Single Cell RNA-seq analysis of Persister Cells to unearth key stress pathways and core, differential genes of those respective pathways
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Updated
Feb 15, 2024 - R
Single Cell RNA-seq analysis of Persister Cells to unearth key stress pathways and core, differential genes of those respective pathways
Workflow for differential gene expression analysis for non-replicate samples using DEGseq
Lightweight RNA-seq pipeline to go from fastqs to differential expression using kallisto and sleuth
RNA-seq Preprocessing for GSEA
Viper is a Snakemake workflow, aimed at performing the RNA-seq workflow of the paper 'Causes and consequences of a glutamine induced normoxic HIF1 activity for the tumor metabolism', Kappler et al. (2019) in a reproducible and automated manner
In this reposity I will give you all script I created. All this script are specialised for one way : annalyse RNA-seq data.
Automated DE analysis by DESeq2
Repo for TotalRNASeq Pipeline Development for AG Hornef, UK Aachen
A repository for RNA-seq workflow
A simple code to analyze OTU table and RNA-seq normalization in R
Performing RNA-Seq data analysis and quality control using DESeq2 in R.
Source code of the paper entitled "The sequential data of differential transcription expression of heart in aldosterone-infusion mice"
Bioinformatic scripts for the manuscript concerning interchromosomal interaction and gene expression patterns of K562 cells treated by hemin
Pipeline for low-level RNA-Seq data processing
Neste projeto descrevo diferentes parâmetros e opções para a execução de programas necessários à análise de Rna-Seq, além de incluir informações e opniões que possam facilitar a escolha da melhor opção que se adque à análise em questão.
RNA-seq workflow: gene-level exploratory analysis and differential expression.
The package mixhvg uses mixture of highly variable gene selection methods to improve gene selection.
This repo is a template for running differential gene expression analysis of RNA-seq count data followed by gene set enrichment analysis. This workflow is run in R using Rmarkdown. It is based around the popular R packages, DESeq2, fGSEA, and others.
Differential gene expression analysis for samples with replicates using STAR-DeSeq2 pipeline
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