scholarly journals Computational Methods for Single-Cell RNA Sequencing

2020 ◽  
Vol 3 (1) ◽  
pp. 339-364 ◽  
Author(s):  
Brian Hie ◽  
Joshua Peters ◽  
Sarah K. Nyquist ◽  
Alex K. Shalek ◽  
Bonnie Berger ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Computational Methods ◽  
Analytical Methods ◽  
High Dimensional ◽  
Computational Tools ◽  
Single Cell Rna Sequencing ◽  
Selection Of

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

scholarly journals Evaluation of UMAP as an alternative to t-SNE for single-cell data

2018 ◽  
Author(s):  
Etienne Becht ◽  
Charles-Antoine Dutertre ◽  
Immanuel W. H. Kwok ◽  
Lai Guan Ng ◽  
Florent Ginhoux ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Reduction Technique ◽  
High Dimensional ◽  
The Past ◽  
Dimensionality Reduction Technique ◽  
Single Cell Rna Sequencing ◽  
Linear Dimensionality Reduction ◽  
Cell Data

AbstractUniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear dimensionality reduction technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

scholarly journals MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyao Liu ◽  
Aatish Thennavan ◽  
Joseph P. Garay ◽  
J. S. Marron ◽  
Charles M. Perou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Consensus Clustering ◽  
Sequencing Data ◽  
Cluster Number ◽  
Single Cell Rna Sequencing ◽  
Clustering Approach ◽  
Cluster Methods ◽  
Optimal Cluster ◽  
Selection Of

AbstractSingle-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.

scholarly journals Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study

2020 ◽  
Vol 21 (6) ◽  
pp. 2181 ◽  
Author(s):  
Chao Feng ◽  
Shufen Liu ◽  
Hao Zhang ◽  
Renchu Guan ◽  
Dan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Dimension Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Component Analysis ◽  
Extraction Methods ◽  
High Dimensional ◽  
Single Cell Rna Sequencing ◽  
Reduction Methods

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

scholarly journals Orchestrating Single-Cell Analysis with Bioconductor

2019 ◽  
Author(s):  
Robert A. Amezquita ◽  
Vince J. Carey ◽  
Lindsay N. Carpp ◽  
Ludwig Geistlinger ◽  
Aaron T. L. Lun ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Biological Data ◽  
High Dimensional ◽  
Software Project ◽  
Sequencing Analysis ◽  
Data Generation ◽  
Data Infrastructure ◽  
Single Cell Rna Sequencing

AbstractRecent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor - an open-source, open-development software project based on the R programming language - has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor.

KS01.6.A Comparative analysis of the immune compartment in human glioblastoma and IDH-mutant WHO grade 4 astrocytoma reveals profound differences in microglia phenotypes

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii4-ii4
Author(s):  
R Sankowski ◽  
M Friedrich ◽  
L Bunse ◽  
H H Heiland ◽  
M Platten ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Rna Sequencing ◽  
Therapeutic Options ◽  
High Dimensional ◽  
Normal Brain ◽  
Who Grade ◽  
Idh Mutations ◽  
Single Cell Rna Sequencing ◽  
Human Gbm

Abstract BACKGROUND Glioblastoma (GBM) are the most common primary brain tumors. If untreated the average survival is around 12–18 months. Unfortunately, despite extensive research efforts, the therapeutic options remain limited. One major aspect complicating therapeutic development is an immunosuppressive tumor microenvironment. Isocitrate dehydrogenase (IDH)-mutant, WHO grade 4 astrocytomas appear histologically indistinguishable from GBM, but show significantly longer survival times. IDH mutations lead to changes in the tumor microenvironment with accrual of the neometabolite R-2-hydroxyglutarate. Previous studies on bulk transcriptomes have shown differences in the immune compartment of both tumor entities that were linked to the differences in clinical behavior. MATERIAL AND METHODS We have conducted high-dimensional comparative analyses of the myeloid compartment in surgically resected human GBM and IDH-mutant WHO grade 4 astrocytomas using single-cell RNA-Sequencing and immunohistochemistry. Histologically normal brain regions from epilepsy patients were used as controls. For analysis, whole-cell suspensions were prepared from freshly resected tumors or controls. Fluorescence activated cell sorting was used for myeloid cell enrichment. Samples were processed using the high-sensitivity single-cell RNA sequencing protocol CEL-Seq2. Seurat and StemID2 algorithms were used for clustering, differential gene expression and pseudotime analysis. Protein validation was achieved using immunohistochemistry. RESULTS We identified profound transcriptional changes of glioma-associated microglia in GBM with respect to control brain samples. Namely, we observed a global upregulation of major histocompatibility complex associated genes in GBM across all clusters. Additionally, we identified distinct myeloid subsets with phagocytic, hypoxia-associated and chemotactic transcriptomic signatures. Pseudotime analysis finely resolved transitional cell states. These changes were dramatically attenuated in IDH-mutant WHO grade 4 astrocytomas. The myeloid cells in these tumors resembled homeostatic microglia and showed an increased expression of cytokine and chemokine genes. CONCLUSION Here, we present a high-dimensional transcriptomic atlas of the myeloid compartment in human GBM and IDH-mutant WHO grade 4 astrocytomas. The identified differences point towards targeted therapeutic options via the modulation of the tumor microenvironment.

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