Journal articles: 'Single cell sequencing data'

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Shi, Qianqian, Xinxing Li, Qirui Peng, Chuanchao Zhang, and Luonan Chen. "scDA: Single cell discriminant analysis for single-cell RNA sequencing data." Computational and Structural Biotechnology Journal 19 (2021): 3234–44. http://dx.doi.org/10.1016/j.csbj.2021.05.046.

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Zhao, Xinlei, Shuang Wu, Nan Fang, Xiao Sun, and Jue Fan. "Evaluation of single-cell classifiers for single-cell RNA sequencing data sets." Briefings in Bioinformatics 21, no. 5 (October 23, 2019): 1581–95. http://dx.doi.org/10.1093/bib/bbz096.

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Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

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Satas, Gryte, and Benjamin J. Raphael. "Haplotype phasing in single-cell DNA-sequencing data." Bioinformatics 34, no. 13 (June 27, 2018): i211—i217. http://dx.doi.org/10.1093/bioinformatics/bty286.

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Vallejos, Catalina A., John C. Marioni, and Sylvia Richardson. "BASiCS: Bayesian Analysis of Single-Cell Sequencing Data." PLOS Computational Biology 11, no. 6 (June 24, 2015): e1004333. http://dx.doi.org/10.1371/journal.pcbi.1004333.

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Schnepp, Patricia M., Mengjie Chen, Evan T. Keller, and Xiang Zhou. "SNV identification from single-cell RNA sequencing data." Human Molecular Genetics 28, no. 21 (August 27, 2019): 3569–83. http://dx.doi.org/10.1093/hmg/ddz207.

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Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

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Gisina, Alisa, Irina Kholodenko, Yan Kim, Maxim Abakumov, Alexey Lupatov, and Konstantin Yarygin. "Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing." International Journal of Molecular Sciences 23, no. 22 (November 17, 2022): 14224. http://dx.doi.org/10.3390/ijms232214224.

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Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.

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Dai, Hao, Lin Li, Tao Zeng, and Luonan Chen. "Cell-specific network constructed by single-cell RNA sequencing data." Nucleic Acids Research 47, no. 11 (March 13, 2019): e62-e62. http://dx.doi.org/10.1093/nar/gkz172.

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Zhang, Yinan, Xiaowei Xie, Peng Wu, and Ping Zhu. "SIEVE: identifying robust single cell variable genes for single-cell RNA sequencing data." Blood Science 3, no. 2 (April 2021): 35–39. http://dx.doi.org/10.1097/bs9.0000000000000072.

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Myers, Matthew A., Simone Zaccaria, and Benjamin J. Raphael. "Identifying tumor clones in sparse single-cell mutation data." Bioinformatics 36, Supplement_1 (July 1, 2020): i186—i193. http://dx.doi.org/10.1093/bioinformatics/btaa449.

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Abstract Motivation Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. Results We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. Availability and implementation SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone. Supplementary information Supplementary data are available at Bioinformatics online.

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Zhao, Peng, Zenglin Xu, Junjie Chen, Yazhou Ren, and Irwin King. "Single Cell Self-Paced Clustering with Transcriptome Sequencing Data." International Journal of Molecular Sciences 23, no. 7 (March 31, 2022): 3900. http://dx.doi.org/10.3390/ijms23073900.

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Single cell RNA sequencing (scRNA-seq) allows researchers to explore tissue heterogeneity, distinguish unusual cell identities, and find novel cellular subtypes by providing transcriptome profiling for individual cells. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the performance of existing single-cell clustering methods is extremely sensitive to the presence of noise data and outliers. Existing clustering algorithms can easily fall into local optimal solutions. There is still no consensus on the best performing method. To address this issue, we introduce a single cell self-paced clustering (scSPaC) method with F-norm based nonnegative matrix factorization (NMF) for scRNA-seq data and a sparse single cell self-paced clustering (sscSPaC) method with l21-norm based nonnegative matrix factorization for scRNA-seq data. We gradually add single cells from simple to complex to our model until all cells are selected. In this way, the influences of noisy data and outliers can be significantly reduced. The proposed method achieved the best performance on both simulation data and real scRNA-seq data. A case study about human clara cells and ependymal cells scRNA-seq data clustering shows that scSPaC is more advantageous near the clustering dividing line.

