Batch Effects Research Articles

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

Identification of key differentially expressed immune related genes in patients with persistent atrial fibrillation: an integrated bioinformation analysis

ObjectiveWe aimed to investigate key differentially expressed immune related genes in persistent atrial fibrillation.MethodsGene expression profiles were downloaded from Gene Expression Omnibus (GEO) using “GEO query” package. “limma” package and “sva” package were used to conduct normalization and eliminate batch effects, respectively. We screened out differentially expressed genes (DEGs) based on “limma” package with the standard of |log fold change (FC)| ≥ 1.5 and false discovery rate (FDR) < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed by “clusterProfler” package. We further applied LASSO to select key DEGs, and intersected key DEGs with immune related genes from ImmPort database. The ROC curve of each DEIRG was constructed to evaluate its diagnostic efficiency for AF.ResultsA total of 103 DEGs we were screened out, of them, 48 genes were down-regulated and 55 genes were up-regulated. Result of functional enrichment analysis show that, most of DEGs were related to immune response, inflammation, and oxidative stress. Ultimately, CYBB, RORB, S100A12, and CHGB were determined as key DEIRGs, each of which displayed a favor efficiency for diagnosing persistent AF.ConclusionCYBB, RORB, S100A12, and CHGB were identified as key DEIRGs in persistent AF, and future studies are needed to further explore the underlying roles of CYBB, RORB, S100A12, and CHGB in persistent AF.

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models

Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.

Refining SARS-CoV-2 intra-host variation by leveraging large-scale sequencing data.

Understanding viral genome evolution during host infection is crucial for grasping viral diversity and evolution. Analyzing intra-host single nucleotide variants (iSNVs) offers insights into new lineage emergence, which is important for predicting and mitigating future viral threats. Despite next-generation sequencing's potential, challenges persist, notably sequencing artifacts leading to false iSNVs. We developed a workflow to enhance iSNV detection in large NGS libraries, using over 130 000 SARS-CoV-2 libraries to distinguish mutations from errors. Our approach integrates bioinformatics protocols, stringent quality control, and dimensionality reduction to tackle batch effects and improve mutation detection reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how related groups of data points are represented within a two-dimensional space, enhancing clustering structure explanation based on genetic similarities. This workflow advances accurate intra-host mutation detection, facilitating a deeper understanding of viral diversity and evolution.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.

A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data

Background: Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison. Methods: In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval. Result &amp; Discussion: By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer. Conclusion: Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).

Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies.

Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms. The analysis pipeline is based on effect size metric profiles of samples in specific cell clusters. The effect size substitutes standard differentiation analyses based on p-values and profiles identified based on these effect size metrics serve as a tool to link cell type clusters between the studied organisms. The algorithms were tested on published scRNA-seq data sets derived from several species and subsequently validated on own data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis. Correlation of the effect size profiles between clusters allowed for the linkage of human and bovine cell types. Moreover, effect size ratios were used to identify differentially regulated genes in control and stimulated samples. The genes identified through effect size profiling were confirmed experimentally using qPCR. We demonstrate that in situations where batch effects dominate cell type variation in single cell small sample size multispecies studies, effect size profiling is a valid alternative to traditional statistical inference techniques.

Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.

The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non-invasive system for identifying HER2 and HR in breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In light of the absence of high-performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I-SPY 1) and I-SPY 2, were incorporated. I-SPY 2 was utilized for model training and validation, while I-SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction. The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I-SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I-SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction. This study proposes a non-invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre-trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic.

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

BackgroundExtracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray’s 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.ResultsEighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.ConclusionsThis study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

ScHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Multivariate testing and effect size measures for batch effect evaluation in radiomic features

While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause “batch effects” that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation.

Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R's ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented.

A deep learning paradigm for medical imaging data

We present a novel deep learning framework to address several challenges in medical imaging analysis. Especially for functional magnetic resonance imaging (fMRI) data, we develop a statistical sampling algorithm to augment small data for deep learning modeling. Also, the ComBat harmonization is utilized for multi-source batch effect correction. Finally, we implement the prediction uncertainty in deep learning through a dropout approximation of the deep Gaussian process. The proposed approach offers effective statistical tools to solve persistent problems in deep learning modeling for medical imaging data. To assess the performance of the proposed approach and demonstrate its effectiveness for various types of data, we apply it to autism fMRI and Pneumonia imaging datasets.

Multi-sequence MRI-based radiomics: An objective method to diagnose early-stage osteonecrosis of the femoral head

ObjectivesThis study investigated the use of radiomics for diagnosing early-stage osteonecrosis of the femoral head (ONFH) by extracting features from multiple MRI sequences and constructing predictive models. Materials and methodsWe conducted a retrospective review, collected MR images of early-stage ONFH (102 from institution A and 20 from institution B) and healthy femoral heads (102 from institution A and 20 from institution B) from two institutions. We extracted radiomics features, handled batch effects using Combat, and normalized features using z-score. We employed the Least absolute shrinkage and selection operator (LASSO) algorithm, along with Max-Relevance and Min-Redundancy (mRMR), to select optimal features for constructing radiomics models based on single, double, and multi-sequence MRI data. We evaluated performance using receiver operating characteristic (ROC) and precision-recall (PR) curves, and compared area under curve of ROC (AUC-ROC) values with the DeLong test. Additionally, we studied the diagnostic performance of the multi-sequence radiomics model and radiologists, compared the diagnostic outcomes of the model and radiologists using the Fisher exact test. ResultsWe studied 122 early-stage ONFH and 122 normal femoral heads. The multi-sequence model exhibited the best diagnostic performance among all models (AUC-ROC, PR-AUC for training set: 0.96, 0.961; validation set: 0.96, 0.97; test set: 0.94, 0.94), and it outperformed three resident radiologists on the external testing group with an accuracy of 87.5 %, sensitivity of 85.00 %, and specificity of 90.00 % (p < 0.01), highlighting the robustness of our findings. ConclusionsOur study underscored the novelty of the multi-sequence radiomics model in diagnosing early-stage ONFH. By leveraging features extracted from multiple imaging sequences, this approach demonstrated high efficacy, indicating its potential to advance early diagnosis for ONFH. These findings provided important guidance for enhancing early diagnosis of ONFH through radiomics methods, offering new avenues and possibilities for clinical practice and patient care.

