Factorization-based Imputation of Expression in Single-cell Transciptomic Analysis (FIESTA) recovers Gene-Cell-State relationships

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) is a powerful gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing single-cell RNA-sequencing methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting ‘zero-inflated’ data may confound data interpretation and visualization.ResultsSince cells have coherent phenotypes defined by conserved molecular circuitries (i.e. multiple gene products working together) and since similar cells utilize similar circuits, information about each each expression value or ‘node’ in a multi-cell, multi-gene scRNA-Seq data set is expected to also be predictable from other nodes in the data set. Based on this logic, several approaches have been proposed to impute missing values by extracting information from non-zero measurements in a data set. In this study, we applied non-negative matrix factorization approaches to a selection of published scRNASeq data sets to recommend new values where original measurements are likely to be inaccurate and where ‘zero’ measurements are predicted to be false negatives. The resulting imputed data model predicts novel cell type markers and expression patterns more closely matching gene expression values from orthogonal measurements and/or predicted literature than the values obtained from other previously published imputation [email protected] and implementationFIESTA is written in R and is available at https://github.com/elnazmirzaei/FIESTA and https://github.com/TheSpikeLab/FIESTA.

Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽

10.1016/j.isci.2021.102357 ◽

2021 ◽

Vol 24 (4) ◽

pp. 102357

Author(s):

Brenda Morsey ◽

Meng Niu ◽

Shetty Ravi Dyavar ◽

Courtney V. Fletcher ◽

Benjamin G. Lamberty ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Related Gene ◽

Disease Related Gene

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

Bioinformatics ◽

10.1093/bioinformatics/btaa278 ◽

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 ◽

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.

TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data

BMC Genomics ◽

10.1186/s12864-019-5477-8 ◽

2019 ◽

Vol 20 (S2) ◽

Cited By ~ 5

Author(s):

Shaokun An ◽

Liang Ma ◽

Lin Wan

Keyword(s):

Gene Expression ◽

Time Series ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Sequencing Data ◽

Embedding Method ◽

bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

10.1101/384586 ◽

2018 ◽

Cited By ~ 7

Author(s):

Wenhao Tang ◽

François Bertaux ◽

Philipp Thomas ◽

Claire Stefanelli ◽

Malika Saint ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Empirical Bayes ◽

Missing Values ◽

Likelihood Function ◽

Differential Expression Analysis ◽

Sequencing Data ◽

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

Single-Cell RNA-Sequencing and Optical Electrophysiology of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Reveal Discordance Between Cardiac Subtype-Associated Gene Expression Patterns and Electrophysiological Phenotypes

Stem Cells and Development ◽

10.1089/scd.2019.0030 ◽

2019 ◽

Vol 28 (10) ◽

pp. 659-673 ◽

Cited By ~ 5

Author(s):

Sherri M. Biendarra-Tiegs ◽

Xing Li ◽

Dan Ye ◽

Emma B. Brandt ◽

Michael J. Ackerman ◽

...

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Single Cell ◽

Rna Sequencing ◽

Pluripotent Stem Cell ◽

Expression Patterns ◽

Induced Pluripotent Stem Cell ◽

Induced Pluripotent

Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries

10.1101/408740 ◽

2018 ◽

Author(s):

Kent A. Riemondy ◽

Monica Ransom ◽

Christopher Alderman ◽

Austin E. Gillen ◽

Rui Fu ◽

...

Keyword(s):

Gene Expression ◽

Dna Sequencing ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Single Cells ◽

