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ategorizing individual cells into one of many known cell-type categories, also known as cell-type annotation, is a critical step in the analysis of single-cell genomics data. The current process of annotation is time intensive and subjective, which has led to different studies describing cell types with labels of varying degrees of resolution. While supervised learning approaches have provided automated solutions to annotation, there remains a significant challenge in fitting a unified model for multiple datasets with inconsistent labels. In this article we propose a new multinomial logistic regression estimator which can be used to model cell-type probabilities by integrating multiple datasets with labels of varying resolution. To compute our estimator, we solve a nonconvex optimization problem using a blockwise proximal gradient descent algorithm. We show through simulation studies that our approach estimates cell-type probabilities more accurately than competitors in a wide variety of scenarios. We apply our method to 10 single-cell RNA-seq datasets and demonstrate its utility in predicting fine resolution cell-type labels on unlabeled data as well as refining cell-type labels on data with existing coarse resolution annotations. Finally, we demonstrate that our method can lead to novel scientific insights in the context of a differential expression analysis comparing peripheral blood gene expression before and after treatment with interferon-β. An R package implementing the method is available in the Supplementary Material and at https://github.com/keshav-motwani/IBMR, and the collection of datasets we analyze is available at https://github.com/keshav-motwani/AnnotatedPBMC.
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Multiresolution Categorical Regression for Interpretable Cell-Type Annotation
Abstract In many categorical response regression applications, the response categories admit a multiresolution structure. That is, subsets of the response categories may naturally be combined into coarser response categories. In such applications, practitioners are often interested in estimating the resolution at which a predictor affects the response category probabilities. In this paper, we propose a method for fitting the multinomial logistic regression model in high dimensions that addresses this problem in a unified and data-driven way. Our method allows practitioners to identify which predictors distinguish between coarse categories but not fine categories, which predictors distinguish between fine categories, and which predictors are irrelevant. For model fitting, we propose a scalable algorithm that can be applied when the coarse categories are defined by either overlapping or nonoverlapping sets of fine categories. Statistical properties of our method reveal that it can take advantage of this multiresolution structure in a way existing estimators cannot. We use our method to model cell-type probabilities as a function of a cell's gene expression profile (i.e., cell-type annotation). Our fitted model provides novel biological insights which may be useful for future automated and manual cell-type annotation methodology.
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- PAR ID:
- 10467974
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 79
- Issue:
- 4
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 3485-3496
- Size(s):
- p. 3485-3496
- Sponsoring Org:
- National Science Foundation
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