DC3: Deconvolution and coupled clustering from bulk and single cell genomics data
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA525433 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:HiChIP data generated for the retinoic acid-induced mESC differentiation at day 4.
2019-08-14 | GSE127807 | GEO
Project description: Not available
| S-EPMC6787340 | biostudies-literature
Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.
2024-01-12 | PXD044157 | Pride
Project description:This paper describes SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets.
2019-08-30 | GSE136148 | GEO
Project description:Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space
| PRJNA794115 | ENA
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220607 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220605 | GEO
Project description:In this study single cell RNA-Seq data was used to train a deconvolution algorithm. The algorithm was validated on paired bulk RNA-Seq profiles.
| EGAS00001006723 | EGA
Project description:SCDC: Deconvolution of Bulk Gene Expression by Single-Cell RNA Sequencing Data
| PRJNA561378 | ENA
Project description:Single-cell expression profiling is a rich resource of cellular heterogeneity. While profiling every sample under study is advantageous, such workflow is time consuming and costly. We devised CPM - a deconvolution algorithm in which cellular heterogeneity is inferred from bulk expression data based on pre-existing collection of single-cell RNA-seq profiles. We applied CPM to investigate individual variation in heterogeneity of murine lung cells during in vivo influenza virus infection, revealing that the relations between cell quantities and clinical outcomes varies in a gradual manner along the cellular activation process. Validation experiments confirmed these gradual changes along the cellular activation trajectory. Additional analysis suggests that clinical outcomes relate to the rate of cell activation at the early stages of this process. These findings demonstrate the utility of CPM as a mapping deconvolution tool at single-cell resolution, and highlight the importance of such fine cell landscape for understanding diversity of clinical outcomes.
2019-03-15 | GSE113530 | GEO