scholarly journals Spectrum: Fast density-aware spectral clustering for single and multi-omic data

2019 ◽  
Author(s):  
Christopher R. John ◽  
David Watson ◽  
Michael Barnes ◽  
Costantino Pitzalis ◽  
Myles J. Lewis
Keyword(s):  
Spectral Clustering ◽  
Personalised Medicine ◽  
Cell Types ◽  
Expression Data ◽  
Rna Seq ◽  
Diffusion Technique ◽  
Eigenvector Analysis ◽  
And Performance ◽  
And Diffusion ◽  
Omic Data

AbstractClustering of single or multi-omic data is key to developing personalised medicine and identifying new cell types. We present Spectrum, a fast spectral clustering method for single and multi-omic expression data. Spectrum is flexible and performs well on single-cell RNA-seq data. The method uses a new density-aware kernel that adapts to data scale and density. It uses a tensor product graph data integration and diffusion technique to reveal underlying structures and reduce noise. We developed a powerful method of eigenvector analysis to determine the number of clusters. Benchmarking Spectrum on 21 datasets demonstrated improvements in runtime and performance relative to other state-of-the-art methods.Contact:[email protected]

scholarly journals Spectrum: fast density-aware spectral clustering for single and multi-omic data

2019 ◽  
Author(s):  
Christopher R John ◽  
David Watson ◽  
Michael R Barnes ◽  
Costantino Pitzalis ◽  
Myles J Lewis
Keyword(s):  
Spectral Clustering ◽  
Optimal Number ◽  
Supplementary Information ◽  
Distribution Analysis ◽  
Rna Seq ◽  
Nearest Neighbours ◽  
Self Tuning ◽  
Non Gaussian ◽  
Shared Structure ◽  
Omic Data

Abstract Motivation Clustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data. Results We present Spectrum, a new spectral clustering method for complex omic data. Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between points that share common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to reduce noise and reveal underlying structures. Spectrum contains a new method for finding the optimal number of clusters (K) involving eigenvector distribution analysis. Spectrum can automatically find K for both Gaussian and non-Gaussian structures. We demonstrate across 21 real expression datasets that Spectrum gives improved runtimes and better clustering results relative to other methods. Availability and implementation Spectrum is available as an R software package from CRAN https://cran.r-project.org/web/packages/Spectrum/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kai Kang ◽  
Caizhi Huang ◽  
Yuanyuan Li ◽  
David M. Umbach ◽  
Leping Li
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Biological Tissues ◽  
Specific Gene ◽  
Specific Cell ◽  
Specific Information ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific

Abstract Background Biological 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 an added new function to aid cell type annotation. The R package would be of interest for the broader R community. Result We 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 the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Conclusions The 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. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

scholarly journals A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Haijing Jin ◽  
Zhandong Liu
Keyword(s):  
Dynamic Testing ◽  
Cell Types ◽  
Future Application ◽  
Expression Data ◽  
Rna Seq ◽  
Deconvolution Analysis ◽  
Tuning Parameters ◽  
Testing Environments ◽  
The Impact ◽  
Unit Component

Abstract Background Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions. Results To systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions. Conclusions We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.

scholarly journals scID: Identification of equivalent transcriptional cell populations across single cell RNA-seq data using discriminant analysis

2018 ◽  
Author(s):  
Katerina Boufea ◽  
Sohan Seth ◽  
Nizar N. Batada
Keyword(s):  
Discriminant Analysis ◽  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Linear Discriminant ◽  
Related Cell ◽  
Technical Factors ◽  
And Performance ◽  
Fisher’S Linear Discriminant

AbstractThe power of single cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging due to technical factors such as sparsity, low number of cells and batch effect. To address these challenges we developed scID (Single Cell IDentification), which uses the framework of Fisher’s Linear Discriminant Analysis to identify transcriptionally related cell types between scRNA-seq datasets. We demonstrate the accuracy and performance of scID relative to existing methods on several published datasets. By increasing power to identify transcriptionally similar cell types across datasets, scID enhances investigator’s ability to extract biological insights from scRNA-seq data.

scholarly journals Generation and network analysis of an RNA-seq transcriptional atlas for the rat.

