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I will do bulk rnaseq analysis with differential gene expression, go, pathway analysis
About this Gig
Got RNA-seq data and need clear, defensible results?
I'll run a fully reproducible bulk RNA-seq pipeline in R/Python and deliver publication-ready figures with an expert interpretation report so you can go straight from raw data to your next manuscript section.
- Quality control & design check metadata review, replicate validation, outlier detection, and PCA
- Differential expression analysis DESeq2 or edgeR with FDR correction (BenjaminiHochberg)
- Publication-ready figures volcano, MA, PCA, clustered heatmap, pathway dot/bubble plots
- Functional enrichment GO (BP/MF/CC) ± KEGG/Reactome via g:Profiler or clusterProfiler
- Organized data tables normalized counts, variance-stabilized values, and DEG tables (CSV/XLSX)
- Detailed report HTML or PDF with full methods, parameters, and plain-language interpretation
What I need from you:
Option A: FASTQ files + sample sheet
Option B: Raw count matrix (genes × samples) + sample sheet
Human, mouse, rat, or any other just let me know.
Please note:
Results are for research purposes only no clinical or diagnostic claims are made. All data is handled confidentially and not shared with third parties.
Technology:
Jupyter Notebook
•
RStudio
Expertise:
Other
Programming language:
Python
•
R
FAQ
Do all packages come with a report included?
Yes. Even the basic one. Report and expert opinion to recommend future direction.
What inputs do you accept?
Counts per million (preferred) or FASTQ.
How many replicates do I need?
≥3 per group for robust DE
What’s a “minor revision”?
Cosmetic plot tweaks, labeling, small filter thresholds. New contrasts, new samples, or re-design = add-on
What are the outputs?
results/normalized_counts.csv results/DEGs_A_vs_B.csv (with log2FC, padj, baseMean, direction) figures/volcano.png, PCA.png, heatmap.png, enrichment_dotplot.png report/report.html (or .pdf)
