scholarly journals CytoTalk: De novo construction of signal transduction networks using single-cell RNA-Seq data

2020 ◽  
Author(s):  
Yuxuan Hu ◽  
Tao Peng ◽  
Lin Gao ◽  
Kai Tan
Keyword(s):  
Signal Transduction ◽  
Single Cell ◽  
De Novo ◽  
Receptor Gene ◽  
Computational Method ◽  
Signal Transduction Pathways ◽  
Rna Seq ◽  
Data Set ◽  
Signal Transduction Networks ◽  
Steiner Forest

AbstractSingle-cell technology has opened the door for studying signal transduction in a complex tissue at unprecedented resolution. However, there is a lack of analytical methods for de novo construction of signal transduction pathways using single-cell omics data. Here we present CytoTalk, a computational method for de novo constructing cell type-specific signal transduction networks using single-cell RNA-Seq data. CytoTalk first constructs intracellular and intercellular gene-gene interaction networks using an information-theoretic measure between two cell types. Candidate signal transduction pathways in the integrated network are identified using the prize-collecting Steiner forest algorithm. We applied CytoTalk to a single-cell RNA-Seq data set on mouse visual cortex and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissue. Compared to published methods, genes in our inferred signaling pathways have significantly higher spatial expression correlation only in cells that are spatially closer to each other, suggesting improved accuracy of CytoTalk. Furthermore, using single-cell RNA-Seq data with receptor gene perturbation, we found that predicted pathways are enriched for differentially expressed genes between the receptor knockout and wild type cells, further validating the accuracy of CytoTalk. In summary, CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

scholarly journals CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

2021 ◽  
Vol 7 (16) ◽  
pp. eabf1356
Author(s):  
Yuxuan Hu ◽  
Tao Peng ◽  
Lin Gao ◽  
Kai Tan
Keyword(s):  
Signal Transduction ◽  
Comparative Analysis ◽  
Single Cell ◽  
Developmental Stages ◽  
De Novo ◽  
Receptor Gene ◽  
Signaling Networks ◽  
Sequencing Data ◽  
Transcriptomic Data ◽  
Steiner Forest

Single-cell technology enables study of signal transduction in a complex tissue at unprecedented resolution. We describe CytoTalk for de novo construction of cell type–specific signaling networks using single-cell transcriptomic data. Using an integrated intracellular and intercellular gene network as the input, CytoTalk identifies candidate pathways using the prize-collecting Steiner forest algorithm. Using high-throughput spatial transcriptomic data and single-cell RNA sequencing data with receptor gene perturbation, we demonstrate that CytoTalk has substantial improvement over existing algorithms. To better understand plasticity of signaling networks across tissues and developmental stages, we perform a comparative analysis of signaling networks between macrophages and endothelial cells across human adult and fetal tissues. Our analysis reveals an overall increased plasticity of signaling networks across adult tissues and specific network nodes that contribute to increased plasticity. CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

scholarly journals SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guo-Wei Li ◽  
Fang Nan ◽  
Guo-Hua Yuan ◽  
Chu-Xiao Liu ◽  
Xindong Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
De Novo ◽  
Single Cells ◽  
High Sensitivity ◽  
Computational Method ◽  
Rna Seq ◽  
Healthy Individuals ◽  
Cleavage And Polyadenylation ◽  
Polyadenylation Sites

AbstractSingle-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3′ tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.

scholarly journals STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

2020 ◽  
Author(s):  
Massimo Andreatta ◽  
Santiago J Carmona
Keyword(s):  
Single Cell ◽  
Distance Measure ◽  
Source Code ◽  
Cell Types ◽  
R Package ◽  
Computational Method ◽  
Biological Variability ◽  
Rna Seq ◽  
Batch Effects ◽  
Guide Trees

Abstract Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

scholarly journals Transcriptome analysis identifies genes related to the waxy coating on blueberry fruit in two northern-adapted rabbiteye breeding populations

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xinpeng Qi ◽  
Elizabeth L. Ogden ◽  
Jose V. Die ◽  
Mark K. Ehlenfeldt ◽  
James J. Polashock ◽  
...  
Keyword(s):  
De Novo ◽  
Acyl Carrier Protein ◽  
Economic Value ◽  
Epicuticular Wax ◽  
Hybrid Breeding ◽  
Rna Seq ◽  
Fresh Market ◽  
Data Set ◽  
Natural Defense ◽  
Breeding Populations

