Biology subjects

Yuval Kluger

Publications and source records attributed to Yuval Kluger.

3 recordsLinked to original sources

bioRxiv2016

DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation

Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance, and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that Deep-CyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured accross several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i)14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data.

Bioinformatics↗

bioRxiv2015

Ritornello: High fidelity control-free chip-seq peak calling

With the advent of next generation high-throughput DNA sequencing technologies, omics experiments have become the mainstay for studying diverse biological effects on a genome wide scale. ChIP-seq is the omics technique that enables genome wide localization of transcription factor binding or epigenetic modification events. Since the inception of ChIP-seq in 2007, many methods have been developed to infer ChIP target binding loci from the resultant reads after mapping them to a reference genome. However, interpreting these data has proven challenging, and as such these algorithms have several shortcomings, including susceptibility to false positives due to artifactual peaks, poor localization of binding sites, and the requirement for a total DNA input control which increases the cost of performing these experiments. We present Ritornello, a new approach with roots in digital signal processing (DSP) that addresses all of these problems. We show that Ritornello generally performs equally or better than the peak callers tested and recommended by the ENCODE consortium, but in contrast, Ritornello does not require a matched total DNA input control to avoid false positives, effectively decreasing the sequencing cost to perform ChIP-seq.

Genomics↗

bioRxiv2015

The interdependent nature of multi-loci associations can be revealed by 4C-Seq

Use of low resolution single cell DNA FISH and population based high resolution chromosome conformation capture techniques have highlighted the importance of pairwise chromatin interactions in gene regulation. However, it is unlikely that these associations act in isolation of other interacting partners within the genome. Indeed, the influence of multi-loci interactions in gene control remains something of an enigma as beyond low-resolution DNA FISH we do not have the appropriate tools to analyze these. Here we present a method that uses standard 4C-seq data to identify multi-loci interactions from the same cell. We demonstrate the feasibility of our method using 4C-seq data sets that identify known pairwise interactions involving the Tcrb and Igk antigen receptor enhancers, in addition to novel tri-loci associations. We further show that enhancer deletions not only interfere with tri-loci interactions in which they participate, but they also disrupt pairwise interactions between other partner enhancers and this disruption is linked to a reduction in their transcriptional output. These findings underscore the functional importance of hubs and provide new insight into chromatin organization as a whole. Our method opens the door for studying multi-loci interactions and their impact on gene regulation in other biological settings.

Genomics↗