Interactive analysis of single-cell data using flexible workflows with SCTK2

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Summary

Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.

Graphical abstract

Highlights

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Includes an intuitive graphical user interface for interactive analysis of scRNA-seq data
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Allows non-computational users to analyze scRNA-seq data with end-to-end workflows
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Provides interoperability between tools across different programming environments
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Produces HTML reports for reproducibility and easy sharing of results

The bigger picture

Single-cell data can be used to understand complex biological systems. However, many single-cell analysis tools can only be used by trained computational biologists and are scattered across different programming languages. The Single-Cell Toolkit (SCTK) is a software package that brings together many different tools in one place and allows non-computational users to analyze their own data using a graphical user interface. Overall, SCTK gives computational and non-computational researchers the ability to access a wide variety of single-cell tools to perform complex analysis workflows.

Abstract

The Single Cell Toolkit (SCTK) is a software package that gives computational and non-computational researchers the ability to utilize a wide variety of tools and complex workflows for single-cell analysis.

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Most cited references 63

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Author and article information

Contributors

Joshua D. Campbell

Journal

Journal ID (nlm-ta): Patterns (N Y)

Journal ID (iso-abbrev): Patterns (N Y)

Title: Patterns

Publisher: Elsevier

ISSN (Electronic): 2666-3899

Publication date PMC-release: 03 August 2023

Publication date Collection: 11 August 2023

Publication date (Electronic): 03 August 2023

Volume: 4

Issue: 8

Electronic Location Identifier: 100814

Affiliations

[1 ]Bioinformatics Program, Boston University, Boston, MA, USA

[2 ]Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA

[3 ]Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA

[4 ]Department of Mathematics and Statistics, Boston University, Boston, MA, USA

Author notes

[∗ ]Corresponding author camp@ 123456bu.edu

[5]

These authors contributed equally

[6]

Lead contact

Article

Publisher Item ID: S2666-3899(23)00182-4 Publisher ID: 100814

DOI: 10.1016/j.patter.2023.100814

PMC ID: 10436054

PubMed ID: 37602214

SO-VID: 9eab6491-70da-4982-b091-446f30ad2090

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 25 July 2022

Date revision received : 27 March 2023

Date accepted : 10 July 2023

Interactive analysis of single-cell data using flexible workflows with SCTK2

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Genome Engineering using CRISPR

Most cited references 63

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

STAR: ultrafast universal RNA-seq aligner.

limma powers differential expression analyses for RNA-sequencing and microarray studies

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