Original: Interpretable visualization of single cell data using Janus autoencodersAnonymous Author(s)
Revised: Interpretable visualization of single cell data using Janus autoencoders

Original: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: The emergence of single-cell transcriptomics and proteomics approaches has resulted in a wealth of high-dimensional data that are challenging to interpret.
Revised: The emergence of single-cell transcriptomics and proteomics approaches has resulted in a wealth of high-dimensional data that are challenging to interpret.

Original: Dimensionality reduction methods, such as UMAP and t-SNE, project data pointsonto a low-dimensional space that preserves cellular similarities from the highdimensional measurement space.
Revised: Dimensionality reduction methods, such as UMAP and t-SNE, project data points onto a low-dimensional space that preserves cellular similarities from the highdimensional measurement space.

Original: However, the projected dimensions typicallyhave no interpretable biological meaning, and the relationships between measuredbiomolecular features are obscured completely.
Revised: However, the projected dimensions typically have no interpretable biological meaning, and the relationships between measured biomolecular features are obscured completely.

Original: These limitations can be overcomeby finding embeddings in which each dimension is a function of a distinct andbiologically meaningful set of features.
Revised: These limitations can be overcome by finding embeddings in which each dimension is a function of a distinct and biologically meaningful set of features.

Original: Here, we introduce Janus autoencoders,a novel neural network architecture capable of finding such low-dimensional embeddings by jointly optimizing multiple distinct one-dimensional embeddings ofa dataset.
Revised: Here, we introduce Janus autoencoders, a novel neural network architecture capable of finding such low-dimensional embeddings by jointly optimizing multiple distinct one-dimensional embeddings of a dataset.

Original: We demonstrate the utility of Janus autoencoders for (1) visualizingmultiomic data such that modality-specific contributions to cell type can be deconvolved and (2) visualizing mass cytometry data such that cell cycle effects can bedistinguished from “true” cell state differences.
Revised: We demonstrate the utility of Janus autoencoders for (1) visualizing multiomic data such that modality-specific contributions to cell state can be deconvolved and (2) visualizing mass cytometry data such that cell cycle effects can be distinguished from “true” cell state differences.

Original: Our initial demonstrations indicatethat Janus autoencoders can uncover relationships between cellular states and theirunderlying cellular features in multiple biological contexts, with the potential togenerally enable highly interpretable visualizations of single cell data.
Revised: Our initial demonstrations indicate that Janus autoencoders can visually represent relationships between cellular states and their underlying cellular features in multiple biological contexts.

Original:  Introduction
Revised:  Introduction

Original: Single-cell sequencing technologies enable the profiling of whole genomes and/or transcriptomesof thousands of single cells in a single experiment [10, 6].
Revised: The advances of sequencing technology enables profiling of whole genomes and/or transcriptomes at single-cell resolution [10, 6].

Original: Single-cell proteomics technologies,such as mass cytometry (CyTOF), CITE-seq, and CODEX, use antibodies to detect proteins ofinterest and allow for the simultaneous quantification of over 50 epitopes in thousands of single cells[2, 14, 8].
Revised: Single-cell proteomics technologies, such as mass cytometry (CyTOF), CITE-seq, and CODEX, use antibodies to detect proteins of interest and allow for the simultaneous quantification of over 50 epitopes in millions of single cells [2, 14, 8].

Original: Such high-dimensional measurements yield information on regulatory mechanisms ofcellular behaviors and enable investigations of cellular and tissue heterogeneity.
Revised: Such highdimensional measurements yield information on regulatory mechanisms of cellular behaviors and enable investigations of cellular and tissue heterogeneity.

Original: However, preciseinterpretations of highly multiplexed data remain challenging.
Revised: However, precise interpretations of highly multiplexed data remain challenging.

Original: Visualization is an essential step in the analysis of high dimensional single cell data.
Revised: Visualization is an essential step in the analysis of high dimensional single cell data.

Original: Dimensionalityreduction techniques allow visualization of -omics data in two-dimensional space.
Revised: Dimensionality reduction techniques allow visualization of -omics data in two-dimensional plots, interpretable by human inspection.