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Weber, Leah L., Palash Sashittal, and Mohammed El-Kebir. "doubletD: detecting doublets in single-cell DNA sequencing data." Bioinformatics 37, Supplement_1 (July 1, 2021): i214—i221. http://dx.doi.org/10.1093/bioinformatics/btab266.

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Abstract Motivation While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance. Results We present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results. Availability and implementation https://github.com/elkebir-group/doubletD. Supplementary information Supplementary data are available at Bioinformatics online.

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Germain, Pierre-Luc, Aaron Lun, Will Macnair, and Mark D. Robinson. "Doublet identification in single-cell sequencing data using scDblFinder." F1000Research 10 (May 16, 2022): 979. http://dx.doi.org/10.12688/f1000research.73600.2.

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Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed scDblFinder, a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.

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Mustachio, Lisa Maria, and Jason Roszik. "Opportunities for Single-Cell Sequencing Technologies and Data Science." Cancers 12, no. 11 (November 19, 2020): 3433. http://dx.doi.org/10.3390/cancers12113433.

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Valecha, Monica, and David Posada. "Somatic variant calling from single-cell DNA sequencing data." Computational and Structural Biotechnology Journal 20 (2022): 2978–85. http://dx.doi.org/10.1016/j.csbj.2022.06.013.

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Germain, Pierre-Luc, Aaron Lun, Will Macnair, and Mark D. Robinson. "Doublet identification in single-cell sequencing data using scDblFinder." F1000Research 10 (September 28, 2021): 979. http://dx.doi.org/10.12688/f1000research.73600.1.

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Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed scDblFinder, a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.

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Vallejos, Catalina A., Davide Risso, Antonio Scialdone, Sandrine Dudoit, and John C. Marioni. "Normalizing single-cell RNA sequencing data: challenges and opportunities." Nature Methods 14, no. 6 (May 15, 2017): 565–71. http://dx.doi.org/10.1038/nmeth.4292.

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Bai, Yu-Long, Melody Baddoo, Erik K. Flemington, Hani N. Nakhoul, and Yao-Zhong Liu. "Screen technical noise in single cell RNA sequencing data." Genomics 112, no. 1 (January 2020): 346–55. http://dx.doi.org/10.1016/j.ygeno.2019.02.014.

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DePasquale, Erica A. K., Daniel J. Schnell, Pieter-Jan Van Camp, Íñigo Valiente-Alandí, Burns C. Blaxall, H. Leighton Grimes, Harinder Singh, and Nathan Salomonis. "DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data." Cell Reports 29, no. 6 (November 2019): 1718–27. http://dx.doi.org/10.1016/j.celrep.2019.09.082.

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Chandra, Shilpi, Gregory Seumois, Ciro Ramirez, Gooyoung Seo, Pandurangan Vijayanand, and Mitchell Kronenberg. "Single cell sequencing reveals mouse MAIT cell diversity." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 65.1. http://dx.doi.org/10.4049/jimmunol.202.supp.65.1.

