scholarly journals Identification of Cell-Type-Specific Spatially Variable Genes Accounting for Excess Zeros

2021 ◽  
Author(s):  
Jinge Yu ◽  
Xiangyu Luo
Keyword(s):  
Negative Binomial ◽  
Spatial Information ◽  
Spatial Relationship ◽  
R Package ◽  
Spatial Effect ◽  
Ductal Adenocarcinoma ◽  
Cell Type ◽  
Gene Expressions ◽  
Excess Zeros ◽  
Cell Type Specific

Spatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions. We develop a statistical approach CTSV to detect cell-type-specific SV genes. CTSV directly models spatial raw count data and considers zero-inflation as well as overdispersion using a zero-inflated negative binomial distribution. It then incorporates cell-type proportions and spatial effect functions in the zero-inflated negative binomial regression framework. The R package pscl (Zeileis et al., 2008) is employed to fit the model. For robustness, a Cauchy combination rule is applied to integrate p-values from mutliple choices of spatial effect functions. Simulation studies show that CTSV not only outperforms the competing methods at the aggregated level but also achieves more power at the cell-type level. By analyzing pancreatic ductal adenocarcinoma spatial transcriptomic data, SV genes identified by CTSV reveal meaningful biological insights at the cell-type level. The R package to implement CTSV is available on GitHub https://github.com/jingeyu/CTSV.

scholarly journals FIREcaller: Detecting Frequently Interacting Regions from Hi-C Data

2019 ◽  
Author(s):  
Cheynna Crowley ◽  
Yuchen Yang ◽  
Yunjiang Qiu ◽  
Benxia Hu ◽  
Armen Abnousi ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Spatial Organization ◽  
R Package ◽  
Specific Gene ◽  
List Type ◽  
Cell Type ◽  
R Software ◽  
Computational Tools ◽  
Cell Type Specific ◽  
User Friendly

AbstractHi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller.Highlights– Frequently Interacting Regions (FIREs) can be used to identify tissue and cell-type-specific cis-regulatory regions.– An R software, FIREcaller, has been developed to identify FIREs and clustered FIREs into super-FIREs.

scholarly journals NF-κB/Rel Transcription Factors in Pancreatic Cancer: Focusing on RelA, c-Rel, and RelB

Cancers ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 937 ◽  
Author(s):  
Derya Kabacaoglu ◽  
Dietrich A. Ruess ◽  
Jiaoyu Ai ◽  
Hana Algül
Keyword(s):  
Transcription Factors ◽  
Ductal Adenocarcinoma ◽  
Cell Type ◽  
Dual Nature ◽  
Ability To Act ◽  
Cell Type Specific ◽  
Additional Tumor ◽  
Light Chain Enhancer ◽  
Oncogenic Function

Regulation of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)/Rel transcription factors (TFs) is extremely cell-type-specific owing to their ability to act disparately in the context of cellular homeostasis driven by cellular fate and the microenvironment. This is also valid for tumor cells in which every single component shows heterogenic effects. Whereas many studies highlighted a per se oncogenic function for NF-κB/Rel TFs across cancers, recent advances in the field revealed their additional tumor-suppressive nature. Specifically, pancreatic ductal adenocarcinoma (PDAC), as one of the deadliest malignant diseases, shows aberrant canonical-noncanonical NF-κB signaling activity. Although decades of work suggest a prominent oncogenic activity of NF-κB signaling in PDAC, emerging evidence points to the opposite including anti-tumor effects. Considering the dual nature of NF-κB signaling and how it is closely linked to many other cancer related signaling pathways, it is essential to dissect the roles of individual Rel TFs in pancreatic carcinogenesis and tumor persistency and progression. Here, we discuss recent knowledge highlighting the role of Rel TFs RelA, RelB, and c-Rel in PDAC development and maintenance. Next to providing rationales for therapeutically harnessing Rel TF function in PDAC, we compile strategies currently in (pre-)clinical evaluation.

Cell type-specific endometrial transcriptome changes during initial recognition of pregnancy in the mare

2019 ◽  
Vol 31 (3) ◽  
pp. 496 ◽  
Author(s):  
Iside Scaravaggi ◽  
Nicole Borel ◽  
Rebekka Romer ◽  
Isabel Imboden ◽  
Susanne E. Ulbrich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Information ◽  
Gene Expression Regulation ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Expression Of Genes ◽  
Prostaglandin F ◽  
Cell Type Specific ◽  
Gene Expression Studies

Previous endometrial gene expression studies during the time of conceptus migration did not provide final conclusions on the mechanisms of maternal recognition of pregnancy (MRP) in the mare. This called for a cell type-specific endometrial gene expression analysis in response to embryo signals to improve the understanding of gene expression regulation in the context of MRP. Laser capture microdissection was used to collect luminal epithelium (LE), glandular epithelium and stroma from endometrial biopsies from Day 12 of pregnancy and Day 12 of the oestrous cycle. RNA sequencing (RNA-Seq) showed greater expression differences between cell types than between pregnant and cyclic states; differences between the pregnant and cyclic states were mainly found in LE. Comparison with a previous RNA-Seq dataset for whole biopsy samples revealed the specific origin of gene expression differences. Furthermore, genes specifically differentially expressed (DE) in one cell type were found that were not detectable as DE in biopsies. Overall, this study revealed spatial information about endometrial gene expression during the phase of initial MRP. The conceptus induced changes in the expression of genes involved in blood vessel development, specific spatial regulation of the immune system, growth factors, regulation of prostaglandin synthesis, transport prostaglandin receptors, specifically prostaglandin F receptor (PTGFR) in the context of prevention of luteolysis.

