Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms. Methods that perform data integration are needed to analyse spatial transcriptomics data from multiple tissue slides. Here, the authors present PRECAST, an efficient data integration method for multiple spatial transcriptomics datasets with complex batch or biological effects between slides. The in situ capturing technologies are unbiased and involve transcriptome-wide expression measurements, while ISH-based methods are targeted and require prior knowledge of the genes of interest. These technologies have provided extraordinary new opportunities for researchers to characterize the transcriptomic landscape within a spatial context; explore how cells influence and are influenced by the cells around them13; identify genes with spatial variations other than cell/domain differences, e.g., cell morphology14; and identify spatial trajectories or RNA velocity in tissues16, among other applications17.Spatially resolved transcriptomics (SRT) encompass a set of recently developed technologies that characterize the gene expression profiles of tissues while retaining information on their physical location. The methodologies used for resolving spatial gene expression are primarily categorized into in situ hybridization (ISH) technologies  that were equally distributed along the anterior-posterior axis of the same mouse using Slide-seqV222. Moreover, when multiple SRT datasets from different clinical/biological conditions are available, integrative analysis to estimate shared embeddings of expressions representing variations between cell/domain types can provide the first step towards detecting genes that are differentially expressed between conditions23. Thus, it is important to develop rigorous methods that are capable of performing data integration across multiple SRT datasets by aligning shared embeddings of biological effects between cell/domain types while accounting for complex batch effects and/or biological effects between slides24.Similar to single-cell RNA-sequencing (scRNA-seq) studies, in SRT studies of a single slide, identifying the cell/domain clusters for each spot with the collation of both spatial information and expression measurements is an important step24, Scanorama25, Seurat V326, Harmony23, scVI27, and scGen28, have been developed for scRNA-seq datasets without any consideration of spatial information. More recently, MEFISTO was proposed as a way to analyze datasets with repeated spatio-temporal measurements29, and PASTE was proposed as a method to stack and/or integrate SRT data from multiple adjacent tissue slices into a single slide, but it is not applicable to the integration of tissue sections from different individuals30. In addition, most existing methods perform data integration in low-dimensional space using the principal components (PCs) of conventional dimension reduction, e.g., principal component analysis (PCA), without considering the consistent loss functions of dimension reduction, alignment across datasets, or spatial clustering.An ideal data integration method for SRT datasets should be capable of the following three tasks: (1) estimating the shared embeddings of biological effects between cell/domain types across SRT datasets and slide-specific embeddings that account for local microenvironments ; (2) aligning the shared embeddings that capture cellular biological variation across datasets with heterogeneous batch effects and/or biological effects between slides ; (3) clustering the aligned embeddings to obtain cell/domain clusters across datasets that promote spatial smoothness . Most existing data integration methods, including MNNTo address the challenges presented by SRT data integration and facilitate the downstream analyses of combinations of multiple tissue slides, we propose the use of a unified and principled probabilistic model, PRECAST, to simultaneously estimate low-dimensional embeddings for biological effects between cell/domain types, perform spatial clustering, and most importantly, align embeddings for normalized gene expression matrices from multiple tissue slides. As a result, PRECAST can resolve aligned representations, provide outstanding visualizations, and achieve higher spatial clustering accuracy for combined tissue slides. The resolved aligned representations and estimated labels from PRECAST can be used in multiple downstream analyses, e.g., removing batch effects, identifying differentially expressed genes under different conditions/stages, and recovering spatial trajectories/RNA velocity, etc. In addition, PRECAST uniquely estimates slide-specific embeddings that capture the spatial dependence in neighboring cells/spots, providing an opportunity to understand the spatial impact of various microenvironments. We illustrate the benefits of using PRECAST through extensive simulations and analysis of a diverse range of example datasets collated with different spatial transcriptomics technologies: 10x Visium datasets of 12 human dorsolateral prefrontal cortex (DLPFC) samples and four hepatocellular carcinoma (HCC) samples, ST datasets from eight mouse liver tissue sections and Slide-seqV2 datasets from 16 OB tissue slides.Unlike other integration methods that take as input the top  PCs and apply multiple steps to remove batch effects and merge the data, PRECAST takes as input the normalized gene expression matrices from multiple tissue slides, factorizes the input to each matrix into a latent factor with a shared distribution in each cell/domain cluster while simultaneously performing spatial dimension reduction and spatial clustering, and aligning and estimating joint embeddings for biological effects between cell/domain types across multiple tissue slides Fig. a. In thePRECAST can be applied to SRT datasets of various resolutions. By increasing the resolution for SRT datasets, PRECAST can reveal fine-scale cell-type distributions. In the following analysis, datasets in Slide-seqV2 with near-single-cell resolution present spatial patterns with much more “noise” than those of Visium due to the heterogeneity of the cell-type distributions. When we merged nearby beads in the Slide-seqV2 datasets, the recovered spatial patterns resembled those from Visium.31 or UMAP32. Second, because the aligned embeddings obtained from PRECAST only carry information on the biological differences between cell/domain types, we provide a module to recover the gene expression matrices with batch effects removed and further identify genes differentially expressed either across different cell/domain clusters and/or under different conditions. Third, using these embeddings, we can identify genes whose spatial variability is not just due to biological differences between cell/domain types. Fourth, with the aligned embeddings estimated by PRECAST, we can perform trajectory inference/RNA velocity analysis to determine either the pseudotime or the pattern of dynamics in spatial spots across multiple tissue slides.We use the estimated cell/domain labels and finely aligned embeddings to showcase some downstream analyses, as depicted in Fig. 23, Seurat V326, fastMNN24, scGen28, Scanorama25, scVI27, MEFISTO29, and PASTE30, all of which can be used to estimate the aligned embeddings among samples, except PASTE, which can only estimate the embeddings of the center slice. The simulation details are provided in the “Methods” section. Briefly, we simulated either the normalized or count matrices of gene expression to compare PRECAST with other methods in terms of performance in data integration, dimension reduction, and spatial clustering. To mimic real data, we also considered the two ways to generate spatial coordinates and cell/domain labels, i.e., Potts models and real data in three DLPFC Visium slices. Using real data in DLPFC, we considered slides either from different or the same donor. In total, we investigated five scenarios: (1) Potts + Count, with three different scales in batch effects ; (2) DLPFC (slides from different donors) + Count, with three different scales in batch effects ; (3) DLPFC (slides from the same donor) + Count; (4) Potts + logCount; and (5) DLPFC (slides from different donors) + logCount. Scenarios 1 and 2 were used to examine the impact of scales in batch effects on data integration performance.We performed comprehensive simulations to evaluate the performance of PRECAST and compare it with that of several other methods Fig. –S2. SpecTo quantify the performance of the data integration, we calculated the F1 scores of the average silhouette coefficients, which summarized two similar metrics of silhouette coefficients into a single quantity, and two versions of the local inverse Simpson’s Index (LISI): integration LISI (iLISI) and cell-type LISI (cLISI). iLISI was employed to assess integration mixing, while cLISI was employed to assess the separation of each domain cluster. In scenarios 1 and 2, data integration performance by all methods dropped as the scale of batch effects increased. However, PRECAST achieved the best performance in all methods, with the highest F1 scores for the average silhouette coefficients. In scenario 3, PRECAST was comparable to PASTE Fig. c. MoreovWe then evaluated the performance of estimating the embeddings induced by neighboring microenvironments in PRECAST. The estimated embeddings due to neighboring microenvironments were highly correlated with the underlying truth  and normalized mutual information (NMI) in all