BEARscc determines robustness of single-cell clusters using simulated technical replicates

ABSTRACTTechnical variance is a major confounding factor in single-cell RNA sequencing, not least because it is not possible to replicate measurements on the same cell. We present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates. We demonstrate that the tool improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments.

Download Full-text

Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

Microbiology Research ◽

10.3390/microbiolres12020022 ◽

2021 ◽

Vol 12 (2) ◽

pp. 317-334

Author(s):

Omar Alaqeeli ◽

Li Xing ◽

Xuekui Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Classification Tree ◽

Area Under The Curve ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Tree Algorithms ◽

R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

Download Full-text

Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing

Nature Neuroscience ◽

10.1038/nn.3881 ◽

2014 ◽

Vol 18 (1) ◽

pp. 145-153 ◽

Cited By ~ 849

Author(s):

Dmitry Usoskin ◽

Alessandro Furlan ◽

Saiful Islam ◽

Hind Abdo ◽

Peter Lönnerberg ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Sensory Neuron ◽

Large Scale ◽

Single Cell Rna Sequencing

Download Full-text

Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing

BMC Genomics ◽

10.1186/s12864-020-07136-2 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Jinghui Li ◽

Siyuan Xing ◽

Guiping Zhao ◽

Maiqing Zheng ◽

Xinting Yang ◽

...

Keyword(s):

Skeletal Muscle ◽

Developmental Stage ◽

Single Cell ◽

Rna Sequencing ◽

Muscle Development ◽

Intramuscular Fat ◽

Meat Production ◽

Cell Clusters ◽

Single Cell Rna Sequencing ◽

Chicken Skeletal Muscle

Abstract Background The development of skeletal muscle is closely related to the efficiency of meat production and meat quality. Chicken skeletal muscle development depends on myogenesis and adipogenesis and occurs in two phases—hyperplasia and hypertrophy. However, cell profiles corresponding to the two-phase muscle development have yet to be determined. Single-cell RNA-sequencing (scRNA-seq) can elucidate the cell subpopulations in tissue and capture the gene expression of individual cells, which can provide new insights into the myogenesis and intramuscular adipogenesis. Results Ten cell clusters at the post-hatching developmental stage at Day 5 and seven cell clusters at the late developmental stage at Day 100 were identified in chicken breast muscles by scRNA-seq. Five myocyte-related clusters and two adipocyte clusters were identified at Day 5, and one myocyte cluster and one adipocyte cluster were identified at Day 100. The pattern of cell clustering varied between the two stages. The cell clusters showed clear boundaries at the terminal differentiation stage at Day 100; by contrast, cell differentiation was not complete at Day 5. APOA1 and COL1A1 were selected from up-regulated genes in the adipocyte cluster and found to be co-expressed with the ADIPOQ adipocyte marker gene in breast muscles by RNA in situ hybridization. Conclusions This study is the first to describe the heterogeneity of chicken skeletal muscle at two developmental stages. The genes APOA1 and COL1A1 were identified as biomarkers for chicken intramuscular fat cells.

Download Full-text

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

10.1101/344242 ◽

2018 ◽

Cited By ~ 2

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.

Download Full-text

Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

10.1101/2019.12.17.879304 ◽

2019 ◽

Cited By ~ 4

Author(s):

Paul Datlinger ◽

André F Rendeiro ◽

Thorina Boenke ◽

Thomas Krausgruber ◽

Daniele Barreca ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Population Genomics ◽

Cost Effective ◽

Mouse Cell ◽

Droplet Microfluidics ◽

Rna Seq ◽

Single Cell Rna Sequencing ◽

Massive Scale ◽

Tcr Activation

AbstractCell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed “single-cell combinatorial fluidic indexing” (scifi) and applied it to single-cell RNA sequencing. The resulting scifi-RNA-seq assay combines one-step combinatorial pre-indexing of single-cell transcriptomes with subsequent single-cell RNA-seq using widely available droplet microfluidics. Pre-indexing allows us to load multiple cells per droplet, which increases the throughput of droplet-based single-cell RNA-seq up to 15-fold, and it provides a straightforward way of multiplexing hundreds of samples in a single scifi-RNA-seq experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow, thereby enabling massive-scale scRNA-seq experiments for a broad range of applications ranging from population genomics to drug screens with scRNA-seq readout. We benchmarked scifi-RNA-seq on various human and mouse cell lines, and we demonstrated its feasibility for human primary material by profiling TCR activation in T cells.

Download Full-text

A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽

10.3390/genes12121947 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1947

Author(s):

Samarendra Das ◽

Anil Rai ◽

Michael L. Merchant ◽

Matthew C. Cave ◽

Shesh N. Rai

Keyword(s):

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Differential Expression Analysis ◽

Individual Performance ◽

Rna Seq ◽

Gene Expressions ◽

Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

Download Full-text

Splatter: simulation of single-cell RNA sequencing data

10.1101/133173 ◽

2017 ◽

Cited By ~ 8

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.

