The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE.

MOTIVATION: Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. RESULTS: To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.

This research explores the complex yet applicable domain of universal visual recognition, where unlabeled datasets may encompass additional, as-of-yet unidentified classes and present a skewed feature distribution within labeled datasets. The primary challenge lies in concurrently distinguishing instances of known classes and uncovering new classes within the unlabeled data. This difficulty is compounded by two factors: category shift and distribution shift. Current methodologies typically overlook the potential of harnessing the inherent structural relationship between labeled and unlabeled datasets, and they fail to effectively isolate the semantic variability of the newly identified classes from the established ones. To overcome these hurdles, we introduce an innovative framework, which we have termed MESA (short for Manifold structure Exploitation with double Spaces dual Alignment), to advance the field of universal visual recognition. Our approach begins by identifying geometrically and semantically related pairs of nearest neighbors between labeled and unlabeled data, which serve as anchor points to foster an intrinsic match between the two datasets. By integrating an affinity score for similarity, we establish a novel cross-view, anchor-guided, self-supervised learning paradigm. This model is designed to spread the learned labels’ information and to conjoin novel semantic insights within the predictive space. To intensify the matching accuracy for the known classes and enhance the differentiation of novel classes, we further implement a confident-prototype-driven, self-supervised learning paradigm in a cross-view manner. This strategy is aimed at revealing the macroscopic category configuration within the embedding space. MESA employs a pioneering bidirectional dual-alignment mechanism that operates simultaneously in the embedding and prediction spaces, hence providing a more robust approach to confronting challenges brought about by distribution and category shifts. Our exhaustive evaluation across several extensive image recognition benchmarks substantiates MESA’s superior performance over the latest cutting-edge methods, marking a significant leap forward in the sphere of universal visual recognition. Finally, MESA is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/AimeeStar/MESA.

The rapid development of single-cell RNA sequencing (scRNA-seq) technology allows us to study gene expression heterogeneity at the cellular level. Cell annotation is the basis for subsequent downstream analysis in single-cell data mining. Existing methods rarely explore the fine-grained semantic knowledge of novel cell types absent from the reference data and usually susceptible to batch effects on the classification of seen cell types. Taking into consideration these limitations, this paper proposes a new and practical task called generalized cell type annotation and discovery for scRNA-seq data. In this task, cells of seen cell types are given class labels, while cells of novel cell types are given cluster labels instead of a unified “unassigned” label. To address this problem, we carefully design a comprehensive evaluation benchmark and propose a novel end-to-end algorithm framework called scGAD. Specifically, scGAD first builds the intrinsic correspondence across the reference and target data by retrieving the geometrically and semantically mutual nearest neighbors as anchor pairs. Then we introduce an anchor-based self-supervised learning module with a connectivity-aware attention mechanism to facilitate model prediction capability on unlabeled target data. To enhance the inter-type separation and intra-type compactness, we further propose a confidential prototypical self-supervised learning module to uncover the consensus category structure of the reference and target data. Extensive results on massive real datasets demonstrate the superiority of scGAD over various state-of-the-art clustering and annotation methods.

The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. Cell type annotation plays an essential role in the substantial downstream analysis of scRNA-seq data. Existing methods usually classify the novel cell types in target data as an “unassigned” group and rarely discover the fine-grained cell type structure among them. Besides, these methods carry risks, such as susceptibility to batch effect between reference and target data, thus further compromising of inherent discrimination of target data. Considering these limitations, here we propose a new and practical task called realistic cell type annotation and discovery for scRNA-seq data. In this task, cells from seen cell types are given class labels, while cells from novel cell types are given cluster labels. To tackle this problem, we propose an end-to-end algorithm framework called scPOT from the perspective of optimal transport (OT). Specifically, we first design an OT-based prototypical representation learning paradigm to encourage both global discriminations of clusters and local consistency of cells to uncover the intrinsic structure of target data. Then we propose an unbalanced OT-based partial alignment strategy with statistical filling to detect the cells from the seen cell types across reference and target data. Notably, scPOT also introduces an easy yet effective solution to automatically estimate the overall cell type number in target data. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scPOT over various state-of-the-art clustering and annotation methods.

The rapid development of single-cell RNA sequencing (scRNA-seq) technology allows us to study gene expression heterogeneity at the cellular level. Cell annotation is the basis for subsequent downstream analysis in single-cell data mining. As more and more well-annotated scRNA-seq reference data become available, many automatic annotation methods have sprung up in order to simplify the cell annotation process on unlabeled target data. However, existing methods rarely explore the fine-grained semantic knowledge of novel cell types absent from the reference data, and they are usually susceptible to batch effects on the classification of seen cell types. Taking into consideration the limitations above, this paper proposes a new and practical task called generalized cell type annotation and discovery for scRNA-seq data whereby target cells are labeled with either seen cell types or cluster labels, instead of a unified 'unassigned' label. To accomplish this, we carefully design a comprehensive evaluation benchmark and propose a novel end-to-end algorithmic framework called scGAD. Specifically, scGAD first builds the intrinsic correspondences on seen and novel cell types by retrieving geometrically and semantically mutual nearest neighbors as anchor pairs. Together with the similarity affinity score, a soft anchor-based self-supervised learning module is then designed to transfer the known label information from reference data to target data and aggregate the new semantic knowledge within target data in the prediction space. To enhance the inter-type separation and intra-type compactness, we further propose a confidential prototype self-supervised learning paradigm to implicitly capture the global topological structure of cells in the embedding space. Such a bidirectional dual alignment mechanism between embedding space and prediction space can better handle batch effect and cell type shift. Extensive results on massive simulation datasets and real datasets demonstrate the superiority of scGAD over various state-of-the-art clustering and annotation methods. We also implement marker gene identification to validate the effectiveness of scGAD in clustering novel cell types and their biological significance. To the best of our knowledge, we are the first to introduce this new and practical task and propose an end-to-end algorithmic framework to solve it. Our method scGAD is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scGAD.

MOTIVATION: The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. The identification of cell types plays an essential role in the analysis of scRNA-seq data, which, in turn, influences the discovery of regulatory genes that induce heterogeneity. As the scale of sequencing data increases, the classical method of combining clustering and differential expression analysis to annotate cells becomes more costly in terms of both labor and resources. Existing scRNA-seq supervised classification method can alleviate this issue through learning a classifier trained on the labeled reference data and then making a prediction based on the unlabeled target data. However, such label transference strategy carries with risks, such as susceptibility to batch effect and further compromise of inherent discrimination of target data. RESULTS: In this article, inspired by unsupervised domain adaptation, we propose a flexible single cell semi-supervised clustering and annotation framework, scSemiCluster, which integrates the reference data and target data for training. We utilize structure similarity regularization on the reference domain to restrict the clustering solutions of the target domain. We also incorporates pairwise constraints in the feature learning process such that cells belonging to the same cluster are close to each other, and cells belonging to different clusters are far from each other in the latent space. Notably, without explicit domain alignment and batch effect correction, scSemiCluster outperforms other state-of-the-art, single-cell supervised classification and semi-supervised clustering annotation algorithms in both simulation and real data. To the best of our knowledge, we are the first to use both deep discriminative clustering and deep generative clustering techniques in the single-cell field. AVAILABILITYAND IMPLEMENTATION: An implementation of scSemiCluster is available from https://github.com/xuebaliang/scSemiCluster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.