Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data
Authors: Fan Zhang
Tianyu Liu
Zihao Chen
Xiaojiang Peng
Chong Chen
Xian-Sheng Hua
Xiao Luo
Hongyu Zhao
Venue: NeurIPS 2024 poster (2024)
Keywords: Computional Biology
Semi-supervised Learning
Transfer Learning
Primary area: machine_learning_for_healthcare
TL;DR: We study the problem of semi-supervised knowledge transfer across multi-omic single-cell data and propose a novel framework named DNACE for the problem.

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.

Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data
Fan Zhang', Tianyu Liu', Zihao Chen², Xiaojiang Peng', Chong Chen', Xian-Sheng Hua?, Xiao Luo Hongyu Zhao Georgia Institute of Technology. Yale University, &Peking University, SShenzhen Technology University, TTerminus Group, University of California, Los Angeles fanzhangdgatech. edu, tianyu. liu@yale. edu, g.e. challenger@pku. edu. cn pengxiaoj edu. en, chenchong. czăgmail. com, buaxiansheng@@mail.. com xiaoluo@cs. ucla. edu, hongyu . zhao@yale edu
Abstract
Knowledge transfer between multi-omic single-cell data aims effectively transfer cell types from scRNA-seu data to unannotated ScATAC-seq data. Several ap- proaches 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--eq data is expensive. Therefore, this paper explores practical yet underexplored problem of knowledge transfer across multi-omic single-cell data under cell type scarcity. To address this problem, we propose 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 pscudo-labels based on optimal transport and merge them into the labeled scRNA- seq data. Moreover, SeATAC-seq data into source-like and target-specific data. For source- samples, 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 for additional supervision. Next, gener- ate virtual scRNA-seq samples with multi-sample Mixup based on the class-wise similarity to reduce cell heterogeneity. Extensive experiments mark datasets suggest the superiority of our DANCE over a series of state-of-the-aee methods. Code available at htthhtp::/// bttttps:/giitub..oom cotttubb.ccm///f/////A/C
adopt divide-and-conge
strategy which divides the
many bench-
1 Introduction
In the realm of biology and medicine, many experimental methods [88 25
53
63 using 51
54
high-throughput sequencing technologies have emerged and characterize diverse properties of single cells. The predominant techniques are single-cell RNA-sequencing (scR NA-sq)
[55]
88
49] and
single-cell ATAC-sequencing (scATAC-seq) [I5] 75) for understanding complicated organisms and tissues at the single cell level. Specifically, SCAA-Se an epigenomic profiling method designed to assess chromatin accessibility and provides an additional layer of information that complements scRNA-seq. enhancing the ability comprehend epigenetic heterogeneity within complex tissues. However, identifying the cell types of ScATAC-seq data is challenging because of the high sparsity, large dimensionality, and increasing scale of scATAC-seq data. Considering the lack of cell type have been proposed to transfer cell
annotations for seATAC-seq data, several methods 38 36
*Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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types from fully-labeled scRNA-seg data to scATAC-seg date Among these methods, scloint [8 introduces scalable transfer learning framework that successtully incorporates atlas-scale scRNA- seq with ScATAC-seq data based on kNN techniques. scBridge [36] integrates multi-omic data heterogeneously by identifying reliable ScATAC-seq cells with smaller omic differences in comparison to scRNA-seq cells. ScNCI. [72] employs prior domain knowledge along with contrastive learning to address the challenge of heterogeneous features from different modalities.
Despite significant progress made by the aforementioned approaches, their success relies on a substantial amount of annotated scRNA-seq data. In reality, annotating both scRNA-seq data and scATAC-seq data is expensive and challenging. Therefore, this study explores practical yet unexplored problem of knowledge transfer across multi-omic single-cell data under label scarcity in both scRNA-seq data and scATAC-seq data. In this study, only a small fraction of scRNA-se -eq data with cell types annotated, while a significant portion of both scRNA- -eq data and ScATAC-seq data lack annotations. In contrast to previous works assuming abundant labeled scRNA-seq data, this setting aligns more closely with practical scenarios.
Addressing this realistic problem requires the consideration of two crucial aspects, Firstly, how to learn representations with distinct cell type discriminability under label scarcity in both scRNA-seq data and ScATAC-seq data? Learning representations with good discriminability relies on a large amount of annotated data, yet collecting labeled data is extremely costly and difficult. Therefore, poses a significant challenge in situations where both scRNA- data and ScATAC-seq data lack a substantial amount of labels. Secondly, how to reduce cell heterogeneity between different omic data while maintaining cell type discriminability? Another challenge is how to reduce the heterogeneous gap between different omic data, which is crucial for cell type transfer. Additionally, while reducing cell heterogeneity, there is high likelihood of compromising cell type discriminability. Even worse, this becomes significantly more challenging under label scarcity.
To answer these questions, we propose a semi-supervised knowledge transfer framework termed Dual label scArcity elimiNation with Cross-omic Multi-samplE Mixup (DANCE). Since the class distribution of the whole scRNA-seq data is unattainable, we start by injecting semantic knowledge using labeled souree scRNA-see data, and then generate pseudo-labes based on optimal transport (OT) for unlabeled scRNA-seq data. Afterward. the unlabeled scRNA-sep data, along with pseudo-labels, are incorporated into the labeled set. For unlabeled target ScATCC-ee data, due to the heterogeneous gap, we adopt fivide-anddconquee strategy, which separates the whole data into source-like data and target-speeift data. For source-like samples, we employ consistency regularization to force the model to make consistent predictions after random perturbations. For target-specific samples, we select ambiguous labels and then filter the incorrect labels, which can guide the optimization in a soft manner. To mitigate the heterogeneous gap, we propose to generate virtual scRNA-seq samples by multi-sample Mixup according to class-wise similarity and then we perform a sample-to-sampl alignment strategy. The effectiveness of DANCE is validated by adopting series of benchmark datasets and conducting comprehensive experiments in comparison to various sate-offther art approaches.
To summarize, the main contribution of this paper includes: (1) New Problem. We study realistic yet underexplored problem of knowledge transfer across multi-omic single-cell data under label scarcity in both scRNA-seq and scATAC-seq data. This problem holds significant implications for reducing the annotation cost of single-cell data. (2) Novel Methodology. We propose a semi-supervised framework named DANCE for this problem. DANCE introduces OT-based dataset expansion and divide-and-conquer strategy for dual label scarcity elimination. Additionally, DANCE employs an effective alignment strategy of cross-omic multi-sample Mixup to reduce cell heterogeneity. (3) Comprehensive Experiments. Through extensive experiments with different settings on various benchmark datasets, we demonstrate the superiority of DANCE against many oftte-of-theart methods.
2
Related Work
2.1 Multi-omic single-cell Data Integration
The integration of multi-omic single-cell data constitutes a fundamental challenge in elucidating biological processes. Multi-omic single-cell data affords a multitude of perspectives on cellular functions, thereby augmenting our understanding of biology. Some approaches [69 44 59 37
We use scATAC-seq as example and there could be other biological layers.

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Injection
L
LIFEI COL.
Shared Encoder
Haratiee Tratsdng
Cross-omic Multi-somple Mixup
Optimal- Tronsport Based Dataset Expansion
Merge
34
Misintte
24
SCRNA-sRR DOSL
Algnment
Log
Pseudo
Divide-ate non-uoe Strategy
00
SL
Source like Samples
Noise
Consistency Learning
Shared Encoder
Jallnn Illon
IB BB
Source Feature
Target Feature
Virtual Sample 201
Torget-specific Samples
Incorrect Cell Type Removal
Figure 1:
An overview of DANCE. We first utilize labeled scRNA-seq samples to inject semantics and expand the scRNA-seq dataset by OT-based pseudo-labeling. Then we employ consistency regularization and ambiguous set learning for source- like and target-specific ScATAC-seq samples. In addition, we perform cross-omic multi-sample Mixup reduce cell heterogeneity.
opt for denoising, batch correction, and integrating single-cell data across numerous experiments, encompassing transcriptomic data and ScATAC-seq data. Nevertheless, the simple application of these methodologies to multi-omic data integration poses computational challenges and frequently yields unsatisfactory results, given the considerable differences in dimensions and sparsity levels among different modalities. Moving a step closer, methodologies such as scAl [25] and MOFA+ K utilize factor analysis and joint clustering to concurrently measure multi-omic data within the same cell. Nevertheless, these methods require pairwise data format, and executing paired measurements presents technical challenges and involves high costs. Therefore, another series of approaches have been proposed to measure multi-omic data from different cells, including manifold alignment (63 39 matrix factorization [8
30
64
84), correlation-bes based methods (5] [4], and neural
network approaches 38 36 72
2 Recent approaches often map multi-omic data into nww-dimensional 66
20), and Graph Neural Networks (GNNs) encoders 665] 60) 44). which focus on knowledge transfer across multi-omic data. For instance, scBridge (36) maps multi-omic single-cell data into shared embedding space and mines reliable samples for dataset expansion, thereby merging the se ScATAC-seq and scRNA-seq data into the same dataset. GLUE g combines a knowledge-basee graph and adversarial alignment explore regulatory interaction across various omic layers. scCLIP (68) introduces a contrastive learning approach to integrate multi-omic single-cell data. It aligns the representations of pairwise multi-omic single-cell data without the usage of cell type labels. Despite their significant strides in integrating heterogeneous cells, all these methods overlook practical challenge reality, i.e., the difficulty of annotating both scRNA-ses -eqq data and ScATAC-seq data. Therefore, there is an urgent need for an approach that can effectively alleviate label scarcity in knowledge transfer across multi-omic single-cell data.
spaces through shared auto-encoders 21
56
2.2 Semi-supervised Learning
To tackle the label scarcity issue in reality, semi-supervised learning has been widely studied with various applications, such as image classification [66]
23
7 6. semantic segmentation [4 14].
and object detection 24
80 Recent studies on superviue learning include consistency
67] and pseudo-labeling approaches 6 23 79). Consistency regularization usually first introduces random perturbations from various sources, including data input [67]. network parameters [47] and deep features 28 By encouraging the model to make consistent predictions under these perturbations, can still learn good representations even under resource constraints. Pscudo-labeling typically assigns pscudo-labels to unlabeled samples using
regularization approaches 446
52
3