Expression Patterns ◽

Complex Mixture ◽

Gene Expression Information ◽

ABSTRACTSingle-cell RNA sequencing (scRNA-seq) methods generate sparse gene expression profiles for thousands of single cells in a single experiment. The information in these profiles is sufficient to classify cell types by distinct expression patterns but the high complexity of scRNA-seq libraries often prevents full characterization of transcriptomes from individual cells. To extract more focused gene expression information from scRNA-seq libraries, we developed a strategy to physically recover the DNA molecules comprising transcriptome subsets, enabling deeper interrogation of the isolated molecules by another round of DNA sequencing. We applied the method in cell-centric and gene-centric modes to isolate cDNA fragments from scRNA-seq libraries. First, we resampled the transcriptomes of rare, single megakaryocytes from a complex mixture of lymphocytes and analyzed them in a second round of DNA sequencing, yielding up to 20-fold greater sequencing depth per cell and increasing the number of genes detected per cell from a median of 1,313 to 2,002. We similarly isolated mRNAs from targeted T cells to improve the reconstruction of their VDJ-rearranged immune receptor mRNAs. Second, we isolatedCD3DmRNA fragments expressed across cells in a scRNA-seq library prepared from a clonal T cell line, increasing the number of cells with detectedCD3Dexpression from 59.7% to 100%. Transcriptome resampling is a general approach to recover targeted gene expression information from single-cell RNA sequencing libraries that enhances the utility of these costly experiments, and may be applicable to the targeted recovery of molecules from other single-cell assays.

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

10.1101/2020.01.03.893867 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hyundoo Jeong ◽

Zhandong Liu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Local Community ◽

Expression Patterns ◽

Imputation Method ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Depth Analysis ◽

AbstractSingle-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 community 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 six datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

Bioinformatics ◽

10.1093/bioinformatics/btaa108 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3131-3138

Author(s):

Ke Jin ◽

Le Ou-Yang ◽

Xing-Ming Zhao ◽

Hong Yan ◽

Xiao-Fei Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Supplementary Information ◽

Expression Levels ◽

Downstream Analysis ◽

Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

scSensitiveGeneDefine: sensitive gene detection in single-cell RNA sequencing data by Shannon entropy

BMC Bioinformatics ◽

10.1186/s12859-021-04136-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zechuan Chen ◽

Zeruo Yang ◽

Xiaojun Yuan ◽

Xiaoming Zhang ◽

Pei Hao

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Shannon Entropy ◽

Expression Patterns ◽

Ground Truth ◽

Unsupervised Clustering ◽

Data Sets ◽

Abstract Background Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation. Result In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as “sensitive genes”. To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labels, after removing the sensitive genes detected by our tools. Conclusion Our study revealed the prevalence of stochastic gene expression patterns in most types of cells, compared the differences among cell marker genes, housekeeping genes (HK genes), and sensitive genes, demonstrated the similarities of functions of sensitive genes in various scRNA-seq data sets, and improved the results of unsupervised clustering towards the ground-truth labels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future.

Comprehensive analysis of a mouse model of spontaneous uveoretinitis using single-cell RNA sequencing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1915571116 ◽

2019 ◽

Vol 116 (52) ◽

pp. 26734-26744 ◽

Cited By ~ 3

Author(s):

Jacob S. Heng ◽

Sean F. Hackett ◽

Genevieve L. Stein-O’Brien ◽

Briana L. Winer ◽

John Williams ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Cell Types ◽

Retinal Cell ◽

Central Tolerance ◽

Unbiased Manner ◽

Lymphoid Structures

Autoimmune uveoretinitis is a significant cause of visual loss, and mouse models offer unique opportunities to study its disease mechanisms.Aire−/−mice fail to express self-antigens in the thymus, exhibit reduced central tolerance, and develop a spontaneous, chronic, and progressive uveoretinitis. Using single-cell RNA sequencing (scRNA-seq), we characterized wild-type andAire−/−retinas to define, in a comprehensive and unbiased manner, the cell populations and gene expression patterns associated with disease. Based on scRNA-seq, immunostaining, and in situ hybridization, we infer that 1) the dominant effector response inAire−/−retinas is Th1-driven, 2) a subset of monocytes convert to either a macrophage/microglia state or a dendritic cell state, 3) the development of tertiary lymphoid structures constitutes part of theAire−/−retinal phenotype, 4) all major resident retinal cell types respond to interferon gamma (IFNG) by changing their patterns of gene expression, and 5) Muller glia up-regulate specific genes in response to IFN gamma and may act as antigen-presenting cells.