Liam tackles complex multimodal single-cell data integration challenges.

Multi-omics characterization of single cells holds outstanding potential for profiling the dynamics and relations of gene regulatory states of thousands of cells. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing both biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data and mosaic integration of paired with unimodal data. Liam learns a joint low-dimensional representation of the measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tunable combination of conditional and adversarial training, which can be optimized using replicate information while retaining selected biological variation. We demonstrate liam's superior performance on multiple paired multimodal data types, including Multiome and CITE-seq data, and in mosaic integration scenarios. Our detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis.

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

ScCross: efficient search for rare subpopulations across multiple single-cell samples.

Identifying rare cell types is an important task to capture the heterogeneity of single-cell data, such as scRNA-seq. The widespread availability of such data enables to aggregate multiple samples, corresponding for example to different donors, into the same study. Yet, such aggregated data is often subject to batch effects between samples. Clustering it therefore generally requires the use of data integration methods, which can lead to overcorrection, making the identification of rare cells difficult. We present scCross, a biclustering method identifying rare subpopulations of cells present across multiple single-cell samples. It jointly identifies a group of cells with specific marker genes by relying on a global sum criterion, computed over entire subpopulation of cells, rather than pairwise comparisons between individual cells. This proves robust with respect to the high variability of scRNA-seq data, in particular batch effects. We show through several case studies that scCross is able to identify rare subpopulations across multiple samples without performing prior data integration. Namely, it identifies a cilium subpopulation with potential new ciliary genes from lung cancer cells, which is not detected by typical alternatives. It also highlights rare subpopulations in human pancreas samples sequenced with different protocols, despite visible shifts in expression levels between batches. We further show that scCross outperforms typical alternatives at identifying a target rare cell type in a controlled experiment with artificially created batch effects. This shows the ability of scCross to efficiently identify rare cell subpopulations characterized by specific genes despite the presence of batch effects. The R and Scala implementation of scCross is freely available on GitHub, at https://github.com/agerniers/scCross/. A snapshot of the code and the data underlying this article are available on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10471063.

A prostate cancer gastrointestinal transcriptional phenotype may be associated with diminished response to AR-targeted therapy.

Prostate cancer is a heterogenous disease, but once it becomes metastatic it eventually becomes treatment resistant. One mechanism of resistance to AR-targeting therapy is lineage plasticity, where the tumor undergoes a transformation to an AR-indifferent phenotype, most studied in the context of neuroendocrine prostate cancer (NEPC). However, activation of additional de- or trans-differentiation programs, including a gastrointestinal (GI) gene expression program, has been suggested as an alternative method of resistance. In this study, we explored the previously identified GI prostate cancer phenotype (PCa-GI) in a large cohort of metastatic castration-resistant prostate cancer (mCRPC) patient biopsy samples. We analyzed a dataset of 634 mCRPC samples with batch effect corrected gene expression data from the West Coast Dream Team (WCDT), the East Coast Dream Team (ECDT), the Fred Hutchinson Cancer Research Center (FHCRC) and the Weill Cornell Medical center (WCM). Survival data was available from the WCDT and ECDT cohorts. We calculated a gene expression GI score using the sum of z-scores of genes from a published set of PCa-GI-defining genes (N=38). Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards regression with endpoint overall survival from time of biopsy to death of any cause. We found that the PCa-GI score had a bimodal distribution, identifying a distinct set of tumors with an activated GI expression pattern. Approximately 35% of samples were classified as PCa-GI high, which was concordant with prior reports. Liver metastases had the highest median score but after excluding liver samples, 29% of the remaining samples were still classified as PCa-GI high, suggesting a distinct phenotype not exclusive to liver metastases. No correlation was observed between GI score and proliferation, AR signaling, or NEPC scores. Furthermore, the PCa-GI score was not associated with genomic alterations in AR, FOXA1, RB1, TP53 or PTEN. However, tumors with MYC amplifications showed significantly higher GI scores (p=0.0001). Patients with PCa-GI tumors had a shorter survival (HR=1.5 [1.1-2.1], p=0.02), but this result was not significant after adjusting for the liver as metastatic site (HR=1.2 [0.82-1.7], p=0.35). Patients with PCa-GI low samples had a better outcome after androgen receptor signaling inhibitors (ASI, abiraterone or enzalutamide) than other therapies (HR=0.37 [0.22-0.61], p=0.0001) while the benefit of ASI was smaller and non-significant for PCa-GI high samples (HR=0.55 [0.29-1.1], p=0.07). A differential pathway analysis identified FOXA2 signaling to be upregulated PCa-GI high tumors (FDR = 3.7 × 10-13). The PCa-GI phenotype is prevalent in clinical mCRPC samples and may represent a distinct biological entity. PCa-GI tumors may respond less to ASI and could offer a strategy to study novel therapeutic targets.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies.

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.