scholarly journals Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics

2021 ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Mikhail Alperovich ◽  
Bo Li ◽  
Yiming Yang
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Biological Diversity ◽  
Scientific Discovery ◽  
State Level ◽  
Cell Types ◽  
Data Driven ◽  
Sequencing Data ◽  
Control Framework ◽  
Downstream Analysis

Quality control (QC) of cells, a critical step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds on QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation in the commonly used QC criteria. We demonstrate that the QC metrics vary both at the tissue and cell state level across technologies, study conditions, and species. We propose data-driven QC (ddqc), an unsupervised adaptive quality control framework that performs flexible and data-driven quality control at the level of cell states while retaining critical biological insights and improved power for downstream analysis. On applying ddqc to 6,228,212 cells and 835 mouse and human samples, we retain a median of 39.7% more cells when compared to conventional data-agnostic QC filters. With ddqc, we recover biologically meaningful trends in gene complexity and ribosomal expression among cell-types enabling exploration of cell states with minimal transcriptional diversity or maximum ribosomal protein expression. Moreover, ddqc allows us to retain cell-types often lost by conventional QC such as metabolically active parenchymal cells, and specialized cells such as neutrophils or gastric chief cells. Taken together, our work proposes a revised paradigm to quality filtering best practices - iterative QC, providing a data-driven quality control framework compatible with observed biological diversity.

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

2021 ◽  
Vol 17 (8) ◽  
pp. e1009290
Author(s):  
Ariel A. Hippen ◽  
Matias M. Falco ◽  
Lukas M. Weber ◽  
Erdogan Pekcan Erkan ◽  
Kaiyang Zhang ◽  
...  
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Sequencing ◽  
Kidney Tissue ◽  
Data Driven ◽  
Probabilistic Framework ◽  
Sequencing Data ◽  
Tumor Tissues ◽  
Single Cell Rna Sequencing ◽  
Different Types

Single-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 certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We 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. Our software package is available at https://bioconductor.org/packages/miQC.

scholarly journals Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Rui Hong ◽  
Yusuke Koga ◽  
Shruthi Bandyadka ◽  
Anastasia Leshchyk ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Programming Environments ◽  
Marker Selection ◽  
Quality Control Metrics ◽  
Single Cell Rna Sequencing ◽  
Standard Quality ◽  
Downstream Analysis

AbstractPerforming comprehensive quality control is necessary to remove technical or biological artifacts in single-cell RNA sequencing (scRNA-seq) data. Artifacts in the scRNA-seq data, such as doublets or ambient RNA, can also hinder downstream clustering and marker selection and need to be assessed. While several algorithms have been developed to perform various quality control tasks, they are only available in different packages across various programming environments. No standardized workflow has been developed to streamline the generation and reporting of all quality control metrics from these tools. We have built an easy-to-use pipeline, named SCTK-QC, in the singleCellTK package that generates a comprehensive set of quality control metrics from a plethora of packages for quality control. We are able to import data from several preprocessing tools including CellRanger, STARSolo, BUSTools, dropEST, Optimus, and SEQC. Standard quality control metrics for each cell are calculated including the total number of UMIs, total number of genes detected, and the percentage of counts mapping to predefined gene sets such as mitochondrial genes. Doublet detection algorithms employed include scrublet, scds, doubletCells, and doubletFinder. DecontX is used to identify contamination in each individual cell. To make the data accessible in downstream analysis workflows, the results can be exported to common data structures in R and Python or to text files for use in any generic workflow. Overall, this pipeline will streamline and standardize quality control analyses for single cell RNA-seq data across different platforms.

scholarly journals Automated quality control and cell identification of droplet-based single-cell data using dropkick

2020 ◽  
Author(s):  
Cody N. Heiser ◽  
Victoria M. Wang ◽  
Bob Chen ◽  
Jacob J. Hughey ◽  
Ken S. Lau
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Software Tool ◽  
Cell Types ◽  
Computational Method ◽  
High Background ◽  
Cell Identification ◽  
Sequencing Technologies ◽  
Downstream Analysis

