Biostatistics solved.
Get tailored data analysis, software, and tech bio solutions for your organisation.
Pre-clinical statistics
Biology RLHF
Bioinformatics
1-1 Statistical Approach Overview
Trusted by biotech companies, universities, and AI leaders
Biotech Startups
Stay on top of your costs and timelines by ensuring your data analysis is accurate.
Universities
Improve dry lab precision, performance, and reliability by deep dives into the technicals of computational biology.
Big Pharma
Maximize support for your high-impact teams by equipping them with insights derived from large-scale datasets.
AI Tech Companies
Enhance the performance of bio-focused AI models by leveraging domain-specific datasets and RL methods from experts.
Get a free consultation today!
Got more questions about your data after the experiment than before? Let us help you uncover insights in genomics, statistics, and proteomics. We can also assist in building biology-focused LLMs powered by high-quality RLHF to supercharge your research.
Never doubt your analysis again
Here are some common themes we help answer
Bioinformatics
How to correct batch effects in RNA-seq?
Are cell type annotations from UMAP clusters in scRNA and snATAC seq reliable?
How to speed up barcode counting?
Is the cloud configuration (AWS, GCP, Azure) efficient?
What bioinformatic processes can be optimised for deployment on the cloud?
How can one move away from Snakemake and CWL to Nextflow or Argo?
How are tools for DGE, alignment, and variant calling chosen for a given analysis?
What’s the best normalisation technique for WGS?
Do RNA-seq & proteomics data correlate?
Statistics
How to stratify treatment and control groups?
What tests can be used on what kind of data?
What test assumptions can we relax for given data?
Can outliers be excluded?
How to compare data with multiple time points, drug concentrations, and categorical variables?
What settings should be used for common statistical tests in Microsoft Excel, GraphPad Prism, or G*Power?
How can statistical power be maximized for a given sample size, and how many mice are required to achieve sufficient power?
RLHF
How do we create a dataset of questions and responses in biology to break modern LLMs?
What strategy works for incorporating and setting up a small-scale RLHF team? What are the rules, feedback loops, and skills that should be prioritised?
What metrics should be used to quantify and evaluate human feedback?
How can consistency be ensured during RLHF?
What is a good reward fucntion?
Want a different question answered? Let us know!
Our process
We believe in messy meetings but crisp documents. After multiple iteration strategies, we have hit upon an effective workflow for biological data analyses.
Understanding the problem
We first grasp your problem and produce a one-page document of what we understand are the challenges in analysis or what you want to get done.
Aligning competencies
Next, we pick a team lead who should lead the solution for our client. This is based on technical competency, problem specifics, and timelines.
Solution and feedback
We then interface with the client to discuss our solution in the appropriate contexts and possible pitfalls in our analysis.