Abstract Background Blueberry is of high economic value. Most blueberry varieties selected for the fresh market have an appealing light blue coating or “bloom” on the fruit due to the presence of a visible heavy epicuticular wax layer. This waxy layer also serves as natural defense against fruit desiccation and deterioration. Results In this study, we attempted to identify gene(s) whose expression is related to the protective waxy coating on blueberry fruit utilizing two unique germplasm populations that segregate for the waxy layer. We bulked RNA from waxy and non-waxy blueberry progenies from the two northern-adapted rabbiteye hybrid breeding populations (‘Nocturne’ x T 300 and ‘Nocturne’ x US 1212), and generated 316.85 million RNA-seq reads. We de novo assembled this data set integrated with other publicly available RNA-seq data and trimmed the assembly into a 91,861 blueberry unigene collection. All unigenes were functionally annotated, resulting in 79 genes potentially related to wax accumulation. We compared the expression pattern of waxy and non-waxy progenies using edgeR and identified overall 1125 genes in the T 300 population and 2864 genes in the US 1212 population with at least a two-fold expression difference. After validating differential expression of several genes by RT-qPCR experiments, a candidate gene, FatB, which encodes acyl-[acyl-carrier-protein] hydrolase, emerged whose expression was closely linked to the segregation of the waxy coating in our populations. This gene was expressed at more than a five-fold higher level in waxy than non-waxy plants of both populations. We amplified and sequenced the cDNA for this gene from three waxy plants of each population, but were unable to amplify the cDNA from three non-waxy plants that were tested from each population. We aligned the Vaccinium deduced FATB protein sequence to FATB protein sequences from other plant species. Within the PF01643 domain, which gives FATB its catalytic function, 80.08% of the amino acids were identical or had conservative replacements between the blueberry and the Cucumis melo sequence (XP_008467164). We then amplified and sequenced a large portion of the FatB gene itself from waxy and non-waxy individuals of both populations. Alignment of the cDNA and gDNA sequences revealed that the blueberry FatB gene consists of six exons and five introns. Although we did not sequence through two very large introns, a comparison of the exon sequences found no significant sequence differences between the waxy and non-waxy plants. This suggests that another gene, which regulates or somehow affects FatB expression, must be segregating in the populations. Conclusions This study is helping to achieve a greater understanding of epicuticular wax biosynthesis in blueberry. In addition, the blueberry unigene collection should facilitate functional annotation of the coming chromosomal level blueberry genome.

scholarly journals Gill Transcriptome Sequencing and De Novo Annotation of Acanthogobius ommaturus in Response to Salinity Stress

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 631
Author(s):  
Zhicheng Sun ◽  
Fangrui Lou ◽  
Yuan Zhang ◽  
Na Song
Keyword(s):  
Signal Transduction ◽  
Salinity Stress ◽  
De Novo ◽  
Enrichment Analysis ◽  
Gill Tissue ◽  
Control Group ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Kegg Pathways ◽  
Pathways Analysis

Acanthogobius ommaturus is a euryhaline fish widely distributed in coastal, bay and estuarine areas, showing a strong tolerance to salinity. In order to understand the mechanism of adaptation to salinity stress, RNA-seq was used to compare the transcriptome responses of Acanthogobius ommaturus to the changes of salinity. Four salinity gradients, 0 psu, 15 psu (control), 30 psu and 45 psu were set to conduct the experiment. In total, 131,225 unigenes were obtained from the gill tissue of A. ommaturus using the Illumina HiSeq 2000 platform (San Diego, USA). Compared with the gene expression profile of the control group, 572 differentially expressed genes (DEGs) were screened, with 150 at 0 psu, 170 at 30 psu, and 252 at 45 psu. Additionally, among these DEGs, Gene Ontology (GO) analysis indicated that binding, metabolic processes and cellular processes were significantly enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis detected 3, 5 and 8 pathways related to signal transduction, metabolism, digestive and endocrine systems at 0 psu, 30 psu and 45 psu, respectively. Based on GO enrichment analysis and manual literature searches, the results of the present study indicated that A. ommaturus mainly responded to energy metabolism, ion transport and signal transduction to resist the damage caused by salinity stress. Eight DEGs were randomly selected for further validation by quantitative real-time PCR (qRT-PCR) and the results were consistent with the RNA-seq data.

scholarly journals Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

2018 ◽  
Author(s):  
Pierre-Cyril Aubin-Frankowski ◽  
Jean-Philippe Vert
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
De Novo ◽  
State Of The Art ◽  
Real Data ◽  
Biological Processes ◽  
Rna Seq ◽  
Cell Cycles ◽  
Linear Differential ◽  
Cell Trajectories

AbstractSingle-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulation networks (GRN) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. In this work we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual data, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data.

scholarly journals Bias, robustness and scalability in differential expression analysis of single-cell RNA-seq data

2017 ◽  
Author(s):  
Charlotte Soneson ◽  
Mark D. Robinson
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Statistical Methods ◽  
Expression Analysis ◽  
Method Development ◽  
Differential Expression Analysis ◽  
Data Sets ◽  
Rna Seq ◽  
Data Set ◽  
Extensive Evaluation

AbstractBackgroundAs single-cell RNA-seq (scRNA-seq) is becoming increasingly common, the amount of publicly available data grows rapidly, generating a useful resource for computational method development and extension of published results. Although processed data matrices are typically made available in public repositories, the procedure to obtain these varies widely between data sets, which may complicate reuse and cross-data set comparison. Moreover, while many statistical methods for performing differential expression analysis of scRNA-seq data are becoming available, their relative merits and the performance compared to methods developed for bulk RNA-seq data are not sufficiently well understood.ResultsWe present conquer, a collection of consistently processed, analysis-ready public single-cell RNA-seq data sets. Each data set has count and transcripts per million (TPM) estimates for genes and transcripts, as well as quality control and exploratory analysis reports. We use a subset of the data sets available in conquer to perform an extensive evaluation of the performance and characteristics of statistical methods for differential gene expression analysis, evaluating a total of 30 statistical approaches on both experimental and simulated scRNA-seq data.ConclusionsConsiderable differences are found between the methods in terms of the number and characteristics of the genes that are called differentially expressed. Pre-filtering of lowly expressed genes can have important effects on the results, particularly for some of the methods originally developed for analysis of bulk RNA-seq data. Generally, however, methods developed for bulk RNA-seq analysis do not perform notably worse than those developed specifically for scRNA-seq.

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