Heterogeneity in human diseases presents clinical challenges in accurate disease characterization and treatment. Recently available high throughput multi-omics data may offer a great opportunity to explore the underlying mechanisms of diseases and improve disease heterogeneity assessment throughout the treatment course. In addition, increasingly accumulated data from existing literature may be informative about disease subtyping. However, the existing clustering procedures, such as Sparse Convex Clustering (SCC), cannot directly utilize the prior information even though SCC produces stable clusters.We develop a clustering procedure, information-incorporated Sparse Convex Clustering, to respond to the need for disease subtyping in precision medicine. Utilizing the text mining approach, the proposed method leverages the existing information from previously published studies through a group lasso penalty to improve disease subtyping and biomarker identification. The proposed method allows taking heterogeneous information, such as multi-omics data. We conduct simulation studies under several scenarios with various accuracy of the prior information to evaluate the performance of our method. The proposed method outperforms other clustering methods, such as SCC, K-means, Sparse K-means, iCluster+, and Bayesian Consensus Clustering. In addition, the proposed method generates more accurate disease subtypes and identifies important biomarkers for future studies in real data analysis of breast and lung cancer-related omics data. In conclusion, we present an information-incorporated clustering procedure that allows coherent pattern discovery and feature selection.The code is available upon request. Disease heterogeneity is common in many complex diseases, such as cancer. As a result, patients with a shared diagnosis may have different clinical responses to the same treatment. Such heterogeneity could be due to disease subtypes with distinct physiological processes. Therefore, accurate identification of these disease subtypes becomes critical for precision medicine. Breast cancer, e.g. four molecular subtypes  with distinct clinical responses to treatments have been identified through gene expression data . Using oClustering, an unsupervised machine learning method, has been widely applied to the disease subtyping problem . In addin = 60 samples by p = 100 features. We aim to find coherent sample clusters associated with the disease of interest. Nevertheless, other noisy patterns within omics datasets may compromise the power to identify clinically meaningful subtypes. For example, in genetics studies, clusters could be driven by sex-related genes, age-related genes, or other unknown confounders-related genes instead of disease-related genes that interest us  AND (breast cancer[Title/Abstract])” in the search field. This search allows us to count how frequently progesterone receptor (PGR) is mentioned together with breast cancer in the same study. Further refinement of the search field is available based on the purpose of the study. More detailed information is available at https://pubmed.ncbi.nlm.nih.gov/help/#search-tags. For illustration, we focus on publications with a pair of a feature and a disease in their titles or abstracts.Denote Step II: Information-incorporated analysis. Before integrating multiple omics datasets, we need a standardization procedure. We perform a feature-level centering and scaling each omics dataset by its standard deviation. Then we concatenate all transformed omics datasets into one big matrix 2-norm. The second term of (2) is related to the fused lasso penalty (hm (AMA)  to solveWhen applying iSCC, the sample weight approach  to selecapproach , which iWhen the numbers of clusters and informative features are known, we can first fix 1 and γ2 . We firsscvxlustr, sparcl, iClusterPlus) and code. We implement the adjusted Rand index (ARI)   to measuex (ARI)  to evaluS1 spherical setting: The numbers of features are S2 spherical setting: The number of features is S3 nonspherical setting with two half-moons: The number of features is For each setting, we evaluate the effect of the accuracy rate of informative feature weights  and MCC (0.59) among all iSCC occur with S2, iSCC with S3. In terms of feature selection, iSCC with S3. The clustering accuracy and the performance of feature selection for iSCC generally decrease when the accuracy rate of feature weights decreases. We only compare feature selection among iSCC, SCC, Sparse K-means, and iCluster+ since K-means and BCC cannot select features.The simulation results are summarized in S1 and S2. In terms of feature selection, iSCC with S1 and S2. In S3, iSCC outperforms other methods when The simulation results when the clusters with imbalanced sample size are summarized in In this section, we apply our method, iSCC, to several multi-omics data from the Cancer Genome Atlas (TCGA) breast cancer and mRNA expression data from human lung cancer. We evaluate the performance of our method against other clustering methods, including SCC, K-means, Sparse K-means, iCluster+, and BCC. We use the above-mentioned BIC-based approach to tune hyperparameters for iSCC and SCC. The gap statistic , colon cancer metastasis (Colon: n = 13), small cell carcinoma , and normal lung .Lung cancer subtyping according to a molecular basis could help predict a patient’s treatment outcome and develop targeted therapy. We use the mRNA gene expression data based on a cohort of people with or without lung cancer . Many literatures have reported that these differentially expressed genes are related to the mechanism and subtypes of lung cancer (MUC1, for example, is associated with a poor prognosis of lung cancer (GRP, has been used as a tumor marker for small-cell lung cancer (IL6 has recently been demonstrated to promote metastasis of non-small cell lung cancer (Our method maintains the highest ARI score of 0.78 compared to others . Supplemg cancer , most haThe clustering analysis for disease subtyping faces two critical challenges: a lack of ways to utilize existing information and incorrectly identifying disease-related clusters. We develop an information-incorporated sparse convex clustering procedure, iSCC, for disease subtyping and biomarker identification. Simulation studies show that the proposed method outperforms other clustering methods when the accuracy rate of feature weights is relatively high. In addition, the real data analysis of omics data demonstrates that iSCC can identify more accurate disease subtypes and select biologically meaningful biomarkers.We incorporate prior information from published studies through text mining techniques to enhance the identification of clinically meaningful disease subtypes and disease-related biomarkers. In our study, the text mining procedure only includes omics data for demonstration purposes. However, the method itself can be applied to other types of features, clinical or nonclinical, as long as there are suitable data resources for text mining. We take a binary weighting scheme for different features based on their co-occurrence with the disease of interest during the information retrieval procedure. This weighting approach will be less affected by the large variation of The proposed method can offer alternative settings beyond the weighting scheme. In the information-incorporated analysis procedure, the second term of solve it . Comparefficient , can be While we have demonstrated the proposed method's superior performance compared to the alternative approach, several directions are still worth exploring in the future. First, the publications resulting from the information retrieval may not always be relevant to disease subtypes. Since text mining is rapidly evolving, more advanced tools are developing, potentially improving to retrieval of more robust and relevant information from unstructured data. Secondly, the current design of the proposed procedure may not benefit less studied diseases with few publications. Researchers may follow the same spirit as the proposed method but consider other data sources or prior knowledge based on experts for these diseases. Therefore, how to efficiently incorporate various types of information requires further investigation. Thirdly, our simulation studies only evaluate the impact of the spherical shape on noisy clustering patterns among spherical and nonspherical settings. It would be valuable to consider further the effect of different shapes of noisy clustering patterns under different setups for future work.In conclusion, we introduce a clustering approach allowing prior information incorporation to leverage the existing and accumulated data resources. The proposed method responds to the need for disease subtyping and biomarker identification in precision medicine.btad417_Supplementary_DataClick here for additional data file.


High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we evaluated seven top-ranked batch correction strategies for mRNA profiles in the context of a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different use scenarios with varying complexity, and found that Harmony, a nonlinear method, consistently outperformed the other tested methods. Furthermore, we provide a framework, benchmark, and metrics for the future assessment of new batch correction methods. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery. The most commonly used multiplex image-based profiling assay is Cell Painting3. Cell Painting uses six dyes that label eight cellular components , Golgi, mitochondria, plasma membrane, cytoplasm, and cytoskeleton) that are imaged in five channels. Thus, each image-based profile captures rich morphological features that are extracted using automatic image processing and analysis pipelines. The approach offers single-cell resolution, captures valuable population heterogeneity and provides information distinct from mRNA profiling 9 and protein profiling 10 at a cost lower than even the least expensive high-throughput bulk mRNA technique 11. Importantly, Cell Painting image-based profiles of cells exposed to different genetic or chemical perturbations have been successfully combined with machine learning strategies to generate predictive models that support key steps in drug discovery and development 1.Image analysis has become a cornerstone of biological and biomedical research. The use of fluorescence labeling combined with advanced optical microscopy now enables visualizing biological morphology, structures and processes at unprecedented spatial and temporal resolution. Furthermore, high-throughput microscopy can now extract precise information about morphological changes caused by thousands of specific genetic or chemical perturbations. Analysis of the resulting image-based profiles can be used to deduce gene functions and disease mechanisms, as well as characterize mechanism and toxicity of potential therapeuticsThe broad applicability and the predictive power of Cell Painting data depends on having access to a large number of image-based profiles that can be used to either generate mechanistic hypotheses or build predictive models. Individual companies have begun to create proprietary datasets, but a large public Cell Painting dataset has been lacking, leaving the field unable to benefit from shared data as has been so crucial in other fields of biology such as genomics.12. These efforts resulted in the 2023 release of the first large-scale public dataset of image-based Cell Painting profiles, capturing data from more than 140,000 chemical and genetic perturbations . A large, public dataset is most useful if it can be successfully queried using new profiles generated by individual laboratories in the future. This requires the community to have developed methods for aligning data generated with technical variation. With this in mind, the Consortium went to great effort to exchange compounds and generate images across twelve different laboratories with various equipment, enabling the development and evaluation of batch correction methods that could then be used by future data