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Scientist in the Lab

SCOTT TYLER

Bench and computational biologist

This website is dedicated to my wet bench and computational biology projects - particularly those that are post-publication. If anyone would like to collaborate on any projects, has suggestions, or needs help with my research - feel free to get in touch!

Taking a data science approach to hypothesis generation and bench validation

Single cell RNAseq: PyMINEr

Mixed Categorical and Numeric Multi-omics: MANAclust

RESEARCH

From a single (brief) command line call, PyMINEr will perform the following informatics tasks:

  • unsupervised clustering

  • basic statistics & enrichment analyses

  • pathway analyses

  • Spearman correlation-based expression graphs that enable analyses by graph theory

  • creation of in silico predicted autocrine/paracrine signaling networks within and across cell types (if using scRNAseq)

  • creation of publication-ready visuals based on these analyses

  • generation of a web page explaining the results of the run

  • Future releases will contain updated clustering methods that work for reconstructing single cell lineages.

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CONTACT US

Thanks for your interest in my research. Feel free to get in touch for any questions or comments regarding my work and publications.

  • linkedin
  • twitter
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Unsupervised Clustering on Patient Cohorts Using:

  • Numeric Datasets

    • Transcriptome (log2(TPM))

    • Microbiome (log2(Counts))

    • Proteomics,

    • Methylome... etc!

  • Categorical Datasets

    • Clinical lab tests

    • Co-morbidities... etc

*And it's missing data compatible!

MANAclust automatically performs:

  • Unsupervised feature selection

  • Dataset integration

  • Unsupervised clustering to find final clusters (FCs)

  • Discovery of which FCs are the same in a given 'ome' to find consensus groups (CGs)

  • Differential expression/abundance analysis for FCs and CGs