scholarly journals iTALK: an R Package to Characterize and Illustrate Intercellular Communication

2019 ◽  
Author(s):  
Yuanxin Wang ◽  
Ruiping Wang ◽  
Shaojun Zhang ◽  
Shumei Song ◽  
Changying Jiang ◽  
...  
Keyword(s):  
Intercellular Communication ◽  
Cell Communication ◽  
R Package ◽  
Therapy Resistance ◽  
Computational Approach ◽  
Sequencing Data ◽  
Communication Signals ◽  
Cellular Processes ◽  
Wide Range ◽  
Single Cell Rna Sequencing

ABSTRACTCrosstalk between tumor cells and other cells within the tumor microenvironment (TME) plays a crucial role in tumor progression, metastases, and therapy resistance. We present iTALK, a computational approach to characterize and illustrate intercellular communication signals in the multicellular tumor ecosystem using single-cell RNA sequencing data. iTALK can in principle be used to dissect the complexity, diversity, and dynamics of cell-cell communication from a wide range of cellular processes.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data

2020 ◽  
Vol 36 (10) ◽  
pp. 3276-3278 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Real Data ◽  
Simulation Method ◽  
R Package ◽  
Supplementary Information ◽  
Expression Data ◽  
Sequencing Data ◽  
Wide Range ◽  
Single Cell Rna Sequencing

Abstract Summary SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. Availability and implementation The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. Supplementary information Supplementary data are available at Bioinformatics online.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

2019 ◽  
Vol 35 (22) ◽  
pp. 4827-4829 ◽  
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xing-Ming Zhao ◽  
Xiaohua Hu ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Ensemble Learning ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
The Individual ◽  
Downstream Analysis ◽  
Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

Sources, isolation strategies and therapeutic outcome of exosomes at a glance

2020 ◽  
Vol 15 (12) ◽  
pp. 2361-2378
Author(s):  
Antara Banerjee ◽  
Samatha M Jain ◽  
Syed S Abrar ◽  
Makalakshmi M Kumar ◽  
Christina Mathew ◽  
...  
Keyword(s):  
Waste Disposal ◽  
Intercellular Communication ◽  
Cell Communication ◽  
Current Application ◽  
Diverse Range ◽  
Cellular Processes ◽  
Therapeutic Outcomes ◽  
Isolation Methods ◽  
Health And Disease ◽  
Therapy And Diagnosis

Extracellular vesicles (EVs) have attracted great attention due to their known role in facilitating intercellular communication in a diverse range of cellular processes. In the 30 years since the discovery of exosomes, a class of EV, they have gone from being considered a cellular waste disposal mechanism to an important aspect of cell-to-cell communication. The exponential interest in exosomes in recent years is due to their key role in health and disease and their potential clinical application in therapy and diagnosis. This review aims to provide an updated picture of the sources, isolation methods, therapeutic outcomes and current application of EVs, in particular exosomes.

scholarly journals scds: computational annotation of doublets in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Abha S Bais ◽  
Dennis Kostka
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Binary Classification ◽  
Single Cells ◽  
Computational Cost ◽  
Original Data ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study’s conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals tailfindr: Alignment-free poly(A) length measurement for Oxford Nanopore RNA and DNA sequencing

2019 ◽  
Author(s):  
Maximilian Krause ◽  
Adnan M. Niazi ◽  
Kornel Labun ◽  
Yamila N. Torres Cleuren ◽  
Florian S. Müller ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Nuclear Export ◽  
Messenger Rna ◽  
Tail Length ◽  
R Package ◽  
Full Length ◽  
Sequencing Data ◽  
Oxford Nanopore ◽  
Wide Range ◽  
Rna And Dna

Polyadenylation at the 3’-end is a major regulator of messenger RNA and its length is known to affect nuclear export, stability and translation, among others. Only recently, strategies have emerged that allow for genome-wide poly(A) length assessment. These methods identify genes connected to poly(A) tail measurements indirectly by short-read alignment to genetic 3’-ends. Concurrently Oxford Nanopore Technologies (ONT) established full-length isoform RNA sequencing containing the entire poly(A) tail. However, assessing poly(A) length through basecalling has so far not been possible due the inability to resolve long homopolymeric stretches in ONT sequencing.Here we presenttailfindr, an R package to estimate poly(A) tail length on ONT long-read sequencing data.tailfindroperates on unaligned, basecalled data. It measures poly(A) tail length from both native RNA and DNA sequencing, which makes poly(A) tail studies by full-length cDNA approaches possible for the first time. We assesstailfindr’sperformance across different poly(A) lengths, demonstrating thattailfindris a versatile tool providing poly(A) tail estimates across a wide range of sequencing conditions.

scholarly journals NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data

2021 ◽  
Author(s):  
Federico Agostinis ◽  
Chiara Romualdi ◽  
Gabriele Sales ◽  
Davide Risso
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Sequencing Data ◽  
Bioconductor Project ◽  
Single Cell Rna Sequencing

Summary: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementation: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals COMUNET: a tool to explore and visualize intercellular communication

2020 ◽  
Vol 36 (15) ◽  
pp. 4296-4300
Author(s):  
Maria Solovey ◽  
Antonio Scialdone
Keyword(s):  
Single Cell ◽  
Intercellular Communication ◽  
Cell Communication ◽  
Cell Types ◽  
R Package ◽  
Communication Patterns ◽  
Supplementary Information ◽  
Multiplex Networks ◽  
Patterns Of Communication ◽  
Multicellular Organisms

Abstract Motivation Intercellular communication plays an essential role in multicellular organisms and several algorithms to analyze it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. Results We developed Cell cOmmunication exploration with MUltiplex NETworks (COMUNET), a tool that streamlines the interpretation of the results from cell–cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication patterns between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. Availability and implementation Our algorithm is implemented in an R package available from https://github.com/ScialdoneLab/COMUNET, along with all the code to perform the analyses reported here. Supplementary information Supplementary data are available at Bioinformatics online.

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