scholarly journals echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline

2020 ◽  
Author(s):  
Brian M. Schilder ◽  
Jack Humphrey ◽  
Towfique Raj
Keyword(s):  
Linkage Disequilibrium ◽  
Fine Mapping ◽  
R Package ◽  
Functional Genomic ◽  
Summary Statistics ◽  
Cell Type ◽  
High Confidence ◽  
Genome Wide ◽  
Cell Type Specific ◽  
End To End

AbstractSummaryecholocatoR integrates a diverse suite of statistical and functional fine-mapping tools in order to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium (LD) panels, quantitative trait loci (QTL) datasets, genome-wide annotations, cell type-specific epigenomics, thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations.Availability and implementationecholocatoR is an open-source R package available through GitHub under the MIT license: https://github.com/RajLabMSSM/echolocatoR

scholarly journals Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yan Sun ◽  
Qichao Yu ◽  
Lei Li ◽  
Zhanlong Mei ◽  
Biaofeng Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Specific Protein ◽  
Embryonic Cells ◽  
Cell Type ◽  
Gene Expressions ◽  
Protein Coding ◽  
C Elegans ◽  
Protein Coding Genes ◽  
Cell Type Specific

Abstract Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

scholarly journals CellWalkR: An R Package for integrating single-cell and bulk data to resolve regulatory elements

2021 ◽  
Author(s):  
Pawel F. Przytycki ◽  
Katherine S. Pollard
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
R Package ◽  
Regulatory Elements ◽  
Random Walk Model ◽  
Open Chromatin ◽  
Cell Type ◽  
Regulatory Regions ◽  
Bulk Data ◽  
Cell Type Specific

AbstractWhile single-cell open chromatin (scATAC-seq) data allows for the identification of cell type-specific regulatory regions, it is much sparser than bulk data. CellWalkR is an R package that performs an integration of external labeling and bulk epigenetic data with scATAC-seq using a network-based random walk model to help overcome this sparsity. Outputs include cell type labels for individual cells and regulatory regions.Availability and implementationCellWalkR is freely available as an R package under a GNU GPL-2.0 License, and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette for analyzing example data.

scholarly journals Single-cell Stereo-seq enables cell type-specific spatial transcriptome characterization in Arabidopsis leaves

2021 ◽  
Author(s):  
Keke Xia ◽  
Hai-Xi Sun ◽  
Jie Li ◽  
Jiming Li ◽  
Yu Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Spatial Information ◽  
Transcriptome Profiling ◽  
Cell Type ◽  
Spatially Resolved ◽  
Cell Type Specific

Understanding the complex functions of plant leaves requires spatially resolved gene expression profiling with single-cell resolution. However, although in situ gene expression profiling technologies have been developed, this goal has not yet been achieved. Here, we present the first in situ single-cell transcriptome profiling in plant, scStereo-seq (single-cell SpaTial Enhanced REsolution Omics-sequencing), which enabled the bona fide single-cell spatial transcriptome of Arabidopsis leaves. We successfully characterized subtle but significant transcriptomic differences between upper and lower epidermal cells. Furthermore, with high-resolution location information, we discovered the cell type-specific spatial gene expression gradients from main vein to leaf edge. By reconstructing those spatial gradients, we show for the first time the distinct spatial developmental trajectories of vascular cells and guard cells. Our findings show the importance of incorporating spatial information for answering complex biological questions in plant, and scStereo-seq offers a powerful single cell spatially resolved transcriptomic strategy for plant biology.

scholarly journals CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

2021 ◽  
Author(s):  
Kai Kang ◽  
Caizhi David Huang ◽  
Yuanyuan Li ◽  
David M. Umbach ◽  
Leping Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
In Silico ◽  
Cell Types ◽  
R Package ◽  
Biological Tissues ◽  
Specific Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific

AbstractBackgroundBiological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and with a new function to aid interpretation of deconvolution outcomes. The R package would be of interest for the broader R community.ResultWe developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating CDSeq-estimated cell types using publicly available single-cell RNA sequencing (scRNA-seq) data (single-cell data from 20 major organs are included in the R package). This function allows users to readily interpret and visualize the CDSeq-estimated cell types. We carried out additional validations of the CDSeqR software with in silico and in vitro mixtures and with real experimental data including RNA-seq data from the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) project.ConclusionsThe existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. However, bulk level analyses neglect tissue heterogeneity and hinder investigation in a cell-type-specific fashion. The CDSeqR package can be viewed as providing in silico single-cell dissection of bulk measurements. It enables researchers to gain cell-type-specific information from bulk RNA-seq data.

scholarly journals Author response: Dissecting cell-type-specific metabolism in pancreatic ductal adenocarcinoma

2020 ◽  
Author(s):  
Allison N Lau ◽  
Zhaoqi Li ◽  
Laura V Danai ◽  
Anna M Westermark ◽  
Alicia M Darnell ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Author Response ◽  
Ductal Adenocarcinoma ◽  
Cell Type ◽  
Cell Type Specific

scholarly journals spatzie: An R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions

2021 ◽  
Author(s):  
Jennifer Hammelman ◽  
Konstantin Krismer ◽  
David K. Gifford
Keyword(s):  
Transcription Factor ◽  
Statistical Tests ◽  
Simulated Data ◽  
R Package ◽  
Cell Type ◽  
Specific Transcription Factor ◽  
Transcription Factor Motif ◽  
Cell Type Specific ◽  
Factor Interactions ◽  
Promoter Interaction

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

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