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.

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

DEqMS is a tool for quantitative proteomic 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

Timeseries analysis of -omics data can be carried out by fitting spline curves to the data and using limma for hypothesis testing. The obtained hits can be clustered based on the spline shape and then used for an over-representation (ORA) analysis.The R package SplineOmics streamlines this whole process and generates reports.

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

Performing RNA-seq data analysis with limma package

GSE147507 SARS-Cov-2 Dataset from Mt. Sinai

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

Reproducible R workflow that generates bulk RNA-seq from single-cell references, injects batch/biology effects, and compares correction methods (ComBat, limma, RUV, fastMNN, SVA) using quantitative metrics and plots. Outputs are bulk-level assays (bulk_counts).

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 Repo contains all of the files and the code used in my undergraduate thesis: Gender Bias in Clinical Studies: A Statistical Approach

Differential Gene Expression (DGE) Analysis in Curated Microarray Data of Breast Cancer Subtypes