📖 Biomedical knowledge mining using GOSemSim and clusterProfiler
Multi-Enrichment Analysis of Gene Set Enrichment Analysis Results
TUCCA's RNA-Seq Workflow for Read Quantification, Differential Expression, and Pathway Enrichment Analysis
Working Demo on GO Enrichment Analysis using topGO, clusterProfiler and Enrichr/enrichR. Visit repo website for HTML output
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…
Bayesian network plot for the enrichment analysis results
Context-aware restriction of the Gene Ontology (GO) for enrichment analysis.
Bulk RNA-seq analysis of colorectal cancer (GSE50760): QC, differential expression (limma), and GO/KEGG/GSEA enrichment in R
End-to-end RNA-seq pipeline on TCGA-BRCA (213 samples): DESeq2 differential expression, clusterProfiler functional enrichment, and ML-based biomarker ranking (Random Forest, LASSO, SVM) achieving >96% classification accuracy
GSApp Pathway Analysis
Single-cell RNA-seq analysis — Pandey et al. 2022
An investment advisory firm needs to segment stock offerings so they may offer their customers understandable investment options.
This repository contains an end-to-end RNA-Seq DGE Analysis pipeline built in RStudio to analyze host transcriptional responses to SARS-CoV-2 across multiple cell lines. Using raw read counts from the GSE147507 benchmark study, this pipeline handles complex multi-condition experimental matrices and implements rigorous quality-control protocols.
Singularity container for running gene ontology anlaysis
Unsupervised ML pipeline that transforms raw customer data into actionable behavioural personas — validated with XGBoost + SHAP explainability.
Project Goal: To segment credit card customers based on spending patterns and interactions for targeted marketing. Role: Conducted EDA, applied clustering algorithms, reduced dimensions, and profiled segments. Helped identify key customer segments, enabling the bank to tailor services and improve customer engagement
Este projeto teste, analisa perfis de expressão gênica de dados de RNA-Seq para identificar genes diferencialmente expressos em pacientes com Doença Arterial Coronariana (DAC) usando R.
Modular Seurat workflow for single-cell/nucleus RNA-seq: QC, clustering, Harmony/CCA integration, markers, GO/KEGG enrichment, and pseudobulk DESeq2 differential expression.
Reproducible bulk RNA-seq differential expression in R (DESeq2 → apeglm → GO enrichment), independently verified against pydeseq2 and a published study.
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