Dataset Information

RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.

ABSTRACT: Spatial transcriptomics (ST) profiles gene expression in intact tissues. However, ST data measured at each spatial location may represent gene expression of multiple cell types, making it difficult to identify cell-type-specific transcriptional variation across spatial contexts. Existing cell-type deconvolutions of ST data often require single-cell transcriptomic references, which can be limited by availability, completeness and platform effect of such references. We present RETROFIT, a reference-free Bayesian method that produces sparse and interpretable solutions to deconvolve cell types underlying each location independent of single-cell transcriptomic references. Results from synthetic and real ST datasets acquired by Slide-seq and Visium platforms demonstrate that RETROFIT outperforms existing reference-based and reference-free methods in estimating cell-type composition and reconstructing gene expression. Applying RETROFIT to human intestinal development ST data reveals spatiotemporal patterns of cellular composition and transcriptional specificity. RETROFIT is available at https://bioconductor.org/packages/release/bioc/html/retrofit.html.

SUBMITTER: Singh R

PROVIDER: S-EPMC10274808 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.

Singh Roopali R He Xi X Park Adam Keebum AK Hardison Ross Cameron RC Zhu Xiang X Li Qunhua Q

bioRxiv : the preprint server for biology 20230609

Spatial transcriptomics (ST) profiles gene expression in intact tissues. However, ST data measured at each spatial location may represent gene expression of multiple cell types, making it difficult to identify cell-type-specific transcriptional variation across spatial contexts. Existing cell-type deconvolutions of ST data often require single-cell transcriptomic references, which can be limited by availability, completeness and platform effect of such references. We present RETROFIT, a referenc ...[more]

PMID: 37333291

Similar Datasets

Project description:BackgroundPatient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment.MethodsWe developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data. DeClust differs from most existing methods by directly incorporating molecular subtyping of solid tumors into the deconvolution process and outputting molecular subtype-specific tumor reference profiles for the cohort rather than individual tumor profiles. In addition, DeClust does not require reference expression profiles or signature matrices as inputs and estimates cancer-type-specific microenvironment signals from bulk tumor transcriptomic data.ResultsDeClust was evaluated on both simulated data and 13 solid tumor datasets from The Cancer Genome Atlas (TCGA). DeClust performed among the best, relative to existing methods, for estimation of cellular composition. Compared to molecular subtypes reported by TCGA or other similar approaches, the subtypes generated by DeClust had higher correlations with cancer-intrinsic genomic alterations (e.g., somatic mutations and copy number variations) and lower correlations with tumor purity. While DeClust-identified subtypes were not more significantly associated with survival in general, DeClust identified a poor prognosis subtype of clear cell renal cancer, papillary renal cancer, and lung adenocarcinoma, all of which were characterized by CDKN2A deletions. As a reference profile-free deconvolution method, the tumor-type-specific stromal profiles and cancer cell-intrinsic subtypes generated by DeClust were supported by single-cell RNA sequencing data.ConclusionsDeClust is a useful tool for cancer cell-intrinsic molecular subtyping of solid tumors. DeClust subtypes, together with the tumor-type-specific stromal profiles generated by this pan-cancer study, may lead to mechanistic and clinical insights across multiple tumor types.

Dataset Information

RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.

Publications

RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets