Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

AbstractSingle-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.

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Exploring dimension-reduced embeddings with Sleepwalk

10.1101/603589 ◽

2019 ◽

Author(s):

Svetlana Ovchinnikova ◽

Simon Anders

Keyword(s):

Big Data ◽

Dimension Reduction ◽

Single Cell ◽

Single Cells ◽

High Dimensional ◽

Rna Seq ◽

Mouse Cursor ◽

Sample Data ◽

Reduction Methods ◽

Full Power

AbstractDimension-reduction methods, such as t-SNE or UMAP, are widely used when exploring high-dimensional data describing many entities, e.g., RNA-seq data for many single cells. However, dimension reduction is commonly prone to introducing artefacts, and we hence need means to see where a dimension-reduced embedding is a faithful representation of the local neighbourhood and where it is not.We present Sleepwalk, a simple but powerful tool that allows the user to interactively explore an embedding, using colour to depict original or any other distances from all points to the cell under the mouse cursor. We show how this approach not only highlights distortions, but also reveals otherwise hidden characteristics of the data, and how Sleep-walk’s comparative modes help integrate multi-sample data and understand differences between embedding and preprocessing methods. Sleepwalk is a versatile and intuitive tool that unlocks the full power of dimension reduction and will be of value not only in single-cell RNA-seq but also in any other area with matrix-shaped big data.

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Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

10.1101/206573 ◽

2017 ◽

Cited By ~ 2

Author(s):

Luke Zappia ◽

Belinda Phipson ◽

Alicia Oshlack

Keyword(s):

Single Cell ◽

Open Source ◽

Rapid Development ◽

Analysis Tool ◽

Rna Seq ◽

Link Type ◽

Analysis Tools ◽

Rapid Pace ◽

Cell Data ◽

Selection Of

AbstractAs single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records of the growth of the field over time.Author summaryIn recent years single-cell RNA-sequeing technologies have emerged that allow scientists to measure the activity of genes in thousands of individual cells simultaneously. This means we can start to look at what each cell in a sample is doing instead of considering an average across all cells in a sample, as was the case with older technologies. However, while access to this kind of data presents a wealth of opportunities it comes with a new set of challenges. Researchers across the world have developed new methods and software tools to make the most of these datasets but the field is moving at such a rapid pace it is difficult to keep up with what is currently available. To make this easier we have developed the scRNA-tools database and website (www.scRNA-tools.org). Our database catalogues analysis tools, recording the tasks they can be used for, where they can be downloaded from and the publications that describe how they work. By looking at this database we can see that developers have focued on methods specific to single-cell data and that they embrace an open-source approach with permissive licensing, sharing of code and preprint publications.

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VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder

Genomics Proteomics & Bioinformatics ◽

10.1016/j.gpb.2018.08.003 ◽

2018 ◽

Vol 16 (5) ◽

pp. 320-331 ◽

Cited By ~ 46

Author(s):

Dongfang Wang ◽

Jin Gu

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Rna Seq ◽

Variational Autoencoder

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Comparative Research of Different Dimension Reduction Methods Combined with RWR Network Smoothing in Single Cell RNA-seq Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/495/1/012043 ◽

2020 ◽

Vol 495 ◽

pp. 012043

Author(s):

Xuesong Xiao ◽

Pengchao Ye ◽

Wenbin Ye ◽

Guoli Ji

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Comparative Research ◽

Rna Seq ◽

Reduction Methods

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SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection

10.1101/461640 ◽

2018 ◽

Cited By ~ 2

Author(s):

Shibiao Wan ◽

Junil Kim ◽

Kyoung Jae Won

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Random Projection ◽

Rna Seq ◽

Running Speed ◽

Large Size ◽

Single Cell Rna Sequencing ◽

Speed And Accuracy

ABSTRACTTo process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm which is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq datasets demonstrate that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size datasets (>40,000 cells), SHARP’s running speed far excels other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

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Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

10.1101/574574 ◽

2019 ◽

Cited By ~ 29

Author(s):

F. William Townes ◽

Stephanie C. Hicks ◽

Martin J. Aryee ◽

Rafael A. Irizarry

Keyword(s):

Feature Selection ◽

Dimension Reduction ◽

Single Cell ◽

Current Practice ◽

Principal Component ◽

Ground Truth ◽

Rna Seq ◽

Normal Distributions ◽

Multinomial Sampling ◽

Negative Controls

AbstractSingle cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero-inflation. Current normalization pro-cedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We pro-pose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform current practice in a downstream clustering assessment using ground-truth datasets.

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UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy

10.1101/051755 ◽

2016 ◽

Cited By ~ 11

Author(s):

Tom Smith ◽

Andreas Heger ◽

Ian Sudbery

Keyword(s):

Single Cell ◽

Open Source ◽

Software Package ◽

Ad Hoc ◽

Pcr Amplification ◽

Rna Seq ◽

Sequencing Errors ◽

Pcr Duplicates ◽

Quantification Accuracy ◽

High Throughout Sequencing

AbstractUnique Molecular Identifiers (UMIs) are random oligonucleotide barcodes that are increasingly used in high-throughout sequencing experiments. Through a UMI, identical copies arising from distinct molecules can be distinguished from those arising through PCR amplification of the same molecule. However, bioinformatic methods to leverage the information from UMIs have yet to be formalised. In particular, sequencing errors in the UMI sequence are often ignored, or else resolved in an ad-hoc manner. We show that errors in the UMI sequence are common and introduce network-based methods to account for these errors when identifying PCR duplicates. Using these methods, we demonstrate improved quantification accuracy both under simulated conditions and real iCLIP and single cell RNA-Seq datasets. Reproducibility between iCLIP replicates and single cell RNA-Seq clustering are both improved using our proposed network-based method, demonstrating the value of properly accounting for errors in UMIs. These methods are implemented in the open source UMI-tools software package (https://github.com/CGATOxford/UMI-tools).

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snakePipes enable flexible, scalable and integrative epigenomic analysis

10.1101/407312 ◽

2018 ◽

Cited By ~ 2

Author(s):

Vivek Bhardwaj ◽

Steffen Heyne ◽

Katarzyna Sikora ◽

Leily Rabbani ◽

Michael Rauer ◽

...

Keyword(s):

Single Cell ◽

Open Source ◽

Large Scale ◽

Integrative Analysis ◽

Exploratory Research ◽

Rna Seq ◽

Link Type ◽

Open Source License ◽

Fast Processing ◽

Downstream Analysis

AbstractThe scale and diversity of epigenomics data has been rapidly increasing and ever more studies now present analyses of data from multiple epigenomic techniques. Performing such integrative analysis is time-consuming, especially for exploratory research, since there are currently no pipelines available that allow fast processing of datasets from multiple epigenomic assays while also allow for flexibility in running or upgrading the workflows. Here we present a solution to this problem: snakePipes, which can process and perform downstream analysis of data from all common epigenomic techniques (ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq) in a single package. We demonstrate how snakePipes can simplify integrative analysis by reproducing and extending the results from a recently published large-scale epigenomics study with a few simple commands. snakePipes are available under an open-source license at https://github.com/maxplanck-ie/snakepipes.

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