Biology subjects

Yosef, N.

Publications and source records attributed to Yosef, N..

79 records · Page 5Linked to original sources

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↗