With the growing complexity of single-cell and spatial genomics data, there is an increasing importance of unbiased and efficient exploratory data analysis tools. One common exploratory data analysis step is the prediction of genes with different levels of activity in a subset of cells or locations inside a tissue. We previously developed singleCellHaystack, a method for predicting differentially expressed genes from single-cell transcriptome data, without relying on comparisons between clusters of cells. Here we present an update to singleCellHaystack, which is now a universally applicable method for predicting differentially active features: (1) singleCellHaystack now accepts continuous features that can be RNA or protein expression, chromatin accessibility or module scores from single-cell, spatial and even bulk genomics data, and (2) it can handle 1D trajectories, 2-3D spatial coordinates, as well as higher-dimensional latent spaces as input coordinates. Performance has been drastically improved, with up to ten times reduction in computational time and scalability to millions of cells, making singleCellHaystack a suitable tool for exploratory analysis of atlas level datasets. singleCellHaystack is available as packages in both R and Python. Recent advances in single-cell and spatial omics technologies allow researchers to obtain abundance measures of transcripts and proteins, or the accessibility of genomic regions at single-cell resolution. These technologies present an unprecedented view of the heterogeneity in cell populations and their spatial distributions within tissues. However, they are also accompanied by new challenges in data analysis.A fundamental step in exploring single-cell transcriptomics data is predicting genes that have different levels of expression in one subset of cells compared to others. Such genes are often referred to as differentially expressed genes (DEGs). Similarly, in spatial transcriptomics data, spatially variable genes (SVGs) are genes with altered expression in part of a tissue. In single-cell ATAC (scATAC-seq) data, differentially accessible regions (DARs) are genomic regions which have a higher accessibility in one group of cells compared to others. In this paper, we will use the term differentially active features (DAFs) to refer to any feature or set of features with differential levels of activity  within an input space, be it the 2D or 3D space within a tissue or latent spaces  of any dimension.7. However, benchmark studies have reported that DEG prediction approaches for bulk RNA-seq do not perform worse than methods designed specifically for single-cell RNA-seq (scRNA-seq), and that the agreement between single-cell DEG prediction approaches is low9. Because the number of clusters tends to be large, a common approach is to compare the cells in each cluster against all other cells, which restricts DEGs that can be detected to genes with high (or low) expression in a single cluster. For the prediction of SVGs, methods have been developed that directly employ the spatial coordinates of cells (or spots or pucks) to detect genes that have non-random distributions of expression in the 2D (or 3D) space of the tissue17. However, most existing methods do not scale well with large datasets, suffer from prohibitively long runtimes, and are limited in the spatial patterns that they can detect in practice. The development of more flexible approaches for discovering complex differential expression patterns is one of the grand challenges of this field18.The majority of single-cell DEG prediction approaches are based on two steps: (1) clustering of cells by similarity, and (2) applying statistical tests between clusters to identify DEGs19. Our method does not rely on comparisons between clusters of cells and is applicable to both scRNA-seq and spatial transcriptomics data. Although singleCellHaystack was superior to other methods in our extensive comparison19, a limitation of the implementation was that it used a hard threshold for defining genes as being either detected or not detected in each cell. Treating detection in a binary way ignores the magnitude of gene expression differences, which might miss some differential expression patterns. Furthermore, singleCellHaystack was not able to handle sparse matrices, limiting its applicability to the ever-increasing dataset sizes.We recently developed singleCellHaystack, a method that predicts DEGs based on the distribution of cells in which they are active within an input space24. Moreover, our approach can also be used on sets of genes  for predicting differential activities of biological pathways in single-cell and spatial transcriptomics data, and to identify DEGs along trajectories. Together, these results illustrate the usefulness of singleCellHaystack for exploring complex biological datasets. The new singleCellHaystack method is implemented as an R package and is available from CRAN (version 1.0.0 and higher) and GitHub, and as a Python package available from PyPI (version 0.0.5) and GitHub.Here, we present a new approach which addresses the above limitations and features several critical improvements. First, our new method uses continuous activity levels, enabling the identification of changes in any kind of features . Second, it uses cross-validation for choosing a suitable flexibility of splines during its modeling steps. Third, the computational time has been drastically reduced by incorporating several engineering improvements to the base code, including the use of sparse matrices. Finally, a Python implementation has been developed which enables the efficient application of singleCellHaystack to atlas level datasets with millions of cells. These improvements, together with the fact that it does not make strong assumptions about the statistical distribution of the input data, make singleCellHaystack applicable to a wide range of data types. In this manuscript we describe applications to single-cell transcriptomics, spatial transcriptomics , scATAC-seq, CITE-seq, and a large collection of bulk RNA-seq samples27, and (2) a matrix of numerical values reflecting the activities of features in each sample (activity matrix). These typically are estimates of the concentrations of RNAs or proteins but can also be scores reflecting the accessibility of genomic regions or the average expression of sets of genes that share common functional annotations. First, singleCellHaystack estimates 19. For each Figure 19. This new method (found in singleCellHaystack version 1.0.0 and higher) uses continuous values, reflecting the magnitude of the activity of each feature around each grid point, allowing it to identify more nuanced patterns of expression, and extending the application to features other than gene expression levels. To investigate differences in DEGs returned by the new continuous version and the original binary version of singleCellHaystack, we applied both versions to 119 scRNA-seq datasets of Tabula Muris and Mouse Cell Atlas29. Although both methods returned generally consistent results , and the opposite result for Lyz2 . Other examples are shown in Supplementary Fig. Our previous method (available in singleCellHaystack version 0.3.2) treated the expression of genes as binary (i.e. either expressed or not) using a hard thresholdTo evaluate the robustness of singleCellHaystack w.r.t. sample sizes, we applied it to down-sampled datasets and compared results with those obtained on the full datasets  enabling broader usability and the application to very large datasets. To show this we applied the Python version to a scRNA-seq dataset with 4 million cells from human fetal tissues30  in each dataset and recorded their runtimes. Unfortunately, most methods do not scale well with increasing dataset size . We evaluated the differences in the mouse brain  in 2 locations in the posterior brain. singleCellHaystack regards Ttr as a top-scoring SVG , but SPARK-X does not . In contrast, genes that are top-scoring according to SPARK-X but not singleCellHaystack are rare. Two such genes are Arpp19  and Ccdc36  in the same posterior brain dataset . We applied both methods to each dataset and plotted the returned p-values (logPlp1 (ranked 20th by singleCellHaystack vs 1139th by SPARK-X) and Mal (ranked 55th by singleCellHaystack vs 1543rd by SPARK-X), all showing strong spatial expression patterns  and Cox4i1 (1123rd by singleCellHaystack vs 183rd by SPARK-X). Although both Zfhx3 and Cox4i1 show some degree of spatially differential expression, the tendency is weaker than those of Plp1 and Mal.In Slide-seqV2 datasets, too, top-scoring SVGs picked up by singleCellHaystack but missed by SPARK-X were relatively common. For example, in the mouse hippocampus sample, several genes were top-ranking SVGs according to singleCellHaystack but not SPARK-X, including rns Fig. . In cont–39) but only 105th by SPARK-X .In HDST datasets, singleCellHaystack and SPARK-X returned highly consistent results Fig. . For exa22. This technology has the difficulty that the number of cells is small relative to other methods, making the identification of SVGs more challenging. In agreement with that, both singleCellHaystack and SPARK-X identified relatively lower numbers of differentially expressed genes.Finally, we compared both methods using three MERFISH datasets . Because of the versatility of the density distribution and relative entropy approach on which singleCellHaystack is based, it is applicable to many other data types and input spaces. In the next sections we illustrate this general applicability using examples on scRNA-seq trajectory (1D) data, CITE-seq data, scATAC-seq data, a large collection of bulk RNA-seq data, and on gene set activity data.28, which contains data from developing thymocytes, progressing from a double negative (Cd4−Cd8−) through a double positive (Cd4+Cd8a+) stage into mature naive T cells characterized as single positive . We processed the 10x Genomics Chromium data using the standard pipeline with Seurat33 and then used monocle334 to order cells from the double negative cluster to the single positive clusters  dataset containing the whole transcriptome, and the expression of more than 200 proteinsWe also applied singleCellHaystack to a single-cell multiome  dataset from human PBMCs downloaded from the 10x Genomics website see “” section23. singleCellHaystack successfully predicted DEGs with differential expression in subsets of the samples and generally showed consistency with other DEG prediction approaches such as the Wilcoxon rank sum test or the Student’s t-test , thus ignoring more subtle differences in expression between subsets of cells. At the same time, the use of a hard threshold can result in an overestimation of the significance of relatively modest differences in expression  cannot be easily conducted. We hope to expand singleCellHaystack to include methods for such comparisons in the future. Secondly, for better or worse, current scRNA-seq data analyses are often cluster-oriented. Methods that are based on making comparisons between clusters (such as the Wilcoxon rank sum test and the t-test) tend to have a bias towards features with activity patterns that fit well