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)

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.

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

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.

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

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).