generators.Thus, to enable construction of such a database, we recently partnered with colleagues from pharmaceutical companies, technology providers, and non-profit organizations to form the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting ConsortiumIn general, batch effects arise from technical, non-biological factors that are commonly encountered during large-scale data collection efforts involving multiple sites. In the context of JUMP Cell Painting Consortium, technical factors  all have effects on the image-based profiles we collected. Moreover, some batch effects occur even across batches from a single site, such as unintentional changes in lamp intensity, staining concentration, and cell seeding or growth rate. Additionally, the hierarchical structure of Cell Painting experiments, with each readout originating from a region in a well from a multi-well plate, which in turn comes from an experimental batch at a particular laboratory, further complicates the nature of the technical noise.14, and suggest that most of these methods should not be used without the guidance of an expert 16. Therefore, it remains unknown whether any of the scRNA-seq batch correction methods can be reliably applied to image-based profiles.Only a handful of batch correction methods have been developed and tested for image-based profiling. No systematic and comprehensive comparison and evaluation of such methods has been performed, making it unclear whether the available methods offer a reliable approach for dealing with batch effects in image-based profiling. Such evaluations for single-cell RNA-seq (scRNA-seq) batch correction methods have revealed that no current method appears to outperform the others 12 to test the performance in the context of five common use cases: multiple batches from a single laboratory, multiple laboratories using the same microscope with few and many compounds, and multiple laboratories using different microscopes with few and many compounds. Given the computational constraints of working with large image-based profiling datasets, we focused our evaluation on population-averaged well-level profiles rather than single-cell level profiles. We analyzed correction methods in the context of the replicate retrieval task (finding the replicate sample of a given compound across batches/laboratories), and we found that existing methods are effective in reducing batch effects in image-based profiles. Among the methods tested, Harmony, a non-linear method developed for processing scRNA-seq data, offered the best balance of batch removal and conservation of biological variance metrics. More broadly, the benchmark dataset, evaluation framework, and metrics we describe here will enable future assessment of novel batch correction methods as they emerge. This paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.Here, we carried out a comprehensive analysis of seven batch correction methods. We used qualitative visualizations along with five metrics that capture reduction in batch effects, and eight metrics that capture preservation of biological signals. We used the newly released public database created by the JUMP Cell Painting Consortium 17. These methods were available in Python and did not require additional preprocessing or metadata. Additionally, the chosen methods were representative of different approaches and included linear methods (Combat 18 and Sphering 19), neural-network based methods (scVI 20 and DESC 21), neighbor-based methods (Scanorama 22 and MNN 23), and a mixture-model based method (Harmony 24).A major part of our work was to comprehensively survey methods for batch correction, as well as strategies for their evaluation. Given the rapid advancements in the field of scRNA-Seq, particularly in the development of methods to address batch correction, we focused our attention on this area. We decided to test a subset of the better-performing methods identified in a recent analysis of scRNA-seq batch correction methods 18 models batch effects as multiplicative and additive noise to the biological signal and uses a Bayesian framework to fit linear models that factor such noise out of the readouts. Sphering19 computes a whitening transformation matrix 25 based on negative controls and applies this transformation to the entire dataset. It requires every batch to include negative control samples for which variations are expected to be solely technical. scVI 20 is a foundational model for representation learning in scRNA-seq data based on variational autoencoders. DESC 21 trains an autoencoder along with an iterative clustering algorithm to remove batch effects and preserve biological variation, and requires the knowledge of the biological variable of interest as input, which may be unknown at the batch correction stage. MNN 23 aligns representations between two batches by finding pairs of samples that are mutually nearest neighbors. Scanorama 22 computes mutual nearest neighbors across all of the batches and uses such neighbors as anchors to align the datasets. Harmony 24 is an iterative algorithm based on expectation-maximization that alternates between finding clusters with high diversity of batches, and computing mixture-based corrections within such clusters. All tested batch correction methods except Sphering require batch labels, Sphering alone requires negative control samples, and only DESC additionally requires biological labels. MNN, Scanorama, and Harmony necessitate recomputing batch correction across the entire dataset whenever new profiles are incorporated. While Sphering, Combat, scVI, and DESC don’t require recomputation, they don’t guarantee perfect corrections for new profiles from an unseen source.We will briefly summarize the main characteristics of these methods, to enable the reader to place our results in the appropriate context. Combat 17, and our interest in assessing its robustness when distributional assumptions are not fulfilled.Although these methods were developed for scRNA-seq data, scRNAseq produces single-cell mRNA profiles that are similar in structure to single-cell morphological profiles from the Cell Painting assay. Although the two profile types have inherent biological and statistical differences, most of the foundational assumptions, such as metrics in the vector space revealing similarities between them, are reasonable for the image-based profiling domain. However, we note a crucial difference in the manner we have applied these methods. Given the sheer volume of data in large image-based profiling datasets, which may contain billions of single cells compared to the millions typically found in scRNA-seq, it is computationally impractical to apply these methods at the single-cell level. Importantly three out of the seven methods require computing batch correction across the entire dataset. This means that subsampling, a strategy often used to manage large datasets, is not a viable option for those. Thus, we evaluated these methods based on their ability to correct batch effects in population-averaged well-level (or “pseudo-bulk”) profiles, rather than single-cell level profiles. This shift does alter the distribution of the features, but we believe this is an acceptable trade-off given the computational constraints and the overall goal of correcting batch effects at a broader level. Particularly for scVI, it’s worth noting that while the method assumes a negative binomial distribution for each feature—an assumption not met in image-based profiling datasets—we still included it in our evaluation. This decision was driven by scVI’s demonstrated high performance in scRNA-SeqBeyond batch correction methods for scRNA-seq, we also reviewed those specifically designed for image-based profiling data analysis. A small handful of past research in this domain incorporated a step for batch correction. Such approaches can be split broadly into two categories: those based on (a) pre-computed feature transformation and (b) representation learning.26 to deal with interplate and intraplate normalization - these aim to reduce local variances, but they are limited when technical variations are strong. Sphering 19 is the most used batch correction method for feature-transformation-based profiles widely applied in Cell Painting pipelines 28 and was included in our testing.Pre-computed feature transformation approaches learn a transformation of features that have been extracted from images. In such workflows, common preprocessing/normalization steps have been described by Bray and Carpenter 28. Qian et al. 29 trained a generative adversarial network (GAN) to capture the batch effects from one batch using the style transfer framework. The GAN is then used to align the data by transferring the learned style to every image in the other batches. Despite their success, GANs face challenges during training related to convergence, mode collapsing and stability 30. More importantly, this approach requires selecting a reference batch, which may not be a trivial decision. Lin and Lu 31 addressed the batch effect problem differently, instead of proposing a separate method to correct the data they adapted the BatchNorm 32 strategy to learn a representation that accounts for batch information jointly with the downstream task. Because BatchNorm32 only works with differentiable neural networks, its scope is limited to gradient-based methods, and it requires integrating the downstream task into the loss function, which may differ depending on the question at hand, precluding correcting the data once for all possible applications of the data. In a similar spirit, Haslum et al. 33 proposed a modification for the teacher-student framework using self-supervised learning models such as DINO 34 and BYOL 35 that accounts for batch effects, reporting the kBET metric and qualitative visualizations to compare performance. Note that such models require data augmentation, which is not always feasible, especially for tabular information. Sypetkowski et al. 36 designed a neural network architecture including learning-based normalization and gradient reversal layers to solve the compound classification as a pretext task to correct for batch effects. Sypetkowski’s approach requires the training of a machine learning model to evaluate the performance, which limits the comparison with other methods. Overall, representation-learning methods for batch correction that require additional annotations as a way of supervision (e.g.37) are not applicable to all datasets, nor do they create a dataset useful for all applications. We therefore did not evaluate these methods in our study.Representation learning methods process the raw pixels from the fluorescent channel images, usually using neural-network-based approaches 36 and RxRx3 38 datasets are resources for image-based profiling, with millions of images associated with thousands of compounds, but derive from a single, highly-quality-controlled laboratory so cannot be used to assess methods aiming to correct more dramatic batch effects. We therefore chose to use the JUMP Cell Painting Consortium data as the only large public dataset that originated from a wide range of laboratories and from a range of instruments, thus capturing diverse batch effects.Next, we considered which dataset to use as a benchmark in our evaluation. RxRx1 17, image-based profiling lacks a comprehensive comparison of batch effect correction methods, and there are no agreed evaluation criteria.Finally, we surveyed various evaluation strategies and considered them for the context of image-based profiling batch correction. In contrast to the RNA-seq domain where multiple benchmark studies exist 39, t-SNE 40, or MDE 41 can expose clusters of biological labels, however it strongly compresses high dimensional data in a 2D space. This can be sensitive to hyper parameters and prone to subjectivity.Evaluation techniques are typically split into qualitative and quantitative categories. Qualitative evaluations based on dimensionality reduction methods such as PCA, UMAP 42 and perturbation replicate retrieval across batches 43 as a measure of the expressivity of the image-based profiles after correction. Used in isolation, this indirect approach may fail to detect the presence of batch effects in situations where the experimental covariates (e.g. plate ID) correlate with the labels of the downstream task (e.g. samples’ replicates).Quantitative evaluation of batch correction methods broadly take two forms: using bespoke metrics to directly quantify batch effect removal and conservation of biological variance, or indirectly examining the expressivity of corrected image-based profiles through subsequent analysis tasks. An indirect approach is to utilize downstream tasks such as image classification 36 propose a direct approach with their batch generalization and batch classification accuracy metrics. Batch generalization utilizes a perturbation classifier to compute the performance ratio between batches used for training the classifier and a held-out set of batches. A high ratio indicates a corrected representation that’s not heavily influenced by the batch. Batch accuracy trains a classifier to predict the batch ID based on the corrected representation; batch accuracy is low if batch correction has performed well. However, this approach also requires the tuning of additional classification pipelines during evaluation, making the comparison of batch correction methods somewhat less straightforward. Lastly, Kim et al.’s 44 evaluation provides another direct approach, using silhouette score, graph connectivity, and local inverse Simpson’s index (LISI) to quantify batch effects across different representation learning methods. Their evaluation is similar to our Scenarios 1 and 2, described below.Sypetkowski et al. 17. 