considered scenarios Fig. a, b, whiWe applied PRECAST and the other methods to the analysis of four published datasets obtained via either Visium, ST, or Slide-seqV2 technologies (see “Methods”). By obtaining the estimated aligned embeddings and cluster labels from PRECAST, we could perform many downstream analyses using all tissue slides. Here, we showcase the differential expression (DE) analyses across detected domains, spatial variation analysis (SVA) adjusting for aligned embeddings as covariates, and trajectory inference/RNA velocity analysis. To examine the clustering performance with low-resolution data, we performed deconvolution analysis to infer the cell compositions of the domains detected by PRECAST.21 that contained 12 tissue slices from three adult donors, comprising four tissue slices from each donor. In all 12 tissue slices, the median number of spots was 3844, and the median number of genes per spot was 1716. The original study provided manual annotations for the tissue layers based on the cytoarchitecture that allowed us to evaluate the performance of both the data integration and accuracy of spatial domain detection by taking the manual annotations as ground truth. For each method, we summarized the inferred embeddings for biological effects between cell/domain types using three components from either tSNE or UMAP and visualized the resulting tSNE/UMAP components with red/green/blue (RGB) colors in the RGB plot 19, HOPX , and ENC1 33 . In total, we detected 1331 DE genes with adjusted )33 Fig. –S12. NexISG15 encodes a ubiquitin-like molecule induced by type I interferon, and ISG15 deficiency increases antiviral responses in humans37. Many studies in the literature have highlighted the importance of immune-brain interactions to the development of many disorders of the central nervous system39. Further enrichment analysis showed the genes from each slice to be highly enriched in many common pathways, suggesting the embeddings for biological effects between cell/domain types were effectively aligned by PRECAST  with nonlaminar patterns. A detailed list of the genes identified at a false discovery rate (FDR) of 1% is available in Supplementary Data AST Fig. –S19.40, SPARK-X41, SpatialDE42, and nnSVG43, as input.To demonstrate the robustness of PRECAST, we applied different methods to select top genes as input. As presented in Supplementary Fig. 44. In all eight sections, the median number of spots was 640, and the average number of genes was 15,302. The original study provided manual annotations based on marker genes that allows us to evaluate the performance of both the data integration and accuracy of spatial domain detection by taking the manual annotations as ground truth. For each method, we summarized the clustering performance of each section and combined sections using both ARI and NMI 45; Cyp2f2, Hal, Sds, and Ctsc for portal veins (Domains 6-7)44; and Gsn, Vim, and Col3a1 for the mesenchyme (Domain 5)44. Further enrichment analysis shows that genes specific to both central veins and portal veins were highly enriched for metabolic processes, with central veins that were more enriched for fatty acid metabolism, while portal veins that were more enriched for amino acid metabolism . In total, we detected 367 DE genes with adjusted 46 as the reference panel. To assess the performance in spatial clustering, we evaluated the associations between the cell type proportions obtained by cell-type deconvolution and the cluster labels estimated by PRECAST and other methods. The results displayed in Fig. 2 than the other methods. In addition, cell-type deconvolution enabled us to spatially map 17 cell types annotated in the MCA dataset for liver tissue sections ons Fig. –S28. We 45. Based on the inferred pseudotime, we identified differentially expressed genes along the cell pseudotime using TSCAN47. The heatmap of the expression of the top 20 most significant genes , we obtained data of a lower resolution (~4000 spots) by collapsing nearby spots in each slide (see “Methods”), and the resulting resolution was similar to that of Visium (average of 10 cells per spot). To further examine the structure of the mouse OB, we relied on the structural annotations in the Allen Brain Atlas48  with a center-to-center distance of 100 μm are used to capture mRNAolution ~000 spotsolution ~000 spotsion Fig. –S34. Morp-values of less than 0.001 in all 12 spatial domains detected by PRECAST (Supplementary Data Sox2ot and Sox11 (RMS)51. The heatmap of the findings shows the good separation of the DE genes across different spatial domains , GCL (Domain 2), GCL/inner plexiform layer , mitral cell layer , outer plexiform layer , GL (Domains 6 and 7), and ONL (Domains 8), with Domains 9–12 belonging to low-quality regions or experiment artifacts. To characterize the transcriptomic properties of the spatial domains identified by PRECAST, we performed DE analysis of the combined 16 tissue slides (see “Methods”). In total, we detected 4131 DE genes with adjusted 52. As shown in Fig. 53. Unsurprisingly, we found that Domains 2–3 (GCL) were dominated by two primary subtypes of granule cell, with a larger proportion of immature neurons in Domain 2 (inner) and an enrichment of mitral and tufted cells in Domain 4 (MCL). Domain 8 (ONL) was primarily enriched in olfactory sensory neurons (OSNs); OSNs express odorant receptors in the olfactory epithelium54. We additionally quantified the association between the inferred cell type proportions and the domain labels estimated by PRECAST and the other methods using MacFadden’s adjusted R2  and two from tumor-adjacent tissues (HCC3 and HCC4) from an HCC patient. The median number of spots was 2748, and the median number of genes per spot was 3635. Figure p-values of less than 0.001 in all nine spatial domains detected by PRECAST, with 539 genes being specific to Domains 1–5, which corresponded to TNE regions , Domain 5 was only present in the tumor-adjacent tissues (HCC3 and HCC4), while Domain 4 was shared across the tumor and tumor-adjacent tissues.To characterize the transcriptomic properties of the spatial domains identified by PRECAST, we performed DE analysis of the combined four tissue slides (see Methods). In total, we detected 2093 DE genes with adjusted To identify SVGs other than those that were merely relevant to domain differences, we performed SVA analysis with the embeddings estimated by PRECAST as covariates for each slice. A detailed list of genes identified at an FDR of 1% is available in Supplementary Data 61. As shown in Fig. TGFB1 and MMP2, which have been used for the classification of TAMs63.Next, to examine the cell compositions of each spatial domain detected by PRECAST, we performed cell-type deconvolution analysis of all four HCC slides using scRNA-seq data as the reference panel (see Methods). The scRNA-seq reference panel consisted of malignant and tumor microenvironment cells, including cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), tumor-associated endothelial cells (TECs), cells of an unknown entity but expressing hepatic progenitor cell markers (HPC-like), and immune cellsSPINK1, RPL30, and IL32, which highlights the importance of genes associated with cell states in HCC66.The estimated aligned embeddings and cluster labels can also be used in RNA velocity analysis to investigate the directed transcriptional dynamics of tumorigenesis when spliced and unspliced mRNA are available (see “Methods”). Interestingly, two cell lineages were identified, with one that originated in Domain 2 (TNE) and spread to Domains 4 and 5 (TNE), followed by Domains 6, 7, and 9 (stroma), and the other that originated in Domains 1 and 3 (TNE) and spread to Domain 8 (stroma). The TNE regions in Domain 2 residing in HCC1 may play a key role in tumorigenesis with Domain 4 (TNE) shared among the four slides and Domain 5 (TNE) in the tumor-adjacent tissues Fig. b. To infPRECAST takes, as input, matrices of normalized expression levels and the physical location of each spot across multiple tissue slides. The output of PRECAST comprises all aligned embeddings for cellular biological effect, slide-specific embeddings that capture spatial dependence in neighboring cells/spots, and estimated cluster labels. In contrast to other existing methods of data integration, PRECAST is a unified and principled probabilistic model that simultaneously estimates embeddings for cellular biological effects, performs spatial clustering, and more importantly, aligns the estimated embeddings across multiple tissue sections. Thus, we recommend applying PRECAST first before a comprehensive data analysis pipeline is deployed. By applying PRECAST, the aligned embeddings and estimated cluster labels can be used for many types of downstream analyses, such as visualization, trajectory analysis, and SVA and DE analysis for combined tissue slices. In more detail, we developed a module to further remove batch effects across multiple tissue slides based on housekeeping genes, making expression data comparable for different cell/domain clusters. This module is also applicable to the examination of expressional differences caused by multiple conditions when such information on tissue slides is obtainable.34 showed that, compared with methods that perform dimension reduction and spatial clustering sequentially20, joint methods can estimate embeddings for cellular biological effects more efficiently while accounting for the uncertainty in obtaining