Microarray Analysis Pipeline in Python

Pancreatic Cancer Biomarkers Identification Codes & Files

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

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…

Polytranscript risk scoring (PTRS)

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."

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

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.

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

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.

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).

This project downloads a GEO dataset, runs differential expression analysis using pyDESeq2, and produces DEG results and a volcano plot.