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Abstract Mucosal-associated invariant T (MAIT) cells are a novel subpopulation of innate-like T lymphocytes that recognize vitamin B metabolites and express an invariant T cell receptor (TCR) α chain. MAIT cells are less abundant in mouse blood (~0.1% of T cells) as compared to human blood (~5%). MAIT cells play an important role in various infectious non-infectious diseases. Given their small number in mouse, these cells have been not been fully characterized. To unravel MAIT cell heterogeneity in thymus and peripheral tissues, we have performed single-cell sequencing of MAIT cells from various organs using 10× single-cell genomics, where we sequenced more than 7,000 cells and identified 10 clusters of MAIT cells by unbiased clustering. Cells from different organs mostly are represented in the different clusters, except one cluster was lung MAIT cell specific and another cluster consisted of cells exclusively from thymus. The lung specific cluster also reveals a tissue residency gene signature. We compared the gene expression profile of MAIT cells with iNKT cells, another T lymphocyte population that recognizes non-peptide antigens with invariant α chains. Our data reveal that most mouse MAIT cells have a Th17/NKT17–like gene signature, although there are some with Th1/NKT1 like transcriptomes, particularly in liver and spleen. A Th2/NKT2 gene expression profile was not observed. The thymus specific cluster showed a gene expression profile similar to the most immature or progenitor iNKT cells (NKT0), consistent with other data suggesting a unique thymus differentiation pathway. Therefore, our study reveals that although MAIT cells are predominantly Th17 and Th1 cells, there is an unexpected degree of heterogeneity. Supported by R01 AI71922

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Apsley, Elizabeth J., and Esther B. E. Becker. "Purkinje Cell Patterning—Insights from Single-Cell Sequencing." Cells 11, no. 18 (September 18, 2022): 2918. http://dx.doi.org/10.3390/cells11182918.

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Despite their homogeneous appearance, Purkinje cells are remarkably diverse with respect to their molecular phenotypes, physiological properties, afferent and efferent connectivity, as well as their vulnerability to insults. Heterogeneity in Purkinje cells arises early in development, with molecularly distinct embryonic cell clusters present soon after Purkinje cell specification. Traditional methods have characterized cerebellar development and cell types, including Purkinje cell subtypes, based on knowledge of selected markers. However, recent single-cell RNA sequencing studies provide vastly increased resolution of the whole cerebellar transcriptome. Here we draw together the results of multiple single-cell transcriptomic studies in developing and adult cerebellum in both mouse and human. We describe how this detailed transcriptomic data has increased our understanding of the intricate development and function of Purkinje cells and provides first clues into features specific to human cerebellar development.

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Pham, Son, Tri Le, Tan Phan, Minh Pham, Huy Nguyen, Loc Lam, Nam Phung, et al. "484 Bioturing browser: interactively explore public single cell sequencing data." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A520. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0484.

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BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

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Tran, Thinh N., and Gary D. Bader. "Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data." PLOS Computational Biology 16, no. 9 (September 9, 2020): e1008205. http://dx.doi.org/10.1371/journal.pcbi.1008205.

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Tian, Yu, Ruiqing Zheng, Zhenlan Liang, Suning Li, Fang-Xiang Wu, and Min Li. "A data-driven clustering recommendation method for single-cell RNA-sequencing data." Tsinghua Science and Technology 26, no. 5 (October 2021): 772–89. http://dx.doi.org/10.26599/tst.2020.9010028.

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Verboom, Karen, Celine Everaert, Nathalie Bolduc, Kenneth J. Livak, Nurten Yigit, Dries Rombaut, Jasper Anckaert, et al. "SMARTer single cell total RNA sequencing." Nucleic Acids Research 47, no. 16 (June 19, 2019): e93-e93. http://dx.doi.org/10.1093/nar/gkz535.

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Abstract Single cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3′ end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation and on two other cancer cell lines sorted in microplates. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

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Hie, Brian, Joshua Peters, Sarah K. Nyquist, Alex K. Shalek, Bonnie Berger, and Bryan D. Bryson. "Computational Methods for Single-Cell RNA Sequencing." Annual Review of Biomedical Data Science 3, no. 1 (July 20, 2020): 339–64. http://dx.doi.org/10.1146/annurev-biodatasci-012220-100601.

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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.

