RESUMEN
Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.
Asunto(s)
ARN , Análisis de la Célula Individual , Humanos , ARN/genética , RNA-Seq , Análisis de la Célula Individual/métodos , Perfilación de la Expresión Génica/métodos , Transcriptoma , Análisis de Secuencia de ARN/métodosRESUMEN
In this work, a novel strategy based on chromatographic fingerprints and some chemometric techniques is proposed for quantitative analysis of the formulated complex system. Here, the formulated complex system means a formulated type of complicated analytical system containing more than one kind of raw material under some concentration composition according to a certain formula. The strategy is elaborated by an example of quantitative determination of mixtures consist of three essential oils. Three key steps of the strategy are as follows: (1) remove baselines of the chromatograms; (2) align retention time; (3) conduct quantitative analysis using multivariate regression with entire chromatographic profiles. Through the determination of concentration compositions of nine mixtures arranged by uniform design, the feasibility of the proposed strategy is validated and the factors that influence the quantitative result are also discussed. This strategy is proved to be viable and the validation indicates that quantitative result obtained using this strategy mainly depends on the efficiency of the alignment method as well as chromatographic peak shape of the chromatograms. Previously, chromatographic fingerprints were only used for identification and/or recognition of some products. This work demonstrates that with the assistance of some effective chemometric techniques, chromatographic fingerprints are also potential and promising in solving quantitative problems of complex analytical systems.