Bioinformatics — Analyze Raw Scientific Data

Why I ask for raw time-series (scientific justification)

• Time-subtype response curves require per-timepoint variance (replicates) to show uncertainty (SEM/CI) and avoid overinterpretation — averaging hides transient dynamics and can create false hypotheses.
• Recreating published figures requires numeric extraction or access to underlying data; many papers provide time-resolved data in supplements or repositories — I will reproduce figures and cite the exact data source.
• Fitting kinetics (time constants, rise/decay τ, AUC) is model-dependent; I will (on your confirmation) fit exponential or mechanistic models and report fit quality (AIC, residuals, CI) and blindspots.

Relevant tools & references I will apply (methods & reproducibility)

If you provide data I will run an analysis pipeline similar to HTSplotter for time-series visualization and the drda optimization approach for robust curve-fitting and confidence intervals (both are open-source bodies of work designed for accurate curve fitting and time-course plotting):

• HTSplotter time-series tool
• drda curve-fitting methods

What I will deliver once you provide data

1. Multi-panel Plotly figure (responsive) with: per-subtype mean±SEM ribbons (or CIs), individual replicate traces (toggleable), normalized and absolute plots, and difference-in-curves significance shading (bootstrapped AUC or time-window tests).
2. Model fits per subtype (exponential rise/decay, logistic, or user-specified), fitted parameter table (τ, amplitude, baseline), AIC/BIC, and 95% CI with residual plots.
3. Downloadable high-resolution PNG/SVG, raw processed data (HDF5/CSV), and reproducible Jupyter/Python and R scripts implementing the analysis and plots.
4. Transparent inline citations for any extracted published data used to recreate figures.

Quick example of the exact inputs I expect (CSV headers)

time,subtype,replicate,response
0,SubtypeA,rep1,0.12
0,SubtypeA,rep2,0.10
0,SubtypeB,rep1,0.08
1,SubtypeA,rep1,0.20
1,SubtypeA,rep2,0.18
1,SubtypeB,rep1,0.15
...

Or a summary table: time,subtype,mean,sem (I will reconstruct replicate-level bootstraps if only mean+sem are provided).

Caveats, biases and quality control I will apply

• Report measurement units and assay modality (fluorescence, cell-count, cytokine concentration) so axis labels are biologically meaningful.
• Flag and handle missing timepoints, outliers, and non-uniform sampling with transparent imputation or interpolation options and sensitivity checks.
• Quantify uncertainty: I will prefer bootstrapped CIs, not only analytic SEMs, and will show raw replicate points to avoid overconfidence.
• State limitations in interpretation (correlation ≠ mechanism), and note possible confounders (batch, assay drift, normalization choices).