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neural network. It often comes with some special techniques, such as dynamic thresholding [78 33
and curriculum learning [29 22 [0]. Then those pseudo-labels together with their corresponding samples are added to the dataset for future use. Besides the advanced approaches in computer vision, other related areas such as node classification [13 62 and reinforcement learning [87] also benefit from cemi-supervised learning. However, to the best of our knowledge, there exists no previous work to study the problem of semi-supervised knowledge transfer across multi-omic single-cell data. Therefore, provide semi-supervised framework termed DANCE to promote the understanding of semi-supervised learning in the field bioinformatics.
3 Problem Definition
In our problem, we have two datasets D(s) and D(t), which denote the source scRNA-seq dataset and the target ScATAC-seq dataset, respectively. In the semi-supervised setting, the scRNA-sea dataset D(s) is separated into two parts, the labeled dataset D)()) and the unlabeled dataset D(s).a, where D(+), {(0)
"i ) consists of N' scRNA-seq samples a: (+) and the corresponding (1)))))))))))) ")) includes N unlabeled scRNA-seq samples x; !s)." The labels y/ [+).1 and D(s), = {(x))))"))
( (e).1
target SeATAC-seq dataset with the size N2 is denoted as D {(x(")))) which includes No ScATAC-seq samples ( We focus on the closed-set setting, thus both scRNA-seq data and ScATAC-seq data are assumed to share the same set of C cell types. shared encoder F-) is adopted to map both the scRNA-seq samples and ScATAC-seq samples into the embedding space. Following the encoder, we utilize a classifier (.) to convert representations into softmax predictions corresponding to different cell types. The representations and predictions are denoted z, F(x;) and p; = sof faaaa HH (z:)). respectively. It should be noted that only a small portion of the scRNA- seq data is labeled, and a significant number of both scRNA- seq samples and scATAC-seq samples lack cell type annotations. We aim to transfer the semantic information related to different cell types from D(e) to D(t), which has two expectations: (1) The cell representations of scRNA-see -se data and ScATAC-seq data are mapped into shared common embedding space with the similarity structure preserved. (2) The cell type annotations on ScATAC-see data are accurate after label transfer.
4
The Proposed Approach
4.1 Framework Overview
This paper investigates the problem of semi-supervised knowledge transfer across multi-omic single- cell data. We employ a shared encoder and classifier for heterogeneous scRNA-seq data and scATAC- seq data. The labeled scRNA-seq data are utilized to inject the semantic knowledge. To overcome label scarcity in source scRNA-seq data, we generate pseudo labels based on optimal transport and merge them into the labeled set. To address label scarcity in target ScATAC-seq data, we perform consistency regularization for source-like samples and learn from ambiguous labels for target-specific samples. To reduce the cell heterogeneity, sulti-sample Mixup and align the heterogencous multi-omic data. The overview of the proposed framework is illustrated in Figure| I and each component will be elaborated in the next sections.
generate virtual scRNA-seq samples through cross-omic
4.2
Optimal Transport-based based Dataset Expansion for Source scRNA-sey Data
The first obstacle is the label scarcity in scRNA-seg data. Recently, -seudo-labeling is a commonly
78 and domain adaptation [8 31 problems, which 83 by assigning the label with the highest confidence to each sample. However, this sample-level prediction-based pseudo-labeling strategy could be biased to easy classes, leading to biased and suboptimal results. To tackle this, we incorporate optimal transport (OT) [12 9 [ into the pseudo-labeling process, which is capable of taking into account prior cell type distributions from a global view to reduce potential noise in pseudo-labeling.
used technique in semi-supervised learning 52
usually operate at the sample level [52
Here first recall the knowledge of OT. OT is constrained optimization problem designed to discover the optimal coupling matrix Q for mapping one probability distribution to another, with the goal of minimizing the overall cost. To represent the cost of transporting data from y and n. introduce cost matrix C' E ghi where |y| and |m] denote the dimension of r and n.

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respectively. By applying the Sinkhorn algorithm 17 to minimize the Sinkhorn distance, we can obtain the optimal coupling matrix Q:
Σ Ci + (-QuilloQus))
YHiN QEI(())))
where II(>. { T
Q1nn = 1 =n] and is set to 10 empirically.
To employ OT for pseudo-labeling, seq data, enabling the injection of semantic knowledge into the model. Similar to traditional cell type annotation problems [6 77,, we leverage a small number of labeled scRNA-seq data and minimize the cross-entropy between the classifier's output and the cell type labels:
begin by training the encoder and classifier labeled scRNA-
Ls Σ
CE(P (e).1 yy )
ED(
where (t) represents the predicted distribution of x; (4).
Then, aim transfer the semantic information from labeled scRNA-seg samples unlabeled ones. In particular, a mini-batch of B unlabeled samples is fed to our neural network to generate the prediction matrix P E I RxC which could be biased easy classes and thus inaccurate. To tackle this, we learn an optimal coupling matrix that should obey prior cell type distributions from global view. Since
do not have prior information, polytope II 1 & lc). where B and l indicate two vector of ones with dimensions of B and C, respectively. In addition, the optimal Q should be close P. Therefore, define the cost matrix C' as lo and Eqn. can be reformulated as:
adopt a uniform distribution in the
1 (-QylloQQ
Σ -logp..j Quj
min I 3j
The coupling matrix Q obtained by optimizing Eqn. 3 can be used generate pseudo-labels for unlabeled ScRNA-seq data. Formally, the pseudo-labels can be defined as:
f( argmar, Qij-
Then the unlabeled scRNA-seq samples and their corresponding pseudo-labels would be added to the labeled scRNA- dataset: D(s). + ( : (0).0
(f). D
(5)
Leveraging OT-based pseudo-labeling for dataset expansion effectively mitigate label scarcity in scRNA-seq data. On the one hand, we employ labeled scRNA-seq data for semantic injection using Eqn. 2 obtaining high-quality pseudo-labels. On the other hand, with the dataset expanded during the current iteration, more information is available for Eqn. in the subsequent iteration. This leads to generating more reliable pseudo-labels in the following iterations. Through continuous iterative training, we can progressively enhance both the scale and quality of the scRNA-seq dataset. Next, we theoretically support the superiority of OT-based dataset expansion. See proof in Appendix A
Theorem 4.1. Let P, = P be the normalized version of P. Then after our optimal transport-based dataset expansion, we have
(1) For any & 0 and large enough a, have CF((P,, P) + CF(Q, Cp, where and C) CE(QU), P.y).
Cp = E
5= E._IP E
(2) Let L
(x)) denote the soft-version of true cell labels, where lig max {1 i belongs flass .8 and 8 > O. Suppose 0 < Cuin I 1// I Cuuxx for all
< < B and 1 S C. Then, CE(Pn, L) + + Cr 2 CF(Q, + Cp, where C1 = log Ciax log Cmin
Remarks. Cp characterizes how far Pn is from I 1 1c). If the mini-batch was uniformly sampled across cell-types, then 1/ ΣB Pij should be close to J. Thus, Cp will be close zero Moreover, for those hard classes, Pij 's are close to C. hence both B ΣØ Pij and Σe log Pij will not too large. Hence, on the whole, Cp can relatively small, which demonstrates that our optimization has the potential to achieve the desired distribution.
5

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4.3 Divide and Conquer for Target ScATAC-seq Data
Through OT-based pseudo-labeling, we effectively address the label scareity issue in scRNA- seq data. However, due to the heterogeneous gap. it would not be optimal when employing similar approach for seATAC-seq data. Here, to tackle label scarcity in ScATAC-seq data, we adopt divide- and-conquer learning strategy, which first divides the ScATAC-seq data into source-like data and target-specific data and then utilizes separate approaches for optimization.
In particular, by comparing the confidence of the predictions and threshold, we divide scATAC-seq data into two parts D and D {a, E D( max P;
DST.
[c] > T), = =D(
where P; is the predicted distribution of at!") The threshold is used control the size of two groups and set to 0.9 according to previous works [6]. These source-like samples are more likely to have accurate pseudo-labels with high confidence using the pre-trained model. contrast, target-specific data are far away from source data distribution and thus the predictions could be noisy, Therefore, we utilize separate strategies for these two parts.
Learning from Source-like Data. For these source-like samples, utilize consistency regulariza- tion to provide supervision 4 32, 50 52 85]. Here, we add random perturbations (e.g., Gaussian noise) to data, and then force the network to make consistent predictions for both original and perturbed ScATAC-seq samples. In particular, we can use U/ argmax((p:) to obtain the pseudo- label of the original ScATAC-seq sample. After that, can get the prediction p; of the perturbed scATAC-seq sample. Thus, the supervised loss can be formulated as:
IDSL
CSL = DSL
Σ De %log(())))
1=1 c=1
where |DSL] represents the number of unlabeled ScATAC-seg samples in DSL In this way, we combine pseudo-labeling with consistency learning to learn from source-like ScATAC-seq samples. Learning from Target-specific ScATAC-seq Data. However, due to cell heterogeneity between scRNA-seq and ScATAC-seq data, many ScATAC-seq samples would be far away from the source modality. They are difficult to predict with low confidence scores 71 85 we introduce soft way to learn from their ambiguous label sets instead of pscudo-labels. Here, for each target-specific scATAC-seq sample, we first select the top-k classes with the highest probabilities to make up the ambiguous label set and then introduce soft supervised loss for reliable guidance.
To tackle this challenge,
To be specific, we first recall the predicted distribution p: for each sample æ; DTS The negative cell types are removed with a significant difference from the cell type with the highest probability. In formulation, the removed labels should satisfy the following conditions:
max p: P.úi 2 p.
(8)
where the threshold μ is initially set to le and gradually decreases during the training process. The left labels make up the candidate label set:
Y (ce Topk max p:lc] -p:li] < <r)
(9)
Then, we employ soft supervision loss to provide reliable guidance for target-specific samples:
DTS
Lrs = DTS
Ony
(10)
Pe pili
Σ poll. JEY O,
jEY,
(11)
otherwise.
where (DTS| denotes the number of sarget-specffee samples. The weight agj can allow the model to pay more attention potential correct cell types with high probabilities.
Next, we provide the theoretical underpinnings to show that the quality of the soft labels from the candidate label set is higher than that of hard labels. The proof can be found in Appendix A

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Theorem 4.2. Suppose we randomly draw n samples (X1...., Xx) from two classes {0, 1), where the proportion of class 1 is p. Further, suppose we can only observe flawed version of these class labels Z (71.
7n) with error rate B > 0. That is, IP + Xi) = B. Under loss (1- - p), consider two estimates of p.
[(p, 2) -i=1 2i log + (n- Ei= log((1
(1) Phard Argmin C(p,Z)
(2) Pson
Argmin C(p, tZ (1 1)((1 Z)), where E (0,1).
Then, there exists to E (0.1) such that for all t E (to, 1), we have E IIpion: - pll I E PPaard - pll
Remarks. In Eqn. 10.
if we take p+[1] = pi] = 1 - p. and ouj t if = Zi, then the
estimation under Eqn. T0
will degenerate Psoft''
4.4 Cross-omic Multi-sample Mixup for Cell Heterogeneity Reduction
Despite the proposed techniques in addressing the label scarcity problem, reducing the heterogeneous gap between scRNA-seq data and scATAC-seq data remains additional challenge. To tackle this challenge, previous works [27
propose many strategies for cross-modal alignment. major prerequisites. On the one hand, strategies based on centroid learning [27] [9] require accurate label information from both source and target modalities to aggregate samples of the same class. On the other hand, strategies based on cross-modal contrastive learning (35 (3 (22 require pairwise information. Neither of these prerequisites is applicable to our problem, pairwise information is difficult acquire in single-cell data, and the majority of annotation information is missing. Towards this end, we propose a sample-to sample-to-sample to--ample alignment strategy termed cross-omic multi-sample Mixup 88 58 86 73. which first calculates the class-wise similarity and then mixes the scRNA-seq samples in the hidden space for cross-omic alignment.
19
35
34
70)
However, these strategies have
In particular, we transform the intrinsic distribution of the source scRNA-seq samples align with se scATC-see samples in the hidden space. Since we cannot get accurate pairwise information, calculate the similarity between scRNA-seq samples and ScATAC-seq samples and generate virtual samples instead. Note that the weights of the classifier are considered as prototypes that contain representative information of different cell types. We first form weight matrix W [wi, 202- we]' | E RCDD, where D denotes the hidden dimension of the embeddings. Then, the wise similarity can be determined using cosine distance:
() St cos(p W. W) = p. W| Ip WT
(12)
where |. represents the L2 normalization. For batch of B(a) seRNA-seq samples and batch of ((t) seATAC-seq samples, we can obtain a similarity matrix S E R B()) x n(O) using Eqn. 12 For the sake of simplicity, we let B(+) = B(t) = B, and the similarity matrix within a mini-batch can be formulated as S B Afterwards, we employ the softmax function to convert into the weight matrix M for Mixup, which corresponds to the probability of sharing the same semantics:
M softmar(S))
(13)
where Mi collects the weights of B ScATAC-sco samples corresponding to the i-th scRNA-seg sample. Then, can fuse the scRNA-seq samples into virtual sample through Mixup:
B Mij zj.
m.
(14)
After getting the fused scRNA-seq samples, we can perform cumple-to-samplee alignment strategy. The loss function can be formulated as:
B
LA
2 =1
(15)
7

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Table 1: Quantitative comparisons (in %) with state-of-the -f-t-----teerrttt approaches with various label ratios. Bold numbers indicate beset results, and underlined numbers indicate second beset results.
Label Ratio
Low
Mid
High
Low
Mid
High
Low
Mid
High
Low
Mid
High
Data
snRNA
10X
ATAC
snRNA
10X, v3 A-sanC
snRNA
10X
v2 ATAC
SnRNA
10X
v2 snmC
20.56
20.13
20.33
44.20
46.55
53.72
27.50
29.
54.33
55.08
49.95
CDAN 44
23.46
21.29
21.84
20.38
26.00
27.76
27.67
22.71
21.51
39.08
32.97
38.08
MCC 26
24.34
32,39
40.84
20.40
48.69
44.39
24.57
31.45
33.03
31.22
36.77
40.20
FixMatch
24.38
33.45
41,60
20.42
44.58
41.98
24.93
31.61
33.04
31.66
36,60
41.32
scloint
38
17.58
19.52
18.86
23.46
24.27
21.86
19.39
18.51
17.78
21.48
24.43
22.11
scNCL
22.62
21.21
22.92
27.16
59.78
60.07
21.98
28.11
30.19
59.65
67.47
68.89
s:Bridge
36
25.57
38.50
41.86
21.64
32.74
35.71
19.39
16.29
42.36
12.70
20.85
38.48
Ours
50.23
$1.02
51.42
75.72
78.67
80.12
50.83
$2.90
54,79
60.12
72.24
78.04
Data
saRNA SMARTer-ATAC
SaRNA_ SMARTer-sinCC
scRNA
SMARTer-ATA
scRNA,
SMARTer-uunC
48.28
$2.39
CDAN 43
20,99
28.65
30.07
33.87
35.37
51.19
22.05
21.50
21.58
34.66
42.43
38.70
MCC 26
20.99
23.41
24.09
34.37
52.46
55.38
21.73
21.30
21.54
32.09
50.35
49.24
FixMatch
21.25
33,80
52.05
54.78
22.07
21.77
22.32
49.38
scloint
38
11.05
13.25
15.89
22.88
18.84
20.21
19.62
18.63
17.77
23.94
23.09
23.28
scNCL 72
19.89
20.07
20.05
49.68
69.50
73.39
19.93
19.91
19.71
45,28
62.55
62.69
scBridge
36
26.44
36.34
42.00
36.01
39.75
36.15
28.32
29.69
32.13
13.13
26.58
30.08
Ours
47.43
52.78
34.95
52.31
70.18
77.94
46.40
48.19
49.92
51.56
77.37
78.62
ore cmR-65AP.le.
CIT-ASAP (Hob)
Figure 2: Qualitative comparisons (in %) CITE-ASAP with different label ratios.
4.5 Summarization
In practice, we first warm up the network through labeled scRNA-seq data, and then conduct the training process in end-to-end manner. The total loss function can be summarized as:
C = Ls + Lst. + ACTS + LA.
(16)
where À is coefficient and set to 0.1 empirically.
The step-by-step training algorithm of DANCE is summarized in Appendix
Experiment
5.1 Experimental Settings
Datasets. To validate the effectiveness of the proposed DANCE, we conduct extensive experiments on several benchmark multi-omic single-cell datasets, with a brief introduction as follows: Mouse Atlas Data [74). The multi-omics data can be accessed from the Tabula Muris mouse dat: along with the quantitative gene activity score matrix. In practice, the dataset is divided into multiple subsets based on the various sources of multi-omics data. CITE-ASAP PBMC Data 45). It is derived from both control and stimulated conditions. CITE-seq encompasses both antibody-derived tag (ADT) matrices and gene expression matrices, while ASAP-seq allows matrices and the chromatin accessibility matrices simultaneously. In this research, we extract the gene expression matrices of CITE-seq and the chromatin accessibility matrices of ASAP-seq construct the scRNA-seq and SCATAC-se datasets. The statistics of these datasets can be seen in Appendix D
access the ADT
Baselines. Various approaches are adopted for performance comparisons, including three domain adaptation methods (DAN 42 CDAN [3), and MCC (26). semi-supervised learning method (FixMatch (52)), and three latest multi-omic single-cell data integration methods (scJoint [38], scNCL 72), and scBridge (6)] brief introduction of these approaches is provided Appendix
https://tabull-mmriis cwwiohub. org/

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Table Ablation studies (in %) of DANCE different settings. Bold numbers indicate beset results.
Dataset
saRNA_ 10X v3 A-ATAC
saRNA 10X v3 A-snmC
seRNA 10X v2-ATAC
scRNA
10X v2-sumC
Label Ratio
Low
Mid
High
Low
Mid
High
Low
Mid
High
Low
Mid
High
DANCE w/o OP
31.87
38.55
40.86
55.60
61.89
67.69
33.74
38.31
44.56
51.66 31.66
59.57
64.18
DANCE w/o St.
48.10
49.09
49.94
70.04
76.61
79.00
49.98
52.56
54.75
74.08
79.33
79.89
DANCE w/o
48.62
49.01
49.95
73.85
77.22
78.91
49.74
53.07
55.26
73.99
79.88
80.85
DANCE w/o CM
44.80
45.63
45.69
55.91
59.92
68.79
46.75
48.01
49.97
70.81
76.34
78.02
Full Model
50.23
51,02
51,42
75.72
78.67
80.12
52.02
54.38
56.30
75.23
81,38
81.76
SCTIM
KRNN-ATIC
43.
430
47.44
45.0
SENCL
Ours
(a) Parameter Sensitivity
(b) t-SNE
(c) A-distance
Figure 3: (a) The parameter sensitivity with respect and À on scRNA_ SMARTe--ATAC with various label ratios. (b) The t-SNE visualization of two modalities (scRNA_ CORRA_IOX__00 v2: red, ATAC: blue) with high label ratio. (c) The A-distance comparison with different label ratios.
5.2 Experimental Results
Performance Comparison. To assess the performance of various knowledge transfer methods, we conduct comprehensive quantitative experiments (Table and qualitative experiments (Figure 2). From the results, several conclusions can be drawn: Firstly, consistent results from both quantita- tive and qualitative experiments reveal that DANCE significantly surpasses existing state-of the-art approaches. We attribute this to the factor that existing methods often only address the issue of label scarcity in the target ScATAC-seq data, overlooking the label scarcity issue in the source scRNA- seq data. Consequently, when cell type annotations in scRNA-seq data are limited, not only does the discriminability of representations suffer, but also the heterogeneous gap cannot be effectively eliminated. DANCE excels in three main aspects. Firstly, it utilizes OT-based pseudo-labeling to augment the scRNA-seq dataset, effectively alleviating label scarcity in scRNA-seq data. Secondly, andiii--andcoouuerr strategy with consistency regularization
for ATAA-sse data, we employ and ambiguous set learning for two subsets of samples, addressing label scarcity in ScATAC-seq data. Additionally, our proposed cross-omic multi-sample Mixup mitigates cell heterogeneity in manner. By combining these aspects, DANCE significantly outperforms previous approaches by a large margin. Additionally, existing methods are data-hungry, heavily relying on cell type annotations. Therefore, when the number of labels decreases, the performance drops rapidly. In contrast, DANCE data-efficient, capable of achieving excellent performance with fewer labels. Consequently, the performance decline due to label reduction is less pronounced, thanks to our pro- posed dual label scarcity elimination strategy. Due to the potential class imbalance issue in single-cell data, simply increasing the number of labels may not improve performance for some methods. For example, on the under high label ratio, which is not better than that of 39.75% under mid label ratio. In contrast, our OT-based pseudo-labeling approach considers distributing pseudo-labels in one-to-many manner, effectively preventing biased predictions. Therefore, on all datasets, increasing the number of labels leads to corresponding performance improvements for our DANCE.
SMARTTr--mmmC dataset, scBridge (36] achieves performance
36.15%
we conduct comprehensive experiments on different model variants to explore the contributions of each proposed module. DANCE w/o OP represents DANCE without the proposed OT-based pseudo-labeling. DANCE w/o SL denotes DANCE without the consistency regularization for source- mikee-like samples. DANCE w/o TS indicates DANCE without the ambiguous set learning for cross-omic maulti-sample Mixup. From the results, it can be observed that the performance of DANCE w/o OP sissignn significantly decreases. This indicates that OT-based pseudo-labeling effectively alleviates the label scarcity issue in seRNA-seq data by generating pseudo-labels for source scRNA-seq data and augmenting the seRNA-seq dataset. Additionally, the slight decrease performance of DANCE w/o SL. suggests that for heterogeneous ScATAC-seq data, applying consistency regularization to source-like
Ablation Study. As depicted in Table 2
samples. DANCE w/o CM signifies DANCE without the

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samples can further enhance model performance. Similarly, the minor decrease in performance of DANCE w/o TS suggests that target-specific samples should not be simply discarded in previous methods. For these samples, collecting potentially correct cell types and removing them can provide weak supervision, thereby aiding performance improvement. Furthermore, the significant decrease in performance of DANCE w/o CM underscores the severity of the cell heterogeneity issue. Our proposed cross-omic multi-sample Mixup effectively eliminates the heterogeneous gap by generating mixed source scRNA-seq samples that simulate the distribution of target ScATAC-seq data and aligning the two modalities. Thus, the effectiveness of all proposed modules is sufficiently validated.
Sensitivity Analysis. In Figure 3 (a). we analyze the sensitivity of two crucial hyper-pen hyper-parameters k and À on scRNA MAARRTer-ATA. Firstly, we fix the other parameters to investigate the sensitivity of the model to different values of k. Here, k denotes the size of the partial label set and is used to control the number of potential correct cell types. As & varies within the range {2, 3, 4, 5, 6, 7), the model's 's performance initially increases before gradually stabilizing, Considering all three scenarios, the optimal performance is achieved at k = 4 or = 5. Next, with other parameters fixed, we analyze the sensitivity of the coefficient X of Lrs in Eqn. [O] Since Lrs provides additional weak supervision by removing incorrect cell types, it typically requires a small coefficient. We change the value of À within the range (0.05, 0.1, 0.1, 0.2, 0.25, 0.3). Similarly, the model's performance firstly increases and then decreases, with better performance observed at 0.1.
t-SNE Visualization. In Figure 3 (b), we make the t-SNE [57 visualization to assess the heteroge- neous gap of representations learned by ScNCL [72 and our DANCE scRNA_108 -2-ATAC. Note that the higher overlap degree between the red and blue parts reflects the lower cell heterogeneity between scRNA-seq and scATAC-seq data. It can be observed that there is almost no overlap between the representations of scNCL (72). indicating that it does not address the issue of cell heterogeneity. In contrast, the representations of our DANCE exhibit a high degree of overlap, indicating that our approach effectively mitigates cell heterogeneity even in the presence of label scarcity.
A-distance Comparison. In Figure 3 (c), compare the A-distance scRNA_ 10X_ v2-ATAC with various label ratios. The A-distance is calculated by employing a model and testing its ability to distinguish between the two modalities. Therefore, low A-distance indicates a small heterogeneous gap. The A-distance is defined as: dist = 2(1 2c), where e is the test error. When c = O, the upper bound of dist, is 2. This indicates that there is significant heterogeneous gap between the two modalities, and the model can easily differentiate between them. From the results, it can be observed that the A-distance of FixMatch (52) and scNCL 772 consistently remains close indicating a significant heterogeneity between scRNA-seq data and scATAC-seq data. In contrast, DANCE successfully reduces cell heterogeneity, resulting in significant decrease in the A-distance.
Conclusion
In this paper, we focus on a realistic yet underexplored problem of knowledge transfer across multi- omic single-cell data under label scarcity in both scRNA-seq and scATAC-seq data, and propose a semi-supervised framework termed DANCE for this problem. Specifically, DANCE operates dual label scarcity elimination by OT-based dataset expansion for scRNA-seg data and divide-and and-conquer strategy for ScATAC-seq data. DANCE further introduces a sample-to-sample alignment strategy of cross-omic multi-sample Mixup to reduce cell heterogeneity. Extensive experiments on various datasets verify the superiority of DANCE in comparison to many state-of-the--rt approaches.
Broader Impacts and Limitations. This study addresses the challenge of knowledge transfer across multi-omic single-cell data, which has significant potential for advancing biological research by reducing annotation costs in both source and target biological data. However, important to acknowledge that this work represents an initial exploration of this field and may have certain limitations. For instance, our method may not perform optimally in complex real-world scenarios, including open-set knowledge transfer and knowledge transfer under label noise. Additionally, this study primarily focuses on static scenarios, while there are complex dynamic applications, such as single-cell RNA velocity inference, that require further investigation. In future research, our goal is to address these challenges and expand our approach to encompass more generalized scenarios. To the best of our knowledge, potential negative impacts resulting from our work have been identified.
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16

---

Therefore, Ephard EZ, p+ 3- 3-233 Var(Phard) Var (21) Eptntt and (ar(p (2t Var (Z1) Hence, we have A Proof of Theorem
Proof of Theorem 4.7 (1) Let P be the projection of P. onto II( 1B, δ lc). Further, let Q be
solution of the Kantorovitch's ' problem
min QEIT 1a.e
Σ
-Gi log Pa-
(17)
10)
1.j
Then, by definition, have
Pog P) 2 (Q. P)
(18)
where (.,-) the standard matrix inner product. From definition, we know that is the solution of
P 2
2
min
Pa
2
1 Plc= 1B,
1 c Jc.
(19)
B
Using Lagrange multiplier, we know that
P= - 1BB"
(20)
where 3= -31,..., BC)"" and By = 1 (1) E1= Li=iPij - t) Therefore,
P.- P) Cpp.
(21)
Further, using standard OT theory, we know that - Q converges to the solution of the problem in Eqn. 17 with maximal entropy, Qx. Therefore, for large enough o, we know that
||Q Oo ll S E/ ||- log pl
(22)
So, using Cauchy's inequality, we have
1QQ - Q - log P) S E.
(23)
Combining Eqn. 18 23 and take Q Q. in Eqn.
18
we get
(Pn, log P) (Q, - log P) + Cp E.
(24)
Therefore, we have
CE(P,, P) + CE(Q, Cp.
(25)
(2) For simplicity, we denote + = (Lij/ (Lij/Prg) Pij) Then, we have
L
Lij 1 EPs (-log
Eeu (-log
Lij
Pv Q, llg P
Pss
P.j
2 log Ciin log Cmax-
(26)
Hence,
P log L) (Q, - log L) + Cp Cl.
(27)
Proof of Theorem 4.2
From the definition, we know that
Phard
Zi: Piont = Σ (17: + (1 - ()(1 Zz)}
(28)
1=1
1=1
- p12² = (EZ) - 2)2 + Var(Zı)
E PPhard
n
- p|| = {(1 t) + (2t 1)EZ, p}² + (2t Var Z1)
Proft
(29)
17

---

Define
f(t) B Phard p/1² E || p||²
(30)
Then, from definition, know that f(1) 0. Further, we know
(2t 1) p} (2IEZ) - (4 - 8t) Var(Z1)
(()) 2 ( -) (
- 2EZ) ) -
8Var( (1) 4Var(Z1) + 2EZ1) (1 EZ1 -p)
(31)
Since 2(1 - -2 + 8Var(Z1) > O, we know that there exists
4Varl 21)
+ 2(1 2EZ (1 EZ1 p)
to
n
8 2 + 8Var(2,)
11
+ 2221122p)(( 23) 3)
"
(0, 1),
(32)
8Var (11
+ + 2p) 233
"
such that (t) 0 when t E (co, 1). So, f(t) 2 f(1) for t (to, 1). Hence,
EB
Plon
p|1 s |phard - -p1²
(33)
B Algorithms
The step-by-step training algorithm of our DANCE summarized in Algorithm The model can get extra supervision from target-specific ScATAC-seq data by removing the incorrect types from the candidate label set, the algorithm is provided in Algorithm
Algorithm 1 Training Algorithm of DANCE
Require: The training datasets D(*),1 D( and D(O),
Ensure: The network parameters in HFF))):
1: Warm up the network using D(s).;
2: repeat
3: Update the pscudo-labels using Eqn. and Eqn.
4: Expanding D(s) using Eqn. 5
5: for - 2, ... . do
6: Sample mini-batch from the training datasets;
7:
Generate the predictions for both scRNA-seg data and scATAC-seq data by propagating the network;
8: Calculate the final loss using Eqn. 16
9: Update the parameters the network using backpropagation;
10: end for
11:
until convergence
C Implementation Details
All the baselines are re-implemented NVIDIA Tesla A100 40G GPUs using PyTorch according to the original settings in the corresponding papers to ensure a fair comparison. A two-layer MLP with an embedding dimension of 64 is employed as the encoder, and another two-layer MLP adopted as the classifier. For all the baselines and our DANCE, we first warm up the model with labeled scRNA-seq data for 30 epochs and then train the model for another 30 epochs with batch size of 32. Three settings with different label ratios (low: 1%, mid: 5%, high: 10%) are set up for experiments on each dataset to validate the sensitivity of these methods the number of labels. We opt for SGD the default optimizer with learning rate of 3e and weight decay of le -3 3.
18

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Algorithm 2 Algorithm of Learning from Target-specific ScATAC-seq Data
Require:
Target-speciffs SeATAC-seq dataset DTS; Threshold μu; The number of classes k. The network parameters in (F())))
Ensure:
repeat
for / 2, , do
Sample mini-batch of BTS samples from DJ's:
Generate the softmax predictions for scRNA-seq data by propagating the network; Select the top-k classes from the predictions to form BdC label sets; for ,.",....,B" BTC do
for y Y; do if Eqn. 8 satisfied then Removing cell type y from Y;; end if end for end for Providing extra guidance by loss Eqn. 10 Decrease the threshold Jr;
end for until convergence
D Introduction of Datasets
We summarize the dataset information included in experiments in Table
Table 3: The statistics of the multi-omic single-cell datasets used to conduct experiments.
Dataset
Source Data Type
Source Duta Sizc
Target Data Type
Target Data Size
Number of Cell Types
Sequence Length
sRNNA 10X v3
4017
ATAC
7962
18
18601
SaRNA_IE 10X x2
7652
ATAC
7962
18603
sRRNA SMARTer
scRNA_I0X
7962
Mouse Atlas Data
SIRNA JOX V
SERNA_ SMARRer
6288
4017
saniC
ATAC
9633
7962
18603
18603
saRNA 10X v2
7652
samC
9633
18
18603
ORNA SMARTar
6171
sanC
9631
18601
sRRNA_IOE 10X x
12261
samC
9633
18603
SMARTer
Data
ScKNA part
#:ATAC part
4502
E
Introduction of Baselines
Here we provide brief introduction of the compared baseline methods as follows:
DAN is deep neural network (DNN) based method for domain adaptation, which adapts the layers and the corresponding task-specife features in a layer wise manner.
incorporates the discriminative information of the predictions from the classifier with adversarial learning for domain adaptation.
CDAN 4
MCC 26 is strong domain adaptation method that achieves fast convergence speed without the explicit use of domain alignment.
FixMatch 52 is strong semi semi-superve supervised learning approach that hybrids pseudo-labeling and consistency regularization self-training.
scJoint a multi-omic single-cell data integration method based on transfer learning and semi-supervised learning, which effectively transfers labels through kNN joint embedding space.
scNCL 72 is transfer learning framework that preserves the intrinsic structure of scATAC-seq data and employs contrastive learning to facilitate the transfer of cell types.
scBridge 36 integrates multi-omic single-cell data a heterogeneous manner by mining reliable cross-modal samples and expanding the single-cell dataset.
19

---

Further Discussion
F.1 Comparison with Classic Pseudo-labeling
As depicted in Figure 4 classic pseudo-labeiie [52] assigns the category label with the highest confidence as the pseudo-label, establishing one-to-one correspondence between a sample and category label. This approach overlooks the potential connections between the sample distribution and the class distribution, especially cases class imbalance. contrast, our pro- posed OT-based pseudo-labeling aligns multiple samples with a category center through the cou- pling matrix Q and the cost matrix Pl This approach considers the connections between the sample and class distribution, effectively ad- dressing the issue of potential biased predictions in scRNA-seq data.
Protatype Clsss
Sample belongs Cass
Prototpe
Sample
Classic PL
OT based PL
Figure An illustration of classic pseudo-labeling and our OT- pseudo-labeling.
F.2 Advantages of Divide and Conquer for Target ScATAC-seq Data
Different from traditional approaches
52 78 that directly discard the utilize these samples to provide weak su- pervision for the model (algorithm can be found in Algorithm 2 By progressively removing the incorrect cell types, the model gets extra guid- ance from these samples. Incorpo- rating consistency regularization for source-like samples (Figure 55) and in- correct cell types removal for target- specific samples, the entire ScATAC- seq dataset D(1) DSL DTC is
Freads
sprget- samples,
Classfer
Stare Meightt
Shore Weight
No Mise-agneeee rod Uncader
Figure 5: An illustration of consistency regularization for source-like ScATAC-seq samples.
fully explored, contributing to the semi- semi-supervised knowledge transfer process.
F.3 Advantages of Cross-omic Multi-sample Mixup
Different from previous alignment strategies that rely labels from seATAC-seq data or paired labels, our proposed cross-omic multi-sample Mixup reconstructs class-wise sim- ilarity through the weights of clas- sifiers (Figure (6). It then fuses scRNA-seq samples by incorporating semantic information from ScATAC- seq data. Without the need for la- bels from ScATAC-seq data or pair- wise labels, we transform the distribu- tion of scRNA-seq samples to align with ScATCC-ee data. This makes our alignment strategy more flexible and convenient, Considering the high cost of annotating single- cell data, our alignment strategy more suitable for the problem supervipe knowledge transfer across multi-omic single-cell data.
Cross-omic Multi-sample Mixup
Ensader
Cuasteer
Seeleth Marti:
Showe Weightt
Weigghts
Viup
Wrigl
Cuisterr
Figure 6: An illustration of cross-omic multi-sample Mixup.
20