2) We evaluate more diverse scenarios .Our evaluation strategy differs from prior evaluations in image-based profiling literature in the following: 1) We evaluated 13 metrics, including those measuring conservation of biological variance and batch effect removal Having selected seven methods and 13 evaluation metrics in five scenarios, we then proceeded to our analysis.Target2 plates (see Dataset description), one of which is present in each of the 13 experimental runs/batches produced by a single laboratory (source_6), a subset of the JUMP Cell Painting Consortium dataset. There were a median of 21 replicates per compound. Given that profiles were generated in the same laboratory, with many replicates and relatively low technical variance, this simplified scenario helped us establish a baseline for the best possible results while also guiding the preprocessing pipeline that could be applied across all more complex scenarios.In this scenario we analyzed 306 “landmark” compounds present on the 45, which rescales individual features; and (2) Sphering 19, which rescales all features in a multivariate manner. We applied MAD normalization per plate, and Sphering across the whole experiment. In both steps, the data was normalized against the negative controls which serve as the reference. In 2D projection of the raw profiles, i.e. profiles not transformed in any way, we saw that profiles are heavily influenced by the batch variation  median absolute deviation (MAD) normalization ariation . Similar17: (1) batch removal metrics that capture how well a method removes confounding variation from data; and (2) conservation of biological variance, termed here as bio-metrics for short, that capture the ability to preserve the variation related to a known biological variable .To quantify batch correction methods, we examined two kinds of metrics 17. To make interpretation easier, every metric here was normalized between 0 and 1, with 0 being the worst possible performance and 1 the perfect performance. These quantitative metrics , others focus on a notion of local neighborhood, and others consider more global arrangement. Because different metrics capture different aspects of the correction procedure, and no individual metric captures both effects, we took them all into consideration when making comparisons between multiple batch correction methods. The average of such metric scores has been shown to be a reasonable ranking criterion  metrics  demonstrBaseline approach, in agreement with established protocols in the field 45. All remaining results in this paper apply this two-step preprocessing.We also compared the methods by displaying the metrics in a cartesian plane . Again, 20, DESC 21, MNN 23, Scanorama 22, Combat 18, and Harmony 24. We reiterate that we used pseudo-bulk profiles and did not attempt batch correction at the single-cell level due to the computational time required to process up to billions of cells included in a typical image-based profiling experiment. Qualitatively, the 2D projection of the profiles after applying six different methods, in addition to our Baseline, shows no clusters associated with any particular batch (Dataset description). This suggests that all the methods were successfully mixing the profiles from different batches as compared to the raw profiles (i.e. 2D projections of corrected profiles), we noticed that Harmony and Combat were better at grouping the data points associated with the same compound than others. This suggests that such methods are more effective at preserving biological information  underperformed, perhaps due to fewer samples compared to typical scRNAseq datasets where millions of single cells are used to train the neural network. Further, scVI’s underperformance might be also attributed to its assumptions about data distribution, specifically the negative binomial distribution for each feature, which are not met in these data.Target2 306 compounds from 43 experimental run/batches produced by three laboratories using the same model of microscope. We considered laboratory ID (identifier) as the variable of interest to be factored out because it is the most dominant confounding source , we found that Harmony , consistently outperforms the other tested methods. We consider Harmony to be a good trade-off: as a non-linear based approach it lacks the high complexity of the tested neural-network-based approaches, for which the available data in high-throughput experiments is often insufficient. In less complex scenarios, simpler methods may suffice; for example, for data generated in the same laboratory with many replicates of the compounds, the Baseline was sufficient to correct most of the batch effects, even though Harmony and Combat performed slightly better. However, we observed that as technical and biological variance increases, linear models such as Combat or Sphering fall short in aligning the data.48 and self-supervised methods tailored for tabular data, 49 may represent promising alternatives. These methods, grounded in machine learning principles, are inherently flexible, allowing for incremental learning and the integration of new data without altering the previously established representations. While still in the exploratory stages for our context, these approaches have demonstrated potential in similar high-dimensionality scenarios and may provide an effective solution to batch alignment challenges that arise when incrementally expanding large datasets 51.Although Harmony outperformed other methods tested, it’s important to point out a possible limitation for extensibility: it requires processing of all the data to re-align new batches against an existing dataset. This has a major impact on the ability of users to align their data with a large public dataset as it requires reprocessing and modifying existing representations. Therefore, further strategies, such as domain adaptation techniques Importantly, we discovered that in the most difficult-to-align scenarios, none of the methods are able to adequately remove the batch effects. In fact, the best methods provide the greatest improvement in the least difficult-to-align scenarios. This raises a call for advancements from the field. Our study focused on bulk (population-averaged) profiles. It is possible that applying batch correction at the level of single cell profiles (likely subsampled), or even at a lower level using raw pixels, may yield better results. Among the methods we tested, Sphering, Combat, DESC, and scVI are suited for training on subsampled data at the single-cell level. Although neural networks have been explored, implementation and evaluation of these methods at the JUMP-dataset scale with billions of cells still represents a challenge.12 is a collection of several datasets that were either generated or reprocessed by the JUMP Cell Painting Consortium. The primary dataset (referred to as cpg0016) was generated during the data production phase of the JUMP-CP project. It comprises a compound dataset , an Open Reading Frame gene overexpression dataset  and a CRISPR gene knockout dataset .The JUMP Cell Painting dataset cpg0016 was generated across 11 data producing sites (or “sources”). Each source, apart from source_7, exchanged their nominated compounds with either two or four other sources where they were assayed using different instruments and microscopes (details are provided in the Methods section of 12). To simplify logistics, the sites were split into two groups; each site exchanged compounds only within their respective groups. In this paper, we use only a subset of the compound dataset, comprising all the 82,414 chemical perturbations from one of the two groups.The compound dataset in JUMP-Target-2-Compound – was run with every batch of data generation. These plates not only allow alignment of data within the JUMP dataset, but also with future datasets generated outside the consortium and can thus be considered as control plates. In this paper, JUMP-Target-2-Compound plates are referred to as Target2 plates for brevity; the remaining plates – comprising the 116,753 chemical perturbations – are referred to as Production plates.A positive control plate of 306 diverse compounds – named negative controls. They are used for detecting and correcting plate to plate variations. They can also be used as a baseline for identifying perturbations with a detectable morphological signal.Dimethyl Sulfoxide (DMSO) -treated wells serve as 46) generated by the JUMP Cell Painting Consortium, we identified a set of eight compounds with the most distinct signatures from each other and from DMSO; these are used as positive controls. The identity of each compound is provided in Supplementary Table 1 of 12.Using a pilot dataset  is a measure of how well a sample is assigned to its cluster. ASW_label metric considers the compound as the cluster ID; while ASW_label/batch metric considers the confounding variable as the cluster ID.PCR_batch: Computes the R2 between each principal component and the confounding variable before and after correction. The difference between the sum of such values before and after correction determines the batch removal efficiency. We reported PCR_batch in Scenario 1 using the Raw as representation before correction. We do not report PCR_batch in Scenario 2 onwards because the Raw representation does not provide an insightful comparison in more complex scenarios.Graph_conn: Uses the k-NN graph to measure the connectivity of each sample and those that belong to the same compound. This metric assumes If the batch effects were effectively removed, then the elements of the same biological concept should be close together.LISI-based: LISI is a metric based on the Simpson’s diversity Index to measure the diversity of a sample’s local neighborhood in the data. iLISI uses the confounding variable to measure such diversity; while cLISI uses the compound annotations to measure such diversity.kBET: Compares the global distribution and the local distribution of the confounding variable for each sample in the dataset. If the confounding variable is effectively removed, then such distributions should be similar.ARI_clusterandNMI_cluster: Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) measure the agreement between two clustering assignments. ARI_cluster and NMI_cluster metrics computes agreement between the clustering assignments of the Louvain