Differential abundance analysis for feature/ observation matrices from platforms such as RNA-seq

A Snakemake workflow and MrBiomics module for performing and visualizing differential (expression) analyses (DEA) on NGS data powered by the R package limma.

DEqMS is a tool for quantitative proteomic analysis

Tools for normalization, evaluation of outliers, technical biases and batch effects and differential expression analysis.

A quick recap of widely used differential analyses methods in R for RNA-seq experiments

Differential expression analysis: DESeq2, edgeR, limma. Realized in python based on rpy2

analyze_geo_microarrays.py : Differential expression analysis of published microarrays datasets from the NCBI Gene Expression Omnibus (GEO)

Simple workflows for the isobaric-labeling proteomic data from Proteome Discoverer with ANOVA, t-testing, DEqMS/limma and annotation via fgsea

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/.

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

GSE147507 SARS-Cov-2 Dataset from Mt. Sinai

Timeseries analysis of -omics data can be carried out by fitting spline curves to the data and using limma for hypothesis testing. For this, the right spline freedom and further hyperparameters must be identified, and the obtained hits clustered based on the spline shape.The R package SplineOmics streamlines this whole process and generates reports

This R script is used to analyze microarray data acquired by an Agilent SureScan Microarray Scanner.

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.

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.

Tuberculosis@LOG and NPR, Macrophage gene expression time series.

Methods overview with scripts looking at mtDNA and haplogroup association with CPTAC LUAD study.

RNA-Seq Data Analysis in R: From Raw Counts to Differential Expression Analysis