low-dimensional features from sequential analysis. A similar strategy has been described in previous self-supervised learning literature68. PRECAST also takes advantage of CAR to account for the local microenvironments of neighboring spots, and an intrinsic CAR component has been used to promote spatial smoothness in the observed expressions of SRT data69. We showed that, by projecting non-cellular biological effects onto cellular biological space, different sample slides exhibit a constant shift in the centroid of each cell/domain type. With the assistance of joint modeling, we can use a subclass of CAR, intrinsic CAR, to simultaneously account for both smoothness in neighboring embeddings and shift across batches due to non-cellular biological effects such as complex batch effects.PRECAST simultaneously performs dimension reduction and spatial clustering while using simple projections to align embeddings, and uniquely estimating embeddings that capture spatial dependence of neighboring cells/spots due to varied microenvironments. Recently, Liu et al.26. Most existing methods of data integration were designed to analyze scRNA-seq data without considering additional spatial information in the SRT data.With the advent of high-throughput technologies for SRT, data integration is particularly relevant for analyzing SRT datasets from multiple tissue slides. Analysis of a single section with current state-of-the-art techniques, e.g., 10x Visium and Slide-seqV2, only covers a tiny area of the region of interest, and it takes a few or dozens of slides to cover the whole tissue/organ. In this case, biological variations between cell/domain types are often confounded by factors related to data generation processes. Methods in data integration, which serve as the first step before downstream analyses, not only align the embeddings but also estimate the shared cell/domain clusters across samplesμm-diameter spots. As common sense implies, with low-resolution datasets, PRECAST recovers aligned embeddings for cellular biological effects and cluster labels with smoother spatial patterns while losing the detailed local spatial information. Whereas, at near-single-cell resolution, the identified aligned embeddings and cluster labels show more fine-scale cell-type distribution patterns. Using four datasets, we demonstrated that PRECAST can successfully perform data integration with aligned embeddings across tissue sections, such that spots across different tissue sections are well mixed while cell/domain clusters are well segregated, improve clutering performance, and detect varied microenvironments in tissues. When applied to an HCC dataset, PRECAST identified five spatial domains belonging to TNE cells that were consistent with both manual annotations and spatial deconvolution. We further performed RNA velocity analysis to show the potential velocity of spots in different regions, shedding light on the tumorigenesis in the context of the tissues. To demonstrate the scalability of PRECAST, we analyzed a Slide-seqV2 dataset of 16 slides equally distributed along the anterior-posterior axis of a mouse OB.We examined the SRT data generated by three major platforms, 10x Visium, ST, and Slide-seqV2, with different spatial resolutions. With 10x Visium, mRNA is captured in 55-μm-diameter spots, while Slide-seqV2 achieves a higher resolution with 10PRECAST provides opportunities for new exciting research routes. Firstly, when SRT datasets of single-cell resolution are available, its use can be extended to the integration of multimodal single-cell data. For example, integrating single-cell ATAC-seq will allow users to examine cell-type-specific regulatory mechanisms in the spatial context of tissues. Secondly, it would be interesting to integrate single-cell-resolution SRT datasets with CITE-seq data, thus integrating spatial transcriptomics with immunophenotyping, in which surface proteins are detected by antibody-derived tags. This would enable the exploration of surface proteins not measured in SRT datasets.M SRT datasets, we observe an nr × p normalized expression matrix r, where p-dimensional normalized expression vector for each spot r on square or hexagonal lattices, among others; while the cluster label of spot sri, yri ∈ {1,⋯,K}, and q-dimensional shared embeddings, zri’s, are unavailable. Without loss of generality, we assume that, for each sample r, xri is centered, and PRECAST models the centered normalized expression vector xri with its latent low-dimensional feature, zri, and class label, yri, asr = diag is a diagonal matrix for residual variance, p-dimensional expression vector into q-dimensional embeddings shared across M datasets, kth cluster, respectively, and vri is a q-dimensional slide-specific latent vector that captures the spatial dependence among neighboring spots and aligns embeddings across datasets. Equation (xri) in p genes with a low-dimensional feature (zri) via a probabilistic PCA model70 with consideration of spatial dependence while Eq. . In detail, we use the following Potts model72 for the latent labels,Cr(βr) is a normalization constant that does not have a closed form, Nri is the neighborhood of spot sri in sample r, and βr is the sample-specific smoothing parameter that captures the label similarity among the neighboring spots. However, we assume a continuous multivariate HMRF for the vector, vri, which captures spatial dependence in the embedding space. In detail, we assume an intrinsic CAR model73 for vrisri in sample r, mri is the number of neighbors of spot i in sample r, sri, and Ψr is a q × q conditional covariance matrix for the elements of vri. Conventionally, the joint distribution of the intrinsic CAR model is non-identifiable, as the mean of the joint distribution of intrinsic CAR is not zero. As shown in the next section, non-cellular biological effects, e.g., batch effects, from each slide can be projected onto the cellular biological space, which can be corrected by the non-zero-mean property of intrinsic CAR.Here, we present a basic overview of PRECAST, and further details are available in the mentclass2pt{minimr can be decomposed into additional parts with respect to non-cellular biological effects as follows:νri is a q-dimensional vector that captures the spatial dependence among neighboring spots; ζri, independent of , is the corresponding W) and non-cellular biological space (Wr) are non-orthogonal, we can project Wrs onto the column spaces of W, i.e., zri∣yri = k ~ N, and For each tissue slide, we assume the normalized expressions in each sample 74. In this study, we used a set of mouse/human housekeeping genes from the Housekeeping and Reference Transcript Atlas75. First, we obtained vectors, L housekeeping genes by matching the set of housekeeping genes and genes passing QC for each dataset. By performing PCA, we obtained the top 10 PCs, yri  on a set of detected DE genes using g:Profiler with the g:SCS multiple testing correction method and applying a significance threshold of 0.0576.After removing unwanted variation of gene expression matrices for multiple slides, we performed DE analyses and enrichment analysis on all four datasets. In detail, we used the FindAllMarkers function in the R package SPARK to identify SVGs adjusted for cell/domain-relevant covariates for each sample. Finally, an FDR of 1% was adopted to identify the significant SVGs.After obtaining aligned embeddings and domain labels for DLPFC and HCC Visium data using PRECAST, we detected the SVGs by adjusting the estimated aligned embeddings as covariates to investigate the role of SVGs beyond differences between cell/domain types. In detail, we used the function spark.vc in the R package 77. We inputted the aligned embeddings and domain clusters estimated by PRECAST into the function slingshot in the R package slingshot for implementation. Because the splicing and unsplicing information was available for the HCC Visium dataset, we ran RNA velocity analysis using the scvelo.tl.velocity function in the Python module scvelo based on the splicing and unsplicing matrices, then the domain clusters estimated by PRECAST were used to visualize the inferred RNA velocity and latent time.To further investigate the development and differentiation of these identified spatial domains/cells by PRECAST, we used the aligned embeddings and domain clusters estimated by PRECAST to perform trajectory inference, or RNA velocity analysis if splicing and unsplicing information was available. For the DLPFC Visium data, mouse liver ST data and mouse OB Slide-seqV2 data, we conducted trajectory inference for the combined spots in all multiple tissue sections using Slingshot55, a supervised learning method used to decompose each spatial transcriptomics pixel into a mixture of individual cell types while accounting for platform effects. We leveraged the results of deconvolution analysis for better biological interpretation of the real data analysis.We performed deconvolution analysis using Robust Cell Type Decomposition (RCTD)46. By removing cell types with cell numbers of less than 18, the reference data retained 4640 cells belonging to 17 cell types: B cells with high Jchain expression (n = 43), erythroid cells with high Hbb-bs expression (n = 555), periportal hepatocytes (n = 26), hepatocytes with high Fabp1 expression (n = 149), Kupffer cells (n = 1046), erythroid cells with high Hbb-bt expression (n = 62), endothelial cells (n = 1196), granulocytes (n = 194), NK