scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization.

MOTIVATION: scATAC-seq has enabled chromatin accessibility landscape profiling at the single-cell level, providing opportunities for determining cell-type-specific regulation codes. However, high dimension, extreme sparsity, and large scale of scATAC-seq data have posed great challenges to cell-type identification. Thus, there has been a growing interest in leveraging the well-annotated scRNA-seq data to help annotate scATAC-seq data. However, substantial computational obstacles remain to transfer information from scRNA-seq to scATAC-seq, especially for their heterogeneous features. RESULTS: We propose a new transfer learning method, scNCL, which utilizes prior knowledge and contrastive learning to tackle the problem of heterogeneous features. Briefly, scNCL transforms scATAC-seq features into gene activity matrix based on prior knowledge. Since feature transformation can cause information loss, scNCL introduces neighborhood contrastive learning to preserve the neighborhood structure of scATAC-seq cells in raw feature space. To learn transferable latent features, scNCL uses a feature projection loss and an alignment loss to harmonize embeddings between scRNA-seq and scATAC-seq. Experiments on various datasets demonstrated that scNCL not only realizes accurate and robust label transfer for common types, but also achieves reliable detection of novel types. scNCL is also computationally efficient and scalable to million-scale datasets. Moreover, we prove scNCL can help refine cell-type annotations in existing scATAC-seq atlases. AVAILABILITY AND IMPLEMENTATION: The source code and data used in this paper can be found in https://github.com/CSUBioGroup/scNCL-release.

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity.

MOTIVATION: Integration of growing single-cell RNA sequencing datasets helps better understand cellular identity and function. The major challenge for integration is removing batch effects while preserving biological heterogeneities. Advances in contrastive learning have inspired several contrastive learning-based batch correction methods. However, existing contrastive-learning-based methods exhibit noticeable ad hoc trade-off between batch mixing and preservation of cellular heterogeneities (mix-heterogeneity trade-off). Therefore, a deliberate mix-heterogeneity trade-off is expected to yield considerable improvements in scRNA-seq dataset integration. RESULTS: We develop a novel contrastive learning-based batch correction framework, CIAIRE, which achieves superior mix-heterogeneity trade-off. The key contributions of CLAIRE are proposal of two complementary strategies: construction strategy and refinement strategy, to improve the appropriateness of positive pairs. Construction strategy dynamically generates positive pairs by augmenting inter-batch mutual nearest neighbors (MNN) with intra-batch k-nearest neighbors (KNN), which improves the coverage of positive pairs for the whole distribution of shared cell types between batches. Refinement strategy aims to automatically reduce the potential false positive pairs from the construction strategy, which resorts to the memory effect of deep neural networks. We demonstrate that CLAIRE possesses superior mix-heterogeneity trade-off over existing contrastive learning-based methods. Benchmark results on six real datasets also show that CLAIRE achieves the best integration performance against eight state-of-the-art methods. Finally, comprehensive experiments are conducted to validate the effectiveness of CLAIRE. AVAILABILITY AND IMPLEMENTATION: The source code and data used in this study can be found in https://github.com/CSUBioGroup/CLAIRE-release. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.

Single-cell RNA-sequencing technology (scRNA-seq) brings research to single-cell resolution. However, a major drawback of scRNA-seq is large sparsity, i.e. expressed genes with no reads due to technical noise or limited sequence depth during the scRNA-seq protocol. This phenomenon is also called 'dropout' events, which likely affect downstream analyses such as differential expression analysis, the clustering and visualization of cell subpopulations, cellular trajectory inference, etc. Therefore, there is a need to develop a method to identify and impute these dropout events. We propose Bubble, which first identifies dropout events from all zeros based on expression rate and coefficient of variation of genes within cell subpopulation, and then leverages an autoencoder constrained by bulk RNA-seq data to only impute those values. Unlike other deep learning-based imputation methods, Bubble fuses the matched bulk RNA-seq data as a constraint to reduce the introduction of false positive signals. Using simulated and several real scRNA-seq datasets, we demonstrate that Bubble enhances the recovery of missing values, gene-to-gene and cell-to-cell correlations, and reduces the introduction of false positive signals. Regarding some crucial downstream analyses of scRNA-seq data, Bubble facilitates the identification of differentially expressed genes, improves the performance of clustering and visualization, and aids the construction of cellular trajectory. More importantly, Bubble provides fast and scalable imputation with minimal memory usage.

A deep matrix factorization based approach for single-cell RNA-seq data clustering.

The rapid development of single-cell sequencing technologies makes it possible to analyze cellular heterogeneity at the single-cell level. Cell clustering is one of the most fundamental and common steps in the heterogeneity analysis. However, due to the high noise level, high dimensionality and high sparsity, accurate cell clustering is still challengeable. Here, we present DeepCI, a new clustering approach for scRNA-seq data. Using two autoencoders to obtain cell embedding and gene embedding, DeepCI can simultaneously learn cell low-dimensional representation and clustering. In addition, the recovered gene expression matrix can be obtained by the matrix multiplication of cell and gene embedding. To evaluate the performance of DeepCI, we performed it on several real scRNA-seq datasets for clustering and visualization analysis. The experimental results show that DeepCI obtains the overall better performance than several popular single cell analysis methods. We also evaluated the imputation performance of DeepCI by a dedicated experiment. The corresponding results show that the imputed gene expression of known specific marker genes can greatly improve the accuracy of cell type classification.

GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets.

Integration of single-cell transcriptome datasets from multiple sources plays an important role in investigating complex biological systems. The key to integration of transcriptome datasets is batch effect removal. Recent methods attempt to apply a contrastive learning strategy to correct batch effects. Despite their encouraging performance, the optimal contrastive learning framework for batch effect removal is still under exploration. We develop an improved contrastive learning-based batch correction framework, GLOBE. GLOBE defines adaptive translation transformations for each cell to guarantee the stability of approximating batch effects. To enhance the consistency of representations alignment, GLOBE utilizes a loss function that is both hardness-aware and consistency-aware to learn batch effect-invariant representations. Moreover, GLOBE computes batch-corrected gene matrix in a transparent approach to support diverse downstream analysis. Benchmarking results on a wide spectrum of datasets show that GLOBE outperforms other state-of-the-art methods in terms of robust batch mixing and superior conservation of biological signals. We further apply GLOBE to integrate two developing mouse neocortex datasets and show GLOBE succeeds in removing batch effects while preserving the contiguous structure of cells in raw data. Finally, a comprehensive study is conducted to validate the effectiveness of GLOBE.

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement.

Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.

Rational Design of Zirconium-doped Titania Photocatalysts with Synergistic Brønsted Acidity and Photoactivity.

The preparation of photocatalysts with high activities under visible-light illumination is challenging. We report the rational design and construction of a zirconium-doped anatase catalyst (S-Zr-TiO2 ) with Brønsted acidity and photoactivity as an efficient catalyst for the degradation of phenol under visible light. Electron microscopy images demonstrate that the zirconium sites are uniformly distributed on the sub-10 nm anatase crystals. UV-visible spectrometry indicates that the S-Zr-TiO2 is a visible-light-responsive catalyst with narrower band gap than conventional anatase. Pyridine-adsorption infrared and acetone-adsorption 13 C NMR spectra confirm the presence of Brønsted acidic sites on the S-Zr-TiO2 sample. Interestingly, the S-Zr-TiO2 catalyst exhibits high catalytic activity in the degradation of phenol under visible-light illumination, owing to a synergistic effect of the Brønsted acidity and photoactivity. Importantly, the S-Zr-TiO2 shows good recyclability.