1,721,022 research outputs found
Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference [version 1; referees: 1 approved, 2 approved with reservations]
Get PDFNovel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1) dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2) cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and pseudotimes; and (4) differential expression analysis along lineages
mbkmeans: Fast clustering for single cell data using mini-batch k-means
Get PDFSingle-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans
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Statistical Analysis and Integration of Multi-Modal Sequencing Data
No full textThe explosion of high-throughput sequencing technologies in the past decade has enabled the measurement of various molecules and biological processes at the bulk level and the single-cell level, resulting in data modalities such as gene expression, methylation, and chromatin accessibility. Each of these modalities has distinct data properties and therefore poses different statistical challenges. It is important to both study these modalities separately as well as jointly in order to understand their individual characteristics and their complex interactions. This dissertation addresses both questions. For the modeling of a single modality, we first introduce a method for the differential analysis of bulk methylation sequencing data. We propose an approach based on change-point detection that identifies regions in the genome that exhibit different methylation levels between groups or over time. We show that our approach gives improved performance compared to existing methods. We then provide a case study on single-cell gene expression data, illustrating the typical workflow and addressing the challenges involved in analyzing such datasets. For the integration of multiple modalities, we introduce a novel method that focuses on a question that has not yet been addressed: the jointly modeling of single-cell single-modality platforms with multi-modality platforms. We apply the method to integrate a single-cell gene expression dataset, a single-cell chromatin accessibility dataset, and a single-cell joint-platform dataset that sequences both gene expression and chromatin accessibility
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Statistical Analysis and Visualization of Single Cell RNA Sequencing Data at Population Scale
Get PDFThe advent of Single-cell transcriptome sequencing (scRNA-Seq) has revolutionized our ability to explore the intricate landscape of cellular diversity within complex biological systems. Initially focused on cataloging cell subtypes and discerning gene expression disparities across cell types, scRNA-Seq has evolved to address broader inquiries, particularly in the realm of human health. While past efforts concentrated on analyzing numerous cells from a few samples, there’s now a growing interest in understanding inter-sample heterogeneity and its implications for phenotypic outcomes, notably in cancer and inflammatory diseases. However, existing bioinformatic methodologies inadequately address population-level analyses, with limited consideration for inter-sample variation. The dissertation introduces a novel framework termed GloScope Representation, which is introduced in the first chapter in detail, for representing the entire single-cell profile of a sample. In the second chapter, We applied GloScope across scRNA-Seq datasets spanning diverse study designs, with sample sizes ranging from 12 to over 300. Through illustrative examples, we showcase how GloScope empowers researchers to undertake pivotal bioinformatic tasks at the sample level, with a primary focus on visualization and quality control assessment. In Chapter 3, we demonstrate GloScope’s efficacy in evaluating and quantifying batch effects, as well as comparing various batch correction methods’ performance in the patient level analysis of scRNASeq data. Furthermore, to assess GloScope ’s advantages and effectiveness in detecting different classes of single-cell differences arising from variations in sample phenotypes, we compared GloScope to existing visualization tool and other sample level analysis tool in Chapter 4. We also developed a simulation pipeline for generating single-cell count data. We utilize this simulation framework to conduct quantitative evaluations of GloScope through a series of simulated experiments
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Clustering of mRNA-Seq Data for Detection of Alternative Splicing Patterns
Get PDFWhereas prior methods of studying expression in a cell returned only estimates of gene expression, sequencing of mRNA can provide estimates of the amount of individual isoforms within the cell. As a result, many standard statistical methods commonly used for analyzing gene expression levels need to be modified in order to take advantage of this additional information. Many methods have been developed to study differential isoform expression between known groups but little research has been done utilizing methods of unsupervised learning, such as clustering. One novel question is whether we can find clusters of samples that are distinguishable not by their gene expression but by their isoform usage. That is, instead of using clustering to find groups with shared changes in gene expression, we want to utilize clustering to find groups with shared changes in isoform usage. Here, we propose a novel approach to clustering mRNA-Seq data that identifies such clusters. In order to utilize both gene and isoform information when clustering, we treat the sequencing data as a vector denoting the relative isoform usage of each isoform in a gene. In simulated data, we show that clustering using relative isoform usage values rather than isoform counts is more sensitive to finding clusters based on changes in isoform usage. In a real data set, we demonstrate its performance in finding a technical artifact that resulted in different batches having different isoform usage patterns. Additionally, we also illustrate its usage on several TCGA data sets. Specifically, we looked at whether groups determined from clustering on relative isoform usage were associated with tumor stage or splicing mutations
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Shrinkage of dispersion parameters in the double exponential family of distributions, with applications to genomic sequencing
Get PDFThe prevalence of sequencing experiments in genomics has led to an increased use of methods for count data in analyzing high-throughput genomic data to perform analyses. The importance of shrinkage methods in improving the performance of statistical methods remains. A common example is that of gene expression data, where the counts per gene are often modeled as some form of an overdispersed Poisson. In this case, shrinkage estimates of the per-gene dispersion parameter have lead to improved estimation of dispersion in the case of a small number of samples. We address a different count setting introduced by the use of sequencing data: comparing differential proportional usage via an overdispersed binomial model. Such a model can be useful for testing differential exon inclusion in mRNA-Seq experiments in addition to the typical differential gene expression analysis. In this setting, there are fewer such shrinkage methods for the dispersion parameter. We introduce a novel method that is developed by modeling the dispersion based on the double exponential family of distributions proposed by Efron (1986), also known as the exponential dispersion model (Jorgensen, 1987). Our methods (WEB-Seq and DEB-Seq) are empirical bayes strategies for producing a shrunken estimate of dispersion that can be applied to any double exponential dispersion family, though we focus on the binomial and poisson. These methods effectively detect differential proportional usage, and have close ties to the weighted likelihood strategy of edgeR developed for gene expression data (Robinson and Smyth, 2007; Robinson et al., 2010). We analyze their behavior on simulated data sets as well as real data for both differential exon usage and differential gene expression. In the exon usage case, we will demonstrate our methods' superior ability to control the FDR and detect truly different features compared to existing methods. In the gene expression setting, our methods fail to control the FDR; however, the rankings of the genes by p-value is among the top performers and proves to be robust to both changes in the probability distribution used to generate the counts and in low sample size situations. We provide implementation of our methods in the R package DoubleExpSeq available from the Comprehensive R Archive Network (CRAN)
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Time-Course Analysis and Clustering of Gene Expression Data
Get PDFHigh-throughput time-course studies collect measurements from samples across time. Inparticular, longer-duration high-throughput time-course studies are becoming more common, such as in the case of 16S sequencing of bacterial communities or single-cell mRNA sequencing of developmental lineages. A common focus of these studies is on significancetesting per gene. However, in many settings, particularly those studying developmental processes, large numbers of genes show temporal changes, and the relevant question is instead to classify genes into different types of temporal changes. We propose a mixture-model clustering method that estimates a functional spline model for the mean of the cluster, inorder to cluster the temporal patterns of genes independent of scale. The model allows fora wide range of likelihood models to suit a variety of data types. In addition, this clusteringstrategy accounts for time-course data under different experimental conditions or developmental lineages, and it provides a method for evaluating, per cluster, significant differences in temporal patterns between conditions. This allows for an integrated analysis of differential expression analysis and clustering. We demonstrate the benefits of our method using simulated data. In addition, we explore several real data sets to illustrate both the context for and the application of the mixture model
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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