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Wu, Min, Junhua Xu, Tao Ding, and Jie Gao. "Mixed Distribution Models Based on Single-Cell RNA Sequencing Data." Interdisciplinary Sciences: Computational Life Sciences 13, no. 3 (March 22, 2021): 362–70. http://dx.doi.org/10.1007/s12539-021-00427-6.

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Yu, Zhenhua, and Fang Du. "AMC: accurate mutation clustering from single-cell DNA sequencing data." Bioinformatics 38, no. 6 (December 24, 2021): 1732–34. http://dx.doi.org/10.1093/bioinformatics/btab857.

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Abstract Summary Single-cell DNA sequencing (scDNA-seq) now enables high-resolution profiles of intra-tumor heterogeneity. Existing methods for phylogenetic inference from scDNA-seq data perform acceptably well on small datasets but suffer from low computational efficiency and/or degraded accuracy on large datasets. Motivated by the fact that mutations sharing common states over single cells can be grouped together, we introduce a new software called AMC (accurate mutation clustering) to accurately cluster mutations, thus improve the efficiency of phylogenetic inference. AMC first employs principal component analysis followed by K-means clustering to find mutation clusters, then infers the maximum likelihood estimates of the genotypes of each cluster. The inferred genotypes can subsequently be used to reconstruct the phylogenetic tree with high efficiency. Comprehensive evaluations on various simulated datasets demonstrate AMC is particularly useful to efficiently reason the mutation clusters on large scDNA-seq datasets. Availability and implementation AMC is freely available at https://github.com/qasimyu/amc. Supplementary information Supplementary data are available at Bioinformatics online.

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Qin, Fei, Xizhi Luo, Feifei Xiao, and Guoshuai Cai. "SCRIP: an accurate simulator for single-cell RNA sequencing data." Bioinformatics 38, no. 5 (December 7, 2021): 1304–11. http://dx.doi.org/10.1093/bioinformatics/btab824.

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Abstract Motivation Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed scRNA-seq information producer (SCRIP), a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics. Results Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean–variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell–cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates. Collectively, this study provides the research community with a rigorous tool for scRNA-seq data simulation. Availability and implementation https://CRAN.R-project.org/package=SCRIP. Supplementary information Supplementary data are available at Bioinformatics online.

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Das, Samarendra, and Shesh N. Rai. "Statistical methods for analysis of single-cell RNA-sequencing data." MethodsX 8 (2021): 101580. http://dx.doi.org/10.1016/j.mex.2021.101580.

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Qi, Ren, Anjun Ma, Qin Ma, and Quan Zou. "Clustering and classification methods for single-cell RNA-sequencing data." Briefings in Bioinformatics 21, no. 4 (July 4, 2019): 1196–208. http://dx.doi.org/10.1093/bib/bbz062.

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Abstract Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.

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Duan, Bin, Shaoqi Chen, Xiaohan Chen, Chenyu Zhu, Chen Tang, Shuguang Wang, Yicheng Gao, Shaliu Fu, and Qi Liu. "Integrating multiple references for single-cell assignment." Nucleic Acids Research 49, no. 14 (May 25, 2021): e80-e80. http://dx.doi.org/10.1093/nar/gkab380.

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Abstract Efficient single-cell assignment is essential for single-cell sequencing data analysis. With the explosive growth of single-cell sequencing data, multiple single-cell sequencing data sources are available for the same kind of tissue, which can be integrated to further improve single-cell assignment; however, an efficient integration strategy is still lacking due to the great challenges of data heterogeneity existing in multiple references. To this end, we present mtSC, a flexible single-cell assignment framework that integrates multiple references based on multitask deep metric learning designed specifically for cell type identification within tissues with multiple single-cell sequencing data as references. We evaluated mtSC on a comprehensive set of publicly available benchmark datasets and demonstrated its state-of-the-art effectiveness for integrative single-cell assignment with multiple references.