algorithm applied over the corrected data and the compound annotations.Isolated_label metrics: Isolated_label_F1 and isolated_label_silhouette metrics aim to capture the effectiveness of the correction methods in compounds not present in all of the batches by computing F1 and ASW scores within each cluster generated by the Louvain algorithm on the compounds present in the least number of batches.We computed five metrics for batch removal efficiency and eight metrics to measure how well the correction preserves biological information, previously reported in single-cell RNA-Seq benchmarks mean average precision as a bio-metric . We measure similarity between samples using cosine similarity.We additionally report mean_average_precision, of each compound and combine their p-values using the Fisher’s method. We computed a q-value for each compound by applying the Benjamini & Hochberg procedure to control for the false discovery rate. The fraction_positive_q metric is the ratio of compounds for which its q-value is lower than 0.05.Following the information retrieval convention, each sample in the dataset is considered a query; K other samples sharing the same compound are considered positive elements to be retrieved; and N samples belonging to the same query’s plate with different compound annotations are considered negative elements. The rank list is computed using the cosine similarity between the corrected profiles. Once computed, we estimate a p-value for each query by computing 10000 shuffled ranked lists mimicking the size K+P (random baseline), where K is the number of positive matches and P is the number of reference samples. Finally, we average the AP, termed MAD scales profile vectors feature-wise using mean absolute deviation For scVI, we shifted the data to the feasible space. (i.e. transform each feature Metrics computed with the scib package are reported only for compounds with at least 5 replicates.The nature of the image-based profiles data involving low number of replicates and high number of compounds limits metrics such as kBET that rely on a higher (>15) number of samples per biological concept.mean_average_precision is the only metric able to capture the performance of the models when there are as few as only two replicates of a compound.41 for embedding visualization with default parameters from the scib package. MDE is a framework that generalizes PCA and UMAP dimensionality reduction methods. Scib implementation scales to large datasets and allows the usage of GPU for faster computation.We use Minimal Distortion Embedding (MDE)


Monogenic diseases are often studied in isolation due to their rarity. Here we utilize multiomics to assess 22 monogenic immune-mediated conditions with age- and sex-matched healthy controls. Despite clearly detectable disease-specific and “pan-disease” signatures, individuals possess stable personal immune states over time. Temporally stable differences among subjects tend to dominate over differences attributable to disease conditions or medication use. Unsupervised principal variation analysis of personal immune states and machine learning classification distinguishing between healthy controls and patients converge to a metric of immune health (IHM). The IHM discriminates healthy from multiple polygenic autoimmune and inflammatory disease states in independent cohorts, marks healthy aging, and is a pre-vaccination predictor of antibody responses to influenza vaccination in the elderly. We identified easy-to-measure circulating protein biomarker surrogates of the IHM that capture immune health variations beyond age. Our work provides a conceptual framework and biomarkers for defining and measuring human immune health. Immune-mediated disease results from a complex interplay of environmental, exposure history, and genetic factors. In contrast to polygenic diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), monogenic diseases offer unique opportunities to highlight important mechanisms by which individual genes and associated pathways contribute to immune function in humans. For example, the study of patients with immunodeficiencies has illuminated the critical roles of the JAK-STAT network in orchestrating microbial defense and inflammatory processes at the organismal level in humans3; similarly, monogenic periodic fever syndromes have deepened our molecular understanding of inflammasomes and their roles in innate immunity and autoinflammatory diseases4.Immune system dysregulation is central to diverse pathologies, including cancer, chronic inflammation, cardiovascular, and neurological diseases8, immune-mediated diseases, in particular those of monogenic origin, have often been studied in isolation. Molecular and cellular attributes and biomarkers shared across diseases remained poorly defined, knowledge of which could help advance our understanding of both common and disease-specific pathophysiology and immune dysregulation, potentially pointing to multi-disease therapeutic targets. Importantly, the contribution of genetics to human immune variations can be highly variable and tends in wane by adulthood9; even monogenic disease patients with primary causal defects in the same gene can exhibit extensive clinical heterogeneity10 with poorly understood molecular and cellular drivers. Thus, dissecting the inter- and intra-patient variations in diverse immune parameters both within and across diseases is critical to understanding disease- and patient-specific dysregulation beyond the causal gene and proximal pathways. Analyzing diverse monogenic diseases may also simultaneously reveal features of a normal, healthy immune system, which remains ill-defined because parameters quantifying immunological health remain elusive11. In principle, immune health metrics should not be defined based on features of the immune systems among healthy individuals alone, but also incorporate common features of immune pathologies as “negative” indicators of health. Simultaneous assessment of immune states in monogenic disease patients and matching healthy subjects may thus reveal quantifiable parameters of human immune health.Aside from comparison of genetic associations and gene expression quantitative trait loci in polygenic diseases14. Integration of transcriptomic, serum protein, and peripheral blood cell frequency data revealed a quantitative metric of immune health through both bottom-up, unsupervised principal variation analysis of personal immune states and supervised machine learning analyses that discriminated between healthy individuals and sick patients. This metric also marks healthy aging and is associated with the antibody responses to influenza vaccination in the elderly. We also uncovered easy-to-measure serum protein surrogates of this metric that capture immune health variations among healthy individuals beyond age. Beyond our specific findings, this rich dataset can serve as a resource for the research community to probe these specific monogenic disorders more deeply, for example, by generating new hypotheses. Our work paves the way for a more quantitative understanding of human immune health and provides a unique dataset for further exploration.Here we have integrated multiomics profiling and clinical information to comparatively analyze 22 monogenic immune-mediated disease cohorts together with age- and sex-matched healthy controls. Using this new dataset, we identified both disease-specific and shared (“pan-disease”) signatures, and importantly, found that both patients and healthy subjects possessed temporally stable personal immune states independent of disease condition or medication useWe employed multiomics analyses of circulating immune cells involving whole blood transcriptomics, measurements of more than 1300 circulating proteins from serum , as well as immune cell frequencies and hematological parameters from a complete blood count (CBC) and clinical flow cytometry  to comparatively analyze samples collected from 364 visits of 228 patients (some patients had multiple samples collected at different visits/timepoints)—spanning 22 monogenic immune-mediated diseases—and 42 age- and sex-matched healthy subjects . Once da15 was applied to the serum protein and transcriptomic data to derive co-expression modules separately for each data modality. This resulted in 12 blood transcriptomic modules  and spleen proteins in PM2  (Extended Data Table 6). In contrast to the highly modular nature of blood transcriptomic measurements (p = 9.68x10−8), small intestine , and adipose tissue  (Extended Data Table 6). These observations are consistent with the notion that whole blood transcriptomic data mainly capture variation in circulating immune cell frequencies and cellular states that give rise to correlated, modular gene expression structures, while circulating protein levels reflect more diverse sources of variation, including those from circulating blood cells but also from tissues and potentially their status such as inflammation. The blood transcriptomic and serum protein measurements thus provide orthogonal, complementary information and together enable comprehensive assessment of phenotypically diverse individuals.To reduce data dimensionality and assess the correlation among parameters, weighted gene correlation network analysis (WGNCA)les TMs;  or intraurements , a largeurements . Interes17 to estimate the relative contributions from the following sources: differences associated with disease, differences among patients with the same disease, medication/treatment effects, and intra-patient variations over time , including those associated with disease and medications as well as inter-subject and temporal differences within individuals. Leveraging data from 63, 62, and 64 subjects for the cell frequencies, whole blood transcriptomics, and serum proteins, respectively, from whom we had collected more than one sample over time , we fit a variance partition modelver time ,g. A larver time -g. Majorver time , suggestver time . Jackkniver time . Consistver time ,b, indic18), TM2 (enriched for heme biosynthesis), and PM2, as well as decreases in TM6 (enriched for NK cells and CD8+ T-cells), NK cell frequencies, and PM6 (enriched for platelet related factors) , including interleukin-23 (IL-23), tumor necrosis factor α soluble receptors 1 and 2, interferon (IFN)-related or -induced proteins , and the shed receptor sCD163 that might reflect macrophage activation in tissues20. Together, these signals may reflect both systemic and tissue inflammation shared across diseases.We next derived and compared disease signatures, although our aim was to generate new hypotheses rather than “deep diving” mechanistically into any specific monogenic disease. We used linear models to derive signatures of individual disease conditions in comparison to matching healthy subjects accounting for age, sex, and major medication groups . Despite21. Although we verified that this increase in IL-23 was not driven purely by changes in cell frequencies by fitting an additional model controlling for major cell subset frequencies  patients and several PIDs such as STAT1 gain-of-function (STAT1 GOF) and X-linked chronic granulomatous disease (X-CGD), relative to healthy subjects , consistng IL-23 , suggest23. Here DADA2, STAT1 GOF, X-CGD, and p47phoxCGD (p47-CGD) had clear IFN-I signatures as reflected by elevation in TM1 , which were also significantly higher than STAT1 GOF . Relative serum concentrations of the IFN-inducible protein I-TAC/CXCL11, as well as STAT1 itself, were higher in X-CGD and STAT1 GOF patients relative to healthy subjects (Extended Data Table 10), with circulating STAT1 protein concentrations significantly higher in X-CGD compared to STAT1 GOF . Consistently, IFN-inducible transcripts in TM1 tended to be elevated in both the CGDs and STAT1 GOF patients compared to healthy, but again the elevations appeared stronger in the CGDs than the STAT1 GOF (Elevated type I IFN (IFN-I) blood transcriptional signatures have been found in monogenic and polygenic inflammatory diseases such as Aicardi-Goutières syndrome and