SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

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Single cell network analysis with a mixture of Nested Effects Models

10.1101/258202 ◽

2018 ◽

Author(s):

Martin Pirkl ◽

Niko Beerenwinkel

Keyword(s):

Single Cell ◽

New Technologies ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Data Sets ◽

Cell Network ◽

A Cell ◽

Supplementary Material ◽

Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

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An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq

10.1101/070193 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jinzhou Yuan ◽

Peter A. Sims

Keyword(s):

Single Cell ◽

Expression Profiling ◽

Large Scale ◽

Single Cell Analysis ◽

Cell Analysis ◽

Cell Capture ◽

Rna Seq ◽

Fluorescent Cell ◽

Recent Developments ◽

Improved Performance

Recent developments have enabled rapid, inexpensive RNA sequencing of thousands of individual cells from a single specimen, raising the possibility of unbiased and comprehensive expression profiling from complex tissues. Microwell arrays are a particularly attractive microfluidic platform for single cell analysis due to their scalability, cell capture efficiency, and compatibility with imaging. We report an automated microwell array platform for single cell RNA-Seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of >50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We evaluate the level of cross-contamination in our platform by both tracking fluorescent cell lysate in sealed microwells and with a human-mouse mixed species RNA-Seq experiment. Finally, we apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.

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ascend: R package for analysis of single cell RNA-seq data

10.1101/207704 ◽

2017 ◽

Cited By ~ 11

Author(s):

Anne Senabouth ◽

Samuel W Lukowski ◽

Jose Alquicira Hernandez ◽

Stacey Andersen ◽

Xin Mei ◽

...

Keyword(s):

Single Cell ◽

R Package ◽

Computational Genomics ◽

Supplementary Information ◽

Rna Seq ◽

Software Packages ◽

Wide Range ◽

Flexible Framework ◽

Supplementary Material ◽

Data Objects

AbstractSummaryascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. ascend is designed to work with scRNA-seq data generated by any high-throughput platform, and includes functions to convert data objects between software packages.AvailabilityThe R package and associated vignettes are freely available at https://github.com/IMB-Computational-Genomics-Lab/[email protected] informationAn example dataset is available at ArrayExpress, accession number E-MTAB-6108

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Improved dropClust R package with integrative analysis support for scRNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz823 ◽

2019 ◽

Author(s):

Debajyoti Sinha ◽

Pradyumn Sinha ◽

Ritwik Saha ◽

Sanghamitra Bandyopadhyay ◽

Debarka Sengupta

Keyword(s):

Single Cell ◽

Large Scale ◽

R Package ◽

Integrative Analysis ◽

Locality Sensitive Hashing ◽

Supplementary Information ◽

Expression Data ◽

Rna Seq ◽

Speed Up ◽

Cell Expression

Abstract Summary DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. Here we present the improved dropClust, a complete R package that is, fast, interoperable and minimally resource intensive. The new dropClust features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets. Availability and implementation dropClust is freely available at https://github.com/debsin/dropClust as an R package. A lightweight online version of the dropClust is available at https://debsinha.shinyapps.io/dropClust/. Supplementary information Supplementary data are available at Bioinformatics online.

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Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud

10.1101/064006 ◽

2016 ◽

Cited By ~ 1

Author(s):

Andrian Yang ◽

Michael Troup ◽

Peijie Lin ◽

Joshua W. K. Ho

Keyword(s):

Single Cell ◽

Large Scale ◽

Low Cost ◽

Supplementary Information ◽

Data Sets ◽

Rna Seq ◽

Single Node ◽

Big Data Technologies ◽

Amazon Web Services ◽

Supplementary Material

AbstractSummarySingle-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellisation of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq data sets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6 – 145.4 times faster using Falco than running on a highly optimised single node analysis. Falco also allows user to the utilise low-cost spot instances of Amazon Web Services (AWS), providing a 65% reduction in cost of analysis.AvailabilityFalco is available via a GNU General Public License at https://github.com/VCCRI/Falco/[email protected] informationSupplementary data are available at BioRXiv online.

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ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

Bioinformatics ◽

10.1093/bioinformatics/btab179 ◽

2021 ◽

Author(s):

Irzam Sarfraz ◽

Muhammad Asif ◽

Joshua D Campbell

Keyword(s):

Single Cell ◽

R Package ◽

Poor Quality ◽

Data Matrix ◽

Supplementary Information ◽

Data Provenance ◽

Rna Seq ◽

Efficient Management ◽

The Matrix ◽

The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

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DEsingle for detecting three types of differential expression in single-cell RNA-seq data

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhun Miao ◽

Ke Deng ◽

Xiaowo Wang ◽

Xuegong Zhang

Keyword(s):

Single Cell ◽

Differential Expression ◽

Negative Binomial ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Binomial Model ◽

Supplementary Data ◽

Rna Seq ◽

Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

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ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

Nucleic Acids Research ◽

10.1093/nar/gkaa412 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W403-W414

Author(s):

Fabrice P A David ◽

Maria Litovchenko ◽

Bart Deplancke ◽

Vincent Gardeux

Keyword(s):

Big Data ◽

Single Cell ◽

Single Cell Analysis ◽

Omics Data ◽

Cell Analysis ◽

Rna Seq ◽

Analysis Pipeline ◽

Web Based ◽

Data Analyses ◽

The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

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DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz453 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5155-5162 ◽

Cited By ~ 10

Author(s):

Chengzhong Ye ◽

Terence P Speed ◽

Agus Salim

Keyword(s):

Single Cell ◽

Differential Expression ◽

Type I Error ◽

R Package ◽

Supplementary Information ◽

Type I ◽

Common Phenomenon ◽

Rna Seq ◽

Capture Process ◽

Technological Platforms

Abstract Motivation Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. Results We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. Availability and implementation The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. Supplementary information Supplementary data are available at Bioinformatics online.

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