cells (n = 114), macrophages (n = 191), dendritic cells (n = 422), B cells with high Fcmr expression (n = 97), T cells (n = 219), plasmacytoid dendritic cells (n = 90), hepatocytes with high Spp1 expression (n = 99), pericentral hepatocytes (n = 119), and hepatic stellate cells (n = 18).For mouse liver ST data, we used scRNA-seq data on liver tissue sections from an adult mouse from the MCA52. The reference data included 17,453 cells belonging to 40 cell types: three astrocyte types , two endothelial types (EC1: n = 897 and EC2: n = 445), two mesenchymal types (Mes1: n = 200 and Mes2: n = 65), three microglials , monocytes (n = 238), two murals , myelinating-oligodendrocyte cells (n = 294), macrophages (n = 197), five olfactory ensheathing cell types , oligodendrocyte progenitor cells (OPC: n = 137), red blood cells (RBCs: n = 103), and 18 neural subtypes . For more details of the cell types, please refer to Tepe et al.52.For the mouse OB Slide-seqV2 data, we used 10X Genomics Chromium scRNA-seq data collected from the wild-type OB61. After filtering out 92 cells of unclassified cell type, the reference data contained 5023 cells belonging to seven cell types: B cells (n = 598), CAFs (n = 724), HPC-like cells (n = 254), malignant cells (702), T cells (n = 1429), TAMs (n = 437), and TECs (n = 879). After acquiring the deconvolution results from the above-mentioned reference, we combined the proportions of B cells, T cells, and TECs into a single set and referred to this as the immune cell proportion.For the HCC Visium data, we leveraged a droplet-based scRNA dataset collected from HCC and intra-hepatic cholangiocarcinoma patients to serve as the reference data for deconvolutionWe conducted comprehensive simulation and real data analyses to compare PRECAST with existing methods of data integration and clustering.26; (2) Harmony23 implemented in the R package harmony; (3) fastMNN24 implemented in the R package batchelor; (4) Scanorama25 implemented in the Python module scanorama. (5) scGen28; (6) scVI27 implemented in the Python module scvi; (7) MEFISTO29 implemented in the Python module mofax; and (8) PASTE30 implemented in the Python module paste. In the real data analyses, PRECAST and all other methods used the same list of selected genes from the preprocessing steps as input. The first six methods were designed for scRNA-seq integration, while MEFISTO can be used for scRNA-seq and SRT data integration, and PASTE is designed for integrating SRT data from multiple adjacent tissue slices into a single slice (see We applied the following single-cell integration methods to benchmark the data integration performance of PRECAST: (1) Seurat V3SC.MEB20, (2) Louvain implemented in the R package igraph78, (3) BayesSpace implemented in the R package BayesSpace79, and (4) BASS implemented in the R package BASS80. SC-MEB and BayesSpace were recently developed to perform spatial clustering based on a discrete Markov random field79, and Louvain is a conventional non-spatial clustering algorithm based on community detection in large networks78, while BASS was a newly developed clustering method for multiple SRT data based on the aligned embeddings from Harmony. In the implementation, we set the default values for them in the respective packages  SC-MEB implemented in the R package We evaluated the methods’ performances in data integration, obtaining embeddings for cellular biological effects, and spatial clustering using the following metrics.23 to quantify the performance in merging the shared cell populations among tissue slides and mixing spots from M tissue slides. cLISI assigns a diversity score to each spot that represents the effective number of cell types in the neighborhoods of that spot. For M datasets with a total of K cell types, accurate integration should maintain a cLISI value close to 1, reflecting the purity of the unique cell types in the neighborhood of each spot, as defined by the aligned embeddings. Erroneous embedding includes neighborhoods with a cLISI of more than 1, while the worst cases have cLISI close to K, suggesting that neighbors have K different types of cells. iLISI has a similar form and implication as cLISI but is based on a sample index set rather than clusters of cell types. Thus, a larger iLISI value means the different samples have more sufficient mixing.To assess performance in batch-effect removal, we used the cell-type/integration local inverse Simpson’s index (LISI), cLISI, and iLISIcluster) and (2) grouping using different datasets as the batch silhouette coefficient (silhbatch). In the data integration, a larger value of silhcluster indicates better preservation of the biological signals between cell/domain types, while a smaller value of silhbatch suggests better mixing of datasets. These two metrics can be summarized using the F1 score as follows81:To simultaneously evaluate the separation of each cell/domain cluster and mixing of multiple datasets, we calculated the average silhouette coefficient of the SRT datasets using two different groupings: (1) grouping using known cell types as the cell/domain-type silhouette coefficient  is the Pearson correlation coefficient between yi and residij.For dimension reduction, we applied two measurements to assess the performance of true latent feature recovery in the simulation studies. The first was the mean canonical correlation between the estimated features and the true one, defined as82 and NMI83. ARI is the corrected version of the Rand index (RI)84 and is used avoid some of the drawbacks of RI82. ARI measures the similarity between two different partitions and ranges from −1 to 1. A larger value of ARI means a higher degree of similarity between two partitions. ARI takes a value of 1 when the two partitions are equal up to a permutation. Whereas NMI is a variant of the mutual information (MI) that normalizes the value of MI to within the range of 0 and 1. When the two partitions are equal up to a permutation, NMI takes a value of 1.To evaluate performance in spatial clustering, we used ARIScenario 1. Raw gene expression count data with different scales of batch effects: domain labels and spatial coordinates from Potts models. For this scenario, we generated the raw gene expression count data for three samples, as well as the spatial coordinates based on Potts models with four neighborhoods. All quantities, such as the labels yris and latent features νris, of spots were randomly simulated from the generative model  Potts model with the smoothing parameter βr = 0.8 + 0.2(r − 1), using the function sampler.mrf from the R package GiRaF. Then, we generated latent features νris from the CAR model with the same Nri as defined in the Potts model and covariance matrix σr,ij = r(0.2r)i−j∣∣, using the function rmatrixnorm in R package LaplacesDemon. We set different values for the smoothing parameter βr and covariance matrices r, to mimic the heterogeneity of three samples. The domain labels yris and latent features νris were fixed once they were obtained.ve model . We genezri in the model , where zri ∈ Rq with q = 10. Structures for μk and Σk are shown in Supplementary Data Wr by generating W, we performed orthogonalization of Wr. Next, we generated ζri in the model  + Wrζri + εri, τrj ~ N, εri ~ N, where τrj is the j-th element of τr, Λr = diag(λrj), j = 1,…,p, λj1 = 2(1 + 1.5∣zj1∣) with λj2 = 2(1 + zj2) with λj3 = 2(1 + 2zj3) with εri makes the distribution of r, we only observed the raw expression j and spot i and spatial coordinates sri for spot i.Next, we generated a high-dimensional normalized expression matrix using Scenario 2. Raw gene expression count data with different scales of batch effects: domain labels and spatial coordinates from DLPFC data. To validate the generalizability of PRECAST, we also generated data based on three DLFPC datasets  from three donors (Visium platform). The domain labels yris for spots were obtained from annotations made by Maynard et al.21, together with the spatial coordinates. To generate the latent features νris, PRECAST was used to fit the three datasets, and we used the estimated features yris and latent features νris were fixed once they were obtained. The other quantities were generated in the same way as in scenario 1, but with a different μk  from the last donor. The domain labels were obtained from Maynard et al.21. As the true shared embeddings were unknown, we did not evaluate the canonical correlations. To generate the count matrix, we randomly added a pseudocount for each raw count from a binomial distribution with size three and probability parameter 0.3, similar to Zeira et al.30. Finally, we obtained the count matrix and spatial coordinates as input for the compared methods.40 to select top SVGs for human DLPFC Visium data, mouse liver ST data and HCC Visium data. However, we used SPARK-X41 to select SVGs for mouse olfactory bulb Slide-seqV2 data, since SPARK cannot handle datasets with a large number of spots. In total, we selected the top 2000 SVGs for each sample using SPARK or SPARK-X. Next, we prioritized genes based on the number of times they were selected as SVGs in all samples and chose the top 2000 genes as input for PRECAST and the other compared analytical methods.By performing QC, we filtered out genes with zero expression in multiple spots, and spots with zero expression of many genes (see “Data resources”). In our analyses, we used SPARKFindVariableFeatures with default settings in the Seurat R package. In