SERVICE // DATA ANALYTICS
Data Analytics
Most scientific datasets arrive messy, inconsistent units, missing values, batch effects, before they can answer anything. We clean, test, and visualize data with the same documentation discipline we apply to genomic pipelines, so the statistics behind a figure are as reviewable as the figure itself.
THE PROBLEM
A plot without a stated test, a stated correction for multiple comparisons, or a stated sample size is not evidence, it's an image. Research teams under deadline pressure often skip straight to visualization, which makes results hard to defend under peer review or client scrutiny.
OUR APPROACH
We treat statistics as the deliverable, not an intermediate step: every reported test states its assumptions, its correction method where relevant, and its limitations, alongside a figure built specifically for the audience it's going to (a manuscript, a board deck, or an internal report).
PROCESS
- 01
Data cleaning
Missing-value handling, outlier assessment, and normalization decisions documented, not applied silently.
- 02
Exploratory analysis
Pattern and correlation discovery to shape which confirmatory tests are actually appropriate.
- 03
Statistical testing
Hypothesis-appropriate tests with stated assumptions, effect sizes, and correction for multiple comparisons where applicable.
- 04
Visualization
Publication-ready figures built to the target venue's conventions, a journal figure is not a slide figure.
Frequently Asked Questions
Can you match our target journal's figure style?+
Yes, tell us the journal or venue during scoping and we'll match font, color, and layout conventions where the journal specifies them.
Do you only work with genomic data?+
No. Data Analytics is discipline-agnostic; the same rigor applies to any structured scientific dataset.