---

XUMAA 103 7asun. Bonn
XIBMA 1OX Soa.aaatt
KIRA TBX 123in (thito
Figure Qualitative comparisons (in %) with state-of-the-art approaches on additional datasets with different label ratios.
FixMatch
scBridge
ScNCL
Ours
FixMatch
scBridge
ScNCL
Ours
Figure 8: The t-SNE visualization of two modalities (scRNA 10X v2: red, ATAC: blue) (the first row) and different cell types (the second row) with high label ratio.
F4 Further Sensitivity Analysis
In addition to these parameters, we also investigate the two empirical hyper-parameters: a and From the results in Table and Table
observe that the performance is not sensitive the choice of a when we change from 5 to 20, so we empirically set its value to 10. We also vary the value of with the range of {0.8, 0.85, 0.9, 0.95). The results below indicate that the method is not sensitive to in the interval (088, 0.95). Therefore, we set T to 0.9 as the default.
Table 4: Sensitivity analysis of a.
Dataset OTEE-SSAP
5
10
15
20
47.01
47.45
47.32
47.14
snRNA 10X v3 A-ATAC
51.16
51.42
51.08
51.17
We progressively remove the incorrect cell types for additional supervision. However, how to define an incorrect cell type may a point of interest. To answer this question, We have included sensitivity analysis of the threshold y by varying it in (0.0005, 0.001, 0.0015, 0.002). The results Table indicate that a threshold of In particular, after the softmax operation, the sum of the confidences for all cell types equals 1 with
brings good performance with significant differences.
21

---

FixMatch
scBridge
SCNCL
Ours
FixMatch
scBridge
ScNCL
Ours
Figure 9: The t-SNE visualization two modalities (scRNA 10X v2: red, snmC: blue) (the first row) and different cell types (the second row) with high label ratio.
ScRNA-samC
SORNA-ATAC
SaRNA-samC
2.0
2.0
2.0
A-distance
1.5
1.5
A-distance
1.5
1.0
1.0
1.0
0.3
FicMatch
0.5
PhcMatch NONCS
0.5
HarMatch
0.0
Low
Mid
High
0.0
Low
Mid
Hligh
0.0
LOW
Mid
Tligh
Label Ratio
Label Ratio
Label Ratio
Figure 10: Comparisons of the A-distance between source scRNA seq data and target ScATAC-seq data with various label ratios.
more than 10 cell types. In this context, the confidence scores are quite small and a difference of 1e 3 indicates sufficient difference.
F.5 Further Comparison of the A-distance
Besides scRNA 10X, v2-ATAC, we further compare the A-distance on 3 more datasets, including scRNA 10X v2-smmmC snRNA 10X v2-ATAC, and snRNA 10X v2--smCC in Figure T0 The results are consistent with the analysis in Section 5.2 which validates the robustness our approach in reducing cell heterogeneity.
F.6 Additional Qualitative Results
We showcase additional qualitative experimental results on more datasets in Figure 7 still be observed that our DANCE achieves the best performance. From the results, we can obtain consistent conclusions as previously mentioned in Section 5.2
E.7
Additional t-SNE Visualization
In Figure and Figure g we make the t-SNE visualization comparison with more baseline methods. From the results in the first row, it can be found that there is almost no overlap degree in FixMatch
(52 and ScNCL. 772). There are slight overlaps scBridge (36), but the heterogeneous gap remains large when certain parts of labels are missing. Compared with these methods, our DANCE achieves the
largest overlap degree since it effectively reduces cell heterogeneity, the second row, each different color represents different cell type. It can be seen that, in the case of label scarcity, DANCE still
22

---

Table 5: Sensitivity analysis of T.
Dataset
0.8
0.85
0.9
0.95
CITE-ASAP
46.98
47.06
47.45
47.33
snRNA_IOX v3 A-ATAC
50.54
51.15
51.42
50.87
Table 6: Sensitivity analysis of μ..
Dataset
5e-4
le-3
1.5e-3
2e-3
CITE-ASAL
46.58
47,45
47.40
47.02
snRNA 10X v3 A-ATAC
50.07
51.42
51.14
51.03
maintains good cell discriminability compared to the other three methods. This indicates that DANCE effectively transfers cell type knowledge from scRNA-seq data to ScATAC-seq data.
Table 7: Ablation OT strategy for target single-cell data.
Label Ratio
Low
Mid
High
w/ OT for scATAC-seq w/o OT for ScATAC-seq (Ours)
34.90
36.87
39.34
45.36
46.38
47.45
F88 Optimal Transport Strategy for Target Single-cell Data
Despite the effectiveness of OT-based dataset expansion for source scRNA- -sq data, due to the presence of cellular heterogeneity, directly applying this strategy to target single-cell data may not be effective. We have added a model variant w/ for scATAC-seq to support our point. The compared results CITE-ASAP dataset are shown in Table 7 We can find that full model outperforms w/ OT for ScATAC-seq, which validates the OT strategy may not be the optimal choice given the significant cellular heterogeneity inherent in scATAC-seg data. This is why introduce the wwvidd-anddconqeee strategy for target samples.
Table 8: Comparison of pseudo-labeling accuracy (%)
Label Ratio
Low
Mid
High
unlabeled scRNA-seq data
78.85
79.17 78.04
80.13
source-like scATAC-seg data
77.12
78.56
F.9 Effectiveness of Divide-and-conquer Strategy
An interesting question is whether the initial prediction's 's reliance on the divide-and-conquer strategy has been accurately labeled. To explore this issue, we have included comparison of pseudo-labeling accuracy between unlabeled seRNA-seq data and source-like ScATAC-seq data Table The results for the SOANN__SARTeersmnnC dataset are presented below. From the findings, it is evident that the accuracy of the source-like scATAC-seg data closely aligns with that unlabeled scRNA-seq data, confirming the effectiveness of our approach.
Table 9: Performance comparison when transferring cell types in the opposite direction.
Label Ratio
Low
Mid
High
scJoint
28. 14
45.40
49.59
scBridge
32.24
51.55
53.62
scNCL
36.46
51.01
52.73
Ours
70.33
70.78
72.89
23

---

F.10 Cell Type Transfer in the Opposite Direction
Even though mainstream algorithms focus on transferring cell type knowledge from scRNA-seq to seATAC-seq data, we are interested exploring the possibility cell type transfer the opposite direction, namely from SeATAC-seq to scRNA-seq. We have conducted an experiment that performs this reverse cell type transfer on the CITE-ASAP dataset. The results presented in Table Table confirm the effectiveness of our method in this reverse direction.
Table 10: Efficiency Comparison.
Method
Memory Cost
Training Time/Epoch
Acc (%)
scJoint
0.7GB
30s
22.07
scBridge
1.4GB
4s
23.38
scNCL
1.0GB
70s
22.53
Ours
1.4GB
30s
46.40
F.1
Computation Cost
The comparison of computation costs is also a point of interest. We have included a comparison of memory and time in Table TO] and is evident that method offers competitive computation cost. Specifically, the performance of scNCL is significantly inferior to ours (our performance improvement exceeds 105.9%), while our memory cost sees only a slight increase with even less training time.
Table 11: Performance comparison with two additional domain adaptation methods.
Dataset
CI-AS
CI-AS
CI-AS
sn-AT
sn-AT
sn-AT
Label Ratio
Low
Mid
High
Low
Mid
High
SLA
32.05
36.99
41.74
35.36
37.48
42.41
COT
31.44
35.86
39.01
35.73
36.79
43.85
Seurat
38.71
41.76
43.25
33.46
39.78
45.09
Harmony
39.03
39.99
44.06
32.17
39.94
44.56
Ours
45.36
46.38
47.45
50.23
51.02
51.42
F.12 Further Comparison with More Baselines
To benchmark our DANCE against more baseline methods, we have included SLA 76.. COT 41
Seurat [54). and Harmony (30) different datasets for performance comparison. The results shown
Table T1 demonstrate the effectiveness of the proposed DANCE.
Table 12: Performance on full MouseAtlas data.
Label Ratio
Low
Mid
High
scJoint
62.36
67.75
70.02
scBridge
64.99
69.86
71.44
scNCL
61.50
69.04
71.17
Ours
70.64
72.21
72.58
F.13 Performance on Full MouseAtlas Data
To evaluate the performance under different batches, we have included the experimental results on the full MouseAtlas dataset. Specifically, we collect different batches of the MouseAtlas dataset and remove some of the batches. The results in T2biica2 indicate that our approach still outperforms other methods, showing the potential of DANCE in batch effect correction.

