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Background Advancements in single-cell RNA sequencing have enabled the analysis of millions of cells, but integrating such data across samples and methods while mitigating batch effects remains challenging. Deep learning approaches address this by learning biologically conserved gene expression representations, yet systematic benchmarking of loss functions and integration performance is lacking. Results We evaluate 16 integration methods using a unified variational autoencoder framework, incorporating batch and cell-type information. Results reveal limitations in the single-cell integration benchmarking index (scIB) for preserving intra-cell-type information. To address this, we introduce a correlation-based loss function and enhance benchmarking metrics to better capture biological conservation. Using cell annotations from lung and breast atlases, our approach improves biological signal preservation. We propose a refined integration framework, scIB-E, and metrics that provide deeper insights into the integration process and offer guidance for advanced developments in integrating increasingly complex single-cell data. Conclusions This benchmark highlights the potential of deep learning-based approaches for single-cell data integration, emphasizing the importance of biologically informed metrics and improved benchmarking strategies.

Diverse machine learning methods promise to forecast gene expression changes in response to novel genetic perturbations. However, these methods’ accuracy is not well characterized. We created a benchmarking platform that combines a panel of 11 large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to a wide variety of methods. We used our platform to assess methods, parameters, and sources of auxiliary data, finding that it is uncommon for expression forecasting methods to outperform simple baselines. Our platform will serve as a resource to improve methods and to identify contexts in which expression forecasting can succeed.

Background Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes (SVGs). Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Results Here, we systematically evaluate 14 methods using 96 spatial datasets and 6 metrics. We compare the methods regarding gene ranking and classification based on real spatial variation, statistical calibration, and computation scalability and investigate the impact of identified SVGs on downstream applications such as spatial domain detection. Finally, we explore the applicability of the methods to spatial ATAC-seq data to examine their effectiveness in identifying spatially variable peaks (SVPs). Overall, SPARK-X outperforms other benchmarked methods and Moran’s I achieves a competitive performance, representing a strong baseline for future method development. Moreover, our results reveal that most methods are poorly calibrated, and more specialized algorithms are needed to identify spatially variable peaks. Conclusions Our benchmarking provides a detailed comparison of SVG detection methods and serves as a reference for both users and method developers.

Background Differences in data distribution, feature dimensions, and quality between different single-cell modalities pose challenges for clustering. Although clustering algorithms have been developed for single-cell transcriptomic or proteomic data, their performance across different omics data types and integration scenarios remains poorly investigated, which limits the selection of methods and future method development. Results In this study, we conduct a systematic and comparative benchmark analysis of 28 computational algorithms on 10 paired transcriptomic and proteomic datasets, evaluating their performance across various metrics in terms of clustering, peak memory, and running time. We also discuss the impact of highly variable genes (HVGs) and cell type granularity on clustering performance. Additionally, the robustness of these clustering methods on two kinds of omics is evaluating by using 30 simulated datasets. Furthermore, to explore the benefits of integrating omics information for clustering tasks, we integrate single-cell transcriptomic and proteomic data using 7 state-of-the-art integration methods and assess the performance of existing single-omics clustering schemes on the integrated features. Conclusions Our findings reveal modality-specific strengths and limitations, highlight the complementary nature of existing methods, and provide actionable insights to guide the selection of appropriate clustering approaches for specific scenarios. Overall, for top performance across two omics, consider scAIDE, scDCC, and FlowSOM, with FlowSOM also offering excellent robustness. For users prioritizing memory efficiency scDCC and scDeepCluster are recommended, while TSCAN, SHARP, and MarkovHC are recommended for users who prioritize time efficiency, and community detection-based methods offer a balance.

Background Spatial transcriptomics preserves spatial context of tissues while capturing gene expression. As the technology advances, researchers are increasingly generating data from multiple tissue sections, creating a growing demand for multi-slice integration methods. These methods aim to generate spatially aware embeddings that jointly capture spatial and transcriptomic information, preserving biological signals while mitigating technical artifacts such as batch effects. However, the reliability of these methods varies, and the growing diversity of technologies makes integration even more challenging. This underscores the need for a comprehensive benchmark to evaluate their performance, which is still lacking. Results To systematically evaluate the performance of multi-slice integration methods, we propose a comprehensive benchmarking framework covering four key tasks that form an upstream-to-downstream pipeline: multi-slice integration, spatial clustering, spatial alignment, slice representation. For each task, we perform detailed analyses of the methods and provide actionable recommendations. Our results reveal substantial data-dependent variation in performance across tasks. We further investigate the relationships between upstream and downstream tasks, showing that downstream performance often depends on upstream quality. Conclusions Our study provides a comprehensive benchmark of 12 multi-slice integration methods across four key tasks using 19 diverse datasets. Our results reveal that method performance is highly dependent on application context, dataset size, and technology. We also identified strong interdependencies between upstream and downstream tasks, highlighting the importance of robust early-stage analysis.

Background/Objectives: Skin cancer is one of the most common diseases in the world, whose early and accurate detection can have a survival rate more than 90% while the chance of mortality is almost 80% in case of late diagnostics. Methods: A modified EfficientNet-B0 is developed based on mobile inverted bottleneck convolution with squeeze and excitation approach. The 3 × 3 convolutional layer is used to capture low-level visual features while the core features are extracted using a sequence of Mobile Inverted Bottleneck Convolution blocks having both 3 × 3 and 5 × 5 kernels. They not only balance fine-grained extraction with broader contextual representation but also increase the network’s learning capacity while maintaining computational cost. The proposed architecture hyperparameters and extracted feature vectors of standard benchmark datasets (HAM10000, ISIC 2019 and MSLD v2.0) of dermoscopic images are optimized with the quantum-behaved particle swarm optimization algorithm (QBPSO). The merit function is formulated by the training loss given in the form of standard classification cross-entropy with label smoothing, mean fitness value (mfval), average accuracy (mAcc), mean computational time (mCT) and other standard performance indicators. Results: Comprehensive scenario-based simulations were performed using the proposed framework on a publicly available dataset and found an mAcc of 99.62% and 92.5%, mfval of 2.912 × 10−10 and 1.7921 × 10−8, mCT of 501.431 s and 752.421 s for HAM10000 and ISIC2019 datasets, respectively. The results are compared with state of the art, pre-trained existing models like EfficentNet-B4, RegNetY-320, ResNetXt-101, EfficentNetV2-M, VGG-16, Deep Lab V3 as well as reported techniques based on Mask RCCN, Deep Belief Net, Ensemble CNN, SCDNet and FixMatch-LS techniques having varying accuracies from 85% to 94.8%. The reliability of the proposed architecture and stability of QBPSO is examined through Monte Carlo simulation of 100 independent runs and their statistical soundings. Conclusions: The proposed framework reduces diagnostic errors and assists dermatologists in clinical decisions for an improved patient outcomes despite the challenges like data imbalance and interpretability.