SLE, respectivelyTAT1 GOF . Togethe25. Together, our data provide a rich resource for the biomedical community and highlight shared and disease-specific cellular, transcriptional, and serum protein signatures of diverse monogenic immune-mediated diseases. The shared signatures in particular point to commonly dysregulated pathways and processes in the immune system independent of disease-specific pathologies.In addition to examining differences in relation to healthy subjects, we also compared each disease against all other diseases excluding the healthy subjects. Surprisingly, this other-disease-as-background map was qualitatively similar to the healthy-as-background map . For exa26 to infer latent components shared among the temporally stable transcriptomic and serum protein parameters  in each data type was shared  with jPCattenuation in inflammation-related processes as evident by: 1) jPC1 was negatively correlated with the neutrophil-to-lymphocyte ratio, which is a known marker of systemic inflammation and elevated in acute infections and cancer28, and positively correlated with B- and T-cell frequencies  and the expectation that patients would have elevated inflammation and potentially poorer immune health, jPC1 score is significantly higher in healthy subjects than patients , consistent with the tendency for less severe disease in p47-CGD compared to X-CGD patients36. Together, our unbiased integration of blood transcriptomic and circulating protein data revealed an emergent axis of immune health that delineates both inter-disease and inter-subject heterogeneity in patient and healthy populations.In addition to the healthy subjects, most disease groups such as STAT3 DN, GATA2, and STAT1 GOF, spanned a wide range along jPC1 . The extThe emergence of pan-disease signatures  and an i37, the IHM score and jPC1 were both negatively correlated with age in healthy individuals . Additionally, the IHM classifier could not have directly learned age-associated signals by training on patients versus healthy subjects because these two groups had indistinguishable age distributions in our cohort  against healthy subjects (“positive” examples) what a healthy immune system should (or should not) look like. Thus, we next used our classifier to assign each sample an “immune health metric” (IHM) score that reflects the probability that the sample belongs to the healthy group see , but lesividuals . Since c40. We estimated the coherent deviation (meta-effect size) between disease and healthy subjects across the four diseases for every transcript and the transcriptional signature scores of the IHM, jPC1, and the top predictive markers from the IHM 42, we found that the IHM is indeed positively associated with antibody responses to influenza vaccination  together play important roles in setting and maintaining personal immune states. Indeed, various immune parameters have been found to be temporally stable over months in healthy individuals; some of these inter-subject differences were associated with responses to perturbations such as vaccination and autoimmune disease flares14. Here we have extended these concepts and observations to diverse monogenic patients with high-penetrance deleterious mutations affecting immune functions.Monogenic diseases are often studied in isolation due to their rarity, and thus the data and insight obtained from one condition cannot be easily compared to those of others. Here a unified approach was taken to simultaneously compare multiple rare immune-mediated conditions with natural genetic perturbations disrupting key pathways. To our surprise, despite penetrant genetic defects and clearly detectable common and disease-specific signatures, we observed that temporally stable, between-subject variation in cellular, transcriptomic, and circulating protein parameters dominates relative to the variation attributable to disease condition, medication, age, and sex. This observation is consistent with the clinical heterogeneity often observed even within single monogenic disorders51. Our findings raise the possibility that a targeted set of parameters comprising select blood immune cell frequencies, proteins, and transcripts could be developed from a multi-disease cohort like ours with the goal of optimizing both information overlap (to increase robustness) and uniqueness  to track the health and disease status of individuals in the general population.In general, there were both shared and modality-specific information provided by the transcriptomic and circulating protein data, suggesting that both should be measured to capture personal biological states when possible. Importantly, our results using the protein and transcriptional signatures were largely independent of circulating immune cell frequency, which is a major driver of blood transcriptomic profiles. Some of the circulating protein modules we uncovered may also reflect tissue status, as was postulated previously in a large proteomic study of older individuals24. This observation suggests that JAK inhibitors, which have been successfully used to treat some inflammatory complications of STAT1 GOF patients52, may also be a therapeutic option for inflammatory complications of CGD. While IFN signatures have been reported in some inflammatory conditions54, their presence and relative magnitude have not been comparatively analyzed across multiple monogenic disorders. These observations and hypotheses highlight the power of the comparative approach taken to study monogenic diseases in this study.Our dataset serves as a valuable resource for hypothesis generation and exploratory analyses by the research community. As an example, we revealed that IFN-stimulated gene transcripts were elevated in the blood of CGD patients and often at higher levels than in STAT1 GOF patients. This was unexpected given that STAT1 GOF patients are known to have increased STAT1 signaling and transcription of IFN-stimulated genes due to their gain-of-function mutations in the STAT1 gene55. Finally, it was negatively correlated with age in healthy subjects in our cohort and in a large independent cohort of healthy adults age ~20-90, consistent with the expectation that immune health declines with age. The IHM is based on a relatively small number of parameters and can be evaluated using circulating proteins from serum alone, and thus can potentially serve as an inexpensive tool for monitoring immune states and functions in diverse populations.Our bottom-up analysis of subject-level immune states revealed an axis (jPC1) of natural subject-to-subject variation captured by both blood transcriptomic and circulating protein data. Surprisingly, this was not driven by differences among diseases or between healthy and sick patients because a similar, correlated principal axis emerged from the data of sick patients or healthy subjects alone. This axis was also highly concordant with the IHM derived through a supervised machine learning analysis for differentiating healthy from sick patients in our cohort. Thus, the unsupervised and supervised analyses independently converged on a measure of immune health potentially applicable to diverse populations. Supporting this notion, the applicability of the IHM was validated in three independent and biologically distinct datasets. First, we showed that the IHM signature was lower  in patients from a meta-analysis of several polygenic autoimmune and inflammatory diseases. Second, it was associated, when evaluated pre-vaccination, with the antibody response to seasonal influenza vaccination in older individuals, pointing to a potential baseline determinant of vaccine responsiveness in this population. This is notable because the baseline immune statuses of the elderly are often highly heterogeneous and shaped by myriad complex factors 59. The age-related signals that we detected in Tregs and neutrophils confirm previous reports that aging contributes to their pathologic potential60.Given the applicability of the IHM in a range of biological scenarios, it is perhaps not surprising that IHM transcriptional scores appeared lower in nearly every peripheral immune cell type from patients with various polygenic or idiopathic immunological diseases. This coherent signature could be, at least partly, driven by cell-extrinsic factors, such as some of cytokines (interferons) and tissue growth/homeostatic factors  revealed by the IHM circulating protein correlate analysis. This result obtained using another independent dataset further validates the notion that the IHM likely has applicability beyond the monogenic conditions explored in this study. Interestingly, these coherent IHM signals across cell types were seen in only a subset of cell types when assessing the cell type specific correlation between the IHM transcriptional score and age in healthy subjects, including LDGs and some regulatory and effector memory T-cell subsets. LDGs (which includes low density neutrophils) and these T-cell subsets have been implicated in a spectrum of immunological and inflammatory conditions, including autoimmunity, cancer, and cardiovascular disease19, as well as mortality and morbidity risks . While the mechanisms behind these associations are not entirely clear, increased RDW is known to reflect dysregulation of erythropoiesis and potential reductions in the rate of RBC turnover62. Conversely, higher NK cell numbers were associated with higher IHM scores. Aging, which is associated with the IHM in our study, is known to be associated with decreased NK cell production in the bone marrow. While it is unclear whether decreased bone marrow output or reduced expansion capacity of specific NK cell subsets played a role in the lower NK cell numbers we observed across multiple diseases, the association of both RDW and NK cell frequency with the IHM suggests that disruption of hematologic homeostasis may be involved.Markers of systemic inflammation , RDW, and NK cell frequencies were some of the key constituents of the IHM. RDW and inflammatory markers were negative indicators of immune health. Increased RDW has been associated with human aging and several pathologies, including heart disease and cancer63. Inflammaging has been linked to increased IL-6 in the literature, although there has been conflicting data63; IL-6 was neither correlated with the IHM in our study nor a key feature of an inflammatory aging (iAge) “clock” recently developed from ~1000 healthy individuals45. That study identified CXCL9/MIG as an informative feature of age-related inflammation. In our data, CXCL9 is a member of the protein module PM2, a key component of the IHM. PM2 also includes other inflammatory cytokines  and IFN-related or -induced proteins . As expected, the IHM was negatively correlated with CXCL9/MIG, but it remained correlated with age even when CXCL9/MIG and PM2 were removed, consistent with our findings that the protein IP-10/CXCL10 was negatively correlated with the IHM independent of age in healthy individuals only. More broadly, the IHM (and jPC1) was surprisingly variable even among apparently healthy subjects; the correlation between circulating proteins  and the IHM in healthy subjects is also independent of age, suggesting that the IHM captures aspects of immune health not linked to age and inflammaging. Thus, the IHM, as measured by easy-to-assay serum protein parameters for example, could be applicable to the healthy population.Inflammaging  has been linked to age-related adverse outcomes such as cardiovascular disease. However, the inflammatory mechanisms or molecules responsible have not been well characterized64. This is partly because the function and pathology of the immune system are wide ranging and thus unified definitions and metrics of general immunological health have been elusive66. Here we have developed a framework for defining and quantifying immune health by searching for personal, temporally stable immune parameters enriched in health  but depleted in patients across diverse pathologies due to perturbations of normal immune functions. The resulting measure was surprisingly generalizable to