I provide reproducible bioinformatics analysis for human bulk RNA-seq projects using R and Python.
This service is suitable for researchers who already have a gene-level integer count matrix or a clearly identified expression matrix. I first validate the matrix structure, gene and sample identifiers, metadata alignment, study design, pairing information, reference group, and sample-level quality before statistical analysis.
Available work may include:
• Bulk RNA-seq differential-expression analysis
• Paired or simple unpaired two-group comparisons
• Sample-level QC and exploratory analysis
• PCA, heatmaps, volcano plots, and expression figures
• GO, Reactome, and KEGG pathway-enrichment analysis
• Complete differential-expression result tables
• Publication-ready scientific data visualization
• Methods, assumptions, limitations, and interpretation notes
• Organized output files and project-specific R or Python scripts
The exact scope is confirmed after reviewing the input data and study design. Raw FASTQ preprocessing, complex longitudinal or interaction designs, and other work outside the agreed scope require a separate quotation.
I work only with de-identified and authorized research data. I do not alter scientific results or guarantee statistical significance. Findings are reported transparently with appropriate quality checks and limitations.
Please contact me before hiring and share the species, data type, matrix format, sample metadata, intended comparison, study design, expected deliverables, and deadline.