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Background There is interest in deriving megakaryocytes (MKs) from pluripotent stem cells (iPSC) for biological studies. We previously found that genomic structural integrity and genotype concordance is maintained in iPSC-derived MKs. Objective To establish a comprehensive dataset of genes and proteins expressed in iPSC-derived MKs. Methods iPSCs were reprogrammed from peripheral blood mononuclear cells (MNCs) and MKs were derived from the iPSCs in 194 healthy European American and African American subjects. mRNA was isolated and gene expression measured by RNA sequencing. Protein expression was measured in 62 of the subjects using mass spectrometry. Results and conclusions MKs expressed genes and proteins known to be important in MK and platelet function and demonstrated good agreement with previous studies in human MKs derived from CD34+ progenitor cells. The percent of cells expressing the MK markers CD41 and CD42a was consistent in biological replicates, but variable across subjects, suggesting that unidentified subject-specific factors determine differentiation of MKs from iPSCs. Gene and protein sets important in platelet function were associated with increasing expression of CD41/42a, while those related to more basic cellular functions were associated with lower CD41/42a expression. There was differential gene expression by the sex and race (but not age) of the subject. Numerous genes and proteins were highly expressed in MKs but not known to play a role in MK or platelet function; these represent excellent candidates for future study of hematopoiesis, platelet formation, and/or platelet function.

Genome-wide association studies have identified common variants associated with platelet-related phenotypes, but because these variants are largely intronic or intergenic, their link to platelet biology is unclear. In 290 normal subjects from the GeneSTAR Research Study (110 African Americans [AAs] and 180 European Americans [EAs]), we generated whole-genome sequence data from whole blood and RNA sequence data from extracted nonribosomal RNA from 185 induced pluripotent stem cell-derived megakaryocyte (MK) cell lines (platelet precursor cells) and 290 blood platelet samples from these subjects. Using eigenMT software to select the peak single-nucleotide polymorphism (SNP) for each expressed gene, and meta-analyzing the results of AAs and EAs, we identify (q-value < 0.05) 946 cis-expression quantitative trait loci (eQTLs) in derived MKs and 1830 cis-eQTLs in blood platelets. Among the 57 eQTLs shared between the 2 tissues, the estimated directions of effect are very consistent (98.2% concordance). A high proportion of detected cis-eQTLs (74.9% in MKs and 84.3% in platelets) are unique to MKs and platelets compared with peak-associated SNP-expressed gene pairs of 48 other tissue types that are reported in version V7 of the Genotype-Tissue Expression Project. The locations of our identified eQTLs are significantly enriched for overlap with several annotation tracks highlighting genomic regions with specific functionality in MKs, including MK-specific DNAse hotspots, H3K27-acetylation marks, H3K4-methylation marks, enhancers, and superenhancers. These results offer insights into the regulatory signature of MKs and platelets, with significant overlap in genes expressed, eQTLs detected, and enrichment within known superenhancers relevant to platelet biology.

Significance Machine learning is now being used across the entire scientific enterprise. Researchers commonly use the predictions from random forests or deep neural networks in downstream statistical analysis as if they were observed data. We show that this approach can lead to extreme bias and uncontrolled variance in downstream statistical models. We propose a statistical adjustment to correct biased inference in regression models using predicted outcomes—regardless of the machine-learning model used to make those predictions., Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes postprediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with arbitrarily complicated machine-learning models including random forests and deep neural nets. Rather than trying to derive the correction from first principles for each machine-learning algorithm, we observe that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for postprediction inference that naturally fits into the standard machine-learning framework where the data are divided into training, testing, and validation sets. We train the prediction model in the training set, estimate the relationship between the observed and predicted outcomes in the testing set, and use that relationship to correct subsequent inference in the validation set. We show our postprediction inference (postpi) approach can correct bias and improve variance estimation and subsequent statistical inference with predicted outcomes. To show the broad range of applicability of our approach, we show postpi can improve inference in two distinct fields: modeling predicted phenotypes in repurposed gene expression data and modeling predicted causes of death in verbal autopsy data. Our method is available through an open-source R package: https://github.com/leekgroup/postpi.

We performed an empirical study of the perceived quality of scientific graphics produced by beginning R users in two plotting systems: the base graphics package (“base R”) and the ggplot2 add-on package. In our experiment, students taking a data science course on the Coursera platform were randomized to complete identical plotting exercises using either base R or ggplot2. This exercise involved creating two plots: one bivariate scatterplot and one plot of a multivariate relationship that necessitated using color or panels. Students evaluated their peers on visual characteristics key to clear scientific communication, including plot clarity and sufficient labeling. We observed that graphics created with the two systems rated similarly on many characteristics. However, ggplot2 graphics were generally perceived by students to be slightly more clear overall with respect to presentation of a scientific relationship. This increase was more pronounced for the multivariate relationship. Through expert analysis of submissions, we also find that certain concrete plot features (e.g., trend lines, axis labels, legends, panels, and color) tend to be used more commonly in one system than the other. These observations may help educators emphasize the use of certain plot features targeted to correct common student mistakes. Supplementary materials for this article are available online.