with one of a few clusters, but are likely to miss features whose differential activity is spread out over several clusters  we refer to Vandenbon and DiezThe updated version of singleCellHaystack (version 1.0.0) includes several improvements. The main improvement is that singleCellHaystack no longer treats expression in a binary way (i.e. detected or not detected), but uses continuous values (see Steps 3–4 below). Secondly, we updated the modeling of . By default, the coordinates in each dimension are rescaled to mean 0 and standard deviation 1. Observations might be gene expression, chromatin accessibility, gene set module scores, etc. and will be denoted as The main inputs to singleCellHaystack are the coordinates of samples inside an input space, and the observations in each sample. Here, samples include single cells, spots, pucks or even bulk samples, depending on the platform used. The input space could be the 2D or 3D space in a tissue or a latent space after typical dimensionality reduction , or the 1D coordinates of samples along a trajectory (pseudotime). Coordinates of samples in 36. Such approaches, however, are not necessarily suitable for high-dimensional spaces nor for spaces that are partly empty. In our methods, by default, the coordinates of grid points are instead decided by running k-means clustering on the sample coordinates 19. Alternatively, the user can specify the coordinates of the grid points to use (option grid.coord), or use seeding, as used in k-means++ clustering, as described before (grid.method = “seeding”)19.Steps 2–4 involve estimating the distribution of samples inside the 19. For each sample, the Euclidean distance to the closest grid point is calculated, and The bandwidth 19. In brief, the density of cells around grid point The reference distribution After this, Whereas the original version of singleCellHaystack treated observations in a binary way (a gene is either detected or not detected in each cell), this updated version of singleCellHaystack addresses this weak point and treats observations in a continuous manner. To do so, the distribution The divergence of feature 19. If the activity of feature This approach is simpler than the original version, because no distinction needs to be made between samples in which a feature was detected or not detectedIn a final step, singleCellHaystack evaluates the statistical significance of First, singleCellHaystack calculates the coefficient of variation (CV = standard deviation/mean) of each feature For each randomized feature Alternatively, B-splines can be used, using function bs. In this case, a suitable degree (between 1 and 5) and degree of freedom (between 1 and 10) is decided in the same way.Using the splines, the expected mean and standard deviation of https://figshare.com/articles/dataset/Robject_files_for_tissues_processed_by_Seurat/5821263 (version 3)28. For the Mouse Cell Atlas data, we downloaded file MCA_BatchRemove_dge.zip from https://figshare.com/articles/MCA_DGE_Data/543586629. This data has been treated to reduce batch effects. Data from both Tabula Muris (32 datasets) and Mouse Cell Atlas (87 datasets) were processed using the Seurat R package (version 4.0.0)33. We used the same pipeline for the processing and normalization of all datasets: genes detected in less than 3 cells and cells with fewer than 100 detected genes were removed. After this initial filtering, cells with extreme UMI counts (bottom 1 percentile and top 1 percentile), or extreme numbers of detected genes (bottom 1 percentile and top 1 percentile), or with a high fraction of mitochondrial reads (> 10%) were removed. The data for the remaining cells of each dataset was normalized , scaled , and highly variable genes (HVGs) were detected . The HVGs were used for principal component analysis (PCA), and the 20 first principal components (PCs) were used for further dimensionality reduction (t-SNE and UMAP) and for clustering of cells . Both the binary (version 0.3.2) and updated (version 1.0.0) singleCellHaystack were applied on the first 20 PCs of each dataset. For the binary version, in each dataset, for each gene, the median expression level of each gene was used as a threshold to define detection. The detection data was used as input. For the updated version, the continuous expression levels were used as input.We obtained the Tabula Muris data from We conducted a comprehensive comparison between genes that are high-scoring according to either the continuous or the binary version of singleCellHaystack (but not both) as follows. From the prediction results of the 119 Tabula Muris and Mouse Cell Atlas datasets, we picked up genes that were among the top 250 high-scoring genes according to one approach, but were ranked at least 1000 ranks lower by the other approach. We calculated the mean expression and coefficient of variation (= standard deviation/mean) of these genes in the dataset in which they were predicted to be DEGs . For each down-sampled dataset, we collected the p-values of all genes as estimated by singleCellHaystack and FindAllMarkers . Finally, we compared results of the down-sampled datasets to the results of the full datasets using the Spearman’s rank correlation of the p-values as a measure of the robustness of the results  using PCA coordinates with 50 components. To check scalability with dataset size we compared the runtimes obtained with the R and Python implementations, using datasets containing 1000, 10,000, 50,000, 100,000, 500,000 and 1 million randomly selected cells from this dataset , through double positive (CD4+CD8+) and into mature, single positive (CD4+CD8− and CD4−CD8+) T cells. To identified the differentiation trajectory we used monocle334. Briefly, the data was first processed using the standard Seurat pipeline (see above), except that 30 PCs were used to calculate UMAP coordinates. We converted the Seurat object into a cell_data_set object with the SeuratWrappers package . Then monocle3 was used to calculate clusters and partitions using the UMAP coordinates with the function cluster_cells. Next, the principal graph is learned using the learn_graph function, and cell were ordered selecting as root the node in the graph starting in the cluster of double negative cells. We used singleCellHaystack using the pseudotime coordinates. We selected the top 1,000 predicted DEGs and clustered them into modules using kmeans, using k = 6.We applied singleCellHaystack to pseudotime projection on the Tabula Muris thymus datahttps://atlas.fredhutch.org/nygc/multimodal-pbmc/. This dataset contains information about the expression of 228 immune marker proteins on over 200 k cells. As input to singleCellHaystack we used the protein based PCA coordinates (50 PCs), and the normalized expression levels included in the downloaded data, which was processed as described here33. To compare with singleCellHaystack, differentially expressed proteins were also predicted using the default Wilcoxon rank sum and the Student’s t-test implemented in Seurat’s FindAllMarkers function, with default parameters except max.cells.per.ident = 500, and the original cell type annotations (celltype.l2) as clusters.Single-cell data from human peripheral blood mononuclear cells (PBMC) data was downloaded from https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets/1.0.0/pbmc_granulocyte_sorted_10k). The raw data (fragments and peak information from cellranger) were processed with Signac37, following the workflow described here: https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html#wnn-analysis-of-10x-multiome-rna-atac-1. Briefly, the expression and chromatin accessibility peak information data were loaded into a Seurat object. For the peaks, the information about genomic ranges was obtained using the function GetGRangesFromEnsDb with the Bioconductor package EnsDb.Hsapiens.v86 (http://bioconductor.org/packages/EnsDb.Hsapiens.v86/). Cells were filtered to have less than 20% of mitochondrial counts, RNA counts between 1000 and 25,000 and ATAC counts between 5 × 103 and 7 × 107. For the RNA data the SCTransform pipeline was used, and UMAP coordinates were calculated using 50 PCs. For the ATAC counts, first term-frequency inverse-document-frequency was calculated with RunTFIDF. Top features were selected with FindTopFeatures and min.cutoff = “q0”. Then, a Latent Semantic Index (LSI) embedding was calculated with RunSVD. ATAC based UMAP was constructed from dimensions 2 to 50 from LSI. singleCellHaystack was run using LSI embedding and ATAC peak counts. To compare with singleCellHaystack, differentially accessible regions (DARs) were also predicted using the default Wilcoxon rank sum and the Student’s t-test implemented in Seurat’s FindAllMarkers function, with default parameters except max.cells.per.ident = 500, and the original cell type annotations as clusters.Single-cell multiome (RNA + ATAC) data from human PBMC was downloaded from the 10x Genomics web site .Data for mouse kidney and brain were obtained through the SeuratData R package20. We filtered out mitochondrial genes and genes with non-zero counts in less than 10 spots, as well as pucks with less than 100 reads in total. Data was normalized using the Seurat R package .We obtained the data from the Broad Institute Single Cell Portal (accession number SCP815)21. We filtered out mitochondrial genes and genes with non-zero counts in less than 10 spots. Data was normalized using the Seurat R package .We obtained the data from the Broad Institute Single Cell Portal (accession number SCP420)22. We filtered out genes with non-zero counts in less than 10 spots, as well as cells with less than 6000 detected genes. Data was normalized using the Seurat R package .RNA counts and cell position information was obtained from Xia et al. (Datasets S12 and S15)32. Because several methods have long runtimes or returned errors on large datasets , and applied singleCellHaystack on the resulting 1958 samples and 18,260 genes, using as input space the first 5 PCs, using 25 grid points and otherwise default parameters. For visualization purposes, we applied UMAP on the first 5 PCs. For comparison, DEGs were also predicted using the default Wilcoxon rank sum and the Student’s t-test implemented in Seurat’s FindAllMarkers function, after clustering samples using default parameters.We downloaded a large collection of RNA-seq samples covering 76 mouse cell types and tissues35. For 292 pathways which had at least 10 associated genes, we collected their genes, and used the Seurat function AddModuleScore to calculate module scores in the spots of the Visium datasets. These module scores reflect the general activity of the genes in each pathway. Subsequently, we ran singleCellHaystack on each Visium datasets, using as input the coordinates of spots and the module scores  of all pathways. High-scoring pathways thus reflect pathways with spatial differences in activity within the tissue. The top 25 high-scoring pathways in each dataset were clustered by similarity using function hclust_haystack of the singleCellHaystack package.We used the R package msigdbr (version 7.5.1) to retrieve sets of mouse genes associated with BIOCARTA pathwaysSupplementary Information.


