Spatial Datasets Research Articles

Abstract C028: Decreased heterogeneity in immune interactions in oral cavity squamous cell carcinoma patients with recurrence

Abstract There have been improvements in clinical risk stratifications for oral cavity squamous cell carcinoma (OSCC) with the AJCC 8th edition staging system. Other studies suggest the addition of a metric of specific immune interactions in and around the tumor may be helpful to further stratify patients, with mixed findings. The advent of spatial imaging has allowed a global view for assessing tumor features in the context of all surrounding cell interactions. We hypothesize that the lack of architectural information could contribute to varied clinical outcomes in previous studies, such as tumor recurrence. This highlights how spatial technologies could aid in understanding the role of tissue architecture and its influence on biology and medicine. To study tissue remodeling spatially, we focused on multiplexed-ion beam imaging (MIBI), a spatial proteomics platform to visualize 40 immuno-onco proteins simultaneously. Our banked patient cohort consists of 20 recurrent and 49 non-recurrent OSCC primary surgery samples. Clinical metadata capturing pathologic stage, alcohol use, smoking history, survival data, and more were also collected. With our pathologist, we curated 290 field-of-views (FOVs) of matched primary tumors, uninvolved lymph nodes, and metastatic lymph nodes (if any). Downstream computational analysis using existing MIBI codebases augmented with in-house spatial analytics has revealed a robust spatial dataset. After using machine-learning algorithms to cell segment our images, we validated these assigned cells and marker staining to further annotate cell populations. When we analyzed direct cell interactions, there was a decrease in heterogeneity in immune interactions within recurrent patients compared to non-recurrent patients. To ascertain how the loss of these interactions affected recurrence prognosis, we investigated their prevalence within the tumor border vicinity. We hypothesize that the proximity and cell-cell interactions near the border will promote a tumor-suppressive environment. Utilizing centroid-to-centroid distance measurements, we distilled the top 30 cell-cell interactions within a 100-micron distance from the tumor border. Cell pairs involving macrophage tissue-resident memory cells and CD8 T memory resident cells were more abundant in recurrent patients, while pairs involving CD4 T regulatory and CD8 cells were found in non-recurrent patients. Reassuringly, cell pairings involving myeloid cells were upregulated in the recurrent cohort as expected. Interestingly, cancer-associated fibroblast cell pairings were upregulated indiscriminately in both patient sub- cohorts. This suggests that the impact of certain known immunosuppressive changes may contribute in varying amounts to disease control. Our data demonstrates differences in the architecture of tumors with known divergent outcomes, which suggests global tissue-level changes could be a predictive factor. Clinically, interrogating these interactions of interest could guide treatment escalation or de-escalation in the future. Citation Format: Connie J. Zhou, Jane Lee, Rebecca Jaszczak, Steven R. Long, Patrick K. Ha, Matthew H. Spitzer. Decreased heterogeneity in immune interactions in oral cavity squamous cell carcinoma patients with recurrence [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C028.

SPathDB: a comprehensive database of spatial pathway activity atlas.

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis.

Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots. SDUCL integrates image features, spatial relationships and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments. https://github.com/WeiMin-Li-visual/SDUCL. Supplementary data are available at Bioinformatics online.

Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved.

BIOM-22. SPATIAL, SINGLE-CELL, AND BULK RNA SEQUENCING IDENTIFY MOLECULAR PROGRAMS UNDERLYING MENINGIOMA BRAIN INVASION

Abstract Brain invasion is a negative prognostic factor for meningiomas, but the molecular programs underlying meningioma brain invasion are poorly understood. To address this, we performed transcriptomic analyses of 3 independent datasets: Bulk RNA sequencing from 199 meningiomas (n=33 with brain invasion), single-cell RNA sequencing of 2 regionally distinct samples from the brain tumor interface (BTI) versus tumor core (TC) (n=22,118 single-cell transcriptomes), and spatial RNA sequencing from 6 samples with (n=4) versus without (n=2) brain invasion (n=18,471 spatial transcriptomes). Single-cell and spatial datasets were integrated with Harmony, cell interactions were defined using CellChat, and cross-platform comparisons were performed with the Seurat data integration pipeline. Bulk RNA sequencing revealed enrichment of 1835 genes in meningiomas with brain invasion compared to those without brain invasion, including glial markers (GFAP, NCAN) and markers of extracellular matrix remodeling (PDGFRA, COL9A1). Single-cell RNA sequencing comparison of BTI versus TC meningioma cells identified 2793 genes that were enriched in BTI cells, 268 of which were conserved across bulk and single-cell transcriptomes from brain-invasive meningiomas. Gene ontology enrichment analysis showed molecular programs underlying cell migration and regulation of neurogenesis at the BTI, while glycolysis and antigen presentation programs were enriched in the TC. Cholesterol, CSPG4, and LIF signaling were active in the TC, while SLIT signaling was enriched at the BTI. Spatial RNA sequencing showed enrichment of 439 genes at the BTI, and a conserved set of 12 genes were enriched in BTI meningioma cells across bulk, single-cell, and spatial datasets (TGM2, APOC1, IGFBP6, PLEKHO1, GFPT2, ZYX, IQGAP3, LUM, S100A11, ARPC1B, CDK1, CYCS). Comparison of tumor microenvironment monocytes at the BTI versus TC demonstrated enrichment of 1571 genes that were associated with macrophage migration and activation. These data elucidate molecular programs underlying meningioma brain invasion and provide insights into meningioma intratumor heterogeneity.

