scholarly journals Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

2019 ◽  
Author(s):  
Haruka Ozaki ◽  
Tetsutaro Hayashi ◽  
Mana Umeda ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Heterogeneity ◽  
Specific Cell ◽  
Read Coverage ◽  
Cell Heterogeneity ◽  
Sequencing Data ◽  
Rna Transcription ◽  
Link Type ◽  
Single Cell Rna Sequencing

AbstractBackgroundRead coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly “full-length” ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking.ResultsHere, we have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of “local” region-specific, cell-to-cell heterogeneity in read coverage, including variability of transcribed regions.ConclusionsMillefy simplifies the examination of cellular heterogeneity in RNA transcription and processing events using scRNA-seq data. Millefy is available as an R package (https://github.com/yuifu/millefy) and a Docker image to help use Millefy on the Jupyter notebook (https://hub.docker.com/r/yuifu/datascience-notebook-millefy).

scholarly journals MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii406-iii406
Author(s):  
Andrew Donson ◽  
Kent Riemondy ◽  
Sujatha Venkataraman ◽  
Ahmed Gilani ◽  
Bridget Sanford ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Cell Heterogeneity ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing ◽  
Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

scholarly journals miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

2021 ◽  
Author(s):  
Ariel A. Hippen ◽  
Matias M. Falco ◽  
Lukas M. Weber ◽  
Erdogan Pekcan Erkan ◽  
Kaiyang Zhang ◽  
...  
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Sequencing ◽  
Data Driven ◽  
Probabilistic Framework ◽  
Sequencing Data ◽  
Link Type ◽  
Tumor Tissues ◽  
Single Cell Rna Sequencing ◽  
Different Types

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on lower-quality tissues, such as archived tumor tissues.ResultsWe propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses.AvailabilitySoftware available at https://github.com/greenelab/miQC. The code used to download datasets, perform the analyses, and reproduce the figures is available at https://github.com/greenelab/mito-filtering.ContactStephanie C. Hicks ([email protected]) and Anna Vähärautio ([email protected])

scholarly journals Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Haruka Ozaki ◽  
Tetsutaro Hayashi ◽  
Mana Umeda ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Read Coverage ◽  
Cell Heterogeneity ◽  
Single Cell Rna Sequencing

scholarly journals Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

2015 ◽  
Vol 33 (2) ◽  
pp. 155-160 ◽  
Author(s):  
Florian Buettner ◽  
Kedar N Natarajan ◽  
F Paolo Casale ◽  
Valentina Proserpio ◽  
Antonio Scialdone ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Computational Analysis ◽  
Cell Heterogeneity ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

2017 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Fernando J. Calero-Nieto ◽  
Liora Haim-Vilmovsky ◽  
Berthold Göttgens ◽  
John C. Marioni
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Capture Efficiency ◽  
Cellular Heterogeneity ◽  
Sequencing Data ◽  
Constant Amount ◽  
Technical Noise ◽  
Data Analyses ◽  
Single Cell Rna Sequencing ◽  
The Cost

AbstractBy profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell, and to scale the observed expression values so that the coverage of spike-in RNA is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample, and on similarities in behaviour (e.g., capture efficiency) between the spike-in and endogenous transcripts. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behaviour between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses such as detection of highly variable genes and clustering. Our results suggest that spike-in normalization is reliable enough for routine use in single-cell RNA sequencing data analyses.

scholarly journals Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fenglin Liu ◽  
Yuanyuan Zhang ◽  
Lei Zhang ◽  
Ziyi Li ◽  
Qiao Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Allele Frequencies ◽  
Cellular Heterogeneity ◽  
Variant Allele ◽  
Detection Methods ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Single Cell Rna Sequencing

Abstract Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

UNCURL-App: Interactive Database-Driven Analysis of Single Cell RNA Sequencing Data

2020 ◽  
Author(s):  
Yue Zhang ◽  
Shunfu Mao ◽  
Sumit Mukherjee ◽  
Sreeram Kannan ◽  
Georg Seelig
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Biological Knowledge ◽  
Analysis Tool ◽  
Sequencing Data ◽  
Link Type ◽  
Analysis Process ◽  
Single Cell Rna Sequencing ◽  
Key Innovations

AbstractAnalysis of single cell RNA sequencing (scRNA-Seq) datasets is a complex and time-consuming process, requiring both biological knowledge and technical skill. In order to simplify and systematize this process, we introduce UNCURL-App, an online GUI-based interactive scRNA-Seq analysis tool. UNCURL-App introduces two key innovations: First, prior knowledge in the form of cell type, anatomy, and Gene Ontology databases is integrated directly with the rest of the analysis process, allowing users to automatically map cell clusters to known cell types based on gene expression. Second, tools for interactive re-analysis allow the user to iteratively create, merge, or delete clusters in order to arrive at an optimal mapping between clusters and cell types.AvailabilityThe website is at https://uncurl.cs.washington.edu/. Source code is available at https://github.com/yjzhang/uncurl_app

scholarly journals McImpute: Matrix completion based imputation for single cell RNA-seq data

2018 ◽  
Author(s):  
Aanchal Mongia ◽  
Debarka Sengupta ◽  
Angshul Majumdar
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Matrix Completion ◽  
Differential Expression Analysis ◽  
Low Rank ◽  
Specific Cell ◽  
Sequencing Data ◽  
Reduction Techniques ◽  
Single Cell Rna Sequencing

AbstractMotivationSingle cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression analysis at single cell resolution, provides a window into dynamics of cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified.ResultsWe introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, performance of dimensionality reduction techniques for cell visualization and gene distribution.Availability and Implementationhttps://github.com/aanchalMongia/McImpute_scRNAseq

scholarly journals A comparison of automatic cell identification methods for single-cell RNA-sequencing data

2019 ◽  
Author(s):  
Tamim Abdelaal ◽  
Lieke Michielsen ◽  
Davy Cats ◽  
Dylan Hoogduin ◽  
Hailiang Mei ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Computation Time ◽  
General Purpose ◽  
Classification Methods ◽  
Sequencing Data ◽  
Identification Methods ◽  
Cell Identification ◽  
Link Type ◽  
Single Cell Rna Sequencing

AbstractBackgroundSingle cell transcriptomics are rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification.ResultsHere, we benchmarked 20 classification methods that automatically assign cell identities including single cell-specific and general-purpose classifiers. The methods were evaluated using eight publicly available single cell RNA-sequencing datasets of different sizes, technologies, species, and complexity. The performance of the methods was evaluated based on their accuracy, percentage of unclassified cells, and computation time. We further evaluated their sensitivity to the input features, their performance across different annotation levels and datasets. We found that most classifiers performed well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose SVM classifier has overall the best performance across the different experiments.ConclusionsWe present a comprehensive evaluation of automatic cell identification methods for single cell RNA-sequencing data. All the code used for the evaluation is available on GitHub (https://github.com/tabdelaal/scRNAseq_Benchmark). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support extension of new methods and new datasets (https://github.com/tabdelaal/scRNAseq_Benchmark/tree/snakemake_and_docker).

Sign in / Sign up

Export Citation Format

Share Document