Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA530575 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8838580.fastq.gz | Fastqsanger.gz | |||
| SRR8838590.fastq.gz | Fastqsanger.gz | |||
| SRR8838581.fastq.gz | Fastqsanger.gz | |||
| SRR8838591.fastq.gz | Fastqsanger.gz | |||
| SRR8838582.fastq.gz | Fastqsanger.gz |
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Project description:The 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-type-specific gene 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 .
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