EPCO-46. SPATIAL TRANSCRIPTOMICS ANALYSIS OF GLIOBLASTOMA REVEALS THREE DISTINCT REGIONAL PROGRAMS OF T-CELL INFILTRATION

Abstract Glioblastoma (GBM) resists T-cell killing by developing an immunosuppressive tumor microenvironment. Transcriptomic technologies, such as single-cell RNA sequencing (scRNA-seq), have characterized immune cell phenotypes and putative cell-cell interactions in GBM. However, the tumor microenvironment surrounding T-cells is still not well characterized, motivating the need for spatial analyses. Two publicly-available GBM spatial transcriptomic datasets were analyzed, with one discovery cohort (N=18) and one validation cohort (N=12). Using cell deconvolution with a scRNA-seq reference dataset, areas of high T-cell infiltration were identified and clustered into regional programs based on transcriptomic signatures. To highlight which ligand-receptor interactions are most prominent in these regional programs, a novel spatial enrichment algorithm was applied. The impact of spatially enriched ligand-receptor interactions on overall survival was evaluated using bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA). Three distinct T-cell regional programs (transitional, immunosuppressive, and infiltrative) were found across both the discovery and validation cohort. The infiltrative T-cell regional program resided on the periphery of the tumor with a neural progenitor phenotype (NRGN, VSNL1, SLC17A7). In contrast, the immunosuppressive T-cell region had a higher proportion of malignant cells and a greater mesenchymal tumor signature (ANXA1, CD44, complement pathway). Furthermore, ligand-receptor enrichment analysis showed that the immunosuppressive region had higher tumor and tumor-associated macrophage (TAM) signaling towards CD4 T-cells along the collagen, midkine, and osteopontin pathways. TCGA survival analysis showed that higher collagen and midkine signals were associated with worse overall and disease free survival. Our results identified three distinct regional programs in GBM and highlight a set of signaling pathways associated with T-cell suppression. Our findings suggest that the disruption of these pathways could reduce immunosuppressive signals from tumor and TAMs to T-cells. Subsequent validation with spatial proteomics data in glioblastoma is ongoing.

EPCO-60. EXPRESSION OF EARLY PROGENITOR MARKERS WITHIN NF2-ASSOCIATED SPINAL EPENDYMOMA

Abstract Spinal ependymomas (SP-EPNs) are intramedullary spinal tumors commonly found in patients with NF2-related schwannomatosis. Recent work using bulkRNA sequencing has suggested that SP-EPNs express a molecular signature most similar to ependymal cells. However, this type of sequencing may miss rare stem cell populations within tumors. The aim of this study was to assess the spatial heterogeneity within NF2-associated SP-EPNs and explore whether a stem cell population exists within SP-EPNs. Spatial transcriptomics (ST) was performed on a SP-EPN sample previously resected from a patient with NF2-related schwannomatosis using the 10X Genomics Visium Cytassist Platform. Libraries were sequenced on the Illumina NovaSeq 6000. 28-bp reads including spatial barcode and UMI sequences and 50-bp probe reads were generated with Illumina NovaSeq 6000 at the Center for Medical Genomics at Indiana University School of Medicine. Space Ranger 2.1 was used to process the raw files after sequencing. Data analysis was performed with R package Seurat 4.9.9.9041 following preprocessing with spaceranger. Sctransform normalized the spatial transcriptome dataset. Clusters were identified using “FindClusters” and “FindNeighbors” functions. There was diffuse expression of astrocyte and ependymal cell markers throughout the SP-EPN sample with smaller areas expressing radial glia and stem cell markers, as well as overlap between these regions. Expression of vascular and connective tissue markers varied throughout the tumor. There was minimal to no expression of neural genes in the sample. ST on our SP-EPN sample revealed a tumor with marked cellular and molecular heterogeneity. Although the primarily expressed ependymal cell and astrocyte markers, there does appear to be small, stem cell-like populations. This suggests that NF2-associated SP-EPNs may have a developmental hierarchy with radial glia-like cells and more mature progenitors within the ependymal cell lineage. This proof-of-concept study offers important findings that provide justification for further investigation using single-cell sequencing.