AbstractA major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in datasets with disparate library sizes confounded by high technical noise (i.e. batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining dataset-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against a conventional thresholding approach and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkick’s weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell analysis Python packages.

scholarly journals Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control

2020 ◽  
Author(s):  
Daniel Osorio ◽  
James J Cai
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Single Cell ◽  
Cell Types ◽  
Supplementary Information ◽  
Human Tissues ◽  
Data Quality Control ◽  
Sequencing Data ◽  
Mouse Tissues ◽  
Systematic Determination

Abstract Motivation Quality control (QC) is a critical step in single-cell RNA-seq (scRNA-seq) data analysis. Low-quality cells are removed from the analysis during the QC process to avoid misinterpretation of the data. An important QC metric is the mitochondrial proportion (mtDNA%), which is used as a threshold to filter out low-quality cells. Early publications in the field established a threshold of 5% and since then, it has been used as a default in several software packages for scRNA-seq data analysis, and adopted as a standard in many scRNA-seq studies. However, the validity of using a uniform threshold across different species, single-cell technologies, tissues and cell types has not been adequately assessed. Results We systematically analyzed 5 530 106 cells reported in 1349 annotated datasets available in the PanglaoDB database and found that the average mtDNA% in scRNA-seq data across human tissues is significantly higher than in mouse tissues. This difference is not confounded by the platform used to generate the data. Based on this finding, we propose new reference values of the mtDNA% for 121 tissues of mouse and 44 tissues of humans. In general, for mouse tissues, the 5% threshold performs well to distinguish between healthy and low-quality cells. However, for human tissues, the 5% threshold should be reconsidered as it fails to accurately discriminate between healthy and low-quality cells in 29.5% (13 of 44) tissues analyzed. We conclude that omitting the mtDNA% QC filter or adopting a suboptimal mtDNA% threshold may lead to erroneous biological interpretations of scRNA-seq data. Availabilityand implementation The code used to download datasets, perform the analyzes and produce the figures is available at https://github.com/dosorio/mtProportion. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SSBER: removing batch effect for single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yin Zhang ◽  
Fei Wang
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Type Composition ◽  
Type Composition ◽  
Downstream Analysis ◽  
Sequencing Platforms

Abstract Background With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream analysis often faces challenges. Although a number of algorithms and tools have been proposed for removing batch effects, the current mainstream algorithms have faced the problem of data overcorrection when the cell type composition varies greatly between batches. Results In this paper, we propose a novel method named SSBER by utilizing biological prior knowledge to guide the correction, aiming to solve the problem of poor batch-effect correction when the cell type composition differs greatly between batches. Conclusions SSBER effectively solves the above problems and outperforms other algorithms when the cell type structure among batches or distribution of cell population varies considerably, or some similar cell types exist across batches.

scholarly journals Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA sequencing data quality control

2020 ◽  
Author(s):  
Daniel Osorio ◽  
James J. Cai
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Single Cell ◽  
Cell Types ◽  
Supplementary Information ◽  
Human Tissues ◽  
Data Quality Control ◽  
Sequencing Data ◽  
Mouse Tissues ◽  
Systematic Determination