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

Within the statistics community, a number of guiding principles for sharing data have emerged; however, these principles are not always made clear to collaborators generating the data. To bridge this divide, we have established a set of guidelines for sharing data. In these, we highlight the need to provide raw data to the statistician, the importance of consistent formatting, and the necessity of including all essential experimental information and pre-processing steps carried out to the statistician. With these guidelines we hope to avoid errors and delays in data analysis.

Introduction: Induced pluripotent stem cells (iPSC) have a significant capacity for self-renewal and can differentiate into any somatic cell type, such as megakaryocytes (MK) and vascular endothelial cells (EC). We have previously shown that iPSC-derived MKs (IPSC-MK) upregulate the gene expression levels of irreversible glycolytic enzymes committing glucose to glycolysis in comparison to their parental iPSC lines (Vaidya et al., 2016). This pattern differed from past comparisons between somatic source cells and iPSCs (Chung et al., 2007; Varum et al., 2011; Zhang et al., 2011). To further corroborate our data, we extended our analysis to iPSC-derived ECs (iPSC-EC). Methods: We studied expression of glycolytic enzyme genes in 30 human iPSC lines and paired iPSC-EC and iPSC-MK lines using RNA-seq. iPSC-EC to iPSC expression ratios for 11 glycolytic enzymes were estimated using a multilevel mixed-effect model regression, and a significance threshold of p < 0.05/11 = 0.0045 was used. A bivariate statistical analysis was used to assess the correlation between iPSC-EC and iPSC-MK data. Results: Of the 11 glycolytic enzymes studied, 4 were highly expressed in iPSC-ECs (Log2(iPSC-EC/iPSC) ≥ 1) while 1 was highly expressed in iPSCs (Log2(iPSC/iPSC-EC) ≥ 1). Transcripts for functionally irreversible enzymes including ADPGK, PFKP, and PKLR displayed higher expression levels in iPSC-ECs compared to iPSCs. Similarly, 6 glycolytic genes demonstrated higher expression in iPSC-MKs, while 2 were more highly expressed in iPSCs. The functionally irreversible enzymes HK1, ADPGK, and PFK exhibited higher expression levels in iPSC-MKs. The ratios of gene expression (derived-lines:iPSCs) for all 37 genes in the iPSC-MK results correlated with those of the iPSC-EC results: Pearson’s r = 0.52, p = 0.001. Conclusion: Both iPSC-ECs and iPSC-MKs upregulate the expression of glycolytic enzymes compared to their parental iPSCs. In light of data showing that differentiated cells are more metabolically active and have more mitochondria than iPSCs (Chandra et al., 2020), these results suggest that differentiation of iPSCs is accompanied by an increase in both glycolytic and TCA cycle activities, with glycolytic generation of pyruvate used for mitochondrial production of ATP.

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the originalrecount2resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Medicine has always been a data science. Collecting and interpreting data is a key component of every interaction between physicians and patients. Data can be anything from blood pressure measurements at a yearly exam to complex radiology images interpreted by experts or algorithms. Interpreting these uncertain data for accurate diagnosis, management, and care is a critical component of every physician’s daily life. The intimate relationship between data science and medicine is apparent in the pages of our most prominent medical journals. Using Pubmed, we pulled the abstracts of all papers published in The New England Journal of Medicine, JAMA, Nature Medicine, The Lancet, PLoS Medicine, and BMJ for the years 2010 - March 2019. We then searched for a list of statistical terms in the text of these abstracts. For these 12,281 abstracts a median of 50% (IQR 30%, 67%) of sentences contained a term that would require statistical training to understand.

Reproducibility and replicability are fundamental requirements of scientific studies. Disagreements over universal definitions for these terms have affected the interpretation of large-scale replication attempts. We provide a visual tool for representing definitions and use it to re-examine these attempts.

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a method—quality surrogate variable analysis (qSVA)—as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.

There has been an increasing concern in both the scientific and lay communities that most published medical findings are false. But what does it mean to be false? Here we describe the range of definitions of false discoveries in the scientific literature. We summarize the philosophical, statistical, and experimental evidence for each type of false discovery. We discuss common underpinning problems with the scientific and data analytic practices and point to tools and behaviors that can be implemented to reduce the problems with published scientific results.

Many modern problems in medicine and public health leverage machine learning methods to predict outcomes based on observable covariates. In an increasingly wide array of settings, these predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomespost-prediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with an arbitrary machine learning method. Rather than trying to derive the correction from the first principles for each machine learning tool, we make the observation that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for thepost-prediction inferencethat naturally fits into the standard machine learning framework, where the data is divided into training, testing, and validation sets. We train the prediction model in the training set,. We estimate the relationship between the observed and predicted outcomes on the testing set and use that model to correct inference on the validation set and subsequent statistical models. We show ourpostpiapproach can correct bias and improve variance estimation (and thus subsequent statistical inference) with predicted outcome data. To show the broad range of applicability of our approach, we showpostpican improve inference in two totally distinct fields: modeling predicted phenotypes in re-purposed gene expression data and modeling predicted causes of death in verbal autopsy data. We have made our method available through an open-source R package: https://github.com/leekgroup/postpi