Differential gene expression analysis using DESeq2
Perform differential gene expression analysis of RNA-seq data using DESeq2
Make discoveries with your data
Recent posts
Create a gene counts matrix from featureCounts
Generate a gene counts matrix when featureCounts run separately on individual aligned files
Parsing and analyzing BAM files
SAMtools for manipulation of BAM files
Entrez programming utilities for downloading the nucleotide and protein sequences from NCBI
NCBI E-utilities for downloading the single or large number of sequences from the NCBI sequence database
A guide to understanding the variant information fields in variant call format (VCF) file
VCF fields information
Gene expression units explained: RPM, RPKM, FPKM, TPM, DESeq, TMM, SCnorm, GeTMM, and ComBat-Seq
bulk and single-cell RNA-seq expression units, count normalization, formula, examples in Python, gene quantification, batch effects, and between-sample and w...
How to use NCBI SRA Toolkit effectively?
Downloading FASTQ files from NCBI SRA database
t-SNE in Python [single cell RNA-seq example and hyperparameter optimization]
t-SNE using sklearn package. This article explains the basics of t-SNE, differences between t-SNE and PCA, example using scRNA-seq data, and results interpre...
k-means clustering in Python [with example]
Learn how to perform k-means clustering in Python. This article discusses the k-means clustering algorithm, it’s implementation in Python, and visualization ...
Sequencing coverage and breadth of coverage
High-through sequencing coverage calculation and coverage recommendations
Heatmap in Python
Heatmap and hierarchical clustering visualization in Python
Principal component analysis (PCA) and visualization using Python (Detailed guide with example)
PCA using sklearn package. This article explains the basics of PCA, sample size requirement, data standardization, and interpretation of the PCA results