The early diagnosis of preeclampsia, a key outlook in improving pregnancy outcomes, still remains elusive. The present study aimed to examine the interleukin-13 and interleukin-4 pathway potential in the early detection of preeclampsia as well as the relationship between interleukin-13 rs2069740(T/A) and rs34255686(C/A) polymorphisms and preeclampsia risk to present a combined model. This study utilized raw data from the GSE149440 microarray dataset, and an expression matrix was constructed using the RMA method and affy package. The genes related to the interleukin-13 and interleukin-4 pathway were extracted from the GSEA, and their expression levels were applied to design multilayer perceptron and PPI graph convolutional neural network models. Moreover, genotyping for the rs2069740(T/A) and rs34255686(C/A) polymorphisms of the interleukin-13 gene were tested using the amplification refractory mutation system PCR method. The outcomes revealed that the expression levels of interleukin-4 and interleukin-13 pathway genes could significantly differentiate early preeclampsia from normal pregnancy. Moreover, the present study’s data suggested significant differences in the genotype distribution, the allelic frequencies and some of the risk markers of the study, in the position of rs34255686 and rs2069740 polymorphisms between the case and control groups. A combined test of two single nucleotide polymorphisms and an expression-based deep learning model could be designed for future preeclampsia diagnostic purposes. Pregnancy complications associated with new-onset elevated hypertension  after 20 weeks’ gestation with accompanying proteinuria  is defined as preeclampsia. The frequency of other abnormal lab values such as thrombocytopenia, impaired liver function, epigastric pain, pulmonary edema or renal insufficiency can be categorized as severe preeclampsia . DespiteIt is thought that cytokines as immunological modulators are involved in the development and progression of this pro-inflammatory situation. In normal pregnancy, T helper 2 cytokines (anti-inflammatory) such as interleukins (ILs) 4, 5 and 13 help to neutralize pro-inflammatory cytokines (T helper 1) such as interleukin 2 and gamma interferon. Several studies have noticed the imbalanced concentration of these pro- and anti-inflammatory cytokines in preeclampsia ,8. GenetThere are two main methods to evaluate the expression changes in a specific pathway. The first one applies statistical models to see which genes are differentially expressed by comparing normal individuals and patients ,14. Thenhttps://www.ncbi.nlm.nih.gov/geo/) database on 3 May 2022. The database contains a vast number of datasets related to various studies. We selected the GSE149440 microarray dataset which combined the GSE149434, GSE149436 and GSE149437 datasets. The dataset contained 735 whole blood samples and three different groups . The platform used was GPL28460 or Affymetrix Human Transcriptome Array 2.0, including 32,800 probes. First, raw CEL files were downloaded through the GEOquery package in R. Initial preprocessing was performed on the data, including backlight removal, data normalization based on the RMA method and data transfer to a logarithmic basis based on 2 using the affy package. Samples with the preterm birth condition were excluded from the study, and only early preeclampsia  and normal pregnancy samples remained. As the dataset contained multiple studies, batch effect removal was conducted using the limma package.In the present study, we first searched the terms “Preeclampsia” and “Normal Pregnancy” in the Gene Expression Omnibus  on 10 May 2022. It is a CSV file containing one column that shows protein–protein interactions organized by their Entrez ID, each one separated by a comma. For instance, the interaction between GAPDH and GRM1 is shown by 2597, 2911. Then, a graph of the interactions was constructed, each node representing a gene and each vertex representing its expression. The edges show the connections between the proteins and the graph type is an undirected graph because the impact of each protein on the other one is unknown based on the collected data. The graph structure at first was designed for all protein interactions in GSE149440. Then, the IL-13 and IL-4 signaling pathway gene list was downloaded from the GSEA (https://www.gsea-msigdb.org/) on 20 May 2022, which linked us to the Reactome database (https://reactome.org/). Afterwards, all interactions except those between the mentioned pathway genes were excluded from the constructed graph. To be able to pool graph signals, we first needed to coarsen the graph . Then, we ended up having multiple graphs—not unlike a pyramid—with each at one level of resolution. The finest graph is where the input data lies. The coarsest graph is where the data at the output of the graph convolutional layers lies. Then, we computed the Laplacian graph for each of our graphs, defined by their adjacency matrices. For learning, we used a CNN graph with the following hyperparameters. Two convolutional layers were employed with 32 convolutional filters and a polynomial order of 20. Size 2 maximum pooling was applied to both convolutional layers. Two fully connected layers, on the other hand, had 512 and 128 units, respectively. The activation function of a rectified linear unit (ReLU) was used and cross-entropy loss was kept to a minimum. For MLP, four layers were constructed, with 111, 42, 21 and 10 ReLUs, respectively. In MLP, only the expression matrix of the pathway genes was considered as the input, which contained 111 genes as input features.We applied neural networks and deep learning to classify healthy and preeclampsia blood samples. There are many types of neural networks; here, we selected MLP and CNN. To design the CNN, a dataset in the form of images was needed. To convert the format of our dataset into an image format, we applied the method used in . First, For the case-control study, approved by the Islamic Azad University Kazerun Branch Ethics Committee (IR.IAU.KAU.REC.1398.004), conducted in 2019 and 2020, 150 preeclampsia patients and 150 normal healthy pregnant women from Zeynabieh Hospital in Shiraz, Iran, were selected. Demographic characteristics are shown in 2O. The cycling conditions were as follows: initial denaturation for 5 min at 94 °C, followed by 30 cycles at 94 °C for 40 s (denaturation), annealing temperature as in After collecting 5 mL peripheral blood samples in EDTA tubes, genomic DNA was extracted using a Genomic DNA Isolation Kit , using the salting out method. To genotype rs2069740(T/A) and rs34255686(C/A) polymorphisms, the amplification refractory mutation system PCR (ARMS-PCR) method was carried out. For each polymorphism, two external primers (to check the performance of the PCR) and two allele-specific internal primers  were designed . Chi-square testing was performed to evaluate the differences in the allele and genotype frequencies. Using the GSE149440, an expression matrix was constructed based on the RMA method. As this study only needed early preeclampsia and normal blood samples, 355 out of the 735 samples were removed from the matrix. The number of samples is demonstrated in In the present study, we designed two deep learning models for diagnostic purposes, GCNN and MLP. The GCNN model used a structure based on IL-13 and IL-4 pathway connections. This means that the model considered the biological aspects of the hypothesis. On the other hand, the MLP model only used the expression matrix as given features for model training. Therefore, we aimed to compare the accuracy of the pathway connection-based model with the usual MLP model that did not consider any biological background. The structure of each model is depicted in prs2069740 = 0.009; prs34255686 = 0.00) and the genotype frequencies for rs34255686(C/A) polymorphisms (p = 0.002) differed significantly between the patient and control groups. No significant difference was observed between the studied groups in the genotype frequency of rs2069740(T/A) polymorphisms (p = 0.099). Moreover, there was no significant difference in the combined genotype analyses except for the TT–AA genotypes (p = 0.001).According to the genotype and allele frequencies of the IL-13 rs2069740(T/A) and rs34255686(C/A) polymorphisms summarized in p = 0.008) and edema (p = 0.047) in the position of rs34255686 and multipara (p = 0.02) and gravida (p = 0.015) markers in the case of rs2069740, no significant difference was detected for other markers  and rs34255686(C/A) polymorphisms. Except in the cases of HELLP syndrome  and rs34255686(C/A) contribute to the risk of preeclampsia. We believe that this combined test of two single nucleotide polymorphisms and an expression-based deep learning model is able to predict preeclampsia in very early pregnancy. It is worth noting that this model and SNP-based testing could be invaluable in screening high-risk pregnancies in clinical practice and could serve as a decision-making reference for clinicians.Therefore, we suggest more investigation of a diagnostic model based on both expression and polymorphism results. Finally, to better understand the pathology of the disease due to its life-threatening properties and likely involvement of more polymorphic sites in its pathogenesis, research into more polymorphisms and the expression patterns of IL-4 and IL-13 pathway genes is needed.


Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression‐based classification with clinical relevance.A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques.By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis.The developed class‐predictor allows identification of CC subgroups with specific biological features and clinical behavior at single‐sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice. Among these, clinical trials demonstrated significant activity for drugs targeting In this light, a series of gene expression studies using transcriptomic technologies on primary CC tumors attempted to elucidate the mechanistic insights of CC and to identify transcriptomic subtypes with a predictive and prognostic relevance. Considering 149 ICC cases, Sia and colleagues demonstrated the presence of two classes, named as proliferation‐ and inflammation‐related, associated with up‐regulation of EGF, RAS, AKT, MET signaling, and immune response‐related pathways, respectively.Despite these results represented a significant improvement in the biological understanding of CC, their clinical implications are still limited. This is due not only to the limited application of RNA‐sequencing in clinical practice, but also to the fact that all these classifications were generated from different and small cohorts and have not been compared and unified, thus limiting their applicability. Indeed, a comprehensive transcriptomic analysis of ICC and ECC for obtaining biological insights and prognostic information across different studies is still lacking. Availability of a classification combining several single studies with small patient cohorts is likely to deliver more solid data on ICC and ECC transcriptomic subtypes possibly identifying new druggable pathways.In the present work, we aim at filling the gap by providing a comprehensive transcriptomic analysis of ICC and ECC tumor tissues derived from all the public cohorts available to date, with the final goal of generating a definitive and uniformed gene expression‐based classification of CC and of identifying lists of markers with prognostic relevance.22.1Gene expression profiles were collected from eight public datasets, including 674 patients, that is, 442 (65.6%) ICC and 232 (34.4%) ECC with no previous history of hepatitis or fluke infection, and generated by microarray (6) or RNA sequencing . Overall, 126 of 664 (19%) were from fresh frozen (FF) tumor tissues, while 538 (81%) were from formalin‐fixed paraffin embedded (FFPE) specimens. For all the downstream analyses, seven datasets were used for discovery; whereas the EGAD00001001693 dataset was used for validation proven that it was the unique with complete information on patient survival  obtained using RSEM (level 3) were downloaded from Firebrowse database (accession date: January 2022). TPM values followed by quantile normalization and log2 transformation were considered. All the microarray data were retrieved from GEO using a bespoke R pipeline. Considering Illumina microarray dataset, robust‐spline normalization followed by log2 scaling were used to normalize gene expression values. Affymetrix and Agilent datasets did not require any additional normalization after downloaded from GEO database.http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and aligned to human reference genome (hg19) using STAR (version 2.7.9a).https://www.R‐project.org/, version 4.1.1, see section “list of R packages”). Figure Regarding the validation set, RNAseq data were downloaded from the EGA repository after the accession permission released by ICGC consortium (application number: DACO‐6992). Raw sequencing data (fastq format) were trimmed to remove low‐quality bases and adapters using trimmomatic (version 0.39),genome hg9 using S2.3n = 13,228) were considered. Then, quantile normalization followed by batch adjustment based on empirical Bayes methodn = 1358) and ECC (n = 676) cohorts, respectively. In particular, the method allows to identify genes that drive biological heterogeneity in a dataset decomposing the total variance of each gene into its biological and technical components by fitting a trend to the endogenous variances.Normalized gene expression data of each dataset included in the ICC and ECC discovery sets were merged and only common transcripts  along with euclidean distance and Ward's linkage method.Detection of distinct subgroups within the ICC and ECC training set was performed by unsupervised clustering analysis through NbClust tool.2.4t‐test (LIMMA).t‐statistic values were used to rank gene list for the gene set enrichment analysis by GSEA2 canonical pathway database.p‐value <0.05 and p‐value <0.01 for HALLMARK and C2 canonical pathway gene sets, respectively. Singscore tool,t‐test statistic was applied to detect significant differences in terms of enrichment score. ImmuCellAIt‐test statistics was applied to detect significant difference between subgroups in terms of immunological infiltration score.Within ICC and ECC discovery sets, differential expression analysis between subgroups were performed using gene level linear models with moderated 2.5n = 19). Considering the ECC cohort, among the top 60 differentially expressed genes for each group based on t‐statistic value, only the transcripts with FDR <0.25 in all subgroups were selected (n = 21). The SMOTE (Synthetic minority over‐sampling technique) algorithm,Starting from differential expression analysis by LIMMA, two different approaches were adopted to define the genes to be considered for the predictor establishment in ICC and ECC discovery set. For ICC cohort, genes with a log2 fold change (log2FC) <−1 and >1 and FDR <0.1 values within each comparison were selected , NbClust algorithm was applied to validation sets. In particular, 19 out of 30 indices were considered to validate the number of the clusters along with euclidean distance and Ward's linkage method.n = 29), the range was set to two and five. At the same time, predictors based on rFSRC algorithm were applied to predict the presence of the four biological subgroups in ICC and ECC validation sets. Diagonal dominant matrixStarting from the same genes considered for the unsupervised clustering analysis in discovery set  and relapse free survival (RFS). OS was calculated from the date of disease diagnosis to death or last follow‐up, while RFS was calculated from the date of disease diagnosis to the first event . Patients were divided into biological subgroups returned by rFSRC predictor and unadjusted 2.81. WGCNA2. UBL3. tidyr4. sva5. singscore6. RColorBrewer7. randomForestSRC8. plyr9. org.Hs.eg.db10. NMF11. NbClust12. multiROC13. lumi14. limma15. lattice16. irr17. GSA18. gplots19. ggthemes20. ggpubr21. ggplot222. ggfortify23. GEOquery24. FunCluster25. fgsea26. factoextra27. edgeR28. dplyr29. DBI30. data.table31. ComplexHeatmap32. circlize33. caret34. Biobase33.1A total of 340 ICC and 203 ECC patients profiled for gene expression data with no previous history of hepatitis or fluke infection were collected from seven different datasets and considered as discovery dataset. Overall, the intersection of the different platforms used to characterize each sample allowed to obtain the expression profile of 13,228 genes. The application of preliminary steps for sample normalization and batch effect adjustment lead to a higher correlation between each sample in the cohorts  and to efficient batch correction Figures  and S2.N = 386) obtained from GTEx database were removed to avoid contamination given by transcripts non biologically associated to CC.Liver‐specific genes , metabolism and TGF‐ß (subgroup B), cell cycle and DNA replication (subgroup C), DNA repair and KRAS down‐regulation (subgroup D). Interestingly, the evaluation of the immunological components revealed the presence of immune cell infiltration in all samples, with subgroup A being enriched only with exhausted T cells Figure . The enr3.3The presence of distinct ICC and ECC biological subgroups within the discovery set opened the possibility to build and compare specific predictors to validate our findings in an independent dataset . For such a purpose, feature selections were performed specifically for ICC and ECC cohorts starting from the differential expression analysis between each subclass. A total of 19 and 21 genes were selected for ICC and ECC samples, respectively and used to build the predictor from training set. Due to imbalance between subgroups, we adopted the SMOTE algorithm in order to oversample the minority class prior to classifier definition on the discovery set. Among the three machine learning algorithms tested, rFSRC showed the best performance with an overall misclassification rate of 0.08 and 0.07 for ICC Table  and ECC 3.4n = 43) and D (n = 1) were characterized by the highest and lowest number of patients, respectively, consistently with the discovery set composition. For the ECC cohort, most of the patients were identified in subgroups A (n = 24) and only a few samples were assigned to the B (n = 2), C (n = 2), and D (n = 1) subgroups.To validate and establish the clinical significance of our predictors, an independent dataset composed of 131 patients  profiled by RNAseq was considered as ICC and ECC validation sets  used to detect the subgroups in the discovery dataset. In order to match the group labels provided by the unsupervised clustering to the predicted subgroup classes, we tabulated the unsupervised labels versus the predictor‐based groups is a 4 × 4 matrix and searched for the dominant diagonal matrix  using column permutation and we evaluated the median expression of genes within each specific subgroup. Considering the ICC cohort, the column permutation showed specific concordance between the subgroups identified by the two methods, as strongly demonstrated by the association between VS_1—C and VS_2—B classes. Interestingly, the 19 genes considered for the classification presented peculiar patterns of up‐regulation  and down‐regulation  demonstrating the presence of biological differences between the subgroups , but also gene expression data derived from different platforms ranging from microarrays to RNAseq. The use of cross‐platform analysis was a crucial step that allowed improving the classification and identification of tumor phenotypes not only when considering different type of microarraysThe building of ICC and ECC discovery sets derived by distinct datasets was made possible by resorting to empirical Bayesan methodology (ComBat), to remove batch effects associated with each dataset, a mandatory step as already reported.In a data‐driven approach, such as ours, the choice and careful evaluation of clustering methods is critical. In the current study, the application of NbClust was instrumental for the robust definition of the optimal number of clusters within ICC and ECC of the discovery sets.In fact, the robustness of the identified subgroups was confirmed by statistical considerations, but was also fully supported by the biologically distinct traits of each identified cluster. For digging deeply into the biology of clusters, the gene set enrichment and single sample scoring analyses were complemented by the evaluation of immune cell infiltration. The biological singularity of each cluster identified by unsupervised classification was indeed further supported by the different profiles of immune infiltrate associated with each subgroup.In detail, beside to the presence of distinct canonical CC gene signaling as TGF‐b, RTK‐RAS‐PIK3K,Having identified interesting subgroups of ICC and ECC tumors endeavored with possible treatment implications, we set to find a way to apply those results to the real‐world daily practice.N = 19; ECC N = 21) were extracted from differential expression analysis between subgroups taking advantage from the SMOTE algorithm for adjusting sample number unbalances. These steps were necessary to obtain a more accurate establishment of the ICC and ECC predictors after the test of three different machine learning algorithms. The rFSRC method showed the best performance in cohorts, given the low error rate in classification (misclassification