---

Table 13: Performance on real-world scenarios.
Labeled scRNA-seq Data
CITE
CITE
SaRNA CANA_IOX_VVI 10X_v3_A
SARNA_SMARTeR
Unlabeled scRNA-seq Data
saRNA V
SaRNA SAAATer
CITE
CITE
ScATAC-scq Data
ASAP
ASAP
ASAP
ASAP
scloint
86.59
87.27
75.94
76.53
scBridge
88.01
89.24
78,99
scNCL
86.24
88.28
75.80
75.42
Ours
93.37
92.48
84.88
83.79
E.14 Real-world Cases
In real-world scenarios, the proposed DANCE can transfer cell types from multiple-soure scRNA-sse seq data to target ScATAC-seq data. We have included experiments to transfer cell type knowledge from labeled scRNA-sep dataset and an unlabeled scRNA-seq dataset to scATAC-seq data. The results in Table 13 indicate that our approach is superior to other methods in practical scenarios.
25

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Reviews:
[{"review_id": "kUnJxV3Iv4", "reviewer": "Reviewer_W1Pi", "summary": "This 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.", "questions": "See the Weaknesses above.", "limitations": "I do not observer any potential negative societal impact of this work.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**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.", "weaknesses": "**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", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are truly grateful for the time you have taken to review our paper, and your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your concerns and provide additional clarification.\n\n> Q1. The exclusion of OT in scATAC-seq data due to cellular heterogeneity needs practical examples or quantitative analysis to substantiate its significance.\n\nA1. Thanks for your suggestion. We have added a model variant w/ OT for scATAC-seq to support our point. The compared results on CITE-ASAP dataset are shown below. We can find that our full model outperforms w/ OT for scATAC-seq, which validates the OT strategy may not be the optimal choice given the significant cellular heterogeneity inherent in scATAC-seq data.\n\n\n| Label Ratio                  | Low   | Mid   | High  |\n| ---------------------------- | ----- | ----- | ----- |\n| w/ OT for scATAC-seq         | 34.90 | 36.87 | 39.34 |\n| w/o OT for scATAC-seq (Ours) | **45.36** | **46.38** | **47.45** |\n\n> Q2. The initial prediction's dependency on the divide-and-conquer strategy could lead to misclassification. Discussion on handling wrongly divided samples or providing statistics is needed.\n\nA2. Thanks for your suggestion. We have added the comparison of the pseudo-labeling accuracy between unlabeled scRNA-seq data and source-like scATAC-seq data. The results on the snRNA_SMARTer-snmC dataset are shown below. From the results, we can observe that the accuracy of source-like scATAC-seq data is close to that of unlabeled scRNA-seq data, verifying the effectiveness of our division. \n\n| Pseudo-labeling Acc (%)     | Low   | Mid   | High  |\n| --------------------------- | ----- | ----- | ----- |\n| unlabeled scRNA-seq data    | 78.85 | 79.17 | 80.13 |\n| source-like scATAC-seq data | 77.12 | 78.04 | 78.56 |\n\nWe have also added a model variant w/o divide-and-conquer on snRNA_10X_v3_A-ATAC dataset for performance comparison. As evidenced by the results in the table below, the incorporation of the divide-and-conquer strategy yields positive gains.\n\n\n| Acc (%)                      | Low   | Mid   | High  |\n| ---------------------------- | ----- | ----- | ----- |\n| w/o divide-and-conquer       | 46.49 | 47.80 | 48.15 |\n| w/ divide-and-conquer (Ours) | **50.23** | **51.02** | **51.42** |\n\n> Q3. Exploring if DANCE can be conducted in the opposite direction (scATAC-seq with OT and scRNA-seq with divide-and-conquer) would be beneficial.\n\nA3. Thanks for your suggestion. We have included an experiment, which transfers cell type knowledge from scATAC-seq data to scRNA-seq data on the CITE-ASAP dataset. From the results below, we can validate the superiority of our method in the opposite direction.\n\n\t\n| Label Ratio | Low   | Mid   | High  |\n| ----------- | ----- | ----- | ----- |\n| scJoint     | 28.14 | 45.40 | 49.59 |\n| scBridge    | 32.24 | 51.55 | 53.62 |\n| scNCL       | 36.46 | 51.01 | 52.73 |\n| Ours        | **70.33** | **70.78** | **72.89** |\n\n\n> Q4. A discussion on the complexity of OT and divide-and-conquer in terms of memory and time compared to existing studies is required.\n\nA4. Thanks for your suggestion. We have included the comparison of memory and time as follows and we can observe that our method has a competitive computation cost. In particular, the performance of scNCL is much worse than ours (the performance increasement of ours is over 105.9%), while our memory cost only increases a little with even less training time. \n\n| CITE-ASAP | Memory Cost | Training Time/Epoch | Acc (%) |\n| --------- | ----------- | -------------------- | ------- |\n| scJoint   | 0.7GB       | 30s                  | 22.07   |\n| scBridge  | 1.4GB       | 4s                   | 23.38   |\n| scNCL     | 1.0GB       | 70s                  | 22.53   |\n| Ours      | 1.4GB       | 30s                  | **46.40**   |\n\n> Q5. Including a discussion on the use of scCLIP [1] in scenarios with scarce labels would enhance the paper.\n\nA5. Thanks for your suggestion. We will include the discussion in the revised version as follows. \n\n\"*scCLIP [1] introduces a contrastive learning approach to integrate multi-omic single-cell data. It aligns the representations of pairwise multi-omic single-cell data without the usage of cell type labels. In contrast, we focus on the cell type knowledge transfer task in label scarcity conditions*.\"\n\n\n**Reference**\n\n[1] Xiong et al.,scCLIP: Multi-modal Single-cell Contrastive Learning Integration Pre-training, NeurIPS Workshop 2023\n\n\nThanks again for appreciating our work and for your constructive suggestions. Please let us know if you have further questions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Pez5oyzka4", "reviewer": "Reviewer_kGak", "summary": "This 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.", "questions": "Please 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.", "limitations": "The author has explained the limitations in Sec. 5.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. 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.", "weaknesses": "1. 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", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your time in reviewing our paper and your insightful comments. Your positive feedback is incredibly encouraging for us! We'd like to address your concerns in the following response.\n\n> Q1. The compared methods, especially the DA method, are relatively old. Comparison with newer methods (e.g., [1], [2], etc.) helps to understand the performance of the methods.\n\nA1. Thanks for your suggestion. We have included the recommended baselines, i.e., SLA [1] and COT [2] on different datasets for performance comparison. The results are shown below, which demonstrate the superiority of our method. \n\n\n| Dataset     | CI-AS | CI-AS | CI-AS | sn-AT | sn-AT | sn-AT |\n| ----------- | ----- | ----- | ----- | ----- | ----- | ----- |\n| Label Ratio | Low   | Mid   | High  | Low   | Mid   | High  |\n| SLA [1]     | 32.05 | 36.99 | 41.74 | 35.36 | 37.48 | 42.41 |\n| COT [2]     | 31.44 | 35.86 | 39.01 | 35.73 | 36.79 | 43.85 |\n| Ours        | **45.36** | **46.38** | **47.45** | **50.23** | **51.02** | **51.42** |\n\n\n\n> Q2. 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.\n\nA2. Thanks for pointing out this typo. The term in Eq. 1 and Eq. 3 should be $\\sigma$, and in Theory 3.2 should be $\\beta$. We will correct all typos in the revised version.\n\n> Q3. 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 $\\tau$ will help to understand the method and promote future work.\n\nA3. Thanks for your suggestion. We have added the parameter sensitivity analysis by varying $\\lambda$ (Eq. 1) and $\\tau$ (Eq. 6). From the results below, we can observe that the performance is not sensitive to the choice of $\\lambda$ when we change $\\lambda$ from 5 to 20, so we empirically set its value to 10. We also vary the value of $\\tau$ with the range of {0.8,0.85,0.9,0.95}. The results below indicate that the method is not sensitive to $\\tau$ in the interval [0.8,0.95]. Therefore, we set $\\tau$ to 0.9 as the default. \n\n| $\\lambda$           | 5     | 10    | 15    | 20    |\n| ------------------- | ----- | ----- | ----- | ----- |\n| CITE-ASAP           | 47.01 | 47.45 | 47.32 | 47.14 |\n| snRNA_10X_v3_A-ATAC | 51.16 | 51.42 | 51.08 | 51.17 |\n\n\n| $\\tau$              | 0.8   | 0.85  | 0.9   | 0.95  |\n| ------------------- | ----- | ----- | ----- | ----- |\n| CITE-ASAP           | 46.98 | 47.06 | 47.45 | 47.33 |\n| snRNA_10X_v3_A-ATAC | 50.54 | 51.15 | 51.42 | 50.87 |\n\n\n> Q4. 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\nA4. Thanks for your comment. We have revised the figures according to your suggestions and uploaded the pdf file in the global response section.\n\n> Q5. 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\nA5. Thanks for your comment. We would like to point out that the innovation of our approach against existing methods is threefold:\n- **Underexplored Pratical Problem**. We focus on an underexplored yet practical problem of knowledge transfer across multi-omic single-cell data under label scarcity, while previous works focus on utilizing fully labeled scRNA-seq data. \n- **A Holistic Framework**. To both challenges of label scarcity and cell heterogeneity, we propose a holistic framework, which consists of OT-based dataset expansion and divide-and-conquer strategy for multi-omics semantic learning, followed by cross-omic multi-sample Mixup for semantics integration. All these designs are totally new for single-cell data integration. \n- **Theortical Analysis**. We provide a comprehensive theoretical analysis to support our designs, which makes our framework more solid. \n- **Superior Performance**. Our method achieves superior performance on benchmark datasets compared with state-of-the-art methods. In particular, the performance increasement on snRNA_10X_v3_A-ATAC is up to 96.4% compared to the best baseline.  \n \n**Reference**\n\n[1] Yu et al., Semi-Supervised Domain Adaptation with Source Label Adaptation, CVPR 2023  \n[2] Liu et al., COT: Unsupervised Domain Adaptation with Clustering and Optimal Transport, CVPR 2023\n\n\nThanks again for appreciating our work and for your constructive suggestions. Please let us know if you have further questions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M02WFXMJQH", "reviewer": "Reviewer_puzU", "summary": "This 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.", "questions": "Please refer to my concerns in the weaknesses section.", "limitations": "No significant limitations of this work are found.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. 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.", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the time you've taken to review our paper and for your insightful comments. Your positive feedback is highly encouraging for us! We'd like to address your concerns in the following response.\n\n> Q1. 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.\n\nA1. Thanks for your comment. The label scarcity in scRNA-seq data is practical as supported in the previous works [1,2,3]. They have developed semi-supervised approaches to address the label scarcity in the scRNA-seq data. We will include the citations in our revised version.\n\n\n> Q2. The uniform distribution assumption in the optimal transport process could be wrong, as most scRNA-seq data are unbalanced across different cell types.\n\nA2. Thanks for your comment. In real-world scenarios, we often have no prior knowledge about the distribution of specific cell types and adopting the uniform distribution assumption is a popular choice without prior information. In future work, we aim to extend our methods to scenarios with prior distribution information for imbalanced scenarios. We will include the discussion of potential future works in our revised version.\n\n> Q3. 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?\n\nA3. Thanks for your question. We have included a sensitivity analysis of the threshold $\\mu$ by varying it in {0.0005,0.001,0.0015,0.002}. The following results indicate that a threshold of 1e-3 brings good performance with significant differences. In particular, after the softmax operation, the sum of the confidences for all cell types equals 1 with more than 10 cell types. In this context, the confidence scores are quite small and a difference of 1e-3 indicates a sufficient difference.\n\n| Threshold $\\mu$     | 5e-4  | 1e-3  | 1.5e-3 | 2e-3  |\n| ------------------- | ----- | ----- | ------ | ----- |\n| CITE-ASAP           | 46.58 | 47.45 | 47.40  | 47.02 |\n| snRNA_10X_v3_A-ATAC | 50.07 | 51.42 | 51.14  | 51.03 |\n\n\n> Q4. What does $S_i$ in Eq. 10 mean?\n\nA4. Thanks for your question. $S_i$ is a typo and it should be $Y_i$ in Eq. 9. We will correct it in the revised version.\n\n> Q5. What is the motivation of mixing scRNA-seq data to form scATAC-seq data? Is such an operation biologically reasonable?\n\nA5. Thanks for your question. We **do not** generate scATAC-seq data from scRNA-seq data. Instead, we generate virtual scRNA-seq data using the Mixup technique and then reduce the semantic gap between scATAC-seq data and this virtual scRNA-seq data in the embedding space. From a biological perspective, scATAC-seq data is characterized by extreme sparsity. This results in input matrices that are significantly heterogeneous compared to scRNA-seq data. To address the challenges in data integration, our motivation is to reduce the distance between scATAC-seq data and the mixed (virtual) scRNA-seq data in the embedding space. \n\n> Q6. Typo: In line 253 cBridge -> scBridge.\n\nA6. Thanks for pointing out the typo. We will correct it in the revised version.\n\n> Q7. 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\nA7. Thanks for your suggestion. We have included the experimental results on the full dataset. The results below indicate that our approach still outperforms other methods. We will include this in our revised version.\n\n| Label Ratio | Low   | Mid   | High  |\n| ----------- | ----- | ----- | ----- |\n| scJoint     | 62.36 | 67.75 | 70.02 |\n| scBridge    | 64.99 | 69.86 | 71.44 |\n| scNCL       | 61.50 | 69.04 | 71.17 |\n| Ours        | **70.64** | **72.21** | **72.58** |\n\n**Reference**\n\n[1] Dong et al., Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022   \n[2] Kimmel et al., scNym: Semi-supervised adversarial neural networks for single cell classification, bioRxiv, 2020  \n[3] We et al., CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data, Bioinformatics, 2021\n\nThanks again for appreciating our work and for your constructive suggestions. Please let us know if you have further questions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uTtZkWTmnE", "reviewer": "Reviewer_L3nP", "summary": "This 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.", "questions": "I 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.", "limitations": "The authors have addressed limitations such as only applicable to open-set settings.", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Overall, 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.", "weaknesses": "I'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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your concerns in the following.\n\n> Q1. I'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. \n\nA1. Thanks for your comment. The label scarcity in scRNA-seq data is practical as supported in the previous works [1,2,3]. They have developed semi-supervised approaches to address the label scarcity in the scRNA-seq data. We will include the citations in our revised version.\n\n> Q2. 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\nA2. Thanks for your question. The experiments in the manuscript are randomly removed. We have added an experiment to compare the performance of removing annotations by batches. Specifically, we collect different batches of MouseAtlas dataset and remove some of the batches. From the following results, we can observe that our approach still outperforms other methods, which verifies the robustness of our approach in different scenarios. We will include this in our revised version.\n\n| Label Ratio | Low   | Mid   | High  |\n| ----------- | ----- | ----- | ----- |\n| scJoint     | 62.36 | 67.75 | 70.02 |\n| scBridge    | 64.99 | 69.86 | 71.44 |\n| scNCL       | 61.50 | 69.04 | 71.17 |\n| Ours        | **70.64** | **72.21** | **72.58** |\n\n\n>Q3. I 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\nA3. Thanks for your suggestion. To solve your concern, we have included Seurat [4] and Harmony [5] for performance comparison. For these two methods, we first learn learn the full annotation on scRNA-seq data, and then transfer the knowledge to scATAC-seq data. From the following results on CITE-ASAP and snRNA_10X_v3_A-ATAC, we can find that our method surpasses other methods consistently. We will include this in our revised version.\n\n| Dataset     | CI-AS | CI-AS | CI-AS | sn-AT | sn-AT | sn-AT |\n| ----------- | ----- | ----- | ----- | ----- | ----- | ----- |\n| Label Ratio | Low   | Mid   | High  | Low   | Mid   | High  |\n| Seurat      | 38.71 | 41.76 | 43.25 | 33.46 | 39.78 | 45.09 |\n| Harmony     | 39.03 | 39.99 | 44.06 | 32.17 | 39.94 | 44.56 |\n| Ours        | **45.36** | **46.38** | **47.45** | **50.23** | **51.02** | **51.42** |\n\n\n\n**Reference**\n\n[1] Dong et al., Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022\n\n[2] Kimmel et al., scNym: Semi-supervised adversarial neural networks for single cell classification, bioRxiv, 2020\n\n[3] We et al., CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data, Bioinformatics, 2021\n\n[4] Stuart et al., Comprehensive integration of single-cell data, Cell, 2019  \n\n[5] Korsunsky et al., Fast, sensitive and accurate integration of single-cell data with Harmony, Nature Methods, 2019\n\nIn light of these responses, we hope we have addressed your concerns, and hope you will consider raising your score. If there are any additional notable points of concern that we have not yet addressed, please do not hesitate to share them, and we will promptly attend to those points.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear Reviewers,

