Tuberculosis@LOG and NPR, Macrophage gene expression time series.
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Updated
Oct 25, 2017 - Python
Tuberculosis@LOG and NPR, Macrophage gene expression time series.
A quick recap of widely used differential analyses methods in R for RNA-seq experiments
GSE147507 SARS-Cov-2 Dataset from Mt. Sinai
DEqMS is a tool for quantitative proteomic analysis
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/.
Gene Expression analysis with BIG-DE
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
TCGA Colorectal Cancer RNA seq Data Analysis Pipeline
This R script is used to analyze microarray data acquired by an Agilent SureScan Microarray Scanner.
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.
Performing RNA-seq data analysis with limma package
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.
Differential Gene Expression (DGE) Analysis in Curated Microarray Data of Breast Cancer Subtypes
A selection of analytical approaches, tools, and utilities for the processing of microbiome data derived from either 16S rRNA amplicon sequencing or shotgun metagenomics.
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