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Vu, Trung Nghia, Ha-Nam Nguyen, Stefano Calza, Krishna R. Kalari, Liewei Wang, and Yudi Pawitan. "Cell-level somatic mutation detection from single-cell RNA sequencing." Bioinformatics 35, no. 22 (April 26, 2019): 4679–87. http://dx.doi.org/10.1093/bioinformatics/btz288.

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Abstract Motivation Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data. Results Mutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method—called SCmut—to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity. Availability and implementation The source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut. Supplementary information Supplementary data are available at Bioinformatics online.

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Shparberg, Rachel, Chitra Umala Dewi, Vikkitharan Gnanasambandapillai, Liwan Liyanage, and Michael D. O'Connor. "Single cell RNA-sequencing data generated from human pluripotent stem cell-derived lens epithelial cells." Data in Brief 34 (February 2021): 106657. http://dx.doi.org/10.1016/j.dib.2020.106657.

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Sturm, Gregor, Tamas Szabo, Georgios Fotakis, Marlene Haider, Dietmar Rieder, Zlatko Trajanoski, and Francesca Finotello. "Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data." Bioinformatics 36, no. 18 (July 2, 2020): 4817–18. http://dx.doi.org/10.1093/bioinformatics/btaa611.

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Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

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Du, Rose, Vince Carey, and Scott T. Weiss. "deconvSeq: deconvolution of cell mixture distribution in sequencing data." Bioinformatics 35, no. 24 (May 30, 2019): 5095–102. http://dx.doi.org/10.1093/bioinformatics/btz444.

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Abstract Motivation Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data. Results DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature quantification, which is specific to the data structure of the sequencing type used. Estimated model coefficients can then be used to predict the cell type mixture within a tissue. Predicted cell type mixtures were validated against actual cell counts in whole blood samples. Using this method, we obtained a mean correlation of 0.998 (95% CI 0.995–0.999) from the RNA sequencing data of 35 whole blood samples and 0.95 (95% CI 0.91–0.98) from the reduced representation bisulfite sequencing data from 35 whole blood samples. Using symmetric balances to obtain the correlation between compositional parts, we found that the lowest correlation occurred for monocytes for both RNA and bisulfite sequencing. Comparison with other methods of decomposition such as deconRNAseq, CIBERSORT, MuSiC and EpiDISH showed that deconvSeq is able to achieve good prediction using mean correlation with far fewer genes or CpG sites in the signature set. Availability and implementation Software implementing deconvSeq is available at https://github.com/rosedu1/deconvSeq. Supplementary information Supplementary data are available at Bioinformatics online.

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Liu, Yunqing, Na Lu, Changwei Bi, Tingyu Han, Guo Zhuojun, Yunchi Zhu, Yixin Li, Chunpeng He, and Zuhong Lu. "FEM: mining biological meaning from cell level in single-cell RNA sequencing data." PeerJ 9 (November 30, 2021): e12570. http://dx.doi.org/10.7717/peerj.12570.

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Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).

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Zhao, Xin, Shouguo Gao, Sachiko Kajigaya, Qingguo Liu, Zhijie Wu, Xingmin Feng, Fengkui Zhang, and Neal S. Young. "Single-Cell RNA Sequencing of Healthy Human Marrow Hematopoietic Cells." Blood 134, Supplement_1 (November 13, 2019): 4997. http://dx.doi.org/10.1182/blood-2019-123249.

