scholarly journals A novel computational complete deconvolution method using RNA-seq data

2018 ◽  
Author(s):  
Kai Kang ◽  
Qian Meng ◽  
Igor Shats ◽  
David M. Umbach ◽  
Melissa Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific

AbstractThe cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell type composition and compared its performance to the performance of two state-of-the art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-typespecificgene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue (MATLAB code is available at https://github.com/kkang7/CDSeq_011).

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of applying cell type profiles derived from blood on mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

scholarly journals Digital sorting of complex tissues for cell type-specific gene expression profiles

2013 ◽  
Vol 14 (1) ◽  
pp. 89 ◽  
Author(s):  
Yi Zhong ◽  
Ying-Wooi Wan ◽  
Kaifang Pang ◽  
Lionel ML Chow ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific

scholarly journals Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion

2021 ◽  
Author(s):  
Weixu Wang ◽  
Jun Yao ◽  
Yi Wang ◽  
Chao Zhang ◽  
Wei Tao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Matrix Completion ◽  
Superior Performance ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Large Patient ◽  
Cell Type Specific

Cell type-specific gene expression (CSE) brings novel biological insights into both physiological and pathological processes compared with bulk tissue gene expression. Although fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq) are two widely used techniques to detect gene expression in a cell type-specific manner, the constraints of cost and labor force make it impractical as a routine on large patient cohorts. Here, we present ENIGMA, an algorithm that deconvolutes bulk RNA-seq into cell type-specific expression matrices and cell type fraction matrices without the need of physical sorting or sequencing of single cells. ENIGMA used cell type signature matrix generated from either FACS RNA-seq or scRNA-seq as reference, and applied matrix completion technique to achieve fast and accurate deconvolution. We demonstrated the superior performance of ENIGMA to previously published algorithms (TCA, bMIND and CIBERSORTx) while requiring much less running time on both simulated and realistic datasets. To prove its value in biological discovery, we applied ENIGMA to bulk RNA-seq from arthritis patients and revealed a pseudo-differentiation trajectory that could reflect monocyte to macrophage transition. We also applied ENIGMA to bulk RNA-seq data of pancreatic islet tissue from type 2 diabetes (T2D) patients and discovered a beta cell-specific gene co-expression module related to senescence and apoptosis that possibly contributed to the pathogenesis of T2D. Together, ENIGMA provides a new framework to improve the CSE estimation by integrating FACS RNA-seq and scRNA-seq with tissue bulk RNA-seq data, and will extend our understandings about cell heterogeneity on population level with no need for experimental tissue disaggregation.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

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