scholarly journals Single cell RNA‐seq in the sea urchin embryo show marked cell‐type specificity in the Delta/Notch pathway

2019 ◽  
Vol 86 (8) ◽  
pp. 931-934 ◽  
Author(s):  
Stephany Foster ◽  
Yee Voan Teo ◽  
Nicola Neretti ◽  
Nathalie Oulhen ◽  
Gary M. Wessel
Keyword(s):  
Single Cell ◽  
Sea Urchin ◽  
Notch Pathway ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Sea Urchin Embryo ◽  
Marked Cell

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Dustin J Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cost Constraints ◽  
Mouse Tissues

Abstract RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

scholarly journals Normalization of single-cell RNA-seq counts by log(x + 1)* or log(1 + x)*

2021 ◽  
Author(s):  
A Sina Booeshaghi ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
Supplementary Information ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
The Past ◽  
Highly Expressed Genes ◽  
Low Levels ◽  
Resolution Cell ◽  
Host Genes

Abstract   Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses don’t hold when examining low-expressed genes, with the result that standard workflows can produce misleading results. Supplementary information Supplementary data and all of the code to reproduce Figure 1 are available here: https://github.com/pachterlab/BP_2020_2/.

scholarly journals The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Peng Xu ◽  
Minghui Wang ◽  
Won-min Song ◽  
Qian Wang ◽  
Guo-Cheng Yuan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Control Cell ◽  
Protein Accumulation ◽  
Human Tissues ◽  
Cellular Interactions ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cell Type Specific

Abstract Background Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood. Methods This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity. Results Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis. Conclusions This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

scholarly journals CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data

2021 ◽  
Vol 4 (6) ◽  
pp. e202001004
Author(s):  
Almut Lütge ◽  
Joanna Zyprych-Walczak ◽  
Urszula Brykczynska Kunzmann ◽  
Helena L Crowell ◽  
Daniela Calini ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Distance Distributions ◽  
A Cell ◽  
Cell Type Specific ◽  
Synthetic Datasets

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in scRNA-seq data using real and synthetic datasets and whereas these metrics target the same question and are used interchangeably, we find differences in scalability, sensitivity, and ability to handle differentially abundant cell types. We find that cell-specific metrics outperform cell type–specific and global metrics and recommend them for both method benchmarks and batch exploration.

scholarly journals Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Qianhui Huang ◽  
Yu Liu ◽  
Yuheng Du ◽  
Lana X. Garmire
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
R Packages

Cell type diversity in scallop adductor muscles revealed by single-cell RNA-Seq

Genomics ◽  
2021 ◽  
Vol 113 (6) ◽  
pp. 3582-3598
Author(s):  
Xiujun Sun ◽  
Li Li ◽  
Biao Wu ◽  
Jianlong Ge ◽  
Yanxin Zheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Adductor Muscles

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

2019 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zhongjie Ma ◽  
Michael Gleicher ◽  
Colin N. Dewey
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Training Set ◽  
Sequence Read Archive ◽  
Cell Ontology ◽  
Cell Type Specific ◽  
Type Classification

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract

scholarly journals RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Zhao ◽  
Zhao-Yu Fang ◽  
Cui-Xiang Lin ◽  
Chao Deng ◽  
Yun-Pei Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Gene Selection ◽  
Clustering Algorithms ◽  
Selection Methods ◽  
Clustering Methods ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Random Forest Classification ◽  
Forest Classification

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

scholarly journals CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  
Keyword(s):  
Single Cell ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Model Systems ◽  
Rna Seq ◽  
Cell Type ◽  
Fine Grained ◽  
Single Nucleus ◽  
Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

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