Biophysical Society Thematic Meeting| Santa Cruz 2018

Genome Biophysics: Integrating Genomics and Biophysics to Understand Structural and Functional Aspects of Genomes

Wednesday Speaker Abstracts

Self-assembling Manifolds in Single-cell RNA Sequencing Data Alexander J Tarashansky 1 ; Yuan Xue 1 ; Pengyang Li 1 ; Stephen R Quake 2,4 ; Bo Wang 1,3 ; 1 Stanford University, Bioengineering, Stanford, California, United States 2 Stanford University, Applied Physics, Stanford, California, United States 3 Stanford University School of Medicine, Developmental Biology, Stanford, California, United States 4 Chan Zuckerberg Biohub, , San Francisco, California, United States Analysis of single-cell transcriptomes remains an open challenge in that existing algorithms all have limitations in their ability to select features that can resolve subtle differences in cell types. Here we present the self-assembling manifolds (SAM) algorithm, a fully unsupervised method for dimensionality reduction and marker gene identification. SAM employs a novel feature selection strategy in which it iteratively rescales gene expression, weighting genes according to their ability to separate distinct groups of cells or cell states. Benchmarking on 48 published datasets against other state-of-the-art methods reveals that SAM consistently improves manifold reconstruction, cell clustering and marker gene identification, especially in datasets that contain cells in dynamic transitions or cell groups that are only distinguishable through subtle differences. We use SAM to analyze the stem cells from the parasitic flatworm, Schistosoma mansoni , which infects more than 250 million people worldwide. SAM is able to identify new stem cell subpopulations in juvenile parasites and their respective associated marker genes which we validate using fluorescent in-situ hybridization. In comparison, other existing methods fail to capture any of these populations. Taken together, we show that SAM is particularly useful for unsupervised, parameter-free analysis of scRNA-seq data from tissues and organisms with little to no a priori knowledge to gain novel biological insights.

Nanopore Translocation of Knotted DNA

Cristian Micheletti SISSA Trieste, Italy

No Abstract

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