different patient populations and healthy individuals. Further refinement and development of such approaches, e.g., by increasing the diversity and number of studied subjects including the incorporation of additional pathologies, utilizing measurements from tissues, and modeling potential modifiers such as sex and genetic factors, hold promise for the development of clinically useful immune health monitoring tools to advance personalized and preventative medicine68.It has been recognized that despite ample clinical tools for assessing general physiologic and organ system function and health , aside from the CBC, such tools are largely missing for the immune system69. However, even when we examined the jPCs, which represent principal components of variation shared by the transcriptomic and serum protein data, there was considerable variation unexplained by age. Furthermore, similar positive and negative circulating protein correlates of the IHM emerged regardless of whether age was included as a co-variate. Thus, our work provides a broadly useful dataset and a conceptual framework and markers for defining and measuring human immune health.As expected, some of the observed immune variations across individuals in our cohort are reflected by information shared across correlated data modalities ; however, all major results presented were robust to variations in circulating immune cell frequencies and still significant when controlling explicitly for cell-frequencies. Our analysis of temporal stability by estimating between-subject variations was limited by a relatively small number of patients with repeat samples. Despite this we observed consistent temporally stable, between-subject variations among data modalities, including cellular, transcriptomic, and circulating protein parameters, that dominate relative to those attributable to disease condition, medication, age, and sex; these results are also robust to resampling noise as suggested by Jackknifing analysis. Although achieving mechanistic insights into any specific monogenic disease was not our goal, we demonstrated how this multimodal data could be used to yield new observations and hypotheses concerning disease etiology and therapeutic targets. For example, through our comparative study of interferon-related transcriptional signatures among several diseases, we were able to suggest JAK inhibitors as a possible therapeutic to further explore for CGD. Lastly, some of the major signals related to the IHM may partially reflect age-related decline of immune health and increase in inflammation in healthy individualsNCT00006150), 01-I-0202 (NCT00018044), 07-I-0033 (NCT00404560), 13-I-0157 (NCT01905826), 93-I-0119 (NCT05104723), 04-H-0012 (NCT00071045), and 94-HG-0105 (NCT00001373). Samples were collected when patients presented to NIH for inpatient or routine outpatient care between September, 2015 and November, 2017. Samples from matching healthy subjects were collected from subjects enrolled on NIH protocols 91-I-0140 (NCT00001281) and 15-I-0162 (NCT02504853). These studies were approved by the NIH Institutional Review Board and complied with all relevant ethical regulations. Informed consent was obtained from all participants.Samples were collected on patients with monogenic immune disorders enrolled on National Institutes of Health (NIH) protocols 00-I-0159  according to manufacturer’s protocol. Two Tempus tubes were collected per patient and healthy donor. The blood sample from each Tempus tube was aliquoted in to two 4.5mL cryovials. These cryovials were directly stored in −80°C freezer for long term.RNA was isolated from tempus blood samples using the Tempus Spin RNA Isolation kit  with following modifications to the manufacturer’s protocol: For each sample, 4ml of tempus blood sample was added to a 50ml conical tube containing 1.5ml of 1x PBS. The tubes were vortexed at full speed for 30 seconds, followed by centrifugation at 3000 g for 1 hour at 4°C. After centrifugation, the supernatant from the tubes was decanted and tubes were placed upside down on clean paper towels for 2 minutes. 400ul of RNA Purification buffer was added, vortexed briefly to resuspend the pellet and transferred the RNA to a purification filter with a pre-wet purification filter with 100ul wash solution I. The tubes were centrifuged at 16,000 g for 30 seconds and liquid waste was discarded. A second wash was done with 500ul wash solution I, followed by centrifugation at 16,000g for 30 seconds. The filter was washed with 500ul of wash solution 2 and centrifuged at 16,000 g for 60 seconds. DNase treatment was performed by adding 100ul of AbsoluteRNA wash solution , followed by 15 mins of incubation at room temperature and 5 mins of incubation with wash solution 2. The tubes were spun at 16,000 for 60 seconds. The liquid waste was discarded, and empty tube was spun at 16,000 g for 30 seconds to remove any residual liquid and the filter was inserted into a new collection tube.The Nucleic Acid Purification Elution Solution was pre-warmed at 45°C. 100ul of this pre-warmed elution solution was added to the filter and incubated at 37°C for 5 minutes. The tubes were spun at 16,000 g for 2 minutes. The eluate was pipetted back to the filter and spun again at 16,000 g for 1 minutes such that the eluate was collected in a new collection tube. 90ul of the eluate was transferred to a new tube.RNA QC was performed using Qubit RNA BR assay  and Agilent RNA . The average RIN was 8.26 and average yield was 4.69 μg for the RNA samples.Serum was collected directly in Serum Separator Tubes and allowed to clot at room temperature for a minimum of 30 minutes. Within two hours of blood collection, the tubes were spun at 1800 g for 10 minutes at room temperature. The top (serum) layer was removed via pipette and stored in individual vials at −80°C.All blood samples at different time points from the same subject were processed together. Before assay, 396 samples were carefully batched into 14 groups according to their age, gender and race but run blindly. One in-house reference sample was simultaneously processed with the real samples in each batch. RNA was amplified from 300 ng of total RNA using Ambion WT Expression Kit . Fragmented single-stranded sense cDNA was terminally biotinylated and hybridized to the Affymetrix Human Gene 1.0 ST Arrays with the probes for 36,079 RefSeq coding and noncoding transcripts and 466 lncRNA transcripts . The arrays were then washed and stained on a GeneChip Fluidics Station 450 (Affymetrix); scanning was carried out with the GeneChip Scanner 3000 and image analyzed with the Affymetrix GeneChip Command Console (AGCC) software 4.0.et al). A total of 358 samples were included in this analysis. Two samples with high levels of hemolysis (hemolysis score 4) and one sample with odd appearance were removed from downstream analysis resulting in 355 total samples. The SOMAscan assay has a total of 1322 SOMAmer Reagents, and of these 12 are hybridization controls, which were removed after hybridization normalization. 5 are nonspecifically-targeted SOMAmers  of human proteins and 4 viral proteins .Proteomic profiles for 1,305 SOMAmers in serum were assessed using the 1.3K SOMAscan assay at the Trans-NIH Center for Human Immunology and Autoimmunity, and Inflammation (CHI), National Institute of Allergy and Infectious Disease, National Institutes of Health . Samples were run according to Somalogic standard operating procedures. If operators identified presence of hemolysis in sample, those were marked for presence of hemolysis (1 low- 4 high). In addition to Somalogic quality control samples, internal QC of the runs (cross checked of hemolyzed samples and outliers) was performed using CHI webtools  was first performed for each well on a plate, and subsequent inter-plate calibration  was used to correct for plate-specific effects between plates sharing the same Somalogic control samples. After these steps, median signal normalization was performed on each group of samples from Somalogic plates that used the same Somalogic control. This median normalization was performed to correct for shifts in the median somamer RFUs across samples that may have been due to technical effects rather than biological ones.The Somalogic SOMAscan 1.3k assay data was normalized using the procedure outlined in2 transformed.Additionally, four bridge samples (QC_CHI), derived from healthy donor blood, were added to every run to allow in-house batch calibration normalization. These QC_CHI samples were mixed pools of serum samples of healthy donors from the Center for Human Immunology. In each batch, the QC_CHI controls were used for inter-plate calibration after the initial inter-plate calibration with the Somalogic control samples. After this step, all relative protein expression values were logPatient medical records were evaluated at the level of individual patient visits by trained medical personnel. Medications used at the time of the visit were documented based on notes from that visit; at the time of entry, medications were matched to the closest corresponding term in MeSH. Medications were documented to include the route, dose, frequency, potency (when applicable), date started and date ended (when available). Medical conditions were obtained from chart review and were documented to include past and current medical history. The conditions were entered by hand into a SQL database and selected from available terms in the Human Phenotype Ontology (HPO). Conditions that were unable to be reasonably matched to HPO terms were entered with free text. Current medical conditions were denoted as one of four options: 1) acute, active; 2) acute, resolved; 3) chronic, flare; 4) chronic, stable; 5) future (for planned procedures or therapies).2. Probesets mapping to multiple genes were discarded. To select a single probeset for each gene, principal components analysis was performed for every group of probesets corresponding to a given gene. The probeset most correlated with the first principal component of this group was chosen as the “best” probeset to represent the expression of this gene. With the microarray data summarized to the gene level, genes were then filtered to remove genes that appeared lowly expressed or showed higher technical variation than biological variation. Lowly expressed genes were identified as discussed in3; briefly, a histogram of the median log2 expression values were plotted and a lowly expressed local maximum was identified. There exists a “plateau” where genes with low median intensity are enriched. A manual threshold was selected to remove all genes in this enriched low intensity area of the histogram. To determine the relative amounts of biological vs technical variation, the variance of a gene in technical control samples  was compared to the variance of the gene across all of the patient/healthy control samples. Those genes with higher variance in the technical controls were removed from further analysis.Data were normalized and summarized to the probeset level using the RMA algorithm implemented in the oligo R packageSubjects had standard complete blood counts (CBCs) performed at the NIH Clinical Center in the Department of Laboratory Medicine. Lymphocyte  flow cytometry quantification was performed using the BD FACS Cantoll flow cytometer. The following parameters were collected on most patients, but were removed in downstream analysis for the given reasons:Hematocrit measurements were removed, as they are highly redundant with hemoglobin measurementsNucleated red blood cell measurements were removed, as they were zero for the majority of patients.MPV, immature granulocytes (concentration and percent WBCs), CRP, and ESR measurements were removed, as they were missing for 14, 62, 53, and 61 samples respectively.Three samples were removed due to inconsistencies found in their data .5.Absolute counts of leukocytes (including TBNK) were used for downstream analysis. The neutrophil to lymphocyte ratio (NLR), the ratio between the neutrophil absolute counts and lymphocyte absolute counts, was included as an additional CBC