Original: Principal component analysis (PCA) is typically performed to linearly transform and project high-dimensional data tocoordinates that preserve variation in cellular state.
Revised: Principal component analysis (PCA) is typically performed to linearly transform and project high-dimensional data to coordinates that preserve variation in cellular state.

Original: Recently popularized non-linear dimensionalityreduction techniques, such as t-stochastic neighbour embedding (t-SNE)
Revised: Recently popularized non-linear dimensionality reduction techniques, such as t-stochastic neighbour embedding (t-SNE)

Original: [11] and uniform manifoldapproximation and projection (UMAP) [1], aim to find a nonlinear mapping from a high-dimensionalmeasurement space to a low-dimensional (typically 2D) space, in which similarities between datapoints are preserved.
Revised: [11] and uniform manifold approximation and projection (UMAP) [1], aim to find a nonlinear mapping from a high-dimensional measurement space to a low-dimensional (typically 2D) space, in which similarities between data points are preserved.

Original: Although UMAP and t-SNE are widely used in analyzing transcriptomics and proteomics data, afew inherent limitations of these type of methods prevent deep and precise biological interpretations.
Revised: Although UMAP and t-SNE are widely used in analyzing transcriptomics and proteomics data, a few inherent limitations of these type of methods prevent deep and precise biological interpretations.

Original: First, the low-dimensional embeddings identified by UMAP and t-SNE are determined by arbitrarynonlinear maps of the high-dimensional input space.
Revised: First, the low-dimensional embeddings identified by UMAP and t-SNE are determined by arbitrary nonlinear maps of the high-dimensional input space.

Original: As such, the dimensions of the low-dimensionalembedding have no interpretable biological meaning.
Revised: As such, the dimensions of the low-dimensional embedding have no interpretable biological meaning.

Original: Second, while these methods preserve thesimilarity of single cells, the relationships between features are lost completely in the nonlinearmapping.
Revised: Second, while these methods preserve the similarity of single cells, the relationships between features are lost completely in the nonlinear mapping.

Original: Third, multi-omics analysis with UMAP or t-SNE involves intensive data normalization,preprocessing and integration [3, 9] which could potentially bias resulting visualizations.
Revised: Third, multi-omics analysis with UMAP or t-SNE involves intensive data normalization, preprocessing and integration [3, 9] which could potentially bias resulting visualizations.

Original: One-SENSE, a related nonlinear dimensionality reduction approach, aims to find embeddings inwhich the dimensions have interpretable biological meaning and relationships between markersof interest are preserved [5].
Revised: One-SENSE, a related nonlinear dimensionality reduction approach, aims to find embeddings in which the dimensions have interpretable biological meaning and relationships between markers of interest are preserved [5].

Original: This is done by splitting measured cellular features into biologicallymeaningful categories using prior knowledge, mapping each category of features to a single dimensionusing t-SNE or UMAP, and plotting the one dimensional embeddings together.
Revised: This is done by splitting measured cellular features into biologically meaningful categories using prior knowledge, mapping each category of features to a single dimension using t-SNE or UMAP, and plotting the one dimensional embeddings together in a biaxial plot.

Original: This yields dimensionsthat each represent a distinct category of features, such that variation along an axis indicates thecategory of features responsible the variation.
Revised: This yields dimensions that each represent a distinct category of features, such that variation along an axis indicates the category of features responsible the variation.

Original: However, one-SENSE visualizations typically lack well-defined clusters and clear distinctions of cellstate [5, 12].
Revised: However, one-SENSE visualizations typically lack well-defined clusters and clear distinctions of cell state [5, 12].

Original: We hypothesize that this  due to the lack of robustness of reduction to a single dimensionusing UMAP and t-SNE.
Revised: We hypothesize that this is due to the lack of robustness of reduction to a single dimension using UMAP and t-SNE.

Original: It has been shown that there are often many appreciably different yetequally faithful low dimensional embeddings of single cell data [4].
Revised: It has been shown that there are often many appreciably different yet equally faithful low dimensional embeddings of single cell data [4].

Original: As there are likely to be severalequivalently faithful one-dimensional embeddings, different pairs of one-dimensional embeddingsmay have very different qualities when jointly visualized in two-dimensional space.
Revised: As there are likely to be several equivalently faithful one-dimensional embeddings, different pairs of one-dimensional embeddings may have very different qualities when jointly visualized in two-dimensional space.

Original: In one-SENSE,the two one-dimensional embeddings are optimized completely separately, so an “optimal” pair ofone-dimensional embeddings  cannot be identified.
Revised: In one-SENSE, the two one-dimensional embeddings are optimized completely separately, so pairs of one-dimensional embeddings suitable for 2D visualization cannot be selected for.

Original: In this work, we seek to visualize single cell data using a nonlinear dimensionality reduction methodin which each reduced dimension represents a distinct, biologically meaningful set of features.
Revised: In this work, we seek to visualize single cell data using a nonlinear dimensionality reduction method in which each reduced dimension represents a distinct, biologically meaningful set of features.

Original: Incontrast with previous methods, we preserve cell state information by jointly optimizing multiple onedimensional embeddings in a context-aware fashion.
Revised: In contrast with previous methods, we preserve cell state information by jointly optimizing multiple onedimensional embeddings in a context-aware fashion.

Original: To perform this optimization, we introduce Janusautoencoders, a novel neural network architecture.
Revised: To perform this optimization, we introduce Janus autoencoders, a novel neural network architecture.

Original: We demonstrate the utility of Janus autoencodersfor two tasks: (1) visualizing multiomic data such that modality-specific contributions to cell statecan be deconvolved, and (2) visualizing mass cytometry data such that cell cycle effects can bedistinguished from “true” cell state differences.
Revised: We demonstrate the utility of Janus autoencoders for two tasks: (1) visualizing multiomic data such that modality-specific contributions to cell state can be deconvolved, and (2) visualizing mass cytometry data such that cell cycle effects can be distinguished from “true” cell state differences.

Original:  Results2.1 Network architecture
Revised:  Results 2.1 Network architecture

Original: In order to jointly optimize multiple one dimensional embeddings of high dimensional data, we havedeveloped a topologically modified autoencoder that we refer to as a Janus ("two-faced") autoencoder(Fig 1).
Revised: To jointly optimize multiple one dimensional embeddings of high dimensional data, we have developed a topologically modified autoencoder that we refer to as a Janus ("two-faced") autoencoder (Fig 1a).

Original: While traditional autoencoders find low dimensional latent spaces in which each dimensionis a function of all input dimensions, Janus autoencoders constrain the information used in eachlatent dimension by restricting the connectivity of the network.
Revised: While traditional autoencoders find low dimensional latent spaces in which each dimension is a function of all input dimensions, Janus autoencoders constrain the information used in each latent dimension by restricting the connectivity of the network.

Original: In particular, Janus autoencoderscontain multiple disjoint encoder modules which map distinct sets of input dimensions to distinctlatent dimensions, each of which can be used for a one-dimensional embedding.
Revised: In particular, Janus autoencoders contain multiple disjoint encoder modules which map distinct sets of input dimensions to distinct latent dimensions, each of which can be used for a one-dimensional embedding.

Original: To ensure that theselatent dimensions yield faithful joint embeddings as well as faithful one-dimensional embeddings,we use multiple decoder modules: one for each latent dimension which aims to reconstruct only theconnected inputs, and one “joint” decoder which aims to reconstruct the full initial input based on alllatent dimensions.
Revised: To ensure that these latent dimensions yield faithful joint embeddings as well as faithful one-dimensional embeddings, we use multiple decoder modules: one for each latent dimension which aims to reconstruct only the connected inputs, and one “joint” decoder which aims to reconstruct the full initial input based on all latent dimensions.

Original: In the case of a Janus autoencoder with 2 latent dimensions, where the input X is split into disjointcategories X 1 , X 2 , with reconstructions ˆ X, ˆ X 1 , ˆ X 2 , we use a weighted mean-squared error lossfunction [Equation] where the decoder weights w 1 , w 2 , w 3 are user-defined parameters which must be tuned for eachspecific application. In initial demonstrations, we have used three hidden layers for each of the encoders and decoders,generally following the rule of thumb that each successive hidden encoding layer should decrease insize by a factor of 2 until enforced to be 