1
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Chen BH, Zhou W. mLiftOver: harmonizing data across Infinium DNA methylation platforms. Bioinformatics 2024; 40:btae423. [PMID: 38963309 PMCID: PMC11233119 DOI: 10.1093/bioinformatics/btae423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 07/05/2024] Open
Abstract
MOTIVATION Infinium DNA methylation BeadChips are widely used for genome-wide DNA methylation profiling at the population scale. Recent updates to probe content and naming conventions in the EPIC version 2 (EPICv2) arrays have complicated integrating new data with previous Infinium array platforms, such as the MethylationEPIC (EPIC) and the HumanMethylation450 (HM450) BeadChip. RESULTS We present mLiftOver, a user-friendly tool that harmonizes probe ID, methylation level, and signal intensity data across different Infinium platforms. It manages probe replicates, missing data imputation, and platform-specific bias for accurate data conversion. We validated the tool by applying HM450-based cancer classifiers to EPICv2 cancer data, achieving high accuracy. Additionally, we successfully integrated EPICv2 healthy tissue data with legacy HM450 data for tissue identity analysis and produced consistent copy number profiles in cancer cells. AVAILABILITY AND IMPLEMENTATION mLiftOver is implemented R and available in the Bioconductor package SeSAMe (version 1.21.13+): https://bioconductor.org/packages/release/bioc/html/sesame.html. Analysis of EPIC and EPICv2 platform-specific bias and high-confidence mapping is available at https://github.com/zhou-lab/InfiniumAnnotationV1/raw/main/Anno/EPICv2/EPICv2ToEPIC_conversion.tsv.gz. The source code is available at https://github.com/zwdzwd/sesame/blob/devel/R/mLiftOver.R under the MIT license.
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Affiliation(s)
- Brian H Chen
- California Pacific Medical Center Research Institute, Sutter Health, San Francisco, CA 94143, United States
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, United States
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
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2
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Planterose Jiménez B, Kayser M, Vidaki A, Caliebe A. Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values. Biom J 2024; 66:e2300090. [PMID: 38813859 DOI: 10.1002/bimj.202300090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 03/25/2024] [Accepted: 04/01/2024] [Indexed: 05/31/2024]
Abstract
Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this shortcoming is usually resolved by complete-case analysis or imputation. Both work-arounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore-Penrose pseudoinverse, and the reduced QR decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference data set, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external data sets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness of fit, missing value type, and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a person's biological age from epigenetic data with promising clinical applications.
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Affiliation(s)
- Benjamin Planterose Jiménez
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Athina Vidaki
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Amke Caliebe
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
- University Medical Centre Schleswig-Holstein, Kiel, Germany
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3
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Goldberg DC, Cloud C, Lee SM, Barnes B, Gruber S, Kim E, Pottekat A, Westphal M, McAuliffe L, Majournie E, KalayilManian M, Zhu Q, Tran C, Hansen M, Parker JB, Kohli RM, Porecha R, Renke N, Zhou W. MSA: scalable DNA methylation screening BeadChip for high-throughput trait association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594606. [PMID: 38826316 PMCID: PMC11142114 DOI: 10.1101/2024.05.17.594606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The Infinium DNA Methylation BeadChips have significantly contributed to population-scale epigenetics research by enabling epigenome-wide trait association discoveries. Here, we design, describe, and experimentally verify a new iteration of this technology, the Methylation Screening Array (MSA), to focus on human trait screening and discovery. This array utilizes extensive data from previous Infinium platform-based epigenome-wide association studies (EWAS). It incorporates knowledge from the latest single-cell and cell type-resolution whole genome methylome profiles. The MSA is engineered to achieve scalable screening of epigenetics-trait association in an ultra-high sample throughput. Our design encompassed diverse human trait associations, including those with genetic, cellular, environmental, and demographical variables and human diseases such as genetic, neurodegenerative, cardiovascular, infectious, and immune diseases. We comprehensively evaluated this array's reproducibility, accuracy, and capacity for cell-type deconvolution and supporting 5-hydroxymethylation profiling in diverse human tissues. Our first atlas data using this platform uncovered the complex chromatin and tissue contexts of DNA modification variations and genetic variants linked to human phenotypes.