Download Full-text

Myeloid heterogeneity in kidney disease as revealed through single cell RNA sequencing

Kidney360 ◽

10.34067/kid.0003682021 ◽

2021 ◽

pp. 10.34067/KID.0003682021

Author(s):

Rachel M B Bell ◽

Laura Denby

Keyword(s):

Kidney Disease ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Myeloid Cells ◽

Rna Seq ◽

Cellular Compartment ◽

Single Cell Rna Sequencing ◽

Health And Disease ◽

Diseased Kidney

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Though this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promises to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying aetiology to further reveal the patterning of immune cells and non-immune renal cells. This review summarises the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies and published findings using scRNA-seq in the context of myeloid cells and the kidney.

Download Full-text

DOP23 Single-cell RNA sequencing identifies an important role for class I histone-deacetylase enzymes in intestinal myofibroblasts from patients with Crohn’s Disease strictures

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjab073.062 ◽

2021 ◽

Vol 15 (Supplement_1) ◽

pp. S062-S062

Author(s):

A Lewis ◽

B Pan-Castillo ◽

G Berti ◽

C Felice ◽

H Gordon ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Line ◽

Rna Sequencing ◽

Histone Deacetylase ◽

Chromatin Accessibility ◽

Collagen I ◽

Class I ◽

Rna Seq ◽

Single Cell Rna Sequencing

Abstract Background Histone-deacetylase (HDAC) enzymes are a broad class of ubiquitously expressed enzymes that modulate histone acetylation, chromatin accessibility and gene expression. In models of Inflammatory bowel disease (IBD), HDAC inhibitors, such as Valproic acid (VPA) are proven anti-inflammatory agents and evidence suggests that they also inhibit fibrosis in non-intestinal organs. However, the role of HDAC enzymes in stricturing Crohn’s disease (CD) has not been characterised; this is key to understanding the molecular mechanism and developing novel therapies. Methods To evaluate HDAC expression in the intestine of SCD patients, we performed unbiased single-cell RNA sequencing (sc-RNA-seq) of over 10,000 cells isolated from full-thickness surgical resection specimens of non-SCD (NSCD; n=2) and SCD intestine (n=3). Approximately, 1000 fibroblasts were identified for further analysis, including a distinct cluster of myofibroblasts. Changes in gene expression were compared between myofibroblasts and other resident intestinal fibroblasts using the sc-RNA-seq analysis pipeline in Partek. Changes in HDAC expression and markers of HDAC activity (H3K27ac) were confirmed by immunohistochemistry in FFPE tissue from patient matched NSCD and SCD intestine (n=14 pairs). The function of HDACs in intestinal fibroblasts in the CCD-18co cell line and primary CD myofibroblast cultures (n=16 cultures) was assessed using VPA, a class I HDAC inhibitor. Cells were analysed using a variety of molecular techniques including ATAC-seq, gene expression arrays, qPCR, western blot and immunofluorescent protein analysis. Results Class I HDAC (HDAC1, p= 2.11E-11; HDAC2, p= 4.28E-11; HDAC3, p= 1.60E-07; and HDAC8, p= 2.67E-03) expression was increased in myofibroblasts compared to other intestinal fibroblasts subtypes. IHC also showed an increase in the percentage of stromal HDAC2 positive cells, coupled with a decrease in the percentage of H3K27ac positive cells, in the mucosa overlying SCD intestine relative to matched NSCD areas. In the CCD-18co cell line and primary myofibroblast cultures, VPA reduced chromatin accessibility at Collagen-I gene promoters and suppressed their transcription. VPA also inhibited TGFB-induced up-regulation of Collagen-I, in part by inhibiting TGFB1|1/SMAD4 signalling. TGFB1|1 was identified as a mesenchymal specific target of VPA and siRNA knockdown of TGFB1|1 was sufficient suppress TGFB-induced up-regulation of Collagen-I. Conclusion In SCD patients, class I HDAC expression is increased in myofibroblasts. Class I HDACs inhibitors impair TGFB-signalling and inhibit Collagen-I expression. Selective targeting of TGFB1|1 offers the opportunity to increase treatment specificity by selectively targeting meschenymal cells.

Download Full-text

SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

10.1101/2020.01.13.904649 ◽

2020 ◽

Cited By ~ 2

Author(s):

Zhirui Hu ◽

Songpeng Zu ◽

Jun S. Liu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Stem Cell Differentiation ◽

Marker Genes ◽

Cell Type ◽

Imputation Methods ◽

Cell Clusters ◽

Main Challenge ◽

Single Cell Rna Sequencing ◽

Gene Modules

AbstractA main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called “dropout”), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

Download Full-text