interactive Bioinformatics Exploratory Tools
This project analyzes how SNAP participation rates relate to structural cost-of-living patterns across U.S. counties. Using PCA, regression, and clustering on county-level cost shares, income, and demographics, the study identifies key affordability gradients and regional disparities that shape SNAP usage.
📊 Computation and processing of models' parameters
Exploratory RNA-seq QC and PCA using the airway dataset
An interactive R Shiny web app for DNA methylation analysis with support for DE, pathway enrichment, power analysis, and machine learning — deployable on HPC via Singularity/Apptainer.
Interactive R Shiny application that illustrates core data science topics (clustering, regression, NLP, time series, optimization, epidemiology, Monte Carlo, Markov chains) through visual storytelling.
From black box to glass box: selecting a representative tree for random forest classification.
Brings bulk and pseudobulk transcriptomics to the tidyverse
Seurat meets tidyverse. The best of both worlds.
This is an initiative to help understand Statistical methods and Machine learning in a naive manner. You will find scripts, and theoretical contents required to clarify concepts, especially for bio-informatic students.
A collection of Poisson lognormal models for multivariate count data analysis
R script for creating beautiful biplots - all in base R, no other packages required.
R Package: Regularized Principal Component Analysis for Spatial Data
Labs from Machine Learning course at Linköping University. Includes, K-nearest neighbors, Neural Networks, PCA, Support Vector Machines, Kernels and much more!
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).
Implementation of polygenic score analysis on GenomICA components
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
🧬 Automated single-cell RNA-seq analysis pipeline with R/Seurat. Features adaptive QC, clustering, DE analysis, and publication plots. Handles real NCBI data with intelligent fallbacks. Zero-configuration, publication-ready results.
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