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Sampling time-dependent artifacts in single-cell genomics studies.

ABSTRACT: Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

SUBMITTER: Massoni-Badosa R

PROVIDER: S-EPMC7212672 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Sampling time-dependent artifacts in single-cell genomics studies.

Massoni-Badosa Ramon R Iacono Giovanni G Moutinho Catia C Kulis Marta M Palau Núria N Marchese Domenica D Rodríguez-Ubreva Javier J Ballestar Esteban E Rodriguez-Esteban Gustavo G Marsal Sara S Aymerich Marta M Colomer Dolors D Campo Elias E Julià Antonio A Martín-Subero José Ignacio JI Heyn Holger H

Genome biology 20200511 1

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental a ...[more]

PMID: 32393363

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Sampling time-dependent artifacts in single-cell genomics studies

Project description:Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

2020-04-27 | GSE132065 | GEO

Sampling time-dependent artifacts in single-cell genomics studies

Project description:Sampling time-dependent artifacts in single-cell genomics studies

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Time of Day Bias for Biological Sampling in Studies of Mammary Cancer

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Project description:Evaluating the classification accuracy of a candidate biomarker signaling the onset of disease or disease status is essential for medical decision making. A good biomarker would accurately identify the patients who are likely to progress or die at a particular time in the future or who are in urgent need for active treatments. To assess the performance of a candidate biomarker, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. In many cases, the standard simple random sampling (SRS) design used for biomarker validation studies is costly and inefficient. In order to improve the efficiency and reduce the cost of biomarker validation, marker-dependent sampling (MDS) may be used. In a MDS design, the selection of patients to assess true survival time is dependent on the result of a biomarker assay. In this article, we introduce a nonparametric estimator for time-dependent AUC under a MDS design. The consistency and the asymptotic normality of the proposed estimator is established. Simulation shows the unbiasedness of the proposed estimator and a significant efficiency gain of the MDS design over the SRS design.

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Statistical Methods for Time-Dependent Variables in Hematopoietic Cell Transplantation Studies.

Project description:The interaction of clinically important yet time-dependent events such as infection and acute graft-versus-host disease (GVHD) on hematopoietic cell transplant outcomes is of particular interest to transplant physicians. Clinically, the development of these events is unknown at the time of transplant, but both events place the patient at risk of morbidity and mortality. Furthermore, the occurrence of one may affect the risk for the development of the other (ie, GVHD results in increased immunosuppression, resulting in infection). While these risks can be determined using traditional Cox modeling, due to their time-varying effects on the outcome, it is challenging to graphically display the patient's expected clinical status over time. Landmark analysis is one of the commonly used methods to present time-dependent variables graphically. It can be a useful tool for describing an outcome of interest with time-dependent variables. In this article, we review the basic concepts of time-dependent variables and describe a landmark study with a single-landmark time point and a dynamic landmark study with multiple landmark time points. We illustrate these methods with a hematopoietic cell transplantation data set with infections.

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Project description:It is not economically feasible to sequence all study subjects in a large cohort. A cost-effective strategy is to sequence only the subjects with the extreme values of a quantitative trait. In the National Heart, Lung, and Blood Institute Exome Sequencing Project, subjects with the highest or lowest values of body mass index, LDL, or blood pressure were selected for whole-exome sequencing. Failure to account for such trait-dependent sampling can cause severe inflation of type I error and substantial loss of power in quantitative trait analysis, especially when combining results from multiple studies with different selection criteria. We present valid and efficient statistical methods for association analysis of sequencing data under trait-dependent sampling. We pay special attention to gene-based analysis of rare variants. Our methods can be used to perform quantitative trait analysis not only for the trait that is used to select subjects for sequencing but for any other traits that are measured. For a particular trait of interest, our approach properly combines the association results from all studies with measurements of that trait. This meta-analysis is substantially more powerful than the analysis of any single study. By contrast, meta-analysis of standard linear regression results (ignoring trait-dependent sampling) can be less powerful than the analysis of a single study. The advantages of the proposed methods are demonstrated through simulation studies and the National Heart, Lung, and Blood Institute Exome Sequencing Project data. The methods are applicable to other types of genetic association studies and nongenetic studies.

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Accelerated failure time model for data from outcome-dependent sampling.

Project description:Outcome-dependent sampling designs such as the case-control or case-cohort design are widely used in epidemiological studies for their outstanding cost-effectiveness. In this article, we propose and develop a smoothed weighted Gehan estimating equation approach for inference in an accelerated failure time model under a general failure time outcome-dependent sampling scheme. The proposed estimating equation is continuously differentiable and can be solved by the standard numerical methods. In addition to developing asymptotic properties of the proposed estimator, we also propose and investigate a new optimal power-based subsamples allocation criteria in the proposed design by maximizing the power function of a significant test. Simulation results show that the proposed estimator is more efficient than other existing competing estimators and the optimal power-based subsamples allocation will provide an ODS design that yield improved power for the test of exposure effect. We illustrate the proposed method with a data set from the Norwegian Mother and Child Cohort Study to evaluate the relationship between exposure to perfluoroalkyl substances and women's subfecundity.

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