inference during differential expression analysis.

#Bioinformatics #DESeq2 #RNAseq #Genomics

DESeq2 PCA Plot

Check out the PCA plot from my RNA-seq analysis! 🧬 🖥️

Streptococcus pneumoniae D39 HOCl-treated samples cluster distinctly from controls, revealing significant expression changes due to oxidative stress.

#RNAseq #Bioinformatics #PCA #DESeq2 #Bacteria

DESeq2 results are in! 🚨 Clear separation in PCA plots between HOCl-treated and control samples, and a list of significantly differentially expressed genes. 🧬

Stay tuned for biological insights and visualizations! 🖥️

#Bioinformatics #RNAseq #DESeq2 #Genes #Bacteria

Besides basic #RProgramming ( #tidyverse · #ggplot2 · #DESeq2 · #GEOquery · #org.Hs.eg.db · #clusterProfiler · #DoubletFinder · #EnhancedVolcano · #celldex · #pheatmap · #SingleR · #pathview) for #RNASeq · advanced packges #Seurat · #SeuratExtend · #monocle3 open new insight into #SingleCellAnalysis

This MA plot reminds me of a space ship

#DESeq2 #bambu

😱This is big… New study suggests #DESeq2 and #edgeR might not be suited for non-small #RNAseq datasets (as little as <10% DEG overlap!) Authors suggest Wilcoxon is much more robust. https://twitter.com/Rahul_B/status/1508038107748315145

Frustrating. The best #DESeq2 shrinking for the dataset I'm looking at is apeglm, but my contrast of interest isn't present in the resultsNames. When I add 'modelMatrix="expanded"' and 'betaPrior=TRUE' to the DESeq command to get that result, it complains about betaPrior.