addition, to examine the impact of different SVG selection methods to choose SVGs on the performance of PRECAST, we applied four methods to select the top 2000 SVGs for each sample of these two datasets. These methods included SPARK, SPARK-X, SpatialDE42, and nnSVG43.We used human DLPFC Visium data and mouse liver ST data, which had manual annotations, to confirm that spatially-aware gene selection methods did not represent a crucial part of PRECAST. Here, we selected the top 2000 highly variable genes (HVGs) for each sample using 21 were layer 1 (n = 5321), layer 2 (n = 2858), layer 3 , layer 4 (n = 3547), layer 5 (n = 7300), layer 6 (n = 6201), white matter (n = 4514), and undetermined spots (n = 352). In the analysis, we treated these manual annotations as the ground truth to evaluate the clustering and data integration performance of the different methods.We downloaded spatial transcriptomic data for human DLPFC obtained on the 10x Visium platform from 10.5281/zenodo.4730634. These data were collected from 12 human postmortem DLPFC tissue sections from three independent neurotypical adult donors, and the raw expression count matrix contained 33,538 genes for each sample, with a total of 47,681 spatial locations. Before conducting the analysis, we first performed QC on each sample to filter out genes with non-zero expression levels for fewer than 20 spots and the spots with non-zero expression levels for less than 20 genes. The filtering step led to sets of 14,535 genes on average in a total of 47,680 spatial locations. The annotated spatial domains in all 12 samples based on the cytoarchitecture in the original studyhttps://zenodo.org/record/4399655. The eight datasets contained 15,302 genes, on average, measured over 4865 spatial spots in total. In the QC steps, we first filtered out genes with non-zero expression levels in fewer than 20 spots and spots with non-zero expression levels for fewer than 20 genes. The filtering step led to a set of 9221 genes, on average, from a total of 4865 locations. The annotated spatial domains in all eight samples, based on marker genes in the original study44 were portal veins (n = 1223), central veins (n = 720), haemoglobin (n = 464), immune-related domain (n = 163), mesenchymal-related domain (n = 110), and undetermined spots (n = 2183), which were used to evaluate the clustering and data integration performances of the different methods in the analyses.We downloaded eight sets of mouse liver ST data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169021. We used the first 16 slides because of the low quality of the last four slides. The data contained 21,571 genes, on average, over all 693,863 spatial locations across 16 sections, which were equally distributed along the anterior-posterior axis of the same mouse. In the QC steps, we first filtered out genes with non-zero expression levels in fewer than 20 spots and spots with non-zero expression levels for fewer than 20 genes. The filtering step led to a set of 14,307 genes, on average, for a total of 594,890 locations. To evaluate the impact of spatial resolution, we further collapsed nearby spots in each tissue slide using square grids of size 70 × 70. Then, the same QC steps were performed to obtain normalized expression for the analysis. To examine the structure of the mouse OB, we relied on the structural annotation in the Allen Brain Atlas48 and used the results of downstream analyses to determine the specific regions of OB.We obtained the data for 20 mouse OB Slide-seqV2 sections in Replicate 2 from CNBI accession number GSE169021 55.These data were from two tissue sections each from tumor and tumor-adjacent regions of an HCC patient, and contained 36,601 genes from over 9813 spatial locations. In the QC steps, we first filtered out genes with non-zero expression levels in fewer than 20 spots and spots with non-zero expression levels for fewer than 20 genes. The filtering step led to a set of 14,851 genes on average from a total of 9813 locations. In this data, manual annotations for the TNE and stroma regions were provided by a pathologist using the Visium companion H&E images. We further performed spatial deconvolution to examine the spatial distribution of malignant cells using RCTDFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-8Reporting SummarySource Data


Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, availability of these data are limited compared to RNA-sequencing data. We developed REPAC, a framework for the analysis of APA from RNA-sequencing data. Using REPAC, we investigate the landscape of APA caused by activation of B cells. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA.The online version contains supplementary material available at 10.1186/s13059-023-02865-5. Mechanisms that control gene expression at the RNA level are often referred to as post-transcriptional regulation (PTR) mechanisms. Splicing and alternative polyadenylation (APA) are well-known examples of PTR that can regulate not only gene expression but also their function. While splicing has been extensively studied since the advent of next generation sequencing (NGS), APA studies are far less common than splicing studies. Indeed, inferring APA events from RNA-Seq data is challenging due to the lack of an intrinsic characteristic , and for this reason, several specialized sequencing methods were developed to pinpoint polyadenylation sites (PAS) –3. AlthoThis poses a challenge to the study of polyadenylation (PA) biology given that the lack of publicly available data severely hinders investigation and hypothesis generation without incurring major experimental costs. To overcome this limitation, several groups have developed methods to quantify PA usage from RNA-Seq data –6. Amongse, QAPA , which lMost of the methods currently available rely on the effect size of the change in the proportion of the expression levels between different PA sites. Performing statistical analysis on proportions of a total (i.e. percentage) imposes many statistical limitations that are often ignored and can lead to inaccurate results. Most multivariate methods that were developed for real values cannot be directly applied to compositional data  because compositional data often breaks many assumptions of these methods . MoreoveIn this work, we present regression of polyadenylation compositions (REPAC) a novel framework to detect differential alternative polyadenylation (APA) using regression of polyadenylation compositions which can appropriately handle the compositional nature of this type of data while allowing for complex designs. We show that REPAC is faster and yields more accurate and robust results in comparison to other methods.recount3 [n = 5 for each condition) with 5000 genes having a longer or shorter isoform. Half of the set had one of the isoforms predominantly expressed in one of the conditions (1250:1250 for short/longer isoforms), and the other half did not have a predominant isoform. The ratio of longer to shorter isoforms was randomly defined within a pre-defined range  occurring between conditions. This quantification can be done with traditional tools  or directly pulled from recount3  bigWig fsee the “. We applWe compared the performance of REPAC to other popular methods for APA detection: DaPars , QAPA 44, and LAWe measured the performance of all the methods using the same simulation experiment. For each method, we used their respective effect size estimators  and testis tissue samples from the GTEx project (V8). We found that the time to quantify PAS with REPAC scaled linearly with the number of samples, with an average of 9 seconds per sample. The time to test for DPU between conditions remained stable at around four minutes , a new framework for the study of differential PA events using traditional bulk RNA-sequencing data. The REPAC method is based on the principles of compositional data (CD) analysis developed by Aitchson .In our simulated data set REPAC outperformed DaPars, which is currently, one of the most popular methods of APA analysis, in both accuracy and speed. When compared to more recent developments such as QAPA, REPAC presented a marginal increase in performance Fig. A. HoweveThe REPAC package takes full advantage of the recount3 framework to skip the data acquisition and processing steps and directly extract the necessary data for differential APA analysis with little resource usage and time. This feature makes over 750,000 samples readily available for analysis Fig. . This isn = 8) was 7.6 times faster to process with REPAC than QAPA (4.2 versus 32 min) and LABRAT (4.2 versus 35 min). Both of these methods already outperforms other methods[All of these overheads translate into a much faster and more accurate analysis with REPAC than other methods. We demonstrated that the analysis of a small data set  and testis tissues from GTEx V8. On average, REPAC took 9 seconds per sample to quantify all PAS, meaning REPAC is scalable for large data sets with 400 samples taking under one hour to complete the entire analysis Fig. B. In conMoreover, the results of this comparison were consistent with previous studies reporting that cells from non-proliferative tissue  tend to express longer 3′-UTR isoforms than cells from proliferative tissues  , 12. IntDespite one of the first pieces of evidence of APA as a functional mechanism being reported during B cell transition to plasma cell , the lanOur analysis of naive versus LPS-activated B cells