allielarocco/Microarray-Analysis

Microarray Analysis Pipeline in Python

Language: Jupyter Notebook - Size: 35.5 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 12 - Forks: 7

Srizon143005/PancreaticCancerBiomarkers

Pancreatic Cancer Biomarkers Identification Codes & Files

Language: Python - Size: 90 MB - Last synced at: 11 months ago - Pushed at: almost 6 years ago - Stars: 7 - Forks: 0

francescopatane96/RNA-seq_Pipeline

Deposited R scripts allow to execute a complete RNA-seq Pipeline, starting from sequence reads (FASTQ files) to mapping/annotate the genome using a reference, to counts the number of reads for every gene. when raw counts are obtained, DESeq2 module permits to find differentially expressed genes (DEG) and to perform statistical analysis. The last module of the project allows you to use clusterprofiler in order to perform ORA and GSEA analysis (over-representation analysis and gene set enrichment analysis) using GeneOntology (GO), disease ontology (DO), KEGG, reactome eg...

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SkadiEye/RZiMM

RZiMM: A Regularized Zero-inflated Mixture Model for scRNA-seq Data

Language: R - Size: 5.18 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

WanluLiuLab/yasim

Language: Python - Size: 2.5 MB - Last synced at: 16 days ago - Pushed at: 12 months ago - Stars: 1 - Forks: 0

hessJ/ptrs

Polytranscript risk scoring (PTRS)

Language: R - Size: 2.06 MB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 1 - Forks: 0

mattiamanna2203/TesiMagistrale

Files used for my Master's thesis in Data Science titled "Identification of Critical Nodes in Differential Co-Expression Networks of TEPs Transcriptome for Diagnostic Applications."

Language: R - Size: 29.2 MB - Last synced at: 2 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

shussin245/DNA-Microarray-Limma

This project involves analyzing DNA microarray data using Bioconductor and R to identify differentially expressed genes in mutant versus wild-type zebrafish. It leverages statistical techniques, including normalization and empirical Bayes moderation, to generate insights and visualizations from genome-wide expression data.

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chenyongrowan/DEGage

DEGage is a novel model-based method for gene differential expression analysis between two groups of scRNA-seq count data. It employs a novel family of discrete distributions for describing the difference of two NB distributions (named DOTNB).

Language: R - Size: 5.35 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 1

groverkaushal/RNAseq-Workflow-Mapping-Assembly-and-Differential-Gene-Expression-Analysis

This project uses an workflow pipeline to generate map and assemble RNAseq reads to a reference genome. Furthermore, we generate counts data and identify differentially expressed genes from 2 conditions.

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thomazggr/MinPipe

Minimal but fully logged pipeline for RNA-seq using FastQC, TrimGalore!, Kallisto and Sleuth to get from raw to differentially expressed genes.

Language: Python - Size: 117 KB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0