scholarly journals The barcode, UMI, set format and BUStools

2019 ◽  
Vol 35 (21) ◽  
pp. 4472-4473 ◽  
Author(s):  
Páll Melsted ◽  
Vasilis Ntranos ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Modular Technology ◽  
Rapid Quantification

Abstract Summary We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific and robust workflows for single-cell RNA-seq analysis. Availability and implementation http://BUStools.github.io/ and http://pachterlab.github.io/kallisto/singlecell.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles

2021 ◽  
Author(s):  
Davide Risso ◽  
Stefano Maria Pagnotta
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Unsupervised Clustering ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Data Transformations ◽  
The Impact

Abstract Motivation Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear. Results Here, we present an Asymmetric Winsorization per Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications. Availability The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst\_analysis. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GPress: a framework for querying general feature format (GFF) files and expression files in a compressed form

2020 ◽  
Vol 36 (18) ◽  
pp. 4810-4812
Author(s):  
Qingxi Meng ◽  
Idoia Ochoa ◽  
Mikel Hernaez
Keyword(s):  
Single Cell ◽  
Data Streams ◽  
General Feature ◽  
Supplementary Information ◽  
Storage Space ◽  
Supplementary Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
General Feature Format ◽  
Original File

Abstract Motivation Sequencing data are often summarized at different annotation levels for further analysis, generally using the general feature format (GFF) or its descendants, gene transfer format (GTF) and GFF3. Existing utilities for accessing these files, like gffutils and gffread, do not focus on reducing the storage space, significantly increasing it in some cases. We propose GPress, a framework for querying GFF files in a compressed form. GPress can also incorporate and compress expression files from both bulk and single-cell RNA-Seq experiments, supporting simultaneous queries on both the GFF and expression files. In brief, GPress applies transformations to the data which are then compressed with the general lossless compressor BSC. To support queries, GPress compresses the data in blocks and creates several index tables for fast retrieval. Results We tested GPress on several GFF files of different organisms, and showed that it achieves on average a 61% reduction in size with respect to gzip (the current de facto compressor for GFF files) while being able to retrieve all annotations for a given identifier or a range of coordinates in a few seconds (when run in a common laptop). In contrast, gffutils provides faster retrieval but doubles the size of the GFF files. When additionally linking an expression file, we show that GPress can reduce its size by more than 68% when compared to gzip (for both bulk and single-cell RNA-Seq experiments), while still retrieving the information within seconds. Finally, applying BSC to the data streams generated by GPress instead of to the original file shows a size reduction of more than 44% on average. Availability and implementation GPress is freely available at https://github.com/qm2/gpress. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The Barcode, UMI, Set format and BUStools

2018 ◽  
Author(s):  
Páll Melsted ◽  
Vasilis Ntranos ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Modular Technology ◽  
Rapid Quantification

AbstractWe introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific, and robust workflows for single-cell RNA-seq analysis.

scholarly journals snakePipes: facilitating flexible, scalable and integrative epigenomic analysis

2019 ◽  
Vol 35 (22) ◽  
pp. 4757-4759 ◽  
Author(s):  
Vivek Bhardwaj ◽  
Steffen Heyne ◽  
Katarzyna Sikora ◽  
Leily Rabbani ◽  
Michael Rauer ◽  
...  
Keyword(s):  
Single Cell ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Rna Seq ◽  
Downstream Analysis ◽  
Scalable Analysis

Abstract Summary Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files. Availability and implementation snakePipes can be installed via conda: `conda install -c mpi-ie -c bioconda -c conda-forge snakePipes’. Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions

2020 ◽  
Vol 36 (11) ◽  
pp. 3588-3589 ◽  
Author(s):  
Kaiyi Zhu ◽  
Dimitris Anastassiou
Keyword(s):  
Single Cell ◽  
R Package ◽  
Two Dimensions ◽  
Imputation Method ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Imputation Methods ◽  
Single Cell Rna Sequencing ◽  
Expression Matrix

Abstract Summary We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols. Availability and implementation The R package of 2DImpute is freely available at GitHub (https://github.com/zky0708/2DImpute). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data

2020 ◽  
Author(s):  
Andrew E Teschendorff ◽  
Alok K Maity ◽  
Xue Hu ◽  
Chen Weiyan ◽  
Matthias Lechner
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Computational Cost ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Current State ◽  
Multipotent Cells ◽  
Comparable Accuracy

Abstract Motivation An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. Availability and implementation CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BBKNN: fast batch alignment of single cell transcriptomes

2019 ◽  
Author(s):  
Krzysztof Polański ◽  
Matthew D Young ◽  
Zhichao Miao ◽  
Kerstin B Meyer ◽  
Sarah A Teichmann ◽  
...  
Keyword(s):  
Data Integration ◽  
Single Cell ◽  
Large Scale ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Batch Effects ◽  
Integration Algorithm ◽  
Data Explosion ◽  
Computationally Intensive

Abstract Motivation Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods. Availability and implementation BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip. Supplementary information Supplementary data are available at Bioinformatics online.

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Single Cell ◽  
Learning Algorithm ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

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