parameter for classification due to its previously described association with multiple medical conditions such as infections and cancermain and set-aside, were created with the purpose of holding out the set-aside group for future validation and testing of specific hypotheses. Subjects with multiple visits were assigned to the main group to allow for the assessment of temporal, intra-subject stability. The rest of the participants were randomly assigned to one of the sub-cohorts to achieve a ratio of approximately 80% main to 20% set-aside for each of the conditions, resulting in 217 and 53 subjects in the two groups, respectively. All analyses unless explicitly stated utilize only the main subjects.From a total of 270 subjects , two sub-cohorts, namely Each measured parameter among technical replicate samples (samples taken from a patient during the same visit) were averaged for downstream analysis after normalization . Samples from the same visit were considered technical replicates, although a visit could be an inpatient visit spanning several days or a one-day outpatient visit . This was done for gene log-intensities, protein log-RFUs, and CBC parameters. We refer to the data after averaging across technical replicates as “sample-level” data.In situations where we wished to investigate data at the subject level rather than sample level, we averaged each parameter over biological replicates in the sample-level data. We refer to the result as “subject-level” data. Note that patient ages associated with a subject for a data type were assigned to be the average age across all visits for which a sample of that data type was collected. The largest time difference between samples from the same subject was 369 days.6 was used to form modules of genes and modules of proteins using the subject-level data data see . The parPrior to module creation with WGCNA, samples were flagged as outliers by cutting an agglomerative hierarchical tree formed from distances between samples in the sample-level data. Data were scaled to unit variance prior to distance calculation. This was done separately for each data type and tree cut heights for the proteomic and transcriptomic data hierarchical trees were manually chosen 75 and 250 in each data type respectively. For both data types, the minimum branch size required so that samples on the branch were not removed was set to 10. The subject-level data was then rederived by averaging as before, but without these outlier samples. Although outliers were removed during the module creation process to avoid these extreme samples creating undue impact on the modules, these samples were included for downstream analyses, as they may have been flagged as outliers due to their extreme phenotypes (e.g. marrow failure) rather than technical noise. Thus, module activity scores were still computed for these outlier samples, even though they were not used to inform the creation of the modules.Module activity scores (sometimes referred to as module eigengenes) for a gene or protein module were calculated for each sample in the following way: First, the subject-level data was recomputed  only includes the subject as a random effect, with all other variation being considered “residual.” The second model (VP_M2) includes subject, condition, and various binary medication variables as random effects. The medication groups included in VP_M2 were Monoclonal antibodies(not including those for TNF and IL1), Anti-fungal, Antibiotic, Anti-TNF, Anti-IL1, Anti-inflammatory, IgG-replacement, IFN-gamma, Immune-stimulator, Immunosuppressant, and Steroids. As patients often were taking different combinations of medications, which potentially changed between repeat samples, the medications were coded as binary variables denoting whether a patient was or was not taking a given medication at the time of sampling. The individual variance contributions assigned to each of the medications were then summed to a single medication-associated variance contribution. Medications were included in the model if they were used by many patients and not highly confounded with one of the condition groups.Variance component models were fit using the variancePartition packageA feature was deemed to be stable if VP_M1 estimated that there was more intrer-subject variation than intra-subject variation in that feature (i.e. 50% or greater of the variation is explained by patient covariate in VP_M1). This determination was made for all data types . In various downstream analyses, only the stable features as determined by this method were used.To evaluate the robustness of these estimates, VP_M1 was performed with 100 replicates of jackknife resampling in which 80% of subjects with repeats and 80% of subjects without repeats were selected. Results were summarized with the mean variance explained by subject across jackknife samples and the 95% confidence interval was taken as 2.5% quantile and the 97.% quantile across jackknife samples.8 was used to fit linear models and test differential expression for each feature . A single model was fit for each feature that accounted for Condition, Gender, and Age, and Visit Type (whether or not the patient reports feeling sick on a given visit): feature ~ condition + age + gender + visit_type.To determine the disease signatures, LimmaT-statistics and p-values were computed for the following contrasts of the coefficients:Healthy was coded as the reference level and a t-statistics were computed for the coefficient for each conditionDisease vs. Healthy signaturesA contrast matrix was made such that each disease was compared to all other diseases .Each disease vs. all other diseasesComparison-specific contrasts were created to compare single diseases to others or groups of diseases to other groups.8 were computed and used to compute p values. Multiple hypothesis correction was performed using the Benjamini-Hochberg9 method to compute FDR-adjusted p values.For tests involving the gene expression or proteomic modules, standard t-statistics  were used to compute p-values due to the lower number of features. For the individual proteomic or transcriptomic features, the empirical Bayes moderated t-statistics The genes with TM1: Interferon were subclustered by computing the Euclidean distance matrix between all genes based on the T-statistics from the differential expression analysis comparing all conditions to Healthy Controls. The genes were clustered using Ward’s method (method = “Ward.D2”) with the hclust function in R. The hierarchical clustering tree was then cut to produce three clusters with the cutree function with k = 3.determining feature stability). Data were averaged to the subject level (see 10 was then used to partition the data into joint (sharing axes variation between the transcriptomic and proteomic data) and individual (unique to a data-type) components. Input data were first z-score normalized for each feature and then each input matrix was scaled by the frobenius norm of that data type so as to not give greater weight to data with more features (i.e. the transcriptomic data). The JIVE algorithm produces 3 matrices for each data-type, representing joint (shared between data types), individual (unique to that data-type) and residual  variation. JIVE PC scores were computed for each subject using the prcomp function from R, using the resulting joint, and individual matrices as inputs. To compute the joint PC scores (jPC’s), the transcriptomic and proteomic joint matrices were concatenated to a single joint matrix prior to calculation of the PC scores.The whole blood microarray and serum proteomics data  were filtered to select only stable features  to determine the total amount of variation present in a given data matrix. This same computation was then applied to the resulting joint and individual matrices. Dividing the variation in the joint and individual matrices by the amount of total variation gives the variance explained by each of these respectively. Lastly to determine residual variation, the joint and individual variation were subtracted from the total variation.8 with use.ranks = FALSE.To determine the gene set enrichments for the JIVE PC’s, the whole blood microarray and serum proteome data were separately correlated with each JIVE PC. Genesets were then tested for enrichment of correlation to each PC in each data type separately, using the two-sided t-test with correlation described in Wu, Di, and Gordon K. Smyth. Nucleic acids research 40.17 (2012), using the cameraPR function from limma A leukocyte composite score was computed for each patient by first averaging repeated observations from a given patient. A Z-score was then computed for the lymphocyte, neutrophil and monocyte counts relative to the healthy mean and standard deviation, for that parameter. The three Z-scores were then averaged across the cell-types to give the final composite score.The Immune Health Metric presented represents the likelihood that a given subject is a healthy control according to the leave one out cross validation (LOO CV) prediction probabilities of our random forest model.Prior to training the models, we subsetted the subjects to those that had measurements from all of the following data: proteomic, transcriptomic, and CBC/TBNK . Biological replicate samples from the same patients were averaged, so that each subject had one associated value for each measured feature. Features included for classification were subsetted to those for which the VP_M1 variance partition model assigned at least 50% of the variation to the patient covariate (i.e. the stable features).Three unimodal classifier schemas were designed: a proteomic module classifier, a transcriptomic module classifier, and a CBC parameter classifier, using the stable features from each respective data type.Two multimodal classifiers were also created: the first included all features from the three unimodal classifiers. The second included all features from the first, but also included the log-RFUs of all singleton proteins . Each classifier described above was then evaluated using leave-one-out cross validation, and an ROC curve was generated from the LOO CV probabilities of being a healthy subject (the positive class).Predicting healthy subjects vs. disease using all subjects, we computed the LOO CV prediction probabilities that an individual was a healthy control, that we termed the Immune Health Metric.main set of subjects. The disease vs. healthy status of set aside subjects was then predicted and an ROC curve was generated from the predicted probabilities of being a healthy subject (the positive class).The second multimodal classifier incorporating module activity scores, immune cell frequencies, and grey module protein RFUs was trained using all subjects in the For each classifier, the global variable importance (GVI) of all features were collected after training the classifier on all subjects used in the creation of the Immune Health Metric.To find the significance of the global variable importance (GVI) for each feature, permutation testing was performed to determine how often the GVI, as estimated by classifiers training on permuted class labels, was higher than the classifier trained on the true labels. A total of 10,000,000 permutations were performed.One-versus-all-condition binary classifiers were created for the largest groups of patient conditions: CGDs (XCGDs and 47CGDs were combined), Job, STAT1 GOF, and FMF. Each one-versus-all classifiers for each group were created analogously to the multimodal classifier including all modules, CBC +TBNK, and grey module proteins created to differentiate healthy subjects from monogenic patients. Feature GVIs were identified and tested analogously as well. Note that for the disease-versus-all classifiers, healthy controls were excluded from the LOO CV model training, prediction, and calculation of feature GVI.Transcriptional signatures for features from the three following categories were created:Immune Health MetricjPC1Features: all features from multimodal classifier that passed GVI testing with an FDR-adjusted p value of