scholarly journals SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics

2020 ◽  
Vol 48 (10) ◽  
pp. e55-e55 ◽  
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.

scholarly journals SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics

2019 ◽  
Author(s):  
Simon Cabello-Aguilar ◽  
Fabien Kon Sun Tack ◽  
Mélissa Alame ◽  
Caroline Fau ◽  
Matthieu Lacroix ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Networks ◽  
Detailed Comparison ◽  
R Package ◽  
Communication Structure ◽  
Entry Level ◽  
Receptor Interactions ◽  
Scoring Schemes ◽  
Intracellular Pathways ◽  
First Time

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.

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.

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

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.

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

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.

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

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.

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

Phytotaxa ◽  
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.

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 ESCO: single cell expression simulation incorporating gene co-expression

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]

scholarly journals ENTRY-LEVEL ENTREPRENEURS AND THE CHOICE-OF-ENTITY CHALLENGE

2013 ◽  
Vol 10 (2) ◽  
Author(s):  
Emily Ann Satterthwaite
Keyword(s):  
Real Option ◽  
Lower Income ◽  
Option Value ◽  
Entry Level ◽  
Deliberation Process ◽  
New Business ◽  
Tax Savings ◽  
First Time ◽  
Real Option Value ◽  
Future Growth

For first-time, lower-income and credit-constrained entrepreneurs (“entry-level entrepreneurs”), the employment tax savings proffered by a longstanding tax shelter known as the “Sub-S Shelter” can be particularly salient. Such hypersalience is problematic from a policy perspective. It not only increases the costs and complexity of the entry-level entrepreneur’s deliberation process concerning the appropriate entity for her business, but it distorts her incentives to choose the entity that best supports her business’s future growth. I argue that because the hypersalience of the Sub-Shelter is likely to be more pronounced for entry-level entrepreneurs than for entrepreneurs with more experience or better access to capital, the burdens of the shelter are distributionally regressive. As an alternative to full-scale reforms that would eliminate the demand for the Sub-S Shelter but may be politically infeasible, I suggest that the shelter’s regressive hypersalience can be addressed by government measures to provide choice-of-entity information tailored to the needs and concerns of entry-level entrepreneurs. Such targeted information can mitigate the hypersalience of the Sub-S Shelter by underscoring the risks of relying on it, while highlighting the real option value of choosing a more flexible business entity such as an LLC. By nudging entry-level entrepreneurs towards neutrality in regard to their choice-of-entity decisions, this approach has the potential to improve both the efficiency and the equity of a key step in formalizing a new business. 

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