Biology subjects

Yosef, N.

Publications and source records attributed to Yosef, N..

13 recordsLinked to original sources

bioRxiv2018

Functional Interpretation of Single-Cell Similarity Maps

We present VISION, a tool for annotating the sources of variation in single cell RNA-seq data in an automated, unbiased and scalable manner. VISION operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of VISION using a relatively homogeneous set of B cells from a cohort of lupus patients and healthy controls and show that it can derive important sources of cellular variation and link them to clinical phenotypes in a stratification free manner. VISION produces an interactive, low latency and feature rich web-based report that can be easily shared amongst researchers.

bioinformatics↗

bioRxiv2018

Single-cell transcriptomics identifies drivers of local inflammation in multiple sclerosis

Cerebrospinal fluid (CSF) protects the central nervous system (CNS) and analyzing CSF aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains superficial. Here, we firstly provide a transcriptional map of single leukocytes in CSF compared to blood. Leukocyte composition and transcriptome were compartment-specific with CSF-enrichment of myeloid dendritic cells and a border-associated phenotype of monocytes.\n\nWe secondly tested how multiple sclerosis (MS) - an autoimmune disease of the CNS - affected both compartments. MS increased transcriptional diversity in blood, while it preferentially increased cell type diversity in CSF. In addition to the known expansion of B lineage cells, we identified an increase of cytotoxic-phenotype and follicular T helper (TFH) cells in the CSF. In mice, TFH cells accordingly promoted B cell infiltration into the CNS and severity of MS animal models. Immune mechanisms in MS are thus highly compartmentalized and indicate local T/B cell interaction.

immunology↗

bioRxiv2018

Reconstructing B cell receptor sequences from short-read single cell RNA-sequencing with BRAPeS

RNA-sequencing of single B cells provides simultaneous measurements of the cell state and its binding specificity. However, in order to uncover the latter further reconstruction of the B cell receptor (BCR) sequence is needed. We present BRAPeS, an algorithm for reconstructing BCRs from short-read paired-end single cell RNA-sequencing. BRAPeS is accurate and achieves a high success rate even at very short (25bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared to long reads. BRAPeS is publicly available in the following link: https://github.com/YosefLab/BRAPeS.

bioinformatics↗

bioRxiv2018

SymSim: simulating multi-faceted variability in single cell RNA sequencing

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in-silico platforms for evaluation and validation. Simulated datasets which resemble the properties of real datasets can aid in method development and prioritization as well as in questions in experimental design by providing an objective ground truth. Here, we present SymSim, a simulator software that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation that is indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. Unlike other simulators, the parameters that govern the simulation process directly represent meaningful properties such as mRNA capture rate, the number of PCR cycles, sequencing depth, or the use of unique molecular identifiers. We demonstrate how SymSim can be used for benchmarking methods for clustering and differential expression and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population and how this number deviates from the theoretical lower bound as the quality of the data decreases. SymSim is publicly available as an R package and allows users to simulate datasets with desired properties or matched with experimental data.

bioinformatics↗

bioRxiv2018

Massively parallel characterization of regulatory dynamics during neural induction

The molecular components governing neural induction remain largely unknown. Here, we applied a suite of genomic and computational tools to comprehensively identify these components. We performed RNA-seq, ChIP-seq (H3K27ac, H3K27me3) and ATAC-seq on human embryonic stem cells (hESCs) at seven early neural differentiation time points (0-72 hours) and identified thousands of induced genes and regulatory regions. We analyzed the function of ~2,500 selected regions using massively parallel reporter assays at all time points. We found numerous temporal enhancers that correlated with similarly timed epigenetic marks and gene expression. Development of a prioritization method that incorporated all genomic data identified key transcription factors (TFs) involved in neural induction. Individual overexpression of eleven TFs and several combinations in hESCs found novel neural induction regulators. Combined, our results provide a comprehensive map of genes and functional regulatory elements involved in neural induction and identify master regulator TFs that are instrumental for this process.\n\nOne Sentence SummaryUsing numerous genomic assays and computational tools we characterized the dynamic changes that take place during neural induction.

genomics↗

bioRxiv2018

Mango: Distributed Visualization for Genomic Analysis

The decreasing cost of DNA sequencing over the past decade has led to an explosion of available sequencing datasets, leaving us with terabytes to petabytes of data to explore and analyze. It is critical for analysts in research and clinical settings to be able to develop new data-driven hypotheses from these datasets through bias identification, analysis of data quality, and testing different algorithms and parameter settings. However, current interactive tools for sequence analysis are designed to run on single machines that do not scale to the size of modern genomic datasets, and rely on precomputed static views, rather than allowing direct interaction with the primary dataset. Mango is a genomic sequence visualization and analysis platform that removes these constraints regarding scalability and staticity by leveraging the power of multi-node compute clusters in the cloud to allow interactive analysis over terabytes of sequencing data. Mango provides both a genome browser graphical user interface and programmable notebook form factor to allow users of varying analytical experience to explore large sequencing datasets on both private clusters and in the cloud. These tools provide a flexible environment for interactive exploration of genomic datasets, while surpassing the computational limits of single-node genomic visualization tools.

