Global computational alignment of tumor and cell line transcriptional profiles

Warren, Allison; Jones, Andrew; Shibue, Tsukasa; Hahn, William C.; Boehm, Jesse S.; Vázquez, Francisca; Tsherniak, Aviad; McFarland, James M.

doi:10.1101/2020.03.25.008342

“…We observed that primary tumors cluster together based on their tissue type ( Figure 3 C). Cell lines, however, show different behaviors – most do cluster with the tumors from a similar tissue of origin, while a group of cell lines cluster together and away from the tumors, regardless of their tissue of origin, as observed in previous studies 43 . To quantify the degree of co-clustering of cell lines and tumors, we compared distances between tumors and cell lines from similar and non-similar tissues, and observed, as expected, a higher similarity between tumors and cell lines from the same tissue ( Supp Figure 10 C).…”

Section: Resultssupporting

confidence: 68%

Predicting clinical drug response from model systems by non-linear subspace-based transfer learning

Mourragui

¹

,

Loog

²

,

Vis

³

et al. 2020

Preprint

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AbstractPre-clinical models have been the workhorse of cancer research for decades. Albeit powerful, these models do not perfectly recapitulate the complexity of human tumors which has led to a disappointing bench-to-bedside attrition rate. The quest for biomarkers of drug response signatures has been particularly challenging, suffering from poor translatability from pre-clinical models to human tumors. To address this problem, we present a novel computational framework, PRECISE+, that employs nonlinear kernel approaches to capture complex biological processes expressed in both pre-clinical models and human tumors. PRECISE+ builds predictors on cell lines that show improved performance over competing approaches for a set of 7 of drugs in Patient-Derived Xenografts, 5 drugs on TCGA cohorts and show significant association with clinical response for 4 drugs in 226 metastatic tumors from the Hartwig Medical Foundation data set. We used the interpretability of PRECISE+ to validate the approach by identifying known biomarkers to targeted therapies and propose novel putative biomarkers of resistance to Paclitaxel and Gemcitabine.

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“…In the single-cell domain, bindSC can clearly be applied to align cells and features simultaneously, which are important for ongoing investigations in the Human Cell Atlas 43 , the NIH HubMap 44 , the Human Tumor Cell Network 45 and on remodeling of tumor microenvironment 46 . Further, bindSC can potentially be applied to other domains, such as integrating patient sample mRNA profiles with cell-line drug-sensitivity data 47 .…”

Section: Integrating Single-cell Rna With Protein Datamentioning

confidence: 99%

Unbiased integration of single cell multi-omics data

Dou

¹

,

Liang

²

,

Mohanty

³

et al. 2020

Preprint

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Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

show abstract

“…In the single-cell domain, bindSC can clearly be applied to align cells and features simultaneously, which are important for ongoing investigations in the Human Cell Atlas 43 , the NIH HubMap 44 , the Human Tumor Cell Network 45 and on remodeling of tumor microenvironment 46 . Further, bindSC can potentially be applied to other domains, such as integrating patient sample mRNA profiles with cell-line drug-sensitivity data 47 .…”

Section: Integrating Single-cell Rna With Protein Datamentioning

confidence: 99%

Unbiased integration of single cell multi-omics data

Dou

¹

,

Liang

²

,

Mohanty

³

et al. 2020

Preprint

View full text Add to dashboard Cite

Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

show abstract

Global computational alignment of tumor and cell line transcriptional profiles

Cited by 25 publications

References 76 publications

Predicting clinical drug response from model systems by non-linear subspace-based transfer learning

Predicting clinical drug response from model systems by non-linear subspace-based transfer learning

Unbiased integration of single cell multi-omics data

Unbiased integration of single cell multi-omics data

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