10x Customer? We’ve Got You Covered

Diamond Age Data Science works with fast-moving biotech and pharmaceutical companies to drive innovation through bioinformatics. As a validated partner to 10x Genomics, we’re eager to hear how we can help you work with 10x data. (You can tell us about your needs here.)

We develop and provide custom solutions for the most challenging problems in computational biology, such as large-scale and heterogeneous single-cell RNA-seq analysis, RNA splicing analysis, CNV analysis, and statistical genetics at large scale. If needed, we can also serve as a “one-stop shop,” analyzing existing data (proprietary or public) and helping to answer scientific and strategic questions such as how to identify mechanisms of action or store complex datasets. 

All Diamond Age consultants have both deep computational expertise and advanced degrees in the life sciences.  

As a validated partner to 10x Genomics, we can help accelerate the analysis of single cell gene expression data by: 

  • Creating custom analysis and visualization
  • Designing and implementing best-practice analysis workflows 
  • QCing both data and results
  • Benchmarking methods and tools
  • Taking clients through to publication

We use all major R and Python tools for single cell gene expression analysis, and can handle challenges including clustering, differential expression, batch correction, cell type annotation, trajectory mapping and more.

Our other services include:

Advising and strategy

  • Review of CRO analysis for correctness and completeness
  • Review of poor or unexpected results to determine causes (e.g., was this a lab problem or an analysis problem?)
  • Experimental design review — making sure your experiment is adequately powered to yield the answers you need
  • Bioinformatics “concierge service” — comprehensive advising on experimental design, CRO selection, data analysis, software selection, etc.
  • Guidance on software engineering, informatics, data management, and knowledge architecture
  • Development of best practices for analyzing high-throughput data
  • Total bioinformatics department replacement

Therapeutics-focused functions

  • Biomarker development
  • Mechanism of action evaluation: proteomics, transcriptomics, and pathway analysis
  • Patient selection
  • New indication nominations

Hands-on data analysis

  • Statistical modeling and machine learning applications for therapeutics development
  • Transcriptional profiling and pathway analysis
  • Statistical genetics and comparative sequence analysis
  • Single-cell RNA-seq: cell type ID, rare population detection, perturbation analysis, development/trajectory analysis
  • Single dataset de novo analysis: proteomics, metabolomics, RNA-seq, ribosome profiling (Ribo-seq)
  • Computational chemistry: new target assessments using computational chemistry approaches and structure-based drug design methods

Engineering and infrastructure

  • Pipeline assessments and recommendations
  • Dashboard and web application builds 
  • Custom analysis pipeline builds
  • Production-grade hardening of first-draft analysis tools and systems

If you don’t see your needs represented here, let us know — we’re happy to develop custom solutions and services.