Clc rna seq12/12/2023 ![]() ![]() ![]() MuSiC 16 also leverages single-cell expression as a reference, instead using a weighted non-negative least-squares regression (NNLS) model for decomposition, with improved performance over BSEQ-sc in several datasets. BSEQ-sc 15 instead generates a reference profile from single-cell expression data that is used in the CIBERSORT model. A major limitation of this approach is the reliance on sorting cells to estimate a reference gene expression panel. CIBERSORT 14 is a SVM-regression-based approach, originally designed for microarray data that utilizes a reference generated from purified cell populations. There exist a number of methods for decomposing bulk expression, many of which are regression-based and leverage cell-type-specific expression data as a reference profile 13. Given the wide availability of these bulk data, the estimation of cell-type proportions, often termed decomposition, can be used to extract large-scale cell-type-specific information. Moreover, many bulk RNA-seq studies that have been performed in recent years resulted in a large body of data that is available public databases such as dbGAP and GEO. Collection of bulk expression data remains an attractive approach for identifying population-level associations, such as differential expression regardless of cell-type specificity. However, these experiments remain costly and noisy compared to bulk RNA-seq 12. Single-cell technologies provide a high-resolution view into cellular heterogeneity and cell-type-specific expression 9, 10, 11. Traditional methods for determining cell-type composition, such as immunohistochemistry or flow cytometry, rely on a limited set of molecular markers and lack in scalability relative to the current rate of data generation 8. In addition, measures of cell composition can be leveraged to identify cell-specific eQTLs 6, 7 or differential expression 6 from bulk data. Cell-type heterogeneity may also be of interest in profiling changes in tissue composition associated with disease, such as cancer 4 or diabetes 5. Variability in cell-type composition can significantly confound analyses of these data, such as in identification of expression quantitative trait loci (eQTLs) or differentially expressed genes 3. We further propose an additional mode of operation that merely requires a set of known marker genes.īulk RNA-seq experiments typically measure total gene expression from heterogeneous tissues, such as tumor and blood samples 1, 2. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. We present Bisque, a tool for estimating cell type proportions in bulk expression. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |