\begin{abstract}

% Contrastive learning in image and text in image and text normally assumes we have paired data from each modality.
% However, obtaining paired dataset is generally not viable in biology as each modality are from different physical cells and other data collection issues like batch effect.
% Most of the prior works assumes biological data to be random paired and apply contrastive learning methods designed under paired assumption.
% In this paper, we seek to respect the reality, face that truth that the biological datasets are not paired by defining the unpaired contrastive learning with treatment group labels.
% We propose the first approach to tackle Unpaired contrastive learning (UpCon) with the help of intra-treatment group matching and inter-treatment group clustering.
% We curated four unpaired biological datasets with two modalities, treatment labels and downstream task labels. We show that randomly paired dataset negatively affect representation learning. We also show that our method UpCon out performs all of the prior baselines designed for paired datasets.
%We aim to provide method that can first measure the (un)pairness in our SSL dataset. Then we seek to propose learning objective(s) that take the (un)pairness of each sample into consideration during learning. Finally, with the hope that

Multimodal learning holds tremendous promise for biology, providing a path to integrate diverse data types and ultimately construct a more complete picture of underlying biological mechanisms. However, most existing approaches for multimodal learning require paired samples---an impractical assumption in biology, where measurement devices often destroy samples (e.g., RNA sequencing). To address this challenge, we introduce IntraPair InterCluster (IPIC), a novel contrastive approach for multimodal learning  that departs from traditional reliance on paired data by requiring only treatment-group labels. IPIC aligns modalities through intra-treatment group matching and inter-treatment group clustering, producing embeddings that are both accurate and biologically meaningful. In experiments on four curated multimodal biological datasets, IPIC consistently outperforms baseline approaches, highlighting its effectiveness in leveraging independently collected single-modality datasets for multimodal contrastive pre-training.
%for unpaired representation learning in biological contexts. Our IPIC method paves the way to efficiently leverage 
%Our IPIC method is the first end-to-end approach for unpaired multimodal learning in biological assays, paving the way to associate previously independently collected single-modality datasets for contrastive pre-training.

% OLD ABSTRACT ------
% In this paper, we introduce a novel approach to contrastive learning, termed Unpaired Contrastive Learning (UpCon), tailored for biological datasets that contain unpaired modalities connected solely through treatment group labels. Unlike existing contrastive learning methods in biology, which assume random pairing between samples across modalities, we acknowledge the reality that biological assays often lack direct pairings due to the destructive nature of data collection processes such as RNA sequencing and imaging. To address this challenge, UpCon leverages intra-treatment group matching and inter-treatment group clustering to bridge the gap in representation learning from unpaired data.
% Our study includes four curated unpaired multimodal biological datasets with treatments and downstream task labels. Experimental results demonstrate that traditional approaches relying on random pairing negatively impact representation learning quality. By contrast, UpCon consistently outperforms baseline methods designed for paired datasets, offering more accurate and biologically relevant embeddings. 
% Our UpCon method is the first end-to-end approach for unpaired contrastive learning in biological assays, paving the way to associate previously independently collected single-modality datasets for contrastive pre-training.
% ----
%, advancing the use of self-supervised learning for complex biological systems where direct pairings are impractical.

\end{abstract}