bioinformatics↗

bioRxiv2018

Connectivity Problems on Heterogeneous Graphs

BackgroundNetwork connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner network problems: given a reference \"database\" graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu.\n\nResults and DiscussionWe introduce the condition Steiner network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of C -{epsilon} , for every C [≥] 2 and{epsilon} > 0, and that this bound is tight. Moving beyond the worst case, we explore a special set of instances where the reference graph grows monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network.\n\nConclusionOur results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting.\n\nAvailabilityOur solver for the general condition Steiner network problem is available at https://github.com/YosefLab/condition_connectivity_problems

bioinformatics↗

bioRxiv2018

Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing

Transcriptome profiles of individual cells reflect true and often unexplored biological diversity, but are also affected by noise of biological and technical nature. This raises the need to explicitly model the resulting uncertainty and take it into account in any downstream analysis, such as dimensionality reduction, clustering, and differential expression. Here, we introduce Single-cell Variational Inference (scVI), a scalable framework for probabilistic representation and analysis of gene expression in single cells. Our model uses variational inference, stochastic optimization and deep neural networks to approximate the parameters that govern the distribution of expression values of each gene in every cell, using a non-linear mapping between the observations and a low-dimensional latent space.\n\nBy doing so, scVI pools information between similar cells or genes while taking nuisance factors of variation such as batch effects and limited sensitivity into account. To evaluate scVI, we conducted a comprehensive comparative analysis to existing methods for distributional modeling and dimensionality reduction, all of which rely on generalized linear models. We first show that scVI scales to over one million cells, whereas competing algorithms can process at most tens of thousands of cells. Next, we show that scVI fits unseen data more closely and can impute missing data more accurately, both indicative of a better generalization capacity. We then utilize scVI to conduct a set of fundamental analysis tasks - including batch correction, visualization, clustering and differential expression - and demonstrate its accuracy in comparison to the state-of-the-art tools in each task. scVI is publicly available, and can be readily used as a principled and inclusive solution for multiple tasks of single-cell RNA sequencing data analysis.

bioinformatics↗

bioRxiv2017

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

Systematic measurement biases make data normalization an essential preprocessing step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple, competing considerations behind the assessment of normalization performance, some of them study-specific. Because normalization can have a large impact on downstream results (e.g., clustering and differential expression), it is critically important that practitioners assess the performance of competing methods.\n\nWe have developed scone -- a flexible framework for assessing normalization performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes performance trade-offs and ranks large numbers of normalization methods by aggregate panel performance. The method is implemented in the open-source Bioconductor R software package scone. We demonstrate the effectiveness of scone on a collection of scRNA-seq datasets, generated with different protocols, including Fluidigm C1 and 10x platforms. We show that top-performing normalization methods lead to better agreement with independent validation data.

genomics↗

bioRxiv2017

Meta-analysis of massive parallel reporter assay enables functional regulatory elements prediction

Deciphering the potential of non-coding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA-sequences and their variants in a single experiment. With increasing number of publically available MPRA datasets, one can now develop data-driven models which, given a DNA-sequence, predict its regulatory activity. Here, we performed a comprehensive meta-analysis of several MPRA datasets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across datasets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell-type specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" Challenge for predicting effects of single nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest.

genomics↗

bioRxiv2017

Use antibiotics in cell culture with caution: genome-wide identification ofantibiotic-induced changes in gene expression and regulation

Standard cell culture guidelines often use media supplemented with antibiotics to prevent cell contamination. However, relatively little is known about the effect of antibiotic use in cell culture on gene expression and the extent to which this treatment could confound results. To comprehensively characterize the effect of antibiotic treatment on gene expression, we performed RNA-seq and ChIP-seq for H3K27ac on HepG2 cells, a human liver cell line commonly used for pharmacokinetic, metabolism and genomic studies, cultured in media supplemented with penicillin-streptomycin (PenStrep) or vehicle control. We identified 205 PenStrep-responsive genes, including transcription factors such as ATF3 that are likely to alter the regulation of other genes. Pathway analyses found a significant enrichment for \"xenobiotic metabolism signaling\" and \"PXR/RXR activation\" pathways. Our H3K27ac ChIP-seq identified 9,514 peaks that are PenStrep responsive. These peaks were enriched near genes that function in cell differentiation, tRNA modification, nuclease activity and protein dephosphorylation. Our results suggest that PenStrep treatment can significantly alter gene expression and regulation in a common liver cell type such as HepG2, advocating that antibiotic treatment should be taken into account when carrying out genetic, genomic or other biological assays in cultured cells.

genomics↗

bioRxiv2017

Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. These methods can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a number of statistical and computational methods have been proposed for analyzing cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Here, we introduce a novel method, Slingshot, for inferring multiple developmental lineages from single-cell gene expression data. Slingshot is a uniquely robust and flexible tool for inferring developmental lineages and ordering cells to reflect continuous, branching processes.

bioinformatics↗

bioRxiv2017

Impulse model-based differential expression analysis of time course sequencing data

The global gene expression trajectories of cellular systems in response to developmental or environmental stimuli often follow the prototypic single-pulse or state-transition patterns which can be modeled with the impulse model. Here we combine the continuous impulse expression model with a sequencing data noise model in ImpulseDE2, a differential expression algorithm for time course sequencing experiments such as RNA-seq, ATAC-seq and ChIP-seq. We show that ImpulseDE2 outperforms currently used differential expression algorithms on data sets with sufficiently many sampled time points. ImpulseDE2 is capable of differentiating between transiently and monotonously changing expression trajectories. This classification separates genes which are responsible for the initial and final cell state phenotypes from genes which drive or are driven by the cell state transition and identifies down-regulation of oxidative-phosphorylation as a molecular signature which can drive human embryonic stem cell differentiation.

bioinformatics↗