Minimum inputs
- Raw count matrix or accepted quantification outputs
- Sample metadata with groups, batches, and contrasts
- Organism, genome/reference version, and gene annotation notes
How a project works
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01
Scope the dataset
Clarify organism, sample groups, metadata, contrast, desired figures, and what data can be shared safely.
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02
Run the workflow
Analyze with transparent scripts, named outputs, quality checks, and checkpoints that make the result traceable.
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03
Deliver clean files
Provide tables, plots, scripts, and a short report that explains what was done and what the results show.
Inputs, analyses, and deliverables
Accepted inputs
Count matrix, normalized matrix, Salmon/quantification summaries, metadata, selected public datasets
Core analyses
Differential expression, PCA/heatmap/volcano, pathway/GSEA review, alternative splicing event summaries
Visualization
Publication-ready static figures with clean labels, legends, dimensions, and export formats
Statistical notes
Contrast definition, filtering assumptions, adjusted p-values, ranked lists, and method limitations
Deliverables
Tables, figures, scripts/notebooks when appropriate, and a concise report or methods summary
Scope boundary
Research support only; no diagnosis, treatment guidance, or patient-specific medical interpretation
What makes a dataset ready for analysis
Useful context
- Target genes, pathways, or splicing events of interest
- Required figure formats for thesis, report, or manuscript use
- Any previous scripts, tables, or analysis decisions to preserve
Safer sharing
- Anonymized or public datasets are preferred for first projects
- Only share data you are allowed to transfer for analysis
- Clinical interpretation and patient-specific advice are excluded