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Hematopoiesis, especially the early events of blood cell formation, has been mainly studied in bulk populations of cells and using progenitor colony formation assays; the familiar hierarchy of cell lineage differentiation and maturation, and associated regulatory factors have been inferred from these methods. However, these techniques often require extensive manipulation of cells, the exposure of cells to unphysiological conditions, aggregation of heterogeneous populations, and prior assumptions concerning cell function and gene expression. New single cell methodology avoids many of these potential experimental deficiencies. Here we have applied single-cell RNA-sequencing(scRNA-seq)to fresh human bone marrow CD34+cells: we profiled 391 single hematopoietic stem/progenitor cells (HSPCs) from four healthy donors by deep sequencing of individual cell transcriptomes. An average of 4560 protein-coding genes were detected per cell. Cells clustered into six distinct groups, which could be assigned to known HSPC subpopulations (Fig 1A), based on expression of lineage-specific genes. Lin-CD34+CD38+cells emerged as locally clustered cell populations (Clusters 2-6, including MEP, GMP, ETP and ProB), while Lin-CD34+CD38-cells formed a single cluster (HSC/MLP). Reconstruction of differentiation trajectories by transcription in single cells revealed four committed lineages derived from stem cell compartment. The earliest fate split separates MEPs from MLPs, which partition further into lymphoid, and granulocyte-monocyte progenitors (Fig 1B). The overall pattern differs from the classical hematopoietic model describing a single binary split between myeloid and lymphoid differentiation immediately downstream of multipotent cells. However, our data align well to recently published scRNA-seq data showing sequential commitment of stem cells to the lymphoid, erythroid/megakaryocytic, and finally myeloid lineages (Setty M, Nat Biotechnol2019; Pellin D, Nat Commun2019). We further examined trends in gene expression in each of the branches and found dynamic expression changes underlying cell fate during early lineage differentiation (Fig 1C). As confirmation, PCA plot of published single-cell assay for transposase-accessible chromatin (scATAC-seq) shows similar differentiation pattern. After projecting scATAC-seq data to our transcriptomic clusters' specific genes, MEP-dependent and myeloid/lymphoid-dependent genes were located on opposing sides of the PC1 with same direction (Fig 1D), indicating transcriptome and epigenome work on differentiation in concerted effort. scRNA-seq provides opportunities for discovery and characterization at the molecular levels of early HSC differentiation and developmental intermediates, retrospectively, without the need to isolate purified populations. However, information inferred from scRNA-seq may be obscured due to missing reads and limited cell numbers. More cells would provide greater detail and higher resolution mapping.Given the low frequency of megakaryocyte progenitors within the CD34+cells as well as the neglected Lin-CD34-BM compartment, we could not fully resolve the separation and maturation of all lineages. Nonetheless, we found good coverage of cell types and a similar HSPC Atlas as other published studies (Velten L, Nat Cell Biol2017; Pellin D, Nat Commun2019)despite our limited numbers of starting cells. Our data accurately reflect the pattern of normal hematopoiesis, which may help to revise and refine characterization of hematopoiesis and provide a general reference framework to investigate the complexities of blood cell production at single-cell resolution - especially when cell numbers are limited, as from patient samples and in marrow failure syndromes. Fig. 1scRNA-seq of human hematopoietic stem and progenitor cells. (A) Unsupervised hierarchical clustering of gene expression data for all cells. C1, HSC/MLP; C2, MEP; C3, GMP; C4, ProB; C5-C6, ETP. (B)Visualization of the HSPC continuum. Each ball represents one cell.(C) Large-scale shifts in gene expression during development of hematopoietic cells.Bars on top indicate locations of individual cells, colored by stages of development, along this developmental trajectory. (D) Projections of five transcriptomic gene modules onto PCA of scATAC-seq data (Buenrostro JD,Cell 2018). Each dot represents a transcriptional factor. Figure 1 Disclosures No relevant conflicts of interest to declare.

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Wang, Mengyuan, Jiatao Gan, Changfeng Han, Yanbing Guo, Kaihao Chen, Ya-zhou Shi, and Ben-gong Zhang. "Imputation Methods for scRNA Sequencing Data." Applied Sciences 12, no. 20 (October 21, 2022): 10684. http://dx.doi.org/10.3390/app122010684.

