scholarly journals Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE

2018 ◽  
Author(s):  
Axel Theorell ◽  
Yenan Troi Bryceson ◽  
Jakob Theorell
Keyword(s):  
Single Cell ◽  
Open Source ◽  
Rna Sequencing ◽  
Biological Markers ◽  
State Of The Art ◽  
Single Cells ◽  
Data Mining Algorithm ◽  
Mass Cytometry ◽  
Sequencing Data ◽  
Biologically Relevant

AbstractTechnological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make this highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi-and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better as the best performing state-of-the-art clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. An open source R implementation of DEPECHE is available at https://github.com/theorell/DepecheR.Author summaryDEPECHE-a data-mining algorithm for mega-variate dataModern experimental technologies facilitate an array of single cells measurements, e.g. at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.

scholarly journals Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

2019 ◽  
Author(s):  
Imad Abugessaisa ◽  
Shuhei Noguchi ◽  
Melissa Cardon ◽  
Akira Hasegawa ◽  
Kazuhide Watanabe ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Assessment Method ◽  
Poor Quality ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Gene Coverage ◽  
The Impact

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.

scholarly journals Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Sequencing Data ◽  
Specific Expression ◽  
Single Nucleotide ◽  
Healthy Donors ◽  
Allele Expression ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

scholarly journals IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis

GigaScience ◽  
2019 ◽  
Vol 8 (10) ◽  
Author(s):  
Yun-Ching Chen ◽  
Abhilash Suresh ◽  
Chingiz Underbayev ◽  
Clare Sun ◽  
Komudi Singh ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Biologically Relevant ◽  
Single Cell Rna Sequencing ◽  
Cell Groups ◽  
Cell Ontology

AbstractBackgroundIn single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.FindingsTo accelerate this process, we have developed IKAP—an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology.ConclusionsBy tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.

scholarly journals SNV identification from single-cell RNA sequencing data

2019 ◽  
Vol 28 (21) ◽  
pp. 3569-3583 ◽  
Author(s):  
Patricia M Schnepp ◽  
Mengjie Chen ◽  
Evan T Keller ◽  
Xiang Zhou
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Cell Rna Sequencing ◽  
Sequencing Studies ◽  
Genomic Regions

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.

scholarly journals scds: computational annotation of doublets in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Abha S Bais ◽  
Dennis Kostka
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Binary Classification ◽  
Single Cells ◽  
Computational Cost ◽  
Original Data ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study’s conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Normalizing single-cell RNA sequencing data with internal spike-in-like genes

2020 ◽  
Author(s):  
Li Lin ◽  
Minfang Song ◽  
Yong Jiang ◽  
Xiaojing Zhao ◽  
Haopeng Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Sequencing Depth ◽  
Sequencing Data ◽  
Crucial Step ◽  
Single Cell Rna Sequencing ◽  
Whole Transcriptome

ABSTRACTNormalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm improves the performance of downstream analyzes.

scholarly journals A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Simon Haile ◽  
Richard D. Corbett ◽  
Veronique G. LeBlanc ◽  
Lisa Wei ◽  
Stephen Pleasance ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Single Cells ◽  
Cell Types ◽  
Full Length ◽  
Sequencing Data ◽  
Total Rna ◽  
Specific Protocol

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

scholarly journals Comprehensive analysis of single-cell RNA sequencing data from healthy human marrow hematopoietic cells

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xin Zhao ◽  
Shouguo Gao ◽  
Sachiko Kajigaya ◽  
Qingguo Liu ◽  
Zhijie Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Noncoding Rna ◽  
Single Cells ◽  
Regulation Of Gene Expression ◽  
Sequencing Data ◽  
Healthy Human ◽  
Hematopoietic Stem ◽  
Single Cell Rna Sequencing

Abstract Objective Single cell methodology enables detection and quantification of transcriptional changes and unravelling dynamic aspects of the transcriptional heterogeneity not accessible using bulk sequencing approaches. We have applied single-cell RNA-sequencing (scRNA-seq) to fresh human bone marrow CD34+ cells and profiled 391 single hematopoietic stem/progenitor cells (HSPCs) from healthy donors to characterize lineage- and stage-specific transcription during hematopoiesis. Results Cells clustered into six distinct groups, which could be assigned to known HSPC subpopulations based on lineage specific genes. Reconstruction of differentiation trajectories in single cells revealed four committed lineages derived from HSCs, as well as dynamic expression changes underlying cell fate during early erythroid-megakaryocytic, lymphoid, and granulocyte-monocyte differentiation. A similar non-hierarchical pattern of hematopoiesis could be derived from analysis of published single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), consistent with a sequential relationship between chromatin dynamics and regulation of gene expression during lineage commitment (first, altered chromatin conformation, then mRNA transcription). Computationally, we have reconstructed molecular trajectories connecting HSCs directly to four hematopoietic lineages. Integration of long noncoding RNA (lncRNA) expression from the same cells demonstrated mRNA transcriptome, lncRNA, and the epigenome were highly homologous in their pattern of gene activation and suppression during hematopoietic cell differentiation.

scholarly journals Adapted single-cell consensus clustering (adaSC3)

2020 ◽  
Author(s):  
Cornelia Fuetterer ◽  
Thomas Augustin ◽  
Christiane Fuchs
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Linear Method ◽  
Principal Component ◽  
Data Sets ◽  
Consensus Clustering ◽  
Sequencing Data ◽  
Reduction Techniques ◽  
Single Cell Rna Sequencing

AbstractThe analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.

scholarly journals CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shadi Darvish Shafighi ◽  
Szymon M. Kiełbasa ◽  
Julieta Sepúlveda-Yáñez ◽  
Ramin Monajemi ◽  
Davy Cats ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
B Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Tumor Heterogeneity ◽  
Single Cells ◽  
Clonal Evolution ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Sequencing Data

Abstract Background Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. Methods Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. Results We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. Conclusions The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS).

Sign in / Sign up

Export Citation Format

Share Document