Accuracy Assessment of an Image-Based Cloud-Based Indoor Mapping Platform as an Alternative for Desktop Applications

Abstract. The generation of 3D spatial datasets, especially in indoor space, has continually been a challenge in developing spatial applications that are aimed for the realistic representation of the real world. Despite their potential, the creation of 3D spatial data remains a challenge, particularly in terms of cost and complexity associated with traditional methods. While this method proves to be a reliable and accurate method of creating such data, this approach entails challenges in economic, computational, and human resource aspects. Alternatively, omnidirectional images have emerged as cost-effective alternatives for representing 3D spaces, offering a comprehensive field of view and facilitating the generation of structure and appearance representations through mesh-based 3D maps. This study evaluates the geometric accuracy of a cloud-based image-based mapping platform for generating 3D representations of indoor spaces, aiming to assess its suitability as an alternative to desktop-based platforms. We obtained 30 sample lines and compare respective measurements from each of the generated meshes to a ground truth value. Results show that while there are no significant differences in the mean errors, the mesh generated from a cloud-based platform produced minimal errors. This demonstrates the potential of this platform for generating 3D representations of indoor space, acceptable in both geometric accuracy and visualization capabilities.

GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data

The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.

Co‐production of a vulnerability assessment for aquatic and riparian ecosystems in the southwestern United States

AbstractTo address impacts of climate change and other stressors on stream ecosystems, managers must prioritize resources and locations for conservation actions to facilitate effective cross‐boundary solutions. Through a science management partnership, we co‐produced a spatially explicit landscape assessment framework for cold‐water fish habitat and riparian corridors in three large watersheds of the southwestern United States. Using literature review and stakeholder workshops, we developed indicators of vulnerability and built spatial datasets depicting areas of low to high vulnerability based on exposure, sensitivity, and adaptive capacity for each resource. We found that, in general, vulnerability was greater for cold‐water fish habitat than for riparian corridors. The Little Colorado River and San Juan River watersheds had the highest percentages of subwatersheds with high vulnerability of cold‐water fish habitat. Conversely, the Upper Rio Grande watershed had the highest percentage with high vulnerability of riparian corridors. Assessments like ours facilitate the management of water resources at the scale of watersheds or river basins and incorporate physical characteristics, land‐use history, current management practices, and status of imperiled species into actionable management plans.

Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/.

REVIEWS OF PERSPECTIVES ON CONTEMPORARY APPROACHES AND TECHNIQUES IN FLUVIAL SYSTEMS EVALUATION AND MANAGEMENT

This paper reviews fluvial geomorphology perspectives and methodologies for environmental sustainability by exploring and discussing major concepts, themes, and methodologies. To meet the multidimensional aspect of fluvial geomorphology research, multidimensional approaches are now used. Fluvial geomorphology examines river channel morphology and how it is shaped by fluid flows interacting with erodible or resistant boundaries. Understanding equilibrium conditions and thresholds is key. A systems perspective is needed - any river segment is influenced by upstream and downstream conditions as part of an integrated drainage basin system. Field surveys, mapping, remote sensing, and GIS provide important morphological data on river planform, cross-sections, longitudinal profile features, sediment characteristics, etc. Statistical analysis helps detect spatial patterns and trends. Assessing channel changes over long periods is often necessary to fully understand contemporary fluvial processes and forms. Sediment storage and release causes significant time lags. Major human impacts include land use changes affecting hydrology, sediment budgets, channel boundaries; and infrastructure interrupting continuity. Sustainable management requires addressing root causes. Watershed level processes like water balances, erosion patterns, etc. strongly influence fluvial systems - coordinating river channel and upland catchment interventions is key. Models like SWAT, MIKE-SHE, and ANSWERS simulate hydrological processes but have limitations. They require robust spatial datasets spanning geology, topography, land use, climate, etc. Overall, a multidimensional, scale-conscious, historically contextualized assessment framework is required for evaluating fluvial systems. The review highlights techniques and datasets for such integrated diagnosis.