We thank you for your careful reviews and constructive suggestions. We acknowledge the positive comments such as "effective and well-handled method" (Reviewer W1Pi), "impressive performance" (Reviewer W1Pi), “well-written and organized” (Reviewer W1Pi, Reviewer puzU), "well-motivated and reproducible" (Reviewer kGak), "well-structured and easy to follow” (Reviewer kGak), "solid theory” (Reviewer kGak), "good experiment” (Reviewer kGak), "technically sound method” (Reviewer puzU), “effective ablation studies” (Reviewer puzU, Reviewer L3nP), “innovative approach” (Reviewer L3nP). 

We have also responded to your questions point by point. The revised figures are uploaded in the pdf file.

Please let us know if you have any follow-up questions. We will be happy to address them.

Best regards,

the Authors

Meta-review: This paper proposes a method to transfer knowledge from single-cell RNA sequencing (scRNA-seq) to single-cell ATAC-sequencing (scATAC-seq), a problem that can be formulated as domain adaptation. The proposed method, Dual Label Scarcity Elimination with Cross-omic Multi-sample Mixup, or DANCE for short, achieves this knowledge transfer through several steps, including expanding the labeled RNA data via pseudo-labeling and using a confidence-based divide-and-conquer algorithm to identify easy/hard scATAC-seq samples with respect to scRNA-seq, thereby efficiently transferring knowledge to the ATAC sequencing domain.

The paper initially received scores of 2x6 (weak accept), 1x5 (borderline accept), and 1x4 (borderline reject). After the rebuttal and during the author-reviewer discussion period, the scores improved to 2x7 (accept), 1x5 (borderline accept), and 1x4 (borderline reject). The reviewers acknowledged the novelty of the idea within the context of the problem, and the AC agrees with this assessment, recommending the paper for acceptance. **Congratulations**.

The AC suggests including all the results discussed during the rebuttal and the author-reviewer discussions. Additionally, the following two aspects should be addressed:

1. Provide an intuitive explanation of the theorems and their implications in the main text. Currently, the theorems appear somewhat disconnected, and an intuitive explanation will help readers appreciate them better.

2. The choice of marginals in the optimal transport (OT) implies that mini-batches should be uniform across the class distribution. This should be discussed and explained.

Congratulations again.
Decision: Accept (poster)