Title: Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data
Venue: NeurIPS 2024
Accepted: yes
Keywords: Computional Biology

Semi-supervised Learning

Transfer Learning
Abstract: Knowledge transfer between multi-omic single-cell data aims to effectively transfer cell types from scRNA-seq data to unannotated scATAC-seq data. Several approaches aim to reduce the heterogeneity of multi-omic data while maintaining the discriminability of cell types with extensive annotated data. However, in reality, the cost of collecting both a large amount of labeled scRNA-seq data and scATAC-seq data is expensive. Therefore, this paper explores a practical yet underexplored problem of knowledge transfer across multi-omic single-cell data under cell type scarcity. To address this problem, we propose a semi-supervised knowledge transfer framework named Dual label scArcity elimiNation with Cross-omic multi-samplE Mixup (DANCE). To overcome the label scarcity in scRNA-seq data, we generate pseudo-labels based on optimal transport and merge them into the labeled scRNA-seq data. Moreover, we adopt a divide-and-conquer strategy which divides the scATAC-seq data into source-like and target-specific data. For source-like samples, we employ consistency regularization with random perturbations while for target-specific samples, we select a few candidate labels and progressively eliminate incorrect cell types from the label set for additional supervision. Next, we generate virtual scRNA-seq samples with multi-sample Mixup based on the class-wise similarity to reduce cell heterogeneity. Extensive experiments on many benchmark datasets suggest the superiority of our DANCE over a series of state-of-the-art methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel method to address the knowledge transfer challenge in multi-omic single-cell data. Specifically, it focuses on scenarios where annotations are available partially in one modality, namely scRNA-seq data, and aims to infer annotations in another modality, the scATAC-seq data, without requiring paired datasets.\n\nThe authors proposed to use a shared encoder to project the two modalities, with additional compoenents including: optimal transport-based dataset expansion for scRNA-seq data, divide and conpuer for target scATAV-seq data, and cross-omic multi-sample mixup. \n\nThe author conducted comprehensive comparison with SOTA method and showed that the proposed method achieves the best performance.\n\nSTRENGTHS:\nOverall, the proposed approach is innovative, and the methodology section is well-structured. An ablation study confirms that each proposed component contributes significantly to the overall superior performance of the methology.\n\nWEAKNESSES:\nI'm not entirely convinced about the prevalence of the problem setting described by the author, where only a small subset of the scRNA-seq data is annotated. It may occur in scenarios such as large experiments conducted in batches, where only certain batches are annotated. It raises questions about the experimental setup. Are annotations removed randomly or based on specific conditions or batches? I wonder how does the availability of annotated subsets (randomly or by batch) affect the overall study results.\n\nQUESTIONS:\nI wonder how the methodology comparision is conducted. As the authors argued in the paper, the SOTA methods in the experimental studies does not deal with scenarios where the scRNA-seq is only partially annotated. Therefore, in the comparison experiment, such as in the \"low label ratio\" setup, is it that only the 1% of the scRNA-seq is used for cross-modality matching? There are popular existing methods such as Seurat and Harmony that provided a potential two-step approach: first learn the full annotation on scRNA-seq, and then transfer to scATAC-seq, which might be a more fair comparison.\n\nLIMITATIONS:\nThe authors have addressed limitations such as only applicable to open-set settings.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a label transfer method from scRNA-seq to scATAC-seq data. Based on the heterogeneity of single-cell data, this work partitions data into several groups and designs effective strategies to tackle them respectively. Experiments demonstrate the effectiveness of the proposed method.\n\nSTRENGTHS:\n1. The proposed method is technically sound, outperforming existing methods on two scRNA-seq and scATAC-seq integration datasets.\n2. Ablation studies demonstrate the effectiveness of each component in the proposed method.\n3. The paper is well written and clearly organized in general, which is easy to read and follow.\n\nWEAKNESSES:\n1. In the Introduction, the authors claim that \"a small fraction of scRNA-seq data with cell types annotated\" aligns more closely with practical scenarios. Do you have any evidence to support the claim? You may provide an example to show that scRNA-seq data are not processed and annotated as a whole, but only a small portion is annotated.