Biology subjects

Lubling, Y.

Publications and source records attributed to Lubling, Y..

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

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↗

bioRxiv2016

Cell cycle dynamics of chromosomal organisation at single-cell resolution

Chromosomes in proliferating metazoan cells undergo dramatic structural metamorphoses every cell cycle, alternating between a highly condensed mitotic structure facilitating chromosome segregation, and a decondensed interphase structure accommodating transcription, gene silencing and DNA replication. These cyclical structural transformations have been evident under the microscope for over a century, but their molecular-level analysis is still lacking. Here we use single-cell Hi-C to study chromosome conformations in thousands of individual cells, and discover a continuum of cis-interaction profiles that finely position individual cells along the cell cycle. We show that chromosomal compartments, topological domains (TADs), contact insulation and long-range loops, all defined by ensemble Hi-C maps, are governed by distinct cell cycle dynamics. In particular, DNA replication correlates with build-up of compartments and reduction in TAD insulation, while loops are generally stable from G1 through S and G2. Analysing whole genome 3D structural models using haploid cell data, we discover a radial architecture of chromosomal compartments with distinct epigenomic signatures. Our single-cell data creates an essential new paradigm for the re-interpretation of chromosome conformation maps through the prism of the cell cycle.

genomics↗