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More and more researchers use single-cell RNA sequencing (scRNA-seq) technology to characterize the transcriptional map at the single-cell level. They use it to study the heterogeneity of complex tissues, transcriptome dynamics, and the diversity of unknown organisms. However, there are generally lots of technical and biological noises in the scRNA-seq data since the randomness of gene expression patterns. These data are often characterized by high-dimension, sparsity, large number of “dropout” values, and affected by batch effects. A large number of “dropout” values in scRNA-seq data seriously conceal the important relationship between genes and hinder the downstream analysis. Therefore, the imputation of dropout values of scRNA-seq data is particularly important. We classify, analyze and compare the current advanced scRNA-seq data imputation methods from different angles. Through the comparison and analysis of the principle, advantages and disadvantages of the algorithm, it can provide suggestions for the selection of imputation methods for specific problems and diverse data, and have basic research significance for the downstream function analysis of data.

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Guo, Tiantian, Yang Chen, Minglei Shi, Xiangyu Li, and Michael Q. Zhang. "Integration of single cell data by disentangled representation learning." Nucleic Acids Research 50, no. 2 (November 24, 2021): e8-e8. http://dx.doi.org/10.1093/nar/gkab978.

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Abstract Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is challenging to integrate different datasets from different laboratories or technologies due to batch effect, which are interspersed with biological variances. To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We applied it to thirteen diverse simulated and real datasets. Benchmark results show that SCIDRL outperforms other methods in most cases and exhibits excellent performances in two common situations: (i) effective integration of batch-shared rare cell types and preservation of batch-specific rare cell types; (ii) reliable integration of datasets with different cell compositions. This demonstrates SCIDRL will offer a valuable tool for researchers to decode the enigma of cell heterogeneity.

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Balzer, Michael S., Ziyuan Ma, Jianfu Zhou, Amin Abedini, and Katalin Susztak. "How to Get Started with Single Cell RNA Sequencing Data Analysis." Journal of the American Society of Nephrology 32, no. 6 (March 15, 2021): 1279–92. http://dx.doi.org/10.1681/asn.2020121742.

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Over the last 5 years, single cell methods have enabled the monitoring of gene and protein expression, genetic, and epigenetic changes in thousands of individual cells in a single experiment. With the improved measurement and the decreasing cost of the reactions and sequencing, the size of these datasets is increasing rapidly. The critical bottleneck remains the analysis of the wealth of information generated by single cell experiments. In this review, we give a simplified overview of the analysis pipelines, as they are typically used in the field today. We aim to enable researchers starting out in single cell analysis to gain an overview of challenges and the most commonly used analytical tools. In addition, we hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.

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Posada, David. "CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples." Molecular Biology and Evolution 37, no. 5 (February 6, 2020): 1535–42. http://dx.doi.org/10.1093/molbev/msaa025.

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Abstract Our capacity to study individual cells has enabled a new level of resolution for understanding complex biological systems such as multicellular organisms or microbial communities. Not surprisingly, several methods have been developed in recent years with a formidable potential to investigate the somatic evolution of single cells in both healthy and pathological tissues. However, single-cell sequencing data can be quite noisy due to different technical biases, so inferences resulting from these new methods need to be carefully contrasted. Here, I introduce CellCoal, a software tool for the coalescent simulation of single-cell sequencing genotypes. CellCoal simulates the history of single-cell samples obtained from somatic cell populations with different demographic histories and produces single-nucleotide variants under a variety of mutation models, sequencing read counts, and genotype likelihoods, considering allelic imbalance, allelic dropout, amplification, and sequencing errors, typical of this type of data. CellCoal is a flexible tool that can be used to understand the implications of different somatic evolutionary processes at the single-cell level, and to benchmark dedicated bioinformatic tools for the analysis of single-cell sequencing data. CellCoal is available at https://github.com/dapogon/cellcoal.

