Biology subjects

Lifshitz, A.

Publications and source records attributed to Lifshitz, A..

3 recordsLinked to original sources

bioRxiv2018

MetaCell: analysis of single cell RNA-seq data using k-NN graph partitions

Single cell RNA-seq (scRNA-seq) has become the method of choice for analyzing mRNA distributions in heterogeneous cell populations. scRNA-seq only partially samples the cells in a tissue and the RNA in each cell, resulting in sparse data that challenge analysis. We develop a methodology that addresses scRNA-seqs sparsity through partitioning the data into metacells: disjoint, homogenous and highly compact groups of cells, each exhibiting only sampling variance. Metacells constitute local building blocks for clustering and quantitative analysis of gene expression, while not enforcing any global structure on the data, thereby maintaining statistical control and minimizing biases. We illustrate the MetaCell framework by re-analyzing cell type and transcriptional gradients in peripheral blood and whole organism scRNA-seq maps. Our algorithms are implemented in the new MetaCell R/C++ software package.

bioinformatics↗

bioRxiv2017

Cnidarian cell type diversity revealed by whole-organism single-cell RNA-seq analysis

A hallmark of animal evolution is the emergence and diversification of cell type-specific transcriptional states. But systematic and unbiased characterization of differentiated gene regulatory programs was so far limited to specific tissues in a few model species. Here, we perform whole-organism single cell transcriptomics to map cell types in the cnidarian Nematostella vectensis, a non-bilaterian animal that display complex tissue-level bodyplan organization. We uncover high diversity of transcriptional states in Nematostella, demonstrating cell type-specific expression for 35% of the genes and 51% of the transcription factors (TFs) detected. We identify eight broad cell clusters corresponding to cell classes such as neurons, muscles, cnidocytes, or digestive cells. These clusters comprise multiple cell modules expressing diverse and specific markers, uncovering in particular a rich repertoire of cells associated with neuronal markers. TF expression and sequence analysis defines the combinatorial code that underlies this cell-specific expression. It also reveals the existence of a complex regulatory lexicon of TF binding motifs encoded at both enhancer and promoters of Nematostella tissue-specific genes. Whole organism single cell RNA-seq is thereby established as a tool for comprehensive study of genome regulation and cell type evolution.

genomics↗

bioRxiv2017

SHAMAN: bin-free randomization, normalization and screening of Hi-C matrices

Genome wide chromosome conformation capture (Hi-C) is used to interrogate contact frequencies among genomic elements at multiple scales and intensities, ranging from high frequency interactions among proximal regulatory elements, through specific long-range loops between insulator binding sites and up to rare and transient cis- and trans-chromosomal contacts. Visualization and statistical analysis of Hi-C data is made difficult by the extreme variation in the background frequencies of chromosomal contacts between elements at short and long genomic distances. Here we introduce SHAMAN for performing Hi-C analysis at dynamic scales, without predefined resolution, and while minimizing biases over very large datasets. Algorithmically, we devise a Markov Chain Monte Carlo-like procedure for randomizing contact matrices such that coverage and contact distance distributions are preserved. We combine this strategy with bin-free assessment of contact enrichment using a K-nearest neighbor approach. We show how to use the new method for visualizing contact hotspots and for quantifying differential contacts in matching Hi-C maps. We demonstrate how contact preferences among regulatory elements, including promoters, enhancers and insulators can be assessed with minimal bias by comparing pooled empirical and randomized matrices. Full support for our methods is available in a new software package that is freely available.

bioinformatics↗