SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics

ABSTRACTSingle-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor interactions underlying cellular networks. We introduce a new, curated ligand-receptor database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted ligand-receptor interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison with related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

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SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics

Nucleic Acids Research ◽

10.1093/nar/gkaa183 ◽

2020 ◽

Vol 48 (10) ◽

pp. e55-e55 ◽

Cited By ~ 21

Author(s):

Simon Cabello-Aguilar ◽

Mélissa Alame ◽

Fabien Kon-Sun-Tack ◽

Caroline Fau ◽

Matthieu Lacroix ◽

...

Keyword(s):

Single Cell ◽

Cellular Networks ◽

Detailed Comparison ◽

R Package ◽

Mouse Epidermis ◽

Communication Structure ◽

Entry Level ◽

Scoring Schemes ◽

Intracellular Pathways ◽

First Time

Abstract Single-cell transcriptomics offers unprecedented opportunities to infer the ligand–receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

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talklr uncovers ligand-receptor mediated intercellular crosstalk

10.1101/2020.02.01.930602 ◽

2020 ◽

Author(s):

Yuliang Wang

Keyword(s):

Single Cell ◽

Holistic Approach ◽

Cell Types ◽

R Package ◽

Tissue Homeostasis ◽

Rna Seq ◽

Receptor Interactions ◽

Multiple Cell ◽

Potential Ligand ◽

Different Cell Types

AbstractSingle cell RNA-seq measures the transcriptomes of many cell types across diverse conditions. However, an emerging challenge is to uncover how different cell types communicate with each other to maintain tissue homeostasis, and how inter-cellular communications are perturbed in diseases. To address this problem, we developed talklr, an information theory-based approach to uncover potential ligand-receptor interactions involved in tissue homeostasis and diseases. Compared to existing approaches that analyze changes in each gene in each cell type separately, talklr uses a holistic approach to simultaneously consider expression changes in both ligands and receptors across multiple cell types and conditions. talklr outperformed existing approaches in identifying ligand-receptor interactions, including those known to be important for tissue-specific functions and diseases across diverse datasets. talklr can reveal important signaling events in many biological problems in an unbiased way, and will be a valuable tool in single cell RNA-seq analysis. talklr is available as both an interactive website and an R package.

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

Bioinformatics ◽

10.1093/bioinformatics/btab179 ◽

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.

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DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

International Journal of Molecular Sciences ◽

10.3390/ijms22031399 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1399

Author(s):

Salim Ghannoum ◽

Waldir Leoncio Netto ◽

Damiano Fantini ◽

Benjamin Ragan-Kelley ◽

Amirabbas Parizadeh ◽

...

Keyword(s):

Single Cell ◽

Biomarker Discovery ◽

Enrichment Analysis ◽

Myxoid Liposarcoma ◽

R Package ◽

Differential Analysis ◽

A Cell ◽

Reproducible Analysis ◽

Transcriptomic Level ◽

User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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Directional changes in Levallois core technologies between Eastern Africa, Arabia, and the Levant during MIS 5

Scientific Reports ◽

10.1038/s41598-021-90744-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

James Blinkhorn ◽

Huw S. Groucutt ◽

Eleanor M. L. Scerri ◽

Michael D. Petraglia ◽

Simon Blockley

Keyword(s):

Homo Sapiens ◽

Environmental Parameters ◽

Detailed Comparison ◽

Eastern Africa ◽

Middle Pleistocene ◽

Reduction Activity ◽

Core Technology ◽

Geographic Expansion ◽

First Time ◽

Mis 5

AbstractMarine Isotope Stage (MIS) 5, ~ 130 to 71 thousand years ago, was a key period for the geographic expansion of Homo sapiens, including engagement with new landscapes within Africa and dispersal into Asia. Occupation of the Levant by Homo sapiens in MIS 5 is well established, while recent research has documented complementary evidence in Arabia. Here, we undertake the first detailed comparison of Levallois core technology from eastern Africa, Arabia, and the Levant during MIS 5, including multiple sites associated with Homo sapiens fossils. We employ quantitative comparisons of individual artefacts that provides a detailed appraisal of Levallois reduction activity in MIS 5, thereby enabling assessment of intra- and inter-assemblage variability for the first time. Our results demonstrate a pattern of geographically structured variability embedded within a shared focus on centripetal Levallois reduction schemes and overlapping core morphologies. We reveal directional changes in core shaping and flake production from eastern Africa to Arabia and the Levant that are independent of differences in geographic or environmental parameters. These results are consistent with a common cultural inheritance between these regions, potentially stemming from a shared late Middle Pleistocene source in eastern Africa.

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Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

Cell Communication and Signaling ◽

10.1186/s12964-021-00754-7 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Lei He ◽

Quan Zhang ◽

Yue Zhang ◽

Yixian Fan ◽

Fahu Yuan ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Single Cell ◽

Cell Communication ◽

Cell Types ◽

Trial Registration ◽

T Cell Subsets ◽

Disease Group ◽

Communication Analysis ◽

Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Bioinformatics ◽

10.1093/bioinformatics/btab257 ◽

2021 ◽

Author(s):

Yixuan Qiu ◽

Jiebiao Wang ◽

Jing Lei ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Supplementary Information ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Correlation Pattern ◽

Tissue Samples ◽

Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

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Synopsis of the genus Bothriochloa (Poaceae: Andropogoneae) in India

Phytotaxa ◽

10.11646/phytotaxa.516.1.3 ◽

2021 ◽

Vol 516 (1) ◽

pp. 43-58

Author(s):

SHAHID NAWAZ LANDGE ◽

RAJENDRA D. SHINDE

Keyword(s):

Papua New Guinea ◽

Morphological Study ◽

Far East ◽

Detailed Comparison ◽

Disjunct Distribution ◽

Indian Species ◽

Taxonomic Study ◽

Morphological Attributes ◽

Morphological Variations ◽

First Time

During the taxonomic study of the genus Bothriochloa from India, B. ewartiana was reported for the first time in Asia from India. Earlier, it was known only from Australia, Lesser Sunda Island (Sumbawa, Timor), Philippines (Luzon), and Papua New Guinea (Madang). We have discussed about its amphitropical disjunct distribution over a vast continental gap with respect to some variability reported in the morphological attributes. A hypothesis behind its seclusion from Far East is also discussed. The images of the habitat and habit of B. ewartiana along with its detailed comparison with a close species B. woodrovii are provided. The taxonomic limits of each Indian species of Bothriochloa along with their ranges of morphological variations and distribution have been discussed in a detail. The Indian endemic B. parameswaranii (synonym nova) has been relegated, based on the morphological study, as a new taxonomic synonym of B. insculpta. Moreover, keys to identify closely allied genera and the species of Bothriochloa in India are provided. At the end, identification, taxonomic notes and the range of variations of Dichanthium foulkesii, D. jainii & D. concanense have been discussed in a detail.

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

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

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.

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ESCO: single cell expression simulation incorporating gene co-expression

10.1101/2020.10.20.347211 ◽

2020 ◽

Author(s):

Jinjin Tian ◽

Jiebiao Wang ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

R Package ◽

Brain Cell ◽

Gene Interactions ◽

Cell Type ◽

Imputation Methods ◽

Biological Interest ◽

A Cell ◽

Cell Expression ◽

Cell Data

AbstractMotivationGene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.ResultsTherefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.AvailabilityThe ESCO implementation is available as R package SplatterESCO (https://github.com/JINJINT/SplatterESCO)[email protected]

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