Discovery of rare cells from voluminous single cell expression data

Jindal, Aashi; Gupta, Pratibha; Jayadeva,; Sengupta, Debarka

doi:10.1038/s41467-018-07234-6

Cited by 109 publications

(116 citation statements)

References 33 publications

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“…Data derived from single-cell assays have enabled researchers to unravel tissue heterogeneity at unprecedented levels of detail, enabling the identification of novel cell types, as well as rare cell populations that were previously unidentifiable using bulk assays [92][93][94]. Unsupervised clustering -the process of grouping cells based on a similarity metric without a known reference -is a fundamental step in deconvoluting heterogeneous single-cell data into clusters that relate to biological concepts, such as discrete cell types or cell states.…”

Section: Clusteringmentioning

confidence: 99%

Orchestrating Single-Cell Analysis with Bioconductor

Carey

Carpp

Lun

et al. 2019

Preprint

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Recent 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. Figure 1: 10 years of Bioconductor in the high-throughput sequencing era. Bioconductor software packages associated with the analysis of sequencing technology were tracked by the total number of packages (left) and the number of distinct IPs (data recorded monthly) visiting their online documentation (right) over the course of ten years. Software packages were uniquely defined by their primary sequencing technology association, with examples of specific terms used for annotation below in parentheses. * Co-second authors. These authors (VJC, LNC, LG, ATLL, FM, KR, DR, CS, LW) contributed equally and are listed alphabetically. † Co-senior authors. These authors (RG, SCH) contributed equally.sparsity, due to biological fluctuations in the measured traits or limited sensitivity in quantifying small numbers of molecules [17][18][19]. In addition, data derived from single-cell assays have revealed more heterogeneity than previously seen [20][21][22][23][24][25][26][27]. This has led to the rapid development of statistical methods to address the increased sparsity and heterogeneity seen in this data [28][29][30][31]. The profound increase in the complexity of data measured at the single-cell level, along with the continued increases in the number of samples measured, have precipitated the need for fundamental changes in data access, management, and infrastructure to make data analyses scalable to empower scientific progress. Specifically, specialized statistical methods along with fast and memory-efficient software implementation are required to reap the full scientific potential of hig...

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Section: Clusteringmentioning

confidence: 99%

Orchestrating Single-Cell Analysis with Bioconductor

Carey

Carpp

Lun

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Rare cell types (<5% of population, including Pericytes and T cells) were not detected in nuclei data. This could be due to single nucleus library preparation, amplification stage efficiency, or detection power of sample size [17]. Overall, tumor cells were the largest portion and comprised of more than 50% of whole brain tissue in both single cell and nucleus data (Fig.…”

Section: Single Nucleus Analysis and Comparison To Single Cell Datamentioning

confidence: 99%

Probing glioblastoma and its microenvironment using single-nucleus and single-cell sequencing

Peng

Rath

Vuong

et al. 2019

Preprint

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Single-cell (scSeq) and single-nucleus sequencing (snSeq) are powerful tools to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without considering unexplored host cell types that contribute to the microenvironment around tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage sequencing of freshly resected primary GBM patient tissue without implementing any tumor enrichment strategies. The sequencing resulted in 902 cells and 1186 nuclei, respectively, passing quality control and with low mitochondrial gene percentage. We customized reference transcriptome by listing gene transcript loci as exons to take into account immature RNA, which greatly improved the alignment rate for singlenucleus data. We applied Cell Ranger pipelines (Version 3.0.2) and Seurat package (Version 2.3.1) and discovered 10 clusters in both scSeq and snSeq. Pathway analysis of each cluster signature in scSeq data along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/macrophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. The analysis of snSeq was able to capture the majority of cell types from patient tissues (tumor and microenvironment cells), but interestingly presented different cell type composition in microenvironment cell types such as microglia/macrophages. Integrating single-cell and single-nucleus transcriptomic data using canonical correlation analysis facilitated a comparison of snSeq and scSeq, contrasting depiction for certain cell types (e.g. NKX6-2 gene in Oligodendrocytes). Differential analysis of pathways between tumor and microenvironment cells unveiled potentially rewired pathways such as double strand break repair pathway. Our results demonstrate the cellular diversity of brain tumor microenvironment and lay a foundation to further investigate the individual tumor and host cell transcriptomes that are influenced not only by their cell identity but also by their interaction with surrounding microenvironment.

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“…Recent development of the Droplet based single cell sequencing technologies has enabled profiling several thousands of cells at a time 2,3 . Large pool of cells present the opportunity of identifying cell types which are previously unseen due to their limited presence or rarity 4 . Clustering of cells is a primary step involved in single cell expression data analysis.…”

Section: Availabilitymentioning

confidence: 99%

dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data

Sinha

Saha

et al. 2019

Preprint

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DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves interfacing with multiple programming languages viz. R, python and C, hindering seamless installation and portability. Here we present dropClust2, a complete R package that's not only fast but also minimally resource intensive. DropClust2 features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets.

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Discovery of rare cells from voluminous single cell expression data

Cited by 109 publications

References 33 publications

Orchestrating Single-Cell Analysis with Bioconductor

Orchestrating Single-Cell Analysis with Bioconductor

Probing glioblastoma and its microenvironment using single-nucleus and single-cell sequencing

dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data

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