detected hundreds of genes impacted by APA, whose majority were 3′-UTR shortening events. On par with previous observations, we founInterestingly, response to type I interferon (IFN-I) was among the most enriched processes in 3′-US. Early response of B cells to IFN-I has been shown to elicit many types of responses . UsiFinally, we also demonstrated that APA has serious implications on downstream analysis  since traditionally gene length is assumed to be the same between conditions and therefore is not accounted for in many approaches. While recent pipelines based on expression estimates at transcript level through quasi-mapping, such as the salmon-tximport , 26 pipeWe demonstrated that REPAC is a robust and powerful tool for exploring the biology of APA. It enables the analysis of over 750,000 samples encompassing thousands of different phenotypes. With REPAC, we want to encourage more studies on APA and how they influence normal and disease tissues. We hope this new tool can help pave the way to develop new hypotheses that can be further explored to understand the biological role of APA as a whole.D is the number of elements of comprising a composition  and k is a positive constant. We apply the isometric log ratio (ilr) transformation to the simplex. Let x be a D-part simplex, then:clr) plane of x. Starting with a initial reference r  reported by REPAC represents the fold-change of the isometric log-ratio transformed compositions. This effect size metric, must be interpreted in the simplex space. Since interpretation of changes in the simplex space are not easy to interpret, the REPAC package also provides the mean composition changes across groups (mcDiff) which can be interpreted as the mean percentage in the PAS usage compared to the reference site. REPAC also provides pre-annotated intervals of 3′UTR events for both human and mouse based on polyAsite database .p  and select PAS overlapping with 3′-UTR regions. A total of 67509 and 85476 PAS coordinates were obtained and used in the downstream analysis for mice and humans, respectively. For the QAPA analysis, we extracted the 3′-UTR sequences using QAPA fasta. These sequences were used to build the index and perform the 3′-UTR expression quantification with salmon [Both REPAC and QAPA rely on pre-annotated PAS to infer DPU. In this work, we used PAS annotations from the PolyAsite database for both mouse mm10) and human (hg38) genomes. For each genome, we processed the PAS using  and humap-value t-statistics and tested the MSigDB GO Biological process collection for negative enrichment using a Monte Carlo adaptive multilevel splitting approach, implemented in the fgsea package [Differential polyadenylation usage analyses were performed using REPAC and QAPA (v1.3)  to detecsee the “” section package . The resp-values with the “calculatepsi” mode. Enrichment analysis of GO BP were conducted in the same manner as REPAC, but instead ranking the genes by psi for QAPA and LABRAT, respectively.For the QAPA analysis, we used the QAPA quant option to load 3′-UTR expression estimates from salmon and compute the PAU’s for each sample. Low expressed 3′-UTRs were filtered  and est weights , 31 was g method . Next, tcreate_pa_rse and recorded the time taken to quantify each subset. Next, we filtered PASs that were lowly expressed  and testis samples from the GTEx tracks of recount3 . For eac package .Additional file 1:Supplementary Figures.Additional file 2:Supplementary Tables.Additional file 3. Peer review history.


KMT2A (MLL) gene rearrangements (KMT2A-r) are associated with a diverse spectrum of acute leukemias. Although most KMT2A-r are restricted to nine partner genes, we have recently revealed that KMT2A-USP2 fusions are often missed during FISH screening of these genetic alterations. Therefore, complementary methods are important for appropriate detection of any KMT2A-r. Here we use a machine learning model to unravel the most appropriate markers for prediction of KMT2A-r in various types of acute leukemia. A Random Forest and LightGBM classifier was trained to predict KMT2A-r in patients with acute leukemia. Our results revealed a set of 20 genes capable of accurately estimating KMT2A-r. The SKIDA1  and LAMP5  overexpression were the better markers associated with KMT2A-r compared to CSPG4 , regardless of the type of acute leukemia. Of importance, high expression levels of LAMP5 estimated the occurrence of all KMT2A-USP2 fusions. Also, we performed drug sensitivity analysis using IC50 data from 345 drugs available in the GDSC database to identify which ones could be used to treat KMT2A-r leukemia. We observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU), Gemcitabine (both antimetabolite chemotherapy drugs), WHI-P97 (JAK-3 inhibitor), Foretinib (MET/VEGFR inhibitor), SNX-2112 (Hsp90 inhibitor), AZD6482 (PI3Kβ inhibitor), KU-60019 (ATM kinase inhibitor), and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor). Moreover, IC50 data from analyses of ex-vivo drug sensitivity to small-molecule inhibitors reveals that Foretinib is a promising drug option for AML patients carrying FLT3 activating mutations. Thus, we provide novel and accurate options for the diagnostic screening and therapy of KMT2A-r leukemia, regardless of leukemia subtype.The  KMT2A (MLL) gene are recurrently associated with the disease phenotype of acute leukemia. Considering the high number of genes involved in KMT2A fusions (KMT2A rearrangements (KMT2A-r) is routinely based on screening methods to guide the detection of this alteration regardless of the partner gene. Such methods include immunophenotyping using an antibody for detection of chondroitin sulfate proteoglycan 4  (in situ hybridization (FISH). Subsequently, the identification of the most frequent KMT2A fusion transcripts is generally performed by reverse transcription polymerase chain reaction (RT-PCR)  in all KMT2A-r cases   or split(RT-PCR) , and lon(RT-PCR) . Recent leukemia . However-r cases .KMT2A-USP2 fusions in most patients, leading to an underestimation of its frequency in acute leukemia  FISH pattern. The alteration is only observed whilst the inversion is accompanied by a 3′ KMT2A deletion. The challenge to accurately identify KMT2A-r is not restricted to FISH methodology. Several studies reveal a varying prevalence of false-negative results associated with NG2 detection by flow cytometry in patients with acute leukemia  and has already been applied to understand cancer progression and to develop predictive models of diagnosis, prognosis and response to treatment in renal carcinoma and ovarian cancer . In thisKMT2A status (KMT2A-r or KMT2A wild-type/KMT2A-WT) and available gene expression data from six different datasets  is composed of four different cohorts with pediatric patients diagnosed with 1) B-cell precursor acute lymphoblastic leukemia (B-ALL), 2) T-cell ALL (T-ALL), 3) acute myeloid leukemia (AML), and 4) acute leukemia of ambiguous lineage (ALAL). The diagnosis of KMT2A-r in TARGET dataset was obtained from the respective clinical data available in the TARGET Data Matrix  files for more details). This identification was based on karyotype and FISH results for the B-ALL samples, FISH with confirmation by RNA-Seq (http://www.vizome.org/aml/). As previously described, the diagnosis of KMT2A-r in this cohort was obtained by conventional karyotype and RNA-Seq. All clinical data are found in the Supplementary Material of the original paper  study EGAS00001004212  are from pediatric patients diagnosed with B-ALL and T-ALL. For this cohort, the KMT2A status was retrieved from the original study, which used conventional karyotype/FISH and RNA-Seq techniques . To identify the patients with KMT2A-r, we used annotation of the structural variant from the “Acute Myeloid Leukemia” study in cBioPortal database , which was based on RNA-Seq analysis  database . In the case of ALAL samples, we used the read counts to calculate FPKM values using the Bioconductor R package “edgeR” version 3.30.3 . For more details on processing steps, please see the methods section in the reference , after gaining access permission to EGA studies EGAS00001004212 and EGAS00001003096, respectively. The reads were trimmed using the software Trimmomatic version 0.39 . The STAR alignment algorithm  was usedKMT2A fusion information were retrieved from the Cancer Cell Line Encyclopedia (CCLE) database (https://sites.broadinstitute.org/ccle/) at the Dependency Map (DepMap) portal . RNA-seq profiling was performed as previously described  values, z-scaled across samples. Genes (rows) were clustered using Pearson correlation, and samples (columns) were clustered using Euclidean distance. This step was performed in the R statistical environment version 4.0.5.Afterwards, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were analyzed to assess the performance of our model. The combination of these parameters with the value of the area under the receiver operating characteristic (ROC) curve (AUC) was used to select the best model. Besides that, we calculated the Shapley values of each feature according to the predictive model to understand their respective contributions using SHAP (SHapley Additive exPlanations) version 0.36.0 . All pre. We used genome protein-coding as a reference Set. p values were controlled for false discovery rate (FDR) using the Benjamini–Hochberg method, and we considered FDR ≤ 0.05 as significant results.The gene list obtained from machine learning step was