error = 0.08).The first step was the building and the validation of a classifier with a reduced number of genes. To such purpose, short gene signatures .The use of gene expression classification questions the use of the same type of therapeutic approach for all CC since both among ECC and ICC clear differences on activation of pathways have been observed. Indeed, survival analyses performed in the ICC validation set disclosed an association between predicted subclass and the patient's clinical outcome, with patients whose tumors fell in subgroup A showing the worst prognosis in terms of overall and relapse‐free survival. Due to the low number of samples, it was not possible to perform the analysis with a statistical significance in the ECC cohort.ICC patients with tumors classified as subgroup A represent 43% and their poor prognosis is supported by up‐regulation of pathways linked to intracellular signaling and epithelial mesenchymal transition and by an enrichment of exhausted T‐cells in their infiltrate. These patients might therefore respond to checkpoint inhibitors provided that appropriate biomarkers are used since their poor prognosis is strongly determined by such a peculiar type of immune infiltrate.From the clinical point of view, these data may have relevance and applications in the near future. The recent results from the TOPAZ‐1 trial showed that the addition of the immune checkpoint inhibitor (ICI) durvalumab to standard first‐line chemotherapy significantly improved OS in advanced CC patients.The integration of different datasets and the building of robust classifiers represent main strengths of our study broadening both biological and clinical implications. Indeed, given the rarity of this tumor entity, here we managed to assemble the largest cohort of CC cases profiled with gene expression sequencing, providing a unified and reproducible tool to define CC subgroups. This effort lays the groundwork for future application in prospective specific cohorts, providing a unified schema for CC patient stratification.Our work is still preliminary and has therefore limitations. A major limitation involves the low number of patients with ECC tumors. This did not hinder identification and validation of ECC subgroups, but definitely interfered with evaluation of their prognostic relevance. Nonetheless, the interesting results obtained in ICC imply that our approach levering public datasets is valuable and can be further implemented in the future when more ECC datasets are available. Availability of larger numbers of ECC profiles will also allow searching for differences between pCC and dCC.So far precision medicine approaches in CC patients mostly relied on genomic alterations ranging from gene fusions to activating or inactivating mutations. Our results open a different perspective suggesting that gene expression data may refine genomic classes offering the opportunity of better tuning target treatments.Indeed, the transcriptomic analysis of publicly available CC datasets allowed identifying and validating the presence of biologically distinct subgroups associated to clinical outcome and providing clinical insight into the disease. Such results represent the starting point for a complete molecular characterization of CC, with the integration of genomics data that will allow a comprehensive description of the tumor. We foresee that genomic data integration into the transcriptome classification of patients with CC will aid prognostication and support personalized treatment.Marco Silvestri: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; resources ; software ; validation ; visualization ; writing – original draft ; writing – review and editing . Trung Nghia Vu: Data curation ; formal analysis ; methodology ; resources ; supervision ; writing – review and editing . Federico Nichetti: Supervision ; visualization ; writing – review and editing . Monica Niger: Supervision ; validation ; writing – review and editing . Serena Di Cosimo: Supervision ; validation ; writing – review and editing . Filippo de Braud: Supervision ; writing – review and editing . Giancarlo Pruneri: Supervision ; writing – review and editing . Yudi Pawitan: Resources ; supervision ; validation ; writing – review and editing . Stefano Calza: Conceptualization ; data curation ; methodology ; resources ; supervision ; visualization ; writing – original draft ; writing – review and editing . Vera Cappelletti: Conceptualization ; investigation ; methodology ; resources ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing .The research leading to these results has received funding from AIRC under IG2018‐ID.21694 project‐P.I. Cappelletti Vera. M.S. is the recipient of an AIRC fellowship . The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.M.N. is a recipient of travel expenses from Celgene, speaker honorarium from Accademia della Medicina, and Incyte; honoraria from Sandoz, Medpoint SRL and Servier for editorial collaboration. Consultant honoraria from EMD Serono, Basilea Pharmaceutica, Servier, Incyte, and MSD Italia. F.D.B. is a consultant advisory board for Roche, EMD Serono, NMS Nerviano Medical Science, Sanofi, MSD, Novartis, Incyte, BMS, Menarini, Astra Zeneca, Pierre Fabre. F.D.B is a speaker for BMS, Healthcare Research & Pharmacoepidemiology, Merck Group, MSD, Pfizer, Servier, Sanofi, Roche, AMGEN, Incyte, Dephaforum, Seagen. F.D.B. is a principal investigator for Novartis, F.Hoffmann‐LaRoche Ltd, BMS, Ignyta Operating INC., Merck Sharp & Dohme Spa, Kymab, Pfizer, Tesaro, MSD, MedImmune LCC, Exelixis Inc., LOXO Oncology Incorporated, DAICHI SANKIO Dev. Limited, Basilea Pharmaceutica International AG, Janssen‐Cilag International NV, Merck KGAA. G.P. is a recipient of travel expenses / honoraria from Lilly, AstraZeneca, Exact Sciences, Gilead. G.P is part of the advisory board of ADSBiotec. No potential conflicts of interest were disclosed by the other authors.Data S1: Supporting InformationClick here for additional data file.


Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70–1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70–0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors. Targeted therapies and chemotherapies are prevalent in cancer treatments. About 7% (11.1%) of US patients with advanced or metastatic cancer benefited from genome-targeted (genome-informed) therapy in 2021 , with 13Recently, a deep neural network-based method known as multi-omics late integration (MOLI) was proposed . This mein vitro but not effective in clinical trials/practice.Gene expression data are the most effective of the four omic data types for pan-cancer drug response prediction . Thus, wll occur , e.g., dTo alleviate this shortcoming, we adopted supervised domain adaptation (DA) . AlthougS and T denote the source (training) and target (test) domains, respectively. This approach falls into one category of inductive transfer learning  using expression data of the top-ranked p  genes of cell lines in GDSC via 5-fold cross-validation (CV); see Methods for details. Subsequently, we compared the performance of LogitDA/KNNDA to that of the baseline ridge regression in the work of We used the labels of the test sets only in the feature selection procedures. Specifically, we selected genes , trained by the top-ranked 50 (220) selected genes of the 370 cell lines screened with erlotinib, resulted in a prediction AUC of 0.94 (0.90) for NSCLC tumors. The high prediction power of LogitDA (KNNDA) for targeted therapies suggests that these predictors may help physicians stratify patients with NSCLC who will respond to erlotinib and spare those who do not from adverse effects, illustrating that these predictors have clinical implications. Finally, we uncovered the pathways of the top-fitted genes of LogitDA/KNNDA for erlotinib and cetuximab, which include pathways relevant to anti-cancer therapies and several metabolic pathways. These results indicate that our methods can efficiently translate cell-line-trained predictors into human tumors.The gene expression data and drug response of 1,001 cell lines screened with 265 drugs in the GDSC dataset  were useGene expression profiles of cell lines, which were RMA-normalized, log-transformed, and aggregated to the level of genes, were downloaded from the GDSC database. The gene expression profiles of the first three test datasets were preprocessed by The GED of cell lines from the GDSC dataset was first standardized by the mean and standard error (s.e.) of each gene. Next, the GED of each cell line was normalized by the house-keeping gene GAPDH across cell lines and homogenized with the GED of test sets by the ComBat function from the sva library in R . The detd, let d. Here, we dichotomized the drug response of cell lines (IC50) into 0 (resistant) or 1 (sensitive) if a drug response is greater or less than or equal to its maximum drug concentration (given in the GDSC website), respectively (For a given drug ectively .Y across domains were not significantly different. The intuition for feature selection procedures 2) and 3) is stated in Supplementary methods. In a pilot study, we also studied Logit (KNN) trained by genes and sifted by unsupervised DA in combination with the latter two proposed feature selection procedures. However, the predictors employing unsupervised DA performed in a manner considerably inferior to those employing supervised DA, given that the remaining procedures were kept the same. Thus, we used supervised DA in this study.The proposed feature selection consisted of three procedures: 1) supervised DA , 2) diffX, the Kolmogorov–Smirnov (KS) test for equality of the conditional distribution of selected genes X given the label Y in the source and target domains, PP, 0.8, and 0.9, as the source (cell lines) and target (PDX/patients) domains are quite different. Let Y in the source and target domains, respectively.Specifically, for each drug and gene X in the source and target domains, respectively, and vice versa.Given the features Proposition 1i =1, …, p. Then, r = 1.assumed that the features The proof is given in Supplementary information.versus resistant cell lines. To reveal explainable classifiers, we kept at most the top-ranked 1,000 genes with the smallest false discovery rate (FDR) values obtained from the two-sample t-test and sorted these genes by the BW ratio. The BW ratio for a gene j of the cell line i in group k is defined as follows:j across all cell lines and the cell lines of group k only, respectively.Genes that passed the DA selection were then prioritized by their differential expression among all overlapping genes in sensitive cell lines p genes, where p ranged from 50 with step size 10 to 200 (denoted by 50 (10)200), 200 (20)400, and 400 (100)1,000) genes of the cell lines, we trained the hyperparameter λ  and p using 5-fold CV with ten repeats. We used grid-search to tune the hyper-parameter as follows. First, let 0.3, i.e., we ran 5-fold CV of LogitDA with grid points 10–3, 10–2.7, …, and 100 and found the grid point whose associated CV score was the maximum, which was termed 0.01. The grid point p and For fixed top-ranked p genes because the default Euclidean distance did not work well for the GED of any two cell lines in our pilot study. For each drug and fixed top-ranked p gene, where p = 50 (10)200, 200 (20) 400, and 400 (100)1,000 genes of the cell lines, we first trained the hyperparameter K of KNN, via 5-fold CV with ten repeats and using the GED and drug response of cell lines, and computed the AUC in the cross-validation experiments. The hyperparameter K was determined by the experiment with the highest averaged CV score. We then fitted all data into this KNN classifier with each top-ranked p gene. Of all the top-p-ranked KNN classifiers trained, the one with the highest averaged CV score determined the value of p, which was one trained KNNDA predictor. We repeated the aforementioned procedures ten times to yield the mean and s.e. of the prediction AUC of KNNDA.For the classifier KNN, we used the distance measure 1 − rho, where rho is Spearman’s rho between any two cell lines with selected The following lemma and Proposition 2 established the theoretical foundation for adjusting the prediction probability cutoff when the drug response rates between cell lines and tumors differ.Lemma. When The proof is given in Supplementary information.Proposition 2i =1, …, p. When the odds ratio between the source and target domains is assumed that the predictors The proof is given in Supplementary information.As Proposition 2 suggests, the prediction probability cutoff will deviate from 0.5 when the population proportion of responders in the target and source domains differ. Due to a lack of information on the ratio of responders in the target domain (patients), we estimated it using test data directly in this study. However, a better estimate of the ratio can be obtained once more responses to these drugs are released. We note that Proposition 2 considers a continuous prediction probability function. It does not apply to the KNN classifier that makes a prediction on a test sample based on the majority voting for its K-nearest neighbors.