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Article ; Online: Hierarchicell: an R-package for estimating power for tests of differential expression with single-cell data.

Zimmerman, Kip D / Langefeld, Carl D

2021 Volume 22, Issue 1, Page(s) 319

Abstract: Background: Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for ... ...

Abstract	Background: Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power. Results: Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. Conclusions: Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.
MeSH term(s)	Gene Expression Profiling ; Humans ; RNA/genetics ; RNA-Seq ; Reproducibility of Results ; Research Design ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2021-05-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2041499-7
ISSN	1471-2164 ; 1471-2164
ISSN (online)	1471-2164
ISSN	1471-2164
DOI	10.1186/s12864-021-07635-w
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Article ; Online: Reply to: A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis.

Zimmerman, Kip D / Evans, Ciaran / Langefeld, Carl D

Nature communications

2022 Volume 13, Issue 1, Page(s) 7852

Language	English
Publishing date	2022-12-22
Publishing country	England
Document type	Letter
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-35520-x
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Article ; Online: A practical solution to pseudoreplication bias in single-cell studies.

Zimmerman, Kip D / Espeland, Mark A / Langefeld, Carl D

Nature communications

2021 Volume 12, Issue 1, Page(s) 738

Abstract: Cells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single- ... ...

Abstract	Cells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.
MeSH term(s)	Computer Simulation ; Quality Control ; Sequence Analysis, RNA/methods ; Transcriptome/genetics
Language	English
Publishing date	2021-02-02
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-21038-1
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Article ; Online: An Isobaric Labeling Approach to Enhance Detection and Quantification of Tissue-Derived Plasma Proteins as Potential Early Disease Biomarkers.

Nazli, Sumaiya / Zimmerman, Kip D / Riojas, Angelica M / Cox, Laura A / Olivier, Michael

Biomolecules

2023 Volume 13, Issue 2

Abstract: The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low ... ...

Abstract	The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low abundances in the plasma. Accurate detection and quantification of low abundance proteins by standard mass spectrometry approaches remain challenging. In addition, it is difficult to link low abundance plasma proteins back to their specific tissues or organs of origin with confidence. To address these challenges, we developed a mass spectrometry approach based on the use of tandem mass tags (TMT) and a tissue reference sample. By applying this approach to nonhuman primate plasma samples, we were able to identify and quantify 820 proteins by using a kidney tissue homogenate as reference. On average, 643 ± 16 proteins were identified per plasma sample. About 58% of proteins identified in replicate experiments were identified both times. A ratio of 50 μg kidney protein to 10 μg plasma protein, and the use of the TMT label with the highest molecular weight (131) for the kidney reference yielded the largest number of proteins in the analysis, and identified low abundance proteins in plasma that are prominently found in the kidney. Overall, this methodology promises efficient quantification of plasma proteins potentially released from specific tissues, thereby increasing the number of putative disease biomarkers for future study.
MeSH term(s)	Animals ; Proteomics/methods ; Biomarkers ; Blood Proteins ; Mass Spectrometry/methods ; Plasma/chemistry
Chemical Substances	Biomarkers ; Blood Proteins
Language	English
Publishing date	2023-01-22
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom13020215
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Article ; Online: Modulation of neural gene networks by estradiol in old rhesus macaque females.

Cervera-Juanes, Rita / Zimmerman, Kip D / Wilhelm, Larry / Zhu, Dongqin / Bodie, Jessica / Kohama, Steven G / Urbanski, Henryk F

GeroScience

2024

Abstract: The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and ... ...

Abstract	The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent subcutaneous bioidentical E2 chronic treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p = 1.6 × 10
Language	English
Publishing date	2024-03-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2886586-8
ISSN	2509-2723 ; 2509-2715
ISSN (online)	2509-2723
ISSN	2509-2715
DOI	10.1007/s11357-024-01133-z
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Article ; Online: The effectiveness of psychiatric genetic counseling training: An analysis of 13 international workshops.

Mack, Tiera / Batallones, Rolan / Morris, Emily / Inglis, Angela / Moldovan, Ramona / McGhee, Kevin / Zimmerman, Kip D / Austin, Jehannine

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics

2024 , Page(s) e32978

Abstract: Studies have consistently shown that psychiatric genetic counseling (pGC) helps people with psychiatric conditions by increasing empowerment and self-efficacy, and addressing emotions like guilt. Yet, it is not routinely provided. Genetic counselors and ... ...