2021 ◽  
Author(s):  
Kim Summers ◽  
Stephen J. Bush ◽  
Chunlei Wu ◽  
David A Hume
Keyword(s):  
Cell Types ◽  
Rat Tissues ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Gene Correlation ◽  
Cell Type Specific ◽  
Critical Interpretation ◽  
Public Repositories

The laboratory rat is an important model for biomedical research. To generate a comprehensive rat transcriptomic atlas, we curated and down-loaded 7700 rat RNA-seq datasets from public repositories, down-sampled them to a common depth and quantified expression. Data from 590 rat tissues and cells, averaged from each Bioproject, can be visualised and queried at http://biogps.org/ratatlas. Gene correlation network (GCN) analysis revealed clusters of transcripts that were tissue or cell-type restricted and contained transcription factors implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes. Many of these clusters overlap with previous data from analysis of other species whilst some (e.g. expressed specifically in immune cells, retina/pineal gland, pituitary and germ cells) are unique to these data. GCN on large subsets of the data related specifically to liver, nervous system, kidney, musculoskeletal system and cardiovascular system enabled deconvolution of cell-type specific signatures. The approach is extensible and the dataset can be used as a point of reference from which to analyse the transcriptomes of cell types and tissues that have not yet been sampled. Sets of strictly co-expressed transcripts provide a resource for critical interpretation of single cell RNA-seq data.

scholarly journals A comparative study of deconvolution methods for RNA-seq data under a dynamic testing landscape

2020 ◽  
Author(s):  
Haijing Jin ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Dynamic Testing ◽  
Cell Types ◽  
Future Application ◽  
Expression Data ◽  
Rna Seq ◽  
Biological Factors ◽  
The Impact ◽  
Unit Component

AbstractDeconvolution analyses have been widely used to track compositional alternations of cell-types in gene expression data. Even though numerous novel methods have been developed in recent years, researchers are still having difficulty selecting optimal deconvolution methods due to the lack of comprehensive benchmarks relative to the newly developed methods. To systematically reveal the pitfalls and challenges of deconvolution analyses, we studied the impact of several technical and biological factors such as simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks that cover comparative analysis of 11 popular deconvolution methods under 1,766 conditions. We hope this study can provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.

scholarly journals The Gene Expression Deconvolution Interactive Tool (GEDIT): Accurate Cell Type Quantification from Gene Expression Data

2019 ◽  
Author(s):  
Brian B. Nadel ◽  
David Lopez ◽  
Dennis J. Montoya ◽  
Feiyang Ma ◽  
Hannah Waddel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Data ◽  
Cell Types ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Samples ◽  
Interactive Tool ◽  
Human And Mouse

AbstractThe cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g. cell flow cytometry) are costly, time consuming, and can introduce bias. Computational approaches that infer cell type abundance from expression data offer an alternate solution. While these methods have gained popularity, most are limited to predicting hematopoietic cell types and do not produce accurate predictions for stromal cell types. Many of these methods are also limited to particular platforms, whether RNA-seq or specific microarrays. We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a tool that overcomes these limitations, compares favorably with existing methods, and provides superior versatility. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include a variety of platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from eight sources spanning a wide variety of stromal and hematopoietic types in both human and mouse. This reference database allows the user to obtain estimates for a wide variety of tissue samples without having to provide their own data. GEDIT also accepts user submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data is available.Author SummaryThe Gene Expression Deconvolution Interactive Tool (GEDIT) is a robust and accurate tool that uses gene expression data to estimate cell type abundances. Extensive testing on a variety of tissue types and technological platforms demonstrates that GEDIT provides greater versatility than other cell type deconvolution tools. GEDIT utilizes reference data describing the expression profile of purified cell types, and we provide in the software package a library of reference matrices from various sources. GEDIT is also flexible and allows the user to supply custom reference matrices. A GUI interface for GEDIT is available at http://webtools.mcdb.ucla.edu/, and source code and reference matrices are available at https://github.com/BNadel/GEDIT.

scholarly journals Transcriptional and morphological profiling of parvalbumin interneuron subpopulations in the mouse hippocampus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Que ◽  
David Lukacsovich ◽  
Wenshu Luo ◽  
Csaba Földy
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Rna Seq ◽  
Neuronal Identity ◽  
Parvalbumin Interneurons ◽  
Different Types ◽  
Parvalbumin Interneuron ◽  
Cam Profile ◽  
Developmental Domains

AbstractThe diversity reflected by >100 different neural cell types fundamentally contributes to brain function and a central idea is that neuronal identity can be inferred from genetic information. Recent large-scale transcriptomic assays seem to confirm this hypothesis, but a lack of morphological information has limited the identification of several known cell types. In this study, we used single-cell RNA-seq in morphologically identified parvalbumin interneurons (PV-INs), and studied their transcriptomic states in the morphological, physiological, and developmental domains. Overall, we find high transcriptomic similarity among PV-INs, with few genes showing divergent expression between morphologically different types. Furthermore, PV-INs show a uniform synaptic cell adhesion molecule (CAM) profile, suggesting that CAM expression in mature PV cells does not reflect wiring specificity after development. Together, our results suggest that while PV-INs differ in anatomy and in vivo activity, their continuous transcriptomic and homogenous biophysical landscapes are not predictive of these distinct identities.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruizhu Huang ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Thomas S.B. Schmidt ◽  
Christian Von Mering ◽  
...  
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Cell Types ◽  
Data Driven ◽  
Rna Seq ◽  
Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

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