Biology subjects

Risso, D.

Publications and source records attributed to Risso, D..

5 recordsLinked to original sources

bioRxiv2018

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells.\n\nIt is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperimentprovides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers.\n\nThe package clusterExperimentis publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.

bioinformatics↗

bioRxiv2018

Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell transcriptome sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial (ZINB) model, that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

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

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

ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

bioinformatics↗