scholarly journals scClassify: hierarchical classification of cells

2019 ◽  
Author(s):  
Yingxin Lin ◽  
Yue Cao ◽  
Hani J Kim ◽  
Agus Salim ◽  
Terence P. Speed ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Hierarchical Classification ◽  
Cell Number ◽  
Cell Type ◽  
Classification Framework ◽  
Single Cell Rna Sequencing ◽  
A Cell ◽  
Type Hierarchy

AbstractCell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.

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

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.

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

scholarly journals Single-cell RNA sequencing of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205883 ◽  
Author(s):  
Joseph C. Mays ◽  
Michael C. Kelly ◽  
Steven L. Coon ◽  
Lynne Holtzclaw ◽  
Martin F. Rath ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pineal Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Mammalian Pineal Gland ◽  
Daily Patterns

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
Author(s):  
William Renthal
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Cell Types ◽  
Susceptibility Genes ◽  
Brain Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

scholarly journals Single-cell RNA sequencing reveals cell type- and artery type-specific vascular remodelling in male spontaneously hypertensive rats

2020 ◽  
Author(s):  
Jun Cheng ◽  
Wenduo Gu ◽  
Ting Lan ◽  
Jiacheng Deng ◽  
Zhichao Ni ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spontaneously Hypertensive Rats ◽  
Cell Types ◽  
Vascular Remodelling ◽  
Cell Type ◽  
Hypertensive Rats ◽  
Spontaneously Hypertensive ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract Aims Hypertension is a major risk factor for cardiovascular diseases. However, vascular remodelling, a hallmark of hypertension, has not been systematically characterized yet. We described systematic vascular remodelling, especially the artery type- and cell type-specific changes, in hypertension using spontaneously hypertensive rats (SHRs). Methods and results Single-cell RNA sequencing was used to depict the cell atlas of mesenteric artery (MA) and aortic artery (AA) from SHRs. More than 20 000 cells were included in the analysis. The number of immune cells more than doubled in aortic aorta in SHRs compared to Wistar Kyoto controls, whereas an expansion of MA mesenchymal stromal cells (MSCs) was observed in SHRs. Comparison of corresponding artery types and cell types identified in integrated datasets unravels dysregulated genes specific for artery types and cell types. Intersection of dysregulated genes with curated gene sets including cytokines, growth factors, extracellular matrix (ECM), receptors, etc. revealed vascular remodelling events involving cell–cell interaction and ECM re-organization. Particularly, AA remodelling encompasses upregulated cytokine genes in smooth muscle cells, endothelial cells, and especially MSCs, whereas in MA, change of genes involving the contractile machinery and downregulation of ECM-related genes were more prominent. Macrophages and T cells within the aorta demonstrated significant dysregulation of cellular interaction with vascular cells. Conclusion Our findings provide the first cell landscape of resistant and conductive arteries in hypertensive animal models. Moreover, it also offers a systematic characterization of the dysregulated gene profiles with unbiased, artery type-specific and cell type-specific manners during hypertensive vascular remodelling.

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

2014 ◽  
Vol 18 (1) ◽  
pp. 145-153 ◽  
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

scholarly journals Pathogenic Autoimmunity in Atherosclerosis Evolves From Initially Protective Apolipoprotein B 100 –Reactive CD4 + T-Regulatory Cells

Circulation ◽  
2020 ◽  
Vol 142 (13) ◽  
pp. 1279-1293 ◽  
Author(s):  
Dennis Wolf ◽  
Teresa Gerhardt ◽  
Holger Winkels ◽  
Nathaly Anto Michel ◽  
Akula Bala Pramod ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Apolipoprotein B ◽  
T Helper Cell ◽  
Helper Cell ◽  
Cell Type ◽  
T Helper ◽  
Single Cell Rna Sequencing ◽  
Helper Cell Type

Background: Throughout the inflammatory response that accompanies atherosclerosis, autoreactive CD4 + T-helper cells accumulate in the atherosclerotic plaque. Apolipoprotein B 100 (apoB), the core protein of low-density lipoprotein, is an autoantigen that drives the generation of pathogenic T-helper type 1 (T H 1) cells with proinflammatory cytokine secretion. Clinical data suggest the existence of apoB-specific CD4 + T cells with an atheroprotective, regulatory T cell (T reg ) phenotype in healthy individuals. Yet, the function of apoB-reactive T regs and their relationship with pathogenic T H 1 cells remain unknown. Methods: To interrogate the function of autoreactive CD4 + T cells in atherosclerosis, we used a novel tetramer of major histocompatibility complex II to track T cells reactive to the mouse self-peptide apo B 978-993 (apoB + ) at the single-cell level. Results: We found that apoB + T cells build an oligoclonal population in lymph nodes of healthy mice that exhibit a T reg -like transcriptome, although only 21% of all apoB + T cells expressed the T reg transcription factor FoxP3 (Forkhead Box P3) protein as detected by flow cytometry. In single-cell RNA sequencing, apoB + T cells formed several clusters with mixed T H signatures that suggested overlapping multilineage phenotypes with pro- and anti-inflammatory transcripts of T H 1, T helper cell type 2 (T H 2), and T helper cell type 17 (T H 17), and of follicular-helper T cells. ApoB + T cells were increased in mice and humans with atherosclerosis and progressively converted into pathogenic T H 1/T H 17-like cells with proinflammatory properties and only a residual T reg transcriptome. Plaque T cells that expanded during progression of atherosclerosis consistently showed a mixed T H 1/T H 17 phenotype in single-cell RNA sequencing. In addition, we observed a loss of FoxP3 in a fraction of apoB + T regs in lineage tracing of hyperlipidemic Apoe –/– mice. In adoptive transfer experiments, converting apoB + T regs failed to protect from atherosclerosis. Conclusions: Our results demonstrate an unexpected mixed phenotype of apoB-reactive autoimmune T cells in atherosclerosis and suggest an initially protective autoimmune response against apoB with a progressive derangement in clinical disease. These findings identify apoB autoreactive T regs as a novel cellular target in atherosclerosis.

scholarly journals CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing

2019 ◽  
Vol 47 (16) ◽  
pp. e95-e95 ◽  
Author(s):  
Jurrian K de Kanter ◽  
Philip Lijnzaad ◽  
Tito Candelli ◽  
Thanasis Margaritis ◽  
Frank C P Holstege
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Reference Data ◽  
Classification Tree ◽  
Cell Types ◽  
Biological Information ◽  
Identification Algorithm ◽  
Intermediate Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing

Abstract Cell type identification is essential for single-cell RNA sequencing (scRNA-seq) studies, currently transforming the life sciences. CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate cell type identification algorithm that is rapid and selective, including the possibility of intermediate or unassigned categories. Evidence for assignment is based on a classification tree of previously available scRNA-seq reference data and includes a confidence score based on the variance in gene expression per cell type. For cell types represented in the reference data, CHETAH’s accuracy is as good as existing methods. Its specificity is superior when cells of an unknown type are encountered, such as malignant cells in tumor samples which it pinpoints as intermediate or unassigned. Although designed for tumor samples in particular, the use of unassigned and intermediate types is also valuable in other exploratory studies. This is exemplified in pancreas datasets where CHETAH highlights cell populations not well represented in the reference dataset, including cells with profiles that lie on a continuum between that of acinar and ductal cell types. Having the possibility of unassigned and intermediate cell types is pivotal for preventing misclassification and can yield important biological information for previously unexplored tissues.

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