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/.

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.

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

A selection of analytical approaches, tools, and utilities for the processing of microbiome data derived from either 16S rRNA amplicon sequencing or shotgun metagenomics.

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

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

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

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

Bioinformatic analysis of gene expression microarray profiles and DGEs on R and R studio

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.

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.

Normalization, outlier detection, statistical analysis, and visualization of microarray data from 79 tumor samples to identify differentially expressed genes between recurrent and non-recurrent prostate cancer.

GSE147507 SARS-Cov-2 Dataset from Mt. Sinai

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