scholarly journals Zero-preserving imputation of single-cell RNA-seq data

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
George C. Linderman ◽  
Jun Zhao ◽  
Manolis Roulis ◽  
Piotr Bielecki ◽  
Richard A. Flavell ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Low Rank ◽  
Matrix Approximation ◽  
Rna Seq ◽  
Sequencing Data ◽  
Theoretical Justification ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

AbstractA key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

scholarly journals SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

2018 ◽  
Author(s):  
Xianwen Ren ◽  
Liangtao Zheng ◽  
Zemin Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Random Projection ◽  
Rna Seq ◽  
Sequencing Data ◽  
Computational Framework ◽  
Human Blood Cells ◽  
Single Cell Rna Sequencing ◽  
Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

scholarly journals Splatter: simulation of single-cell RNA sequencing data

2017 ◽  
Author(s):  
Luke Zappia ◽  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Real Data ◽  
Cell Types ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Simulation Based ◽  
Single Cell Rna Sequencing ◽  
Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

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.

LRSK: a low-rank self-representation K-means method for clustering single-cell RNA-sequencing data

2020 ◽  
Vol 16 (5) ◽  
pp. 465-473
Author(s):  
Ye-Sen Sun ◽  
Le Ou-Yang ◽  
Dao-Qing Dai
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Quantitative Research ◽  
Cellular Level ◽  
Low Rank ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

The development of single-cell RNA-sequencing (scRNA-seq) technologies brings tremendous opportunities for quantitative research and analyses at the cellular level.

scholarly journals Joint Gene Network Construction by Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Meichen Dong ◽  
Yiping He ◽  
Yuchao Jiang ◽  
Fei Zou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Graphical Models ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Single Gene ◽  
Matrix Completion ◽  
Low Rank ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

In contrast to differential gene expression analysis at single-gene level, gene regulatory networks (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recently, single-cell RNA sequencing (scRNA-seq) data has started to be used for constructing GRNs at a much finer resolution than bulk RNA-seq data and microarray data. However, scRNA-seq data are inherently sparse which hinders the direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs under different but related conditions with single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) using the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson (ZIP) model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma identifies novel findings in addition to confirming well-known biological results.

scholarly journals McImpute: Matrix completion based imputation for single cell RNA-seq data

2018 ◽  
Author(s):  
Aanchal Mongia ◽  
Debarka Sengupta ◽  
Angshul Majumdar
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Matrix Completion ◽  
Differential Expression Analysis ◽  
Low Rank ◽  
Specific Cell ◽  
Sequencing Data ◽  
Reduction Techniques ◽  
Single Cell Rna Sequencing

AbstractMotivationSingle cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression analysis at single cell resolution, provides a window into dynamics of cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified.ResultsWe introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, performance of dimensionality reduction techniques for cell visualization and gene distribution.Availability and Implementationhttps://github.com/aanchalMongia/McImpute_scRNAseq

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