A robust approach to Gaussian process implementation

Abstract. Gaussian process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes computationally intensive when the training spatial dataset has a large number of observations. To address this challenge, we introduce a scalable GP algorithm, termed MuyGPs, which incorporates nearest-neighbor and leave-one-out cross-validation during training. This approach enables the evaluation of large spatial datasets with state-of-the-art accuracy and speed in certain spatial problems. Despite these advantages, conventional quadratic loss functions used in the MuyGPs optimization, such as root mean squared error (RMSE), are highly influenced by outliers. We explore the behavior of MuyGPs in cases involving outlying observations and, subsequently, develop a robust approach to handle and mitigate their impact. Specifically, we introduce a novel leave-one-out loss function based on the pseudo-Huber function (LOOPH) that effectively accounts for outliers in large spatial datasets within the MuyGPs framework. Our simulation study shows that the LOOPH loss method maintains accuracy despite outlying observations, establishing MuyGPs as a powerful tool for mitigating unusual observation impacts in the large data regime. In the analysis of US ozone data, MuyGPs provides accurate predictions and uncertainty quantification, demonstrating its utility in managing data anomalies. Through these efforts, we advance the understanding of GP regression in spatial contexts.

Adding Highly Variable Genes to Spatially Variable Genes Can Improve Cell Type Clustering Performance in Spatial Transcriptomics Data.

Spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample's spatial context. Various methods have been developed for detecting spatially variable genes (SV genes), whose gene expression over the tissue space shows strong spatial autocorrelation. Such genes are often used to define clusters in cells or spots downstream. However, highly variable (HV) genes, whose quantitative gene expressions show significant variation from cell to cell, are conventionally used in clustering analyses. In this report, we investigate whether adding highly variable genes to spatially variable genes can improve the cell type clustering performance in spatial transcriptomics data. We tested the clustering performance of HV genes, SV genes, and the union of both gene sets (concatenation) on over 50 real spatial transcriptomics datasets across multiple platforms, using a variety of spatial and non-spatial metrics. Our results show that combining HV genes and SV genes can improve overall cell-type clustering performance.

SDFSD-v1.0: A Sub-Meter SAR Dataset for Fine-Grained Ship Detection

In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse. Existing SAR ship datasets mostly consist of medium to low resolution imagery, leading to coarse ship categories and limited background scenarios. We developed the “annotate entire image, then slice” workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. The data cover major ports and shipping routes globally, with varied and complex backgrounds, offering diverse annotation information. Several State-of-the-Art rotational detection models were used to evaluate the dataset, providing a baseline for ship detection and fine-grained ship detection. The SDFSD is a high spatial resolution ship detection dataset that could drive advancements in research on ship detection and fine-grained detection in SAR imagery.

Enhancing spatial domain detection in spatial transcriptomics with EnSDD

Advancements in spatial transcriptomics have transformed our understanding of organ function and tissue microenvironment. However, accurately identifying spatial domains to depict genome heterogeneity and cellular interactions remains a challenge. In this study, we propose EnSDD (Ensemble-learning for Spatial Domain Detection), a method that ingeniously integrates eight state-of-the-art spatial domain detection methods to automatically identify spatial domains. A key innovation of EnSDD is its dynamic weighting mechanism within the ensemble learning process, which optimizes the contribution of each base model and provides a performance evaluation metric without the need for ground truth data. By leveraging the spatial domains identified through EnSDD, we incorporate the detection of domain-specific spatially variable genes and the spatial distribution of cell types, thereby providing deeper insights into tissue heterogeneity. We validate EnSDD across diverse spatial transcriptomics datasets from various tissue organizational structures. Our results demonstrate that EnSDD significantly enhances spatial domain identification accuracy, identifies genes with spatial expression patterns, and reveals domain-specific cell type enrichment patterns, offering invaluable insights into tissue spatial heterogeneity and regionalization.

A General Method for Resampling Autocorrelated Spatial Data

AbstractComparing spatial data sets is a ubiquitous task in data analysis, however the presence of spatial autocorrelation means that standard estimates of variance will be wrong and tend to over‐estimate the statistical significance of correlations and other observations. While there are a number of existing approaches to this problem, none are ideal, requiring detailed analytical calculations, which are hard to generalize or detailed modeling of the data generating process, which may not be straightforward. In this work we propose an approach based on permuting or resampling at fixed spatial autocorrelation, measured by Moran's I, in order to generate a null model that accounts for spatial dependence. Testing on real and synthetic data, we find that, as long as the spatial autocorrelation is not too strong, this approach works just as well as if we knew the data generating process exactly and allows us to compute P‐values with the correct Type‐I error rate.

ScTML: a pan-cancer single-cell landscape of multiple mutation types.

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

Enhancing local-scale groundwater quality predictions using advanced machine learning approaches

Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014–2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca2⁺), magnesium (Mg2⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO₄2⁻), chloride (Cl⁻), nitrate (NO₃⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R2, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R2 greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R2 greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.

Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Participants (N=2394) of the Beiwe Substudy of the Nurses' Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants' key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.