less than 0.20Signatures in the indexes and features categories both were formed by taking the 150 genes from the stable microarray features with highest correlation to the feature . Selected genes were then subsetted to those with a Spearman correlation to the feature of interest of more than 0.35 in magnitude. Genes in the signature were then divided into two groups: those positively correlated with the index/feature of interest, and those negatively correlated. Module signatures were all simply composed of the genes that the module was comprised of (stable and unstable). All these genes were placed in the positive correlates group of the signature, as we used a signed WGCNA performed to derive the modules.To assign each subject in the validation study a signature score, we subsetted the genes in the surrogate signatures to those also measured in the validation studies and we then averaged the z-scores of each gene/protein  for each gene in the signature. Note that z-scores of proteins in the ‘negative correlates’ group were flipped in sign prior to averaging.The proteomic IHM surrogate was derived and computed analogously to the transcriptional surrogate signatures as described above, with one small modification: to ensure that the signature was not reliant on proteins that had substantive relative differential abundance in serum compared to plasma (the data in which we planned to test these signatures), we removed any Somamers that fell into different dilution groups between plasma assays and serum assays.11 were used to test our transcriptional surrogate signatures in other data sets. Briefly, CGPs from the same study and platform were combined to ensure that samples were not being replicated across studies. Samples from the same patient in a study were removed manually. Several CGPs used in the OMiCC jamboree were removed for the following reasons:Comparison group pairs (CGPs) for the OMiCC Jamboreeet al11 were removed as many genes in the signatures to be tested were not present in the platforms used.CGPs/studies of systemic lupus erythematosus (SLE) appearing in Lau CGP ‘GSE9006-Diabetes_Mellitus,_Type_1-PBMC_newlydiagnosed_paired with 1 month follow up::GSE9006-Healthy-PBMC_unpaired’ was not included because samples in this CGP were follow up samples from another CGP, GSE9006-Diabetes_Mellitus,_Type_1-PBMC_newly diagnosed_unpaired::GSE9006-Healthy-PBMC_unpairedCGPs ‘Jam_human_RA_GSE26554-JIA-PBMC::Jam_human_RA_GSE26554-Control-PBMC’, ‘Jam_Human_RA_JIA-PBMC::Jam_Human_RA_Controls-PBMC’, ‘Jam_human_RA_GSE26554-OligoarticularJIA-PBMC::Jam_human_RA_GSE26554-Control-PBMC’, and ‘Jam_Human_RA_JIA-PBMC::Jam_Human_RA_Controls-PBMC’, were removed because the all had many overlapping samples with another CGP already included in our study, Jam_Human_RA_JIA-PBMC::Jam_Human_RA_Controls-PBMC.CGP ‘Jam_human_RA_GSE61281-Psoriatric_arthritis-Whole_blood::Cutaneouspsoriasis without arthritis_GSE61281-Cutaneous_psoriasis_without_arthritis-Whole_blood’ was removed because the control patients had psoriasis.Additionally, some samples were removed within certain studiesGSM545843, GSM545845 were removed as these were technical replicates of other samples in the studyGSE21942We removed additional biological replicates from patients that were sampled longitudinally and we selected the last sample for each patientGSE30210We removed additional biological and technical replicates from the same individual. The last sample was selected for each patient.Samples GSM214490 and GSM214492 were removed as they were believed to have unreliable diagnoses according to the original publicationGSE8650We removed patients who were experiencing clinical remission of symptomsGSE15645We removed patients with non-active sarcoidGSE42834A complete listing of the studies and all case/control samples in the meta-analysis can be found in Supplementary Table 1912 was then used to compute meta effect sizes of each of the surrogate signature scores. Meta-analysis was also performed for all genes that overlapped with those in our the monogenic microarray data and Wilcoxon tests were also used to determine whether genes belonging to each transcriptomic surrogate signature tended to have higher meta-effect sizes than genes that did not belong to the signature.Each study was quantile normalized within the study. The standard pipeline from the metaIntegrator packageet al13 as the previous aging signature. These proteins were compared to the proteins from the Immune Health metric proteomic surrogate with a one-sided Fisher’s exact test, with the alternative hypothesis being that the overlap was greater than that expected by chance.We considered the proteins passing an FDR-adjusted significance threshold of 0.05 from Supplementary table 3 of Tanaka 14, Kyoto Encyclopedia of Genes and Genomes15, Reactome16, and Gene Ontology Biological Processes17. For each module, FDR multiple hypothesis corrections were performed on all gene sets (pooled across collections).Gene modules from the transcriptomics data were tested using hypergeometric tests for the following collections of gene sets: The Li blood transcriptomic modulesProteomic modules were tested for gene set enrichments analogously after converting each protein targets of Somamers to their respective gene according to the SomaScan assay. Proteins that mapped to multiple genes were removed from the analysis. Additionally, some genes corresponded to multiple proteins. In this case, when testing a gene module, genes that mapped to both proteins in and outside of the module were removed from the module and the background proteins.18. Gene sets were made for various tissues by looking for proteins enriched for that tissue based on the HPA. The following categories were considered for enrichments: “enriched”, “enhanced”, and “tissue enriched.”An analogous analysis was performed for the proteomic modules using gene sets from the Human Protein AtlasThe correlation, without removing the effect of age, was computed simply by computing the Spearman correlation of every protein with the IHM surrogate signature. We additionally computed partial correlations where the effect of age had been removed from both the protein data and IHM transcriptional surrogate signature by using the limma removeBatchEffect function with age as the single covariate, which fits a linear model (feature ~age) to remove the effect of age prior to computing the correlation of each protein with the IHM transcriptional signature.https://ddbj.nig.ac.jp/public/ddbj_database/gea/experiment/E-GEAD-000/E-GEAD-397/. For each cell-type, the log cpm values with TMM normalization were computed using edgeR. We noted a large batch effect due to the “Phase” of the study and thus removed the phase effect at the individual gene level using limma’s removeBatchEffect function. After this, genes were z-scored normalized and signature scores were computed as described in the section above Data were downloaded from limma. Processed expression data from SDY212 was downloaded from ImmuneSpace. Each dataset was filtered to baseline, pre-vaccination samples from subjects over the age of 60. High and low antibody response labels for each subject were derived from HAI titer measurements using the maximum residual after baseline adjustment (maxRBA) end point 20. IHM signature scores were calculated in each subject using the MetaIntegrator R package. Briefly, the signature score for each subject was calculated from normalized, log2 transformed gene expression data by taking the geometric mean of positive signature genes and subtracting the geometric mean of negative signature genes. The standardized mean difference of baseline IHM scores between high and low antibody responders was estimated by fitting a random effects model using the metafor R package.Gene expression profiles for Yale vaccination subjects were quantile normalized using the R package Linear models were fit using the lm function in R both with and without including cell frequencies in the model. Cell frequencies were included as percent of total white blood cells and included major cell populations from the CBC/TBNK, specifically neutrophils, monocytes, CD4 T-cells, CD8 T-cells, B cells, NK cells, eosinophils, and basophils. The percent mediation, which reflects how much of the main effect can be explained by additional covariates, was calculated as: 1 – coefficient_without_controlling_for_cell_freq / coefficient_with_controlling_for_cell_freq.AksentijevichI., and SchnappaufO. (2021). Molecular mechanisms of phenotypic variability in monogenic autoinflammatory diseases. Nat. Rev. Rheumatol.17, 405–425.34035534Almarza NovoaE., KasbekarS., ThrasherA.J., KohnD.B., SevillaJ., NguyenT., SchwartzJ.D., and BuerenJ.A. (2018). Leukocyte adhesion deficiency-I: A comprehensive review of all published cases. J. Allergy Clin. Immunol. Pract.6, 1418–1420.e10.29371071ArnoldD.E., and HeimallJ.R. (2017). A Review of Chronic Granulomatous Disease. Adv. Ther.34, 2543–2557.29168144BergersonJ.R.E., and FreemanA.F. (2019). An Update on Syndromes with a Hyper-IgE Phenotype. Immunol. Allergy Clin. North Am.39, 49–61.30466772BustamanteJ., Boisson-DupuisS., AbelL., and CasanovaJ.-L. (2014). Mendelian susceptibility to mycobacterial disease: Genetic, immunological, and clinical features of inborn errors of IFN-γ immunity. Semin. Immunol.26, 454–470.25453225CoulterT.I., ChandraA., BaconC.M., BabarJ., CurtisJ., ScreatonN., GoodladJ.R., FarmerG., SteeleC.L., LeahyT.R.,  (2017). Clinical spectrum and features of activated phosphoinositide 3-kinase δ syndrome: A large patient cohort study. J. Allergy Clin. Immunol.139, 597–606.e4.27555459CudriciC., DeuitchN., and AksentijevichI. (2020). Revisiting TNF Receptor-Associated Periodic Syndrome (TRAPS): Current Perspectives. Int. J. Mol. Sci.21, 3263.32380704HenricksonS.E., JongcoA.M., ThomsenK.F., GarabedianE.K., and ThomsenI.P. (2018). Noninfectious Manifestations and Complications of Chronic Granulomatous Disease. J. Pediatr. Infect. Dis. Soc.7, S18–S24.ManthiramK., ZhouQ., AksentijevichI., and KastnerD.L. (2017). The monogenic autoinflammatory diseases define new pathways in human innate immunity and inflammation. Nat. Immunol.18, 832–842.28722725MeytsI., and AksentijevichI. (2018). Deficiency of Adenosine Deaminase 2 (DADA2): Updates on the Phenotype, Genetics, Pathogenesis, and Treatment. J. Clin. Immunol.38, 569–578.29951947MiotC., ImaiK., ImaiC., ManciniA.J., KucukZ.Y., KawaiT., NishikomoriR., ItoE., PellierI., Dupuis GirodS.,  (2017). Hematopoietic stem cell transplantation in 29 patients hemizygous for hypomorphic IKBKG/NEMO mutations. Blood130, 1456–1467.28679735PeledA., SarigO., SunG., SamuelovL., MaC.A., ZhangY., DimaggioT., NelsonC.G., StoneK.D., FreemanA.F.,  (2019). Loss-of-function mutations in caspase recruitment domain-containing protein 14 (CARD14) are associated with a severe variant of atopic dermatitis. J. Allergy Clin. Immunol.143, 173–181.e10.30248356SchwabC., GabryschA., OlbrichP., PatiñoV., WarnatzK., WolffD., HoshinoA., KobayashiM., ImaiK., TakagiM.,  (2018). Phenotype, penetrance, and treatment of 133 cytotoxic T-lymphocyte antigen 4–insufficient subjects. J. Allergy Clin. Immunol.142, 1932–1946.29729943SpinnerM.A., SanchezL.A., HsuA.P., ShawP.A., ZerbeC.S., CalvoK.R., ArthurD.C., GuW., GouldC.M., BrewerC.C.,  (2014). GATA2 deficiency: a protean disorder of hematopoiesis, lymphatics, and immunity. Blood123, 809–821.24227816TangyeS.G., Al-HerzW., BousfihaA., ChatilaT., Cunningham-RundlesC., EtzioniA., FrancoJ.L., HollandS.M., KleinC., MorioT.,  (2020). Human Inborn Errors of Immunity: 2019 Update on the Classification from the International Union of Immunological Societies Expert Committee. J. Clin. Immunol.40, 24–64.31953710ToubianaJ., OkadaS., HillerJ., OleastroM., Lagos GomezM., Aldave BecerraJ.C., Ouachée-ChardinM., FouyssacF., GirishaK.M., EtzioniA.,  (2016). Heterozygous STAT1 gain-of-function mutations underlie an unexpectedly broad clinical phenotype. Blood127, 3154–3164.27114460TownsleyD.M., DumitriuB., and YoungN.S. (2014). Bone marrow failure and the telomeropathies. Blood124, 2775–2783.25237198