AbstractMotivationQuality control (QC) is a critical step in single-cell RNA-seq (scRNA-seq) data analysis. Low-quality cells are removed from the analysis during the QC process to avoid misinterpretation of the data. One of the important QC metrics is the mitochondrial proportion (mtDNA%), which is used as a threshold to filter out low-quality cells. Early publications in the field established a threshold of 5% and since then, it has been used as a default in several software packages for scRNA-seq data analysis and adopted as a standard in many scRNA-seq studies. However, the validity of using a uniform threshold across different species, single-cell technologies, tissues, and cell types has not been adequately assessed.ResultsWe systematically analyzed 5,530,106 cells reported in 1,349 annotated datasets available in the PanglaoDB database and found that the average mtDNA% in scRNA-seq data across human tissues is significantly higher than in mouse tissues. This difference is not confounded by the platform used to generate the data. Based on this finding, we propose new reference values of the mtDNA% for 121 tissues of mice and 44 tissues of humans. In general, for mouse tissues, the 5% threshold performs well to distinguish between healthy and low-quality cells. However, for human tissues, the 5% threshold should be reconsidered as it fails to accurately discriminate between healthy and low-quality cells in 29.5% (13 of 44) tissues analyzed. We conclude that omitting the mtDNA% QC filter or adopting a suboptimal mtDNA% threshold may lead to erroneous biological interpretations of scRNA-seq data.AvailabilityThe code used to download datasets, perform the analyzes, and produce the figures is available at https://github.com/dosorio/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Deep Learning model to Automate the process of mapping Cancer Cells to Cell Lines & Cancer Types from Single Cell RNA-Seq Data

2021 ◽  
pp. 17-26
Author(s):  
Vatsal Patel
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
Cell Lines ◽  
Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Study Heterogeneity ◽  
Broad Domain ◽  
Cancer Types ◽  
Downstream Analysis

Single Cell RNA Sequencing has given us a broad domain to study heterogeneity & expression profiles of cells. Downstream analysis of such data has led us to important observation and classification of cell types. However, these approaches demand great exertion and effort added that it seems the only way to proceed ahead for the first time. Results of such verified analysis have led us to create labels from our dataset. We can use the same labeled data as an input to a neural network and this way we would be able to automate the tedious & time-consuming process of downstream analysis. In this paper, we have automated the process of mapping cancer cells to cancer cell lines & cancer types. For the same, we have used pan-cancer single cell sequencing data of 53513 cells from 198 cell lines reflecting 22 cancer types.

scholarly journals Accurate and fast cell marker gene identification with COSG

2021 ◽  
Author(s):  
Min Dai ◽  
Xiaobing Pei ◽  
Xiu-Jie Wang
Keyword(s):  
Single Cell ◽  
Marker Gene ◽  
Cell Types ◽  
Superior Performance ◽  
Gene Identification ◽  
Marker Genes ◽  
Sequencing Data ◽  
Cell Type Specificity ◽  
Spatially Resolved ◽  
Downstream Analysis

Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify marker genes to distinguish different cell types still remains as a big challenge. We developed COSG as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets demonstrates the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods. Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.

scholarly journals Analysis of single-cell RNA sequencing data based on autoencoders

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Andrea Tangherloni ◽  
Federico Ricciuti ◽  
Daniela Besozzi ◽  
Pietro Liò ◽  
Ana Cvejic
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Batch Effect ◽  
Learning Approaches ◽  
Sequencing Data ◽  
Starting Point ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis ◽  
Low Dimensional

Abstract Background Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches. Results Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of scRNA-Seq data, so that the deployment of AE-based tools might represent the way forward in this context. We introduce here scAEspy, a unifying tool that embodies: (1) four of the most advanced AEs, (2) two novel AEs that we developed on purpose, (3) different loss functions. We show that scAEspy can be coupled with various batch-effect removal tools to integrate data by different scRNA-Seq platforms, in order to better identify the cell-types. We benchmarked scAEspy against the most used batch-effect removal tools, showing that our AE-based strategies outperform the existing solutions. Conclusions scAEspy is a user-friendly tool that enables using the most recent and promising AEs to analyse scRNA-Seq data by only setting up two user-defined parameters. Thanks to its modularity, scAEspy can be easily extended to accommodate new AEs to further improve the downstream analysis of scRNA-Seq data. Considering the relevant results we achieved, scAEspy can be considered as a starting point to build a more comprehensive toolkit designed to integrate multi single-cell omics.

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