Scott Tyler
At the intersection of bench and computational biology
We work at the intersection of wet bench, statistics, and computational biology. Developing new algorithms and experimental designs to single cell biology to uncover the beautiful mysteries of the universe, and our place in it. Feel free to get in touch!
Taking a data science approach to hypothesis generation and bench validation
Anti-correlation based feature selection
Statistics: PMD a linear correlation-like metric for Poisson sampling
Batch correction quandaries in single cell genomics
Featured Research
From a single (brief) command line call, PyMINEr will perform the following informatics tasks:
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unsupervised clustering
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basic statistics & enrichment analyses
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pathway analyses
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Spearman correlation-based expression graphs that enable analyses by graph theory
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creation of in silico predicted autocrine/paracrine signaling networks within and across cell types (if using scRNAseq)
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creation of publication-ready visuals based on these analyses
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generation of a web page explaining the results of the run
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Future releases will contain updated clustering methods that work for reconstructing single cell lineages.
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.
Unsupervised Clustering on Patient Cohorts Using:
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Numeric Datasets
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Transcriptome (log2(TPM))
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Microbiome (log2(Counts))
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Proteomics,
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Methylome... etc!
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Categorical Datasets
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Clinical lab tests
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Co-morbidities... etc
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*And it's missing data compatible!
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MANAclust automatically performs:
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Unsupervised feature selection
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Dataset integration
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Unsupervised clustering to find final clusters (FCs)
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Discovery of which FCs are the same in a given 'ome' to find consensus groups (CGs)
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Differential expression/abundance analysis for FCs and CGs