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Methods for single-cell CRISPR screens and multiomic data: constructing powerful well-calibrated tests, circumventing unmeasured confounding, and accounting for denoising and imputation

Award Number: R01MH123184
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Project summary/abstract In this application, we request continuation of MH123184, which aimed to understand how genetic variation alters transcription in specific cells and thereby produces psychopathology. Our research developed statistical methods to integrate single cell and tissue-level transcriptomic data. We targeted methods to identify gene communities, defined in terms of cell type and spatiotemporal window, to understand how genes act in concert to confer risk for psychopathology. We also took advantage of an exciting new avenue of research to approach these challenges, namely CRISPR screening. This innovation has emerged as a powerful tool to characterize the effects of genetic perturbations on the entire transcriptome at a single-cell level. Here we propose research covering three related themes, all of which capitalize on CRISPR advancements: (1) develop powerful and well-calibrated tests for the effect of CRISPR perturbations on gene expression by inferring latent factors; (2) develop methods for removing the effect of unmeasured confounders in high throughput screens; and (3) develop methods for imputation and denoising for multiomic data that facilitate downstream testing of omic readouts. Each of these aims is motivated by pressing needs in the field. First, due to small samples and the sparsity of the response variable, it is essential that we enhance the power and interpretability of CRISPR tests by accounting for co- regulation and convergent function of genes. Aim 1 achieves this purpose by estimating latent factors that represent co-regulated genes and by inferring a similarity matrix among gene perturbations. As CRISPR screens advance to more biologically complex settings, such as model organisms, unmeasured confounders will play a more important role, and new methods are needed to control for these effects. Aim 2 develops two approaches to this challenge: an innovative use of negative control variables, as motivated by the causal literature, and key advances to the classic surrogate variable analysis method. For the field to move toward efficient use of multiomic data, data derived from multiple sources will be required. These resources will invariably have missing data. Methods to account for imputation of missing data are needed. Tools developed for variational autoencoders show great promise; however, as described in Aim 3, they need to be paired with semiparametric inference tools to ensure robust and well calibrated downstream analysis. By applying what we learn from these three aims to available resources, most from distributed resources and some from our collaborations, we expect to shed more light on the neurobiological mechanisms of mental illness. We are well positioned to move between theory and data because we have a diverse team of investigators lead by the PI (Roeder), who has decades of experience in statistical genomic field and co-investigators Wasserman and Lei, who are experts in theory and methods for high dimensional and causal inference. The proposed research fits Goal 1 of NIMH’s Strategic Plan, advancing basic science of brain, genomics, and behavior to understand mental illnesses.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $592,551 )
2024	2024	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	002	5	7/2/2024	COMPETING CONTINUATION	$59,255
2024	2024	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	001	5	3/29/2024	COMPETING CONTINUATION	$533,296
2024	2023	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	000	4	3/28/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $592,551

Issue Date FY: 2023 ( Subtotal = $503,876 )
2023	2023	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	000	4	2/27/2023	NON-COMPETING CONTINUATION	$503,876
														Subtotal = $503,876

Issue Date FY: 2022 ( Subtotal = $503,817 )
2022	2022	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	001	3	6/7/2022	NON-COMPETING CONTINUATION	$50,383
2022	2022	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	000	3	2/17/2022	NON-COMPETING CONTINUATION	$453,434
														Subtotal = $503,817

Issue Date FY: 2021 ( Subtotal = $505,168 )
2021	2021	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	000	2	2/24/2021	NON-COMPETING CONTINUATION	$505,168
														Subtotal = $505,168

Issue Date FY: 2020 ( Subtotal = $516,076 )
2020	2020	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Mental Health Research Grants	000	1	4/13/2020	NEW	$516,076
														Subtotal = $516,076

Grand Total All Awards = $2,621,488

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Methods for single-cell CRISPR screens and multiomic data: constructing powerful well-calibrated tests, circumventing unmeasured confounding, and accounting for denoising and imputation

Award Number: R01MH123184

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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