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Wen, Zi-Hang, Jeremy L. Langsam, Lu Zhang, Wenjun Shen, and Xin Zhou. "A Bayesian factorization method to recover single-cell RNA sequencing data." Cell Reports Methods 2, no. 1 (January 2022): 100133. http://dx.doi.org/10.1016/j.crmeth.2021.100133.

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Tsyvina, Viachaslau, Alex Zelikovsky, Sagi Snir, and Pavel Skums. "Inference of mutability landscapes of tumors from single cell sequencing data." PLOS Computational Biology 16, no. 11 (November 30, 2020): e1008454. http://dx.doi.org/10.1371/journal.pcbi.1008454.

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One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.

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Lei, Haoyun, Bochuan Lyu, E. Michael Gertz, Alejandro A. Schäffer, Xulian Shi, Kui Wu, Guibo Li, et al. "Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data." Journal of Computational Biology 27, no. 4 (April 1, 2020): 565–98. http://dx.doi.org/10.1089/cmb.2019.0302.

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Fischer, David S., Anna K. Fiedler, Eric M. Kernfeld, Ryan M. J. Genga, Aimée Bastidas-Ponce, Mostafa Bakhti, Heiko Lickert, Jan Hasenauer, Rene Maehr, and Fabian J. Theis. "Inferring population dynamics from single-cell RNA-sequencing time series data." Nature Biotechnology 37, no. 4 (April 2019): 461–68. http://dx.doi.org/10.1038/s41587-019-0088-0.

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Hu, Ming-Wen, Dong Won Kim, Sheng Liu, Donald J. Zack, Seth Blackshaw, and Jiang Qian. "PanoView: An iterative clustering method for single-cell RNA sequencing data." PLOS Computational Biology 15, no. 8 (August 30, 2019): e1007040. http://dx.doi.org/10.1371/journal.pcbi.1007040.

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Vu, Trung Nghia, Quin F. Wills, Krishna R. Kalari, Nifang Niu, Liewei Wang, Yudi Pawitan, and Mattias Rantalainen. "Isoform-level gene expression patterns in single-cell RNA-sequencing data." Bioinformatics 34, no. 14 (February 27, 2018): 2392–400. http://dx.doi.org/10.1093/bioinformatics/bty100.

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Pouyan, Maziyar Baran, and Dennis Kostka. "Random forest based similarity learning for single cell RNA sequencing data." Bioinformatics 34, no. 13 (June 27, 2018): i79—i88. http://dx.doi.org/10.1093/bioinformatics/bty260.

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Wu, Peng, Yan Gao, Weilong Guo, and Ping Zhu. "Using local alignment to enhance single-cell bisulfite sequencing data efficiency." Bioinformatics 35, no. 18 (February 19, 2019): 3273–78. http://dx.doi.org/10.1093/bioinformatics/btz125.

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Abstract Motivation Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue. Results We comprehensively characterize single-cell BS-seq data and reveal chimerical molecules to be the major source of alignment failures. These chimerical molecules are produced by recombination of genomic proximal sequences with microhomology regions (MR) after bisulfite conversion. In addition, we find DNA methylation within MR is highly variable, suggesting the necessity of removing these regions to accurately estimate DNA methylation levels. We further develop scBS-map to perform quality control and local alignment of bisulfite sequencing data, chimerical molecule determination and MR removal. Using scBS-map, we show remarkable increases in uniquely mapped reads, genomic coverage and number of CpG sites, and recover more functional elements with precise DNA methylation estimation. Availability and implementation The scBS-map software is freely available at https://github.com/wupengomics/scBS-map. Supplementary information Supplementary data are available at Bioinformatics online.

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AlJanahi, Aisha A., Mark Danielsen, and Cynthia E. Dunbar. "An Introduction to the Analysis of Single-Cell RNA-Sequencing Data." Molecular Therapy - Methods & Clinical Development 10 (September 2018): 189–96. http://dx.doi.org/10.1016/j.omtm.2018.07.003.

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Journal articles on the topic 'Single cell sequencing data'

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