\n2. The uniform distribution assumption in the optimal transport process could be wrong, as most scRNA-seq data are unbalanced across different cell types.\n3. In Eq. 8, the negative cell types are removed with a significant difference from the cell type with the highest probability. However, does a threshold of 1e-3 represent significant differences?\n4. What does $S_i$ in Eq. 10 mean?\n5. What is the motivation of mixing scRNA-seq data to form scATAC-seq data? Is such an operation biologically reasonable?\n6. Typo: In line 253 cBridge -> scBridge\n7. Currently the author split the MouseAtlas dataset into several subsets with different combinations for evaluation. I recommend the author provide the experimental results on the full dataset.\n\nQUESTIONS:\nPlease refer to my concerns in the weaknesses section.\n\nLIMITATIONS:\nNo significant limitations of this work are found.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a semi-supervised knowledge transfer framework called DANCE, designed to effectively transfer cell type annotations from scRNA-seq data to unannotated scATAC-seq data under conditions of label scarcity. It is similar to the unsupervised domain adaptation task in computer vision. DANCE addresses the challenge of heterogeneous multi-omic data by generating pseudo-labels based on optimal transport, employing a divide-and-conquer strategy, and using cross-omic multi-sample Mixup to reduce cell heterogeneity. Extensive experiments demonstrate DANCE's superiority over state-of-the-art methods.\n\nSTRENGTHS:\n1. The paper is well-motivated and seems to be reproducible.\n2. The paper is well-structured and easy to follow.\n3. The theory involved in the method seems relatively solid.\n4. The experiment fully proves the effectiveness and superiority of the DANCE.\n\nWEAKNESSES:\n1. The compared methods, especially the DA method, are relatively old. Comparison with newer methods (e.g., [a], [b], etc.) helps to understand the performance of the methods.\n2. Repeated use of symbols may cause confusion and misunderstanding among readers. Eq. (1), (3), (16), and Theory 3.2 all contain $\\lambda$. As far as I understand, the meanings of these symbols may be different.\n3. The method includes too many empirical hyperparameters, and the ‘crucial’ parameters selected by parameter analysis seem not comprehensive enough. A more comprehensive description of each $\\lambda$ and threshold $\\tao$ will help to understand the method and promote future work.\n4. The quality of images seems to be low, affecting comprehension. To be specific, the method process is not clearly and comprehensively shown in Figure 1. The text in Figure 2 is too small.\n\n[a] Semi-Supervised Domain Adaptation with Source Label Adaptation\n[b] COT: Unsupervised Domain Adaptation with Clustering and Optimal Transport\n\nQUESTIONS:\nPlease refer to the strengths and weaknesses of the paper. In addition, in my opinion, the proposed method seems to be a patchwork of methods in domain adaptation, cross-modal alignment, etc. Maybe its technical innovation is questionable. A more targeted and detailed description is recommended.\n\nLIMITATIONS:\nThe author has explained the limitations in Sec. 5.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenge of knowledge transfer across multi-omic single-cell data under label scarcity. The proposed semi-supervised framework, DANCE, uses optimal transport to generate pseudo-labels and a divide-and-conquer strategy for handling scATAC-seq data. The framework demonstrates superior performance on benchmark datasets, offering a practical solution to label scarcity in multi-omic data.\n\nSTRENGTHS:\n**Pros:**\n1. The introduction of optimal transport (OT) into the single-cell domain effectively addresses label scarcity and imbalance issues, supported by ablation studies.\n2. The divide-and-conquer strategy for scATAC-seq data and Mixup to alleviate cellular heterogeneity are well-handled.\n3. The performance gain is impressive, showing improvements as label availability increases.\n4. The paper is well-written and organized, making it enjoyable to read.\n\nWEAKNESSES:\n**Cons:**\n1. The exclusion of OT in scATAC-seq data due to cellular heterogeneity needs practical examples or quantitative analysis to substantiate its significance.\n2. The initial prediction's dependency in the divide-and-conquer strategy could lead to misclassification. Discussion on handling wrongly divided samples or providing statistics is needed.\n3. Exploring if DANCE can be conducted in the opposite direction (scATAC-seq with OT and scRNA-seq with divide-and-conquer) would be beneficial.\n4. Discussion on the complexity of OT and divide-and-conquer in terms of memory and time compared to existing studies is required.\n5. Including a discussion on the use of scCLIP [1] in scenarios with scarce labels would enhance the paper.\n\n[1] https://openreview.net/forum?id=KMtM5ZHxct\n\nQUESTIONS:\nSee the Weaknesses above.\n\nLIMITATIONS:\nI do not observer any potential negative societal impact of this work.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-31"}