α = 0.7 and KNNDA with α = 0.7 to the ten external test sets. To compare with the results of the baseline ridge regression  were selected by the proposed procedures (see Methods for details), we trained logistic ridge regression (KNN) with 5-fold CV using the GED of the prioritized features of GDSC cell lines screened with seven drugs, which included docetaxel, erlotinib, sorafenib, cetuximab, gemcitabine, paclitaxel, and cisplatin, in a total of ten sets. These drugs were chosen because we planned to compare LogitDA and KNNDA to the baseline logistic ridge regression  and MOLIPα = 0.6, 0.7, 0.8, and 0.9, and we denote the resulting predictor as LogitDA_α, where = 0.6, 0.7,0.8, and 0.9.As the training set (GDSC cell lines) is quite different from the test sets (PDX and patient data) , the cut_0.6 were equivalent to those of LogitDA_0.7 for the ten drugs. However, to satisfy the DA condition required by Proposition 1, namely, the marginal conditional distribution of selected genes X given the label Y in both domains is equal, the value of α should be large, so we chose LogitDA_0.7. Proposition 1 shows that features that satisfy the conditions will be domain-invariant. That is, if the features perform well in the source domain, they will also perform well in the target domain._0.7 was higher than that of LogitDA_0.8 and LogitDA_0.9, except that they had the same CV score for cetuximab (PDX). Thus, among the LogitDA predictors, we suggest using LogitDA_0.7 for the prediction of the drug response of patients/PDX and denote it by LogitDA, henceforth, for simplicity; for details, we refer to Moreover, we observed that each averaged CV score of LogitDAα = 0.7, 0.8, and 0.9 for the KS test and summarized the 5-fold CV result in _0.7 performed better than KNNDA_0.8, as the former had higher (lower) averaged CV scores for five (two) drugs than the latter; the differences ranged from 1% to 4%. Moreover, KNND_0.8 outperformed KNNDA_0.9 in terms of higher averaged CV scores for nine of the ten drugs. Thus, we suggest using KNNDA_0.7 among these non-linear predictors for the test sets. For simplicity, we denote KNNDA_0.7 by KNNDA henceforth.Taking the training result of LigitDA into account, we trained the non-linear KNNDA with Next, Of the ten drugs, the predictor KNNDA achieved a prediction AUC >0.8 for four drugs. Specifically, KNNDA using the top-ranked 110, 220, 110, and 60 genes resulted in prediction AUCs of 0.87, 0.90, 0.95, and 1.00 for docetaxel, erlotinib, cetuximab (PDX), and erlotinib (PDX), respectively. This result shows that KNNDA may also be useful for precision oncology.We further compared LogitDA to KNNDA. Of the ten drugs, LogitDA had a significantly higher (21% higher) (11% lower) prediction AUC compared to KNNDA for gemcitabine (PDX) (docetaxel) and performed equivalent to KNNDA for the remaining eight drugs. Thus, these predictors performed equivalently; we refer to As shown in Next, we compared LogitDA and KNNDA to the deep neural network (DNN)-based method MOLI , which oversus resistant (non-responders) samples is reversed from the training to the test sets (from about 1:2 to 10:1 for cisplatin); in other words, our assumption that the ratio of sensitive to resistant samples in both domains is equal was not met.The prediction AUC of LogitDA for both cisplatin and gemcitabine (TCGA) was only 62%, which may be because the ratio of sensitive (responders) EGFR expression has been used as a biomarker to treat colorectal cancer (CRC) patients with wild-type KRAS in the US (patients with metastatic CRC and HNSCC in the EU). However, EGFR expression does not predict a response to cetuximab . The higFurthermore, we compared LogitDA (KNNDA) to MOLI complete (multi-omics data). LogitDA (KNNDA) has a significantly higher prediction AUC 19%–40% (19%–42%) in the test set of cetuximab (PDX), erlotinib (PDX), gemcitabine , and docetaxel (TCGA) and performed equivalent (<6% differences of test AUC) to MOLI complete (multi-omics data) for the remaining drugs, except that KNNDA had 9% less test AUC for paclitaxel; please refer to For targeted therapies such as erlotinib and cetuximab, Sharifi-Noghabi and colleagues further trained MOLI on multi-omics data of five drugs targeting the EGFR pathway (MOLI complete (pan-drug)), which consisted of >3,000 samples. It is interesting that LogitDA (KNNDA) (using merely hundreds of samples) outperformed MOLI (pan-drug) for erlotinib (PDX) and cetuximab (PDX) with 28% and 13% (28% and 15%) higher prediction AUCs, respectively.In addition to the high prediction AUC for the aforementioned drugs, our approach also has the advantages of being interpretable and using much fewer (50–650) genes that are interpretable in comparison with the baseline logistic ridge regression and MOLI, which used more than 12,000 genes, except for docetaxel, for which ∼6,370 were used, as shown in EGFR and KRAS wild-type patients with NSCLC; LogitDA correctly predicted all 12 resistant tumors and five of eight tumors sensitive to erlotinib combined with feature selection using DE genes and BW ratio. Thus, it was of interest to evaluate the contribution of DA. We trained logistic ridge regression and KNN with the features selected by the two aforementioned feature selections  for the ten drugs. http://www.ingenuity.com) and uncovered the relevant pathways in de novo Biosynthesis (As LogitDA and KNNDA perform well in the prediction AUC for erlotinib and cetuximab (PDX), it is of interest to find the pathways in which the fitted genes of these predictors are involved. Thus, we first submitted the top-ranked 220 genes of LogitDA and KNNDA for erlotinib into the database Ingenuity Pathway Analysis  and uncovered DNA repair, metabolic processes, and lysosome-associated pathways; the details of the pathways are listed in Our feature selection approach can be used in combination with any classifier or regression model, not restricted to the logistic ridge regression and KNN demonstrated here, to predict the response of cancer patients using gene expression data. In particular, the ablation study shows that DA increases the prediction power by ∼24% (21%) from LogitDA-DA (KNNDA-DA). Following standard practice, we have chosen the K value of KNNDA that yielded the largest average AUC from 5-fold CV. To see the impact of the selection of K, we computed the test AUC of KNNDA with various values of K in  repeats .LogitDA (KNNDA) performed very well on prediction for five (four) out of the ten targeted therapies and chemotherapies (AUC >0.81), i.e., erlotinib (two sets), cetuximab, gemcitabine, and docetaxel. Thus, these predictors may efficiently uncover novel biomarkers and pathways, although large test sets are warranted. In addition to the high prediction AUC for the aforementioned drugs, our approach also has the advantage of using much fewer (50–650) genes than the baseline logistic ridge regression and MOLI, which used more than 5,100 or 12,000 genes.EGFR and KRAS wild-type patients with NSCLC, whereas there is no currently effective predictive marker of drug response for these patients. Thus, LogitDA may be useful for stratifying such NSCLC patients for erlotinib in clinical practice. Although the test AUCs for chemotherapies such as paclitaxel and cisplatin were only 0.68 and 0.62, respectively, their prediction accuracy achieved 0.88 and 0.88 for paclitaxel (PDX) and cisplatin (66 patients in TCGA), using the adjusted cutoffs for the prediction probability.Notably, using the novel adjusted cutoff of prediction probability, LogitDA achieved a prediction accuracy of 0.70 or higher for seven of the ten drugs. In particular, the prediction accuracy of LogitDA increases to 0.76 from 0.56 (using the default cutoff of 0.5), using the adjusted cutoff of 0.09, as Proposition 2 suggests. Moreover, its prediction accuracy increased to 0.80 if we focused on 20 As LogitDA and KNNDA performed well in prediction responses to erlotinib and cetuximab, we used the fitted genes of the predictors to uncover several important metabolic pathways for these drugs, in addition to DNA repair pathways. The aforementioned pathways play essential roles in tumor malignancy and response to anti-cancer therapies.It is interesting to point out that LogitDA performed particularly well for certain targeted therapies. LogitDA used 370 cell lines for training and achieved test AUCs of 0.94 and 1.00 for erlotinib . In contrast, deep learning-based methods, e.g., MOLI aggregated related samples (of drugs targeting the same EGFR pathway) to a larger training set  and used multi omics data to train the classifier, increased the test AUC from 0.63 to 0.72 for erlotinib (PDX). These differences may be because our approach prioritizes important features, limiting the number of parameters in the logistic ridge regression to at most 1,000 genes to fit. Nevertheless, MOLI (gene expression) performed very well in the prediction of chemotherapies, e.g., with a prediction AUC of 0.75 for cisplatin (TCGA), which outperformed LogitDA (using 130 genes) and KNNDA (using 190 genes).This study employed GED, which has been shown to be the most predictive data type among omics data , to pred


Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods’ performance and DM signatures’ quality in the absence of gold standard data solving a long-existing problem in DM analysis. DNA methylation is an epigenetic mark that plays a significant role in many biological processes, such as regulating gene expression and chromatin remodeling (reviewed in ). MethylDifferential methylation (DM) analysis is resulting in differentially methylated cytosines (DMCs), -probes (DMPs), or -regions (DMRs). Here, we refer to the list of DMCs or DMPs as a methylation signature (or simply a signature). A signature is supposed to accurately classify samples into two or more groups and capture the majority of the DNA methylation alterations associated with a disorder or other case state . Many siGiven the variety of available models for DM analysis, the choice of a particular method is a challenging task ,13,14, mBenchmarks based on experimental data are limited to imprinted DMRs , gold-stBenchmarks based on permutation of labels in compared groups  usually p-values for each DMR [Concordance of the produced results can also serve as grounds for methods’ comparison. The similarity of the methods can be estimated by the percentage of overlapping DMCs between methods , the coreach DMR , or Kendeach DMR . Estimateach DMR ,14,19. AExisting approaches for methods’ evaluation either lack quantitative metrics or require ‘ground truth’ data that limit their applicability to simulated or partly experimentally validated data and, therefore, significantly constrain their practical use. In addition, these approaches do not take into account dataset heterogeneity, while the metrics computed for a particular method may vary significantly across datasets. The label-permuting-based approach, although it can be applied for a particular real dataset, only allows for FPR computation, which can have limited sensitivity. Moreover, new methods are being developed