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Affiliation(s)
- David C Goldberg
- Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, PA, 19104, USA
| | - Cameron Cloud
- Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, PA, 19104, USA
| | - Sol Moe Lee
- Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, PA, 19104, USA
| | | | | | - Elliot Kim
- Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, PA, 19104, USA
| | | | | | | | | | | | | | | | | | - Jared B Parker
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rahul M Kohli
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | - Wanding Zhou
- Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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4
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Lee SM, Loo C, Prasasya R, Bartolomei M, Kohli R, Zhou W. Low-input and single-cell methods for Infinium DNA methylation BeadChips. Nucleic Acids Res 2024; 52:e38. [PMID: 38407446 PMCID: PMC11040145 DOI: 10.1093/nar/gkae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 02/27/2024] Open
Abstract
The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection P-value calculation achieved higher sensitivities for low-input datasets and was validated in over 100 000 public diverse methylome profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.
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Affiliation(s)
- Sol Moe Lee
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA 19104, USA
| | - Christian E Loo
- Graduate Group in Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rexxi D Prasasya
- Department of Cell and Developmental Biology, Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Marisa S Bartolomei
- Department of Cell and Developmental Biology, Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Rahul M Kohli
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Dai L, Johnson-Buck A, Laird PW, Tewari M, Walter NG. Ultrasensitive amplification-free quantification of a methyl CpG-rich cancer biomarker by single-molecule kinetic fingerprinting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.06.587997. [PMID: 38645159 PMCID: PMC11030368 DOI: 10.1101/2024.04.06.587997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The most well-studied epigenetic marker in humans is the 5-methyl modification of cytosine in DNA, which has great potential as a disease biomarker in liquid biopsies of cell-free DNA. Currently, quantification of DNA methylation relies heavily on bisulfite conversion followed by PCR amplification and NGS or microarray analysis. PCR is subject to potential bias in differential amplification of bisulfite-converted methylated versus unmethylated sequences. Here, we combine bisulfite conversion with single-molecule kinetic fingerprinting to develop an amplification-free assay for DNA methylation at the branched-chain amino acid transaminase 1 (BCAT1) promoter. Our assay selectively responds to methylated sequences with a limit of detection below 1 fM and a specificity of 99.9999%. Evaluating complex genomic DNA matrices, we reliably distinguish 2-5% DNA methylation at the BCAT1 promoter in whole blood DNA from completely unmethylated whole-genome amplified DNA. Taken together, these results demonstrate the feasibility and sensitivity of our amplification-free, single-molecule quantification approach to improve the early detection of methylated cancer DNA biomarkers.
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Affiliation(s)
- Liuhan Dai
- Single Molecule Analysis Group, Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
- Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander Johnson-Buck
- Single Molecule Analysis Group, Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter W. Laird
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Muneesh Tewari
- Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nils G. Walter
- Single Molecule Analysis Group, Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
- Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI 48109, USA
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Lee SM, Loo CE, Prasasya RD, Bartolomei MS, Kohli RM, Zhou W. Low-input and single-cell methods for Infinium DNA methylation BeadChips. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558252. [PMID: 37786695 PMCID: PMC10541608 DOI: 10.1101/2023.09.18.558252] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection p -values calculation achieved higher sensitivities for low-input datasets and was validated in over 100,000 public datasets with diverse methylation profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.
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8
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Inkster AM, Wong MT, Matthews AM, Brown CJ, Robinson WP. Who's afraid of the X? Incorporating the X and Y chromosomes into the analysis of DNA methylation array data. Epigenetics Chromatin 2023; 16:1. [PMID: 36609459 PMCID: PMC9825011 DOI: 10.1186/s13072-022-00477-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Many human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data. RESULTS With Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters. CONCLUSION While there may be no single "best" approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.
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Affiliation(s)
- Amy M Inkster
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada.
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada.
| | - Martin T Wong
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
| | - Allison M Matthews
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, V6T 1Z7, Canada
| | - Carolyn J Brown
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
| | - Wendy P Robinson
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
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Salas LA, Zhang Z, Koestler DC, Butler RA, Hansen HM, Molinaro AM, Wiencke JK, Kelsey KT, Christensen BC. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nat Commun 2022; 13:761. [PMID: 35140201 PMCID: PMC8828780 DOI: 10.1038/s41467-021-27864-7] [Citation(s) in RCA: 94] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022] Open
Abstract
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.
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Affiliation(s)
- Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Devin C Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Rondi A Butler
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Helen M Hansen
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Karl T Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
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