scholarly journals 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions

2020 ◽  
Vol 36 (11) ◽  
pp. 3588-3589 ◽  
Author(s):  
Kaiyi Zhu ◽  
Dimitris Anastassiou
Keyword(s):  
Single Cell ◽  
R Package ◽  
Two Dimensions ◽  
Imputation Method ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Imputation Methods ◽  
Single Cell Rna Sequencing ◽  
Expression Matrix

Abstract Summary We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols. Availability and implementation The R package of 2DImpute is freely available at GitHub (https://github.com/zky0708/2DImpute). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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

2017 ◽  
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.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data

2021 ◽  
Author(s):  
Federico Agostinis ◽  
Chiara Romualdi ◽  
Gabriele Sales ◽  
Davide Risso
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Sequencing Data ◽  
Bioconductor Project ◽  
Single Cell Rna Sequencing

Summary: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementation: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data

2020 ◽  
Author(s):  
Andrew E Teschendorff ◽  
Alok K Maity ◽  
Xue Hu ◽  
Chen Weiyan ◽  
Matthias Lechner
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Computational Cost ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Current State ◽  
Multipotent Cells ◽  
Comparable Accuracy

Abstract Motivation An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. Availability and implementation CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. Supplementary information Supplementary data are available at Bioinformatics online.

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

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.

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

2019 ◽  
Vol 35 (24) ◽  
pp. 5155-5162 ◽  
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.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals scDoc: correcting drop-out events in single-cell RNA-seq data

2020 ◽  
Vol 36 (15) ◽  
pp. 4233-4239
Author(s):  
Di Ran ◽  
Shanshan Zhang ◽  
Nicholas Lytal ◽  
Lingling An
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Drop Out ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Cell Subpopulation ◽  
Rna Seq ◽  
Imputation Methods ◽  
Similarity Estimation ◽  
Differential Expression Detection

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of ‘drop-out’ events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. Results scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. Availability and implementation R code is available at https://github.com/anlingUA/scDoc. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

scholarly journals Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles

2021 ◽  
Author(s):  
Davide Risso ◽  
Stefano Maria Pagnotta
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Unsupervised Clustering ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Data Transformations ◽  
The Impact

Abstract Motivation Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear. Results Here, we present an Asymmetric Winsorization per Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications. Availability The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst\_analysis. Supplementary information Supplementary data are available at Bioinformatics online.

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