constantly, making it critical to develop an approach that can evaluate the quality of the signature for the available dataset in an effective manner.To address these problems, we applied the Hobotnica metric (H-score)  that we ■To infer the influence of a data type (microarray vs. NGS derived) on the quality of DM signatures;■To evaluate the concordance of DM models within each data type;■To qualify the impact of signature’s subsetting;■To contrast the quality of DM signatures obtained on the simulated and real experimental NGS data;■To evaluate the relation and discordance between the H-score and existing quality metrics.In this study, with regard to DM models evaluation, we pursue the following tasks: T-test, and dmpFinder with and without variance shrinkage. The sizes of the obtained DM signatures ranged from 1 to 78,359. For four datasets, none of the observed methods detected DM signatures. For one dataset, only a T-test returned a non-empty signature. Within the same dataset, signature lengths were rather similar, though they varied widely among datasets  for all WGBS and RRBS datasets   .For all simulated datasets, the length of the DM signatures increased in concordance with the specified methylation difference . methylKIn contrast to the results for experimental NGS data, DM signatures for simulated data have large intersections not only for beta-binomial methods  but for For most methods, recall, precision, and accuracy improved with the increase in methylation difference . The DSSThe H-scores for all methods except methylKit are close to 1, even for a methylation difference of 0.1, and slightly increase with an increase in the difference in methylation E, opposiSince Hobotnica by design characterizes the ability of a signature to separate groups of samples, we explored the connection of H-score patterns with regard to the precision metric . The H-sThe label permutation-based FPR was quite low and comparable across most methods, with RADMeth having the highest rate, and remained consistent regardless of the changes in methylation difference for most methods except RADMeth and HMM-DM . Most H-In a few cases, signatures consisting of false positives received high H-scores with a significant separation. Even though there was no visible difference between the absolute values of intragroup and intergroup distances , rankingSelecting a suitable method for DM analysis is challenging due to the large and increasing number of available models. Model assessment can be an issue due to the lack of gold standard data. Existing evaluation approaches are indirect or use simulated data that constrain their efficiency and applicability. In this study, we present an evaluation strategy for microarray and NGS datasets based on the previously developed Hobotnica approach. We demonstrate that the DM signatures based on the microarray data are of good quality and highly convergent: the signatures produced by the methods in all datasets have comparable lengths and a significant overlap, which is reflected in highly significant and very similar H-scores. On the other hand, DM analysis based on NGS data is inconsistent across methods. Different methods return signatures of significantly different lengths and content for the same dataset, which is reflected in variable and often non-significant H-scores.Results obtained for the simulated NGS data differ significantly from those obtained for experimental NGS data. The content of DM signatures for simulated data shows higher convergence, and corresponding H-scores have higher values with less variability. This illustrates that existing simulation tools do not fully reflect the complexity of real data. Therefore, the results of the evaluation based on simulated data should be considered with caution. Thus, having in mind that simulated NGS data cannot be considered a “gold standard”, the direct strategy of the evaluation, such as the Hobotnica approach, is critical for the DM quality estimation.Although hundreds or thousands of statistically significant DMC can be detected, evaluating the effective length of a signature can assist with most practical applications. However, it is often not clear whether a subset signature of top DMC incorporates ample relevant information. Despite the fact that, in some cases, extra-short signatures of 10 DMC were substantial for data stratification , in other cases, short signatures delivered lower H-scores and worse data separation , compared to full-length signatures (notably for NGS data). Thus, the length of a subset signature should be chosen not only considering pragmatic reasons (necessary for the interpretation or sufficient for diagnostics) but needs to be explicitly tested in regard to its quality.Detecting and assessing an approach’s limitations is as important as its design or validation. In this work, we limited the Hobotnica applicability to DMC only, while the DMR format for methylation signatures is often more widely used and more easily interpreted. The DMR format, however, poses severe problems for signatures comparison since setting a formal criterion, whether two intersecting yet differing regions should be treated as the same entity or not, is a challenging task. In addition, each region can be characterized not only by the mean level of methylation but also by the length of the region, as well as the density of sites in it.In our study, we detected other scenarios that may constrain Hobotnica under specific conditions. The Hobotnica approach may lose sensitivity when applied for extra-short signatures. During pairwise distances computation, two samples may not have shared DMC, covered in both samples. The Euclidean distance between samples in such a scenario cannot be calculated. This can be addressed by increasing the tested signature’s length. In other cases, Hobotnica may be oversensitive and deliver high scores for false-positive signatures with the existing, albeit negligible, stratification effect. Practically, these cases, although found to be quite rare, can easily be distinguished by assessing not-ranked sample distances, which is a part of the Hobotnica analysis workflow. For the rest of the cases, Hobotnica was shown to provide a meaningful, sensitive, and robust evaluation of DM signatures for all platforms.Our study provides the following practical recommendations for DM analysis. Micro-array-based approaches due to high convergence and performance should be favored when the study design allows, for specific applications, demanding increased robustness and recruiting known methylation sites as most transnational and clinical applications. NGS-based datasets, due to the high variability of the results obtained by different methods, should be processed with several different DM methods, and the resulting signatures should be validated either with limited experimental data or with Hobotnica, which allows for valid quality estimation of DM analysis performance for a newly generated dataset in the absence of gold standard data. The evaluation of newly developed methods for DM analysis should not be performed only on simulated data due to a significant bias in the results. This bias should be at least partially compensated for with tests on ’ground truth’ data, no matter how limited, as well as experimental datasets for evaluating which Hobotnica provides means.Microarray DM methods were evaluated on 14 datasets  normalization. The result All datasets were preprocessed in a uniform way . The metT-test using the T-test and limma functions were executed with the RnBeads package [T-test, a list of differentially methylated CpG sites was obtained using the FDR [DM analysis was performed using four methods: limma with M-values input, two-sided Welch  package . For lim the FDR  threshol the FDR  thresholSix NGS datasets (WGBS and RRBS) were used for differential methylation analysis . The numBoth WGBS and RRBS datasets were preprocessed in a similar manner, except for a deduplication step additionally performed for WGBS data only . Reads fThe reads were mapped to human reference genome hg38 for GSE149608, GSE148060, GSE150592, GSE138598, and GSE119980 datasets, and to mm39 for GSE103886 dataset using Bismark (version 0.22.3) . WGBS daSix software packages were applied to each dataset for DMC identification between two groups: methylKit , BSmoothp-value adjustment lower than 0.05 were included in a signature for the MethylSig, methylKit, DSS, and RADMeth methods. Signatures were sorted based on the adjusted p-value.DMCs with a default The BSmooth smoothing procedure was applied to methylation level values before low-coverage filtering. After smoothing, only the sites with a methylation ratio defined for all samples were selected for further DMC identification. The tested CpG sites were sorted based on the corresponding absolute t-statistics value. Sites that had t-statistics between the 5% and 95% quantiles were included in the resulting signature. BSmooth was not applied to the RRBS datasets.HMM-DM was applied to each chromosome separately with default parameters. DMCs in hypomethylated and hypermethylated states with posterior probabilities greater than 0.95 were added to the resulting signature. The posterior probability value was used to sort the sites in the final HMM-DM signature.DMCs with absolute methylation differences lower than 0.15 were excluded from the signature for all the methods.Two groups of ten RRBS paired-end simulated datasets were generated using the RRBSSim simulator . The firThe resulting FASTQ files were processed in the same way as real NGS datasets. In a group of case samples, the difference in the CpG methylation levels was introduced similarly to that described in . For eacIn each experiment, all DM methods applicable to RRBS data were tested: MethylSig, DSS with and without smoothing, methylKit with and without overdispersion correction, RADMeth, and HMM-DM. Precision, recall, accuracy, and H-score metrics were calculated for the obtained signatures. To calculate the false positive rates, we permuted the sample labels once for each simulation. All obtained DMCs were considered false positives, and the rest of the covered CpG were considered true negatives. To investigate the relationship between the H-score patterns and the precision metric, the dependence of the H-score on the precision value was calculated for a set of synthetic signatures of the same length as a ground truth signature with a different proportion of the true positive DMCs.p-value from a distribution of H-scores of random signatures with the same length.Hobotnica  evaluatep-value). Hobotnica was applied to beta values (microarrays) and methylation ratio values (NGS) with Euclidean distance. The H-score was set to zero if a distance matrix contained at least one NA entry or a method did not return any DMCs. H-scores were defined only for signatures of length greater than or equal to two sites. Whether an empty signature should be dismissed from the evaluation  or needs to be set to the minimum, i.e., 0 (given no possible stratification to groups can be performed for samples), is a precarious question. We evaluated both scenarios and assessed the results separately.The H-score was calculated for each signature and its smaller subset .H-score distributions of the observed DM methods were compared to detect differences between the methods. First, the H-scores distributions from microarray and NGS results were tested for normality using the Shapiro–Wilk normality test. The Friedman test  was applIn this study, we performed a benchmark for DM methods on multiple NGS and microarray datasets based on the resulting DM signature’s quality. We applied the rank statistic approach Hobotnica to assess models’ performance in the absence of gold standard data. The observed heterogeneity of signatures’ quality across experimental platforms, DM models, and biological datasets confirmed the necessity for the signatures’ quality assessment in newly conducted analyses. Hobotnica provides provide robust, sensitive, and informative estimation for DM signature quality, solving a long-existing problem in DM analysis.


Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the  The most easily observed symptom is fluctuating skeletal muscle weakness . The devResearch revealed that gut microbiomes may contain biomarkers that can be used to evaluate several neurological diseases, such as Parkinson’s disease . A growiMachine learning (ML) methods, as a strategy of artificial intelligence (AI), that can successfully recognize patterns in clinical data, it can be efficiently used for triage, screening, diagnosis, and biomarker identification, and the joint use of manual and ML evaluations can offer more efficient and accurate results than the use of one method alone . NumerouSome studies have reported that the microbiota composition in the fecal samples of MG groups differed from those of healthy control groups . Gut mic2.2.1.In this prospective study, 19 individuals with MG and 10 individuals without were consecutively recruited from Fu-Jen Catholic University Hospital. Individuals were enrolled in the MG group if they (1) were given a diagnosis of MG on the basis of having the combination of symptoms and signs that are characteristic of muscle weakness with diurnal changes and either (2a) had a positive test result for specific autoantibodies or (2b) had a positive electrophysiological diagnosis obtained using single-fiber electromyography and repetitive nerve stimulation . None of2.2.After the participants have completed the informed consent form and agreed to participate in the study, fecal samples from each volunteer were collected after enrollment. Volunteers self-collected Fresh stool samples after defecation in the hospital and immediately transferred the samples to a laboratory freezer at −80°C for cryopreservation.Each stage in the process, including the sample testing and polymerase chain reaction (PCR) and library creation and sequencing, can affect the quality of the data, and the accuracy of analytical findings is directly influenced by the quality of data. Therefore, quality control measures were implemented at each stage of the process to ensure data accuracy.2.3.Genomic DNA was extracted from the samples using the EasyPrep Stool Genomic DNA Kit . The DNA concentration was determined and adjusted to 5 ng/μL for subsequent processing. In accordance with the 16S Metagenomic Sequencing Library Preparation protocol (Illumina), the specific primer set 341F: 5’-CCTACGGGNGGCWGCAG-3′, 806R: 5’-GACTACHVGGGTATCTAATCC-3′ was employed to amplify the variable regions V3 and V4 of the 16S rRNA gene. A PCR was conducted using KAPA HiFi HotStart ReadyMix (Roche) and 12.5 ng of genomic DNA (gDNA) under the following conditions: 95°C for 3 min, 25 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 30 s, and a final extension of 72°C for 5 min. The reaction was subsequently maintained at 4°C. The products of the PCR were evaluated using 1.5% agarose gel, and samples with a bright main strip at approximately 500 bp were selected for further library preparation. The selected samples were purified using AMPure XP beads.A sequencing library was prepared using the 16S Metagenomic Sequencing Library Preparation procedure (Illumina). To summarize, the 16S rRNA V3–V4 region PCR amplicon was subjected to a secondary PCR, which was conducted using the Nextera XT Index Kit with dual indices and Illumina sequencing adapters from Illumina. The indexed PCR product was evaluated for quality by using the Qubit 4.0 Fluorometer (Thermo Scientific) and a Qsep100™ system. The indexed PCR products were mixed in equal amounts to create a sequencing library. The library was sequenced on an Illumina MiSeq platform, which generated 300-bp paired reads.2.4.Amplicon sequencing was performed using 300-bp paired-end raw reads, and each sample was demultiplexed on the basis of their barcode identification. Primer and adapter sequences were removed from the paired-end reads by using the QIIME2 cutadapt plugin . To cons2.5.The taxa that significantly differed between the MG and non-MG samples were identified, and an analysis of the overlap between the taxa of these samples was conducted. Significant biomarkers were identified through Linear discriminant analysis effect size (LEfSe) analysis . Subsequ2.6.This study applied four ML methods, namely least absolute shrinkage and selection operator (Lasso), XGBoost, random forest (RF), and classification and regression trees (CART). Because taxonomy or ASVs-based ML approaches provide different types of information, the present study proposed an ML analytical strategy that combines the benefits and valuable information of each approach that can be used to effectively screen key taxon features. LOOCV was executed for the construction of each ML model. In essence, LOOCV is similar to k-fold cross-validation. The primary difference between the two is that k-fold cross-validation involves validation with one of several equally sized folds that have been randomly divided from the data whereas LOOCV involves using a single subset of the data for all rounds of the validation process . Figure The performance of the model was evaluated on the basis of its accuracy (ACC), precision (PRE), sensitivity (SEN), specificity (SPE) and area under the receiver operating characteristic curve (AUC). The study experiments were conducted using Python (version 3.8.8) and Jupyter Notebook (version 6.3.0) softwares . XGBoost3.Individuals who met the criteria for a diagnosis of MG were included in the present study. The mean age at enrollment was 51.5 years, and the majority of the participants were women (68%). The mean disease duration was 59.2 months. In addition, 36% of the patients with MG had a history of an MG crisis, and 21% had experienced life-threatening events at the onset of the disease. The clinical characteristics of the 19 individuals in the MG groups and 10 individuals in the non-MG group were obtained from their medical records . The two3.1.Roseburia, Oscillospira, and Mitsuokella were more abundant in the non-MG group  were used for training, the four ML models were trained using eight taxonomic features (described above) to complete predictive classification and diagnosis of MG. 3.3.p = 0.43), indicating that XGBoost performed well regardless of which data set was used. Through the combination of two distinct datasets analysis, XGBoost emerges as the superior ML method for effectively distinguishing between MG and non-MG subjects. This robust outcome underscores the promising potential of ML methods in disease diagnosis within gut microbiomes.The four ML models were trained with all 1,544 ASV features (full ASV–based data) to investigate the effectiveness of diagnostic classifications made on the basis of all ASVs. 3.4.To further assess the performance of XGBoost compared to traditional machine learning methods, we utilized the receiver operating characteristic (ROC) curve for additional verification. The performance of XGBoost remained similar when different forms of data were used as inputs . For pur3.5.Lachnospiraceae and Ruminococcaceae families. The XGBoost algorithm was reapplied with the 31 HIASVs used to differentiate individuals with and without MG. In the XGBoost trained with the HIASVs, the dimensionality of the feature space was reduced, and the model had the highest AUC (90.53) and performed slightly better than the other ML models . Our ML models based on different ASVs verified this finding, and feature selection revealed that the family Lachnospiraceae was the most crucial with respect to MG. Genera from the family Ruminococcaceae and Lachnospiraceae were determined to be the most crucial for determining a diagnosis of MG when the model was trained using the HIASVs. Lachnospiraceae and Ruminococcaceae were discovered to be the two most abundant families of Clostridiales and have been reported to be associated with the maintenance of gut health and the production of short chain fatty acids (SCFAs)  . The two (SCFAs) .Lachnospiraceae has been indicated to potentially influence healthy gut activity, and literature reviews have revealed that different members of this family are associated with different diseases. Lachnospiraceae was reported to be involved in autoimmune disorders, such as multiple sclerosis and inflammatory bowel diseases  had the same performance as that trained using the full ASV–based data. We identified the overlapping key features selected by these models to improve the ML model’s prediction power. Incorporating two sets of data to train an ML model using 31 HIASVs led to the model having the most accurate prediction. Most microbiome studies have used key operational taxonomic units to distinguish between study groups or used LDS-based taxonomic feature extraction to identify significantly different relative abundances between target groups. Our study combined genetic information  and biological information  to achieve more accurate prediction results. LOOCV was also applied and ensured that an unbiased estimate of the model’s performance was obtained because every instance in the data set is used for both training and validation. LOOCV is also more computationally expensive and particularly useful when the size of a data set is small. It allows for the data to be used to the fullest, for both training and validation . Our useClostridium correlated with a decrease in SCFA  can be found below: NCBI – The studies involving humans were approved by Research Ethic Committee of Fu-Jen Catholic University Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.C-CC and W-NL was involved in the study design, conducted the experiments, and writing the first draft of the paper. C-CC and H-CC were responsible for data collection. W-NL was responsible for proofreading and paper revision. C-CC, T-CL, and C-JL conducted the experiments, analyzed and interpreted the data. All authors have read and agreed to the published version of the manuscript.