Provides easy to use, objective oriented functions for preprocessing methylation data produced by an Illumina Infinium BeadChip and detecting differentially methylated positions and regions within the DNA.
-
Updated
Apr 17, 2023 - R
Provides easy to use, objective oriented functions for preprocessing methylation data produced by an Illumina Infinium BeadChip and detecting differentially methylated positions and regions within the DNA.
Tuberculosis@LOG and NPR, Macrophage gene expression time series.
This scripts involves five major steps including GEO dataset download, data normalization, data manipulation, fetching phenodata and feature data and differentially expressed genes (DEGs) analysis using R and bioconductor packages.
Gene Expression analysis with BIG-DE
TCGA Colorectal Cancer RNA seq Data Analysis Pipeline
Differential Gene Expression (DGE) Analysis in Curated Microarray Data of Breast Cancer Subtypes
Performing RNA-seq data analysis with limma package
The Tasmanian devil is endangered due to Devil Facial Tumour Disease (DFTD), a contagious cancer with no current treatment. Previous study has investigated the anticancer properties of Tasmanian devil cathelicidins. This report intends to reproduce the enrichment analysis results of the study and further analyze their data.
GSE147507 SARS-Cov-2 Dataset from Mt. Sinai
This R script is used to analyze microarray data acquired by an Agilent SureScan Microarray Scanner.
A selection of analytical approaches, tools, and utilities for the processing of microbiome data derived from either 16S rRNA amplicon sequencing or shotgun metagenomics.
Bioinformatic analysis of gene expression microarray profiles and DGEs on R and R studio
EuroBioc2020 SPEAQeasy workshop https://eurobioc2020.bioconductor.org by Nick Eagles and Josh Stolz. For more information about SPEAQeasy check http://research.libd.org/SPEAQeasy/. For an example on how to use this RNA-seq processing pipeline and analyze the output files check http://research.libd.org/SPEAQeasy-example/.
Simple workflows for the isobaric-labeling proteomic data from Proteome Discoverer with ANOVA, t-testing, DEqMS/limma and annotation via fgsea
analyze_geo_microarrays.py : Differential expression analysis of published microarrays datasets from the NCBI Gene Expression Omnibus (GEO)
Differential expression analysis: DESeq2, edgeR, limma. Realized in python based on rpy2
Add a description, image, and links to the limma topic page so that developers can more easily learn about it.
To associate your repository with the limma topic, visit your repo's landing page and select "manage topics."