evaluated for known functional processes by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the over-representation analysis (ORA) methodology in the WEB-based GEne SeT AnaLysis Toolkit . We usedhttps://www.cancerrxgene.org/downloads/bulk_download). We also downloaded IC50 values of some small-molecule inhibitors from the Supplementary Table S8 of Beat AML dataset  z-score values of the drugs with data available in the Genomics of Drug Sensitivity in Cancer (GDSC) repository  were dow dataset . The datKMT2A-r cell lines demonstrated sensitivity was used as an input in the web resource Drug-Gene Interaction database   . The resp-values < 0.05 were considered statistically significant. All analyses presented in this study, with exception of the machine learning step, were performed in the R statistical environment version 4.0.5.The ROC curves and AUC values were generated by the CRAN R package “pROC” version 1.17.0.1 . AssociaKMT2A-r cases (7.3%) and classified the other molecular subgroups as KMT2A-WT . As expected, KMT2A-r were more frequently in the pediatric age group, and less frequently in the older adults, when compared to KMT2A-WT .A total of 1,659 acute leukemia samples were evaluated in this study, consisting of 899 AML, 415 B-ALL, 273 T-ALL and 72 ALAL from six different datasets. These samples were mostly obtained at the time of diagnosis (90.3%) and refer to pediatric patients (61.4%) . The freKMT2A-r and 1,232 KMT2A-WT cases. First, we performed a feature selection analysis to reduce the dimensionality of the dataset. As a result, 247 genes were selected due to their potential to predict KMT2A-r . Among them, 176 genes had increased expression in KMT2A-r acute leukemias  with the “Transcriptional misregulation in cancer” pathway  T-ALL, while this same biological process has the involvement of PBX3, RUNX2, SMAD1, MEF2C, HOXA9 and HOXA10 in KMT2A-AFF1 (or MLL-AF4) B-ALL. The SUPT3H gene is a chromatin regulator and PROM1 is a signaling mediator, both are also associated with KMT2A-AFF1 (or MLL-AF4) in B-ALL.For the algorithm development, we selected the data from four of the six datasets: TARGET, Beat AML, ALL-Ekert, and AML-Griffioen. We used gene expression data of 14,287 genes from 1,332 acute leukemia samples, including 100  KMT2A-r , such aseukemias . The enrKMT2A-r and 853 KMT2A-WT) and 30% testing  data subsets   . TogetheKMT2A-r. The performance measures of these models can be accessed in KMT2A-r, with AUC of 0.984, accuracy of 0.970, sensitivity of 0.905, and specificity of 0.974 (KMT2A-r cases with a good performance (19 of 21 KMT2A-r and 369 of 379 KMT2A-WT cases). We observed some feature modifications in Shapley values (SKIDA1 and LAMP5) did not change.With training and testing data subsets now composed of 20 genes, we used LightGBM algorithms again to build four additional different models also based on selected features and clinical variables associated with of 0.974 . It is ny values , howeverKMT2A-r prediction model, we evaluated its performance in an independent subset. The TCGA cohort included 151 AML patients classified according to KMT2A status: KMT2A-WT (n = 143) and KMT2A-r (n = 8). The KMT2A partner genes in these patients were: MLLT10 (n = 1), ELL (n = 3), MLLT3/AF9 (n = 2), and MLLT4 (n = 2). With a whole new set of data, our model was able to correctly predict six of eight KMT2A-r samples and 141 of 143 KMT2A-WT cases. Therefore, we created a good predictor for KMT2A-r samples (AUC = 0.868), with higher accuracy, sensitivity, and specificity , MLLT10 (n = 3), MLLT3/AF9 (n = 3), AFF1/AF4 (n = 1), and CBL (n = 1). Two patients had out-of-frame fusions with MGMT (n = 1) or within the 7q22.3 region (n = 1). Therefore, we evaluated whether the other genes (11 of 20 genes) would still be able to cluster KMT2A-r patients. As expected, the two samples with out-of-frame did not present a similar expression profile of the other KMT2A fusions, as well as one sample with KMT2A-USP2, which had higher expression of NEDD4 and lower expression of SKIDA1, HOXA9 and MEIS1 , consisting of 163 nd MEIS1 . On the escribed . We obseKMT2A-r identification, we next evaluated the two most important genes ranked by the selected machine learning model . LAMP5 expression was also not different in DEK-NUP214 and complex karyotype samples when compared to KMT2A-r in AML, as well as hypodiploid cases in B-ALL. Besides that, LAMP5 does not appear to be a good biomarker for KMT2A-r in T-ALL. Similarly, CSPG4 expression was comparable between KMT2A-r and all other subgroups of T-ALL. On the other hand, SKIDA1 had the highest expression in KMT2A-r as compared to all remaining subgroups of T-ALL (p < 0.01).In order to find an optimal biomarker for and ALAL . AlthougKMT2A-r classification, we plotted ROC curves and calculated their respective AUC values among acute leukemia subtypes  and LAMP5  performed better than CSPG4 . Considering each subtype of leukemia, the most divergent performance was observed in T-ALL, in which SKIDA1 had the best estimates , followed by LAMP5 , and CSPG4 . The most similar results were associated with ALAL, in which those three genes provided optimal estimates of KMT2A-r. Next, we obtained the best thresholds indicated by ROC curves for SKIDA1, LAMP5 and CSPG4 in acute leukemia , AML , and B-ALL  to validate the predictive power of these genes as biomarkers in the TCGA (151 AML patients) and the French (173 B-ALL patients) cohorts. These results were demonstrated through confusion matrices  and specificity (0.734 vs. CSPG4 = 0.573) in AML, while LAMP5 had greater performance in B-ALL  if the overall acute leukemia threshold was used. Similar results were also demonstrated when thresholds for the respective acute leukemia subtype (AML or B-ALL) were used. It is important to highlight that the validation cohort contains three B-ALL patients with KMT2A-USP2. Of note, LAMP5 was extremely overexpressed in these patients , thus allowing the prediction of this gene fusion in all cases. On the other hand, CSPG4 failed to predict it in one of these cases, and SKIDA1 was not able to predict this rearrangement in all cases either.To evaluate the performance of these genes on subtypes . In genematrices  and perfmatrices . OverallKMT2A is a promiscuous gene and the distribution of fusion partners varies according to leukemia subtypes, we hypothesized that SKIDA1 and LAMP5 expression could be associated with specific gene rearrangements . Notably, CSPG4 expression was lower in KMT2A fusions with ELL, MLLT1, MLLT4, and SEPT6. Conversely, SKIDA1 had the lowest expression variation across KMT2A fusions  using the IC50s of 345 drugs available in the GDSC database (KMT2A-r (n = 6) with KMT2A-WT (n = 27), we observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU) and Gemcitabine , WHI-P97 , Foretinib , SNX-2112 , AZD6482 , KU-60019 , and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor; p = 0.03). On the other hand, KMT2A-r cell lines were more resistant to the γ-Secretase inhibitor, Avagacestat (p = 0.005) . We did not observe significant differences between KMT2A-r and other molecular subgroups . When we evaluated the IC50 values in the Beat AML data, we observed that samples with FLT3 activating mutations were significantly more sensitive compared with FLT3-WT samples (Since database . After c= 0.005) . Due to oretinib , we alsoubgroups . Interes samples .KMT2A-r cell lines demonstrated sensitivity, in the DGIdb database. As result, 127 potential targets were found for three drugs   . Next, wKMT2A-WT .KMT2A-r at the molecular level is challenging, considering the high number of gene fusions attributed to this group of alterations  and provide gene expression profiles. Although it is important to implement this technology for additional clinical benefits  is located at 10p12.31, in very close proximity to MLLT10 (downstream) and ∼340 kb upstream NEBL. Both genes have already been found as partners of KMT2A fusions , we speculate that 10p12.2 rearrangements may disturb the expression of other genes within this region or this association could be restricted to T-ALL. Additionally, a recent murine study revealed that Skida1 might cooperate to sustain hematopoietic stem cells and hematopoietic committed progenitor cells presenting KMT2A fusion  or t in childhood ALL  as a new potential therapeutic agent for this leukemia subtype. Foretinib is an oral multikinase inhibitor targeting MET, RON, AXL, VEGFR, c-KIT, FLT3, and PDGFR signaling pathways. Based on that, we verified that patient’s cells harboring FLT3 mutations, a frequent secondary event in KMT2A-r leukemia, showed higher sensitivity to Foretinib, suggesting that this drug might turn into a therapeutic option for these subgroups. Foretinib has already been used in several cancer treatment trials, including Hepatocellular Carcinoma (NCT00920192), Breast Cancer , Non-Small-Cell Lung Cancer , Solid Tumours , Head and Neck (NCT00725764), Papillary Renal Cell Carcinoma (NCT00726323), and Gastric Carcinoma (NCT00725712). In leukemia, this drug induces mitotic catastrophe in chronic myelogenous leukemia (CML) cells via JNK-dependent inhibition of Plk1 expression and triggers apoptosis by a caspase 2-mediated mechanism  with interaction annotation to 5-Fluorouracil. The upregulation of thymidine phosphorylase (TYMP) is commonly associated with 5-fluorouracil resistance  to predict KMT2A-r regardless of acute leukemia subtypes. Further studies are needed in order to validate these markers and to translate their application into the routine diagnostics of acute leukemia, such as comparing its predictive value compared to CSPG4/NG2 by immunophenotyping. As a highlight, LAMP5 expression was able to predict even the rare gene fusion KMT2A-USP2, which is often missed by routine methods, and that gene product also appears to be a potential treatment target in KMT2A-r leukemia. At last, our analysis suggested Foretinib as one therapeutic option for patients with KMT2A-r, especially those with AML and carrying FLT3 activating mutations. Further clinical studies should now explore the application of this drug for the treatment of KMT2A-r acute leukemia.In this study, we observed that our algorithm delivered highly accurate predictions for


Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns.Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions.Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model : Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients  and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction).The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis. Machine learning  and bio-statistics offer a wide range of methodologies to build models able to estimate several aspects of cancer from gene expression data . A distinguishing feature of machine learning models is that they afford to predict from data rather than infer, a typical paradigm of statistics . PredictPrevious work introduced double discretization procedures to characterize GED applicable when modeling bladder cancer survival rate (supervised binary classification) . The preGene expression data were collected by  and releentclass1pt{minimamentclasspt{minima“Log-z”Each GED was log-transformed and values standardized. Continuous values were discretized by Classification and Regression Trees .“Uniform”Cumulative distribution function of each GED was estimated and, through a quantile function, mapped to a uniform distribution with data normalization in the interval . The number of quantiles (51) introduced a pre-binning of the data followed by CART discretization after uniform mapping.“Normal”Cumulative distribution function of each GED was estimated through a quantile function mapped to a normal distribution with data standardization. The number of quantiles (51) introduced a pre-binning of the data followed by CART discretization after normal mapping. It should be noted that Uniform and Normal data mapping produced a “double discretization” on data as demonstrated in the previous exploration: operational sequences are synthesized in Fig. Three alternative discretization approaches derived from the procedure previously investigated in  constituuantiles 1 introduTrees are hierarchical structures, starting with a root value (a feature or GED) and descendant sub-trees generated from parent nodes: each split into branches is called edge. The end of a branch is called a leaf. The characteristics of trees are the existence of exactly one path (connected sequence of edges) between any pair of nodes and their acyclicity because there is no loop in their topology. Large numbers of trees operating as an ensemble are called forests. Feature spaces could be represented by forest embedding, collecting leaf value sequence for each observation to obtain a similarity matrix resembling the procedure applied by  on prostDimensionality reduction produces a representation that helps identify relevant data patterns. For example, in , the autinternal cluster validation indexes like Davies-Bouldin index  [External cluster validation methods have been estimated for the final comparison between pipelines. Validation indicators included Fowlkes-Mallows index [Several clustering techniques were implemented to sub-divide the dimensionally reduced forest embedding matrix and assess the goodness of the resulting bi-dimensional maps: hierarchical density-based spatial clustering of applications with noise , mini-be., DBI) , silhouee., DBI) , and Cale., DBI) . Internaws index , Rand inws index , adjustews index , normaliws index , homogenws index . ExternaThe investigation was subdivided into two experimental conditions, each with a different tree ensemble embedding. The first condition was a complete embedding of the GED by the tree ensemble from which t-SNE and UMAP generated a bi-dimensional prognostic map, revealing cancer patients’ population patterns. The second condition was a partial embedding obtained by training the tree ensemble on 75% of the data. This situation simulates the addition of 25% unseen patients to an existing model to verify the behavior of each analysis pipeline under extreme circumstances. Indeed, it is unlikely that the model will categorize a large cohort of patients all at once; consequently, the second experimental condition could be interpreted as a “stress test” to check model reliability compared to the baseline condition of fully embedded data.Demonstration of how the proposed analysis sequence leads to the creation of bi-dimensional prognostic maps to support medical decision-making Evaluation of a partial GED embedding to simulate the addition of new patients to an existing forest embedding Investigation of the parameter space for t-SNE and UMAP to highlight those that impact the low dimensional embedding and should be tuned when employing these techniquesTest the performance of a classification model on six classes of tumor outcomes using the original GED set  while scanning the configuration of parameters that optimizes data fitting. Traditionally parameter evaluation could be examined through linear regression to find non-deterministic linear relationships between parameter values . In the analysis . RegardiPrognosis prediction is still considered a challenge in bladder cancer . In thisTwo methodologies were employed to score the genes’ influence in predicting tumor stage and survival. It could be possible that genes of the set pre-selected by the database authors might have different involvement in the pathological status of the patients; thus, they could be evaluated concerning their importance in determining the disease outcome. Using the labels obtained during the complete embedding by the single and double discretization pipelines with t-SNE or UMAP and a forest of trees classifier, the genes after preprocessing Fig.  were ranGenerally, discretization transforms features closer to a knowledge-level representation than continuous data . In the ractions , and genMedical doctors may not be acquainted with machine learning techniques; consequently, an effective tool for GED interpretation might enhance the visual understanding of multi-dimensional GED datasets . Research presented by this study focuses on 2D t-SNE and UMAP reduction because we explored the possibility of producing discernible patterns in the data that summarize the disease progression in patients. Similar to other works in the literature, this investigation adopts an intermediate step to transform GED data distributions by decision tree embedding. This phase should pull out interesting characteristics in the input samples that are otherwise not directly observable. For example, pathways of associated genes or gene pairs with combined effects may be reflected by high correlations or network attributes . A diffeThe benefit of discretization was demonstrated by a supervised machine learning experiment that tried to classify the six classes of outcomes from the raw or preprocessed GED. While the original dataset resulted in a balanced accuracy slightly above the chance level (The relevance characterization using relative gene importance identified a subset of genes by their prominence in defining tumor stage and patient survival. Results of this machine learning experiment are included in Table Table This study evaluated if GED discretization approaches could be integrated into a new analysis pipeline extending patient identification by tumor stage and survival. Complete data embedding created precise prognostic maps suitable for data-driven medical decision-making. In a second numerical experiment using partially embedded data to simulate new patients’ inclusion in the model, performance seems stable only applying the Uniform double stage discretization sequence and UMAP non-linear reduction. Findings on both experimental conditions support using the UMAP technique in omics data analysis as emerging in recent literature on the same topic. However, a further investigation of UMAP parameter space did not identify a significant subset of relevant parameters to consider for speeding up algorithm tuning. This situation underlines the importance of adjusting multiple UMAP parameters for precision medicine studies. A machine learning procedure to establish gene importance in determining six classes of outcomes has been demonstrated through feature permutation or recursive feature elimination. Through this methodologies, a subset of relevant genes for bladder cancer prognosis has been identified. Another machine learning experiment showed how the classification of patients using the preprocessed data with single or double discretization pipelines achieved higher accuracy than unprocessed data. The numerical experiments in the current investigation testing three distinct preprocessing sequences based on single or double discretizations helped to discriminate more effectively six possible patients’ outcomes given a bladder cancer GED dataset from a cross-sectional study.