Abstract	Studies have consistently shown that psychiatric genetic counseling (pGC) helps people with psychiatric conditions by increasing empowerment and self-efficacy, and addressing emotions like guilt. Yet, it is not routinely provided. Genetic counselors and trainees express low confidence in their ability to provide meaningful pGC, especially in the absence of adequate training. Therefore, to address this gap a "Psychiatric Genetic Counseling for Genetic Counselors" (PG4GC) workshop was developed and delivered to 13 groups of participants (primarily qualified genetic counselors and trainees) between 2015 and 2023 (10 workshops were delivered in-person, and three virtually). Participants completed quantitative questionnaires both before and after completing the workshop to assess their comfort, knowledge, behavior, and feeling of being equipped to provide pGC. In total, 232 individuals completed the pre-workshop questionnaire and 154 completed the post-workshop questionnaire. Participants felt more comfortable, knowledgeable, and equipped to provide pGC, and reported being more likely to address psychiatric concerns after the workshop, regardless of whether they were trainees or practicing professionals and whether they completed the workshop in-person or virtually. This study suggests that the PG4GC workshop is an effective educational tool in pGC training that may aid in broader implementation of the service.
Language	English
Publishing date	2024-03-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2108616-3
ISSN	1552-485X ; 1552-4841 ; 0148-7299
ISSN (online)	1552-485X
ISSN	1552-4841 ; 0148-7299
DOI	10.1002/ajmg.b.32978
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Zs.A 1376/B: Show issues

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Article: Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data.

Ampong, Isaac / Zimmerman, Kip D / Nathanielsz, Peter W / Cox, Laura A / Olivier, Michael

Metabolites

2022 Volume 12, Issue 5

Abstract: Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in ... ...

Abstract	Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and
Language	English
Publishing date	2022-05-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo12050429
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Article: Modulation of neural gene networks by estradiol in old rhesus macaque females.

Cervera-Juanes, Rita / Zimmerman, Kip D / Wilhelm, Larry / Zhu, Dongqin / Bodie, Jessica / Kohama, Steven G / Urbanski, Henryk F

bioRxiv : the preprint server for biology

2023

Abstract	The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent E2 treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (
Language	English
Publishing date	2023-12-18
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.18.572105
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Article ; Online: Moderate maternal nutrient reduction in pregnancy alters fatty acid oxidation and RNA splicing in the nonhuman primate fetal liver.

Zimmerman, Kip D / Chan, Jeannie / Glenn, Jeremy P / Birnbaum, Shifra / Li, Cun / Nathanielsz, Peter W / Olivier, Michael / Cox, Laura A

Journal of developmental origins of health and disease

2023 Volume 14, Issue 3, Page(s) 381–388

Abstract: Fetal liver tissue collected from a nonhuman primate (NHP) baboon model of maternal nutrient reduction (MNR) at four gestational time points (90, 120, 140, and 165 days gestation [dG], term in the baboon is ∼185 dG) was used to quantify MNR effects on ... ...

Abstract	Fetal liver tissue collected from a nonhuman primate (NHP) baboon model of maternal nutrient reduction (MNR) at four gestational time points (90, 120, 140, and 165 days gestation [dG], term in the baboon is ∼185 dG) was used to quantify MNR effects on the fetal liver transcriptome. 28 transcripts demonstrated different expression patterns between MNR and control livers during the second half of gestation, a developmental period when the fetus undergoes rapid weight gain and fat accumulation. Differentially expressed transcripts were enriched for fatty acid oxidation and RNA splicing-related pathways. Increased RNA splicing activity in MNR was reflected in greater abundances of transcript splice variant isoforms in the MNR group. It can be hypothesized that the increase in splice variants is deployed in an effort to adapt to the poor
MeSH term(s)	Pregnancy ; Animals ; Female ; Fetal Development/genetics ; Papio ; Nutrients ; Liver/metabolism ; Fatty Acids/metabolism
Chemical Substances	Fatty Acids
Language	English
Publishing date	2023-03-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2554780-X
ISSN	2040-1752 ; 2040-1744
ISSN (online)	2040-1752
ISSN	2040-1744
DOI	10.1017/S204017442300003X
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Article ; Online: Assessment of label-free quantification and missing value imputation for proteomics in non-human primates.

Hamid, Zeeshan / Zimmerman, Kip D / Guillen-Ahlers, Hector / Li, Cun / Nathanielsz, Peter / Cox, Laura A / Olivier, Michael

BMC genomics

2022 Volume 23, Issue 1, Page(s) 496

Abstract: Background: Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data ... ...

Abstract	Background: Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs). Results: Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy. Conclusions: Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.
MeSH term(s)	Algorithms ; Animals ; Bayes Theorem ; Primates ; Proteomics/methods ; Software
Language	English
Publishing date	2022-07-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2041499-7
ISSN	1471-2164 ; 1471-2164
ISSN (online)	1471-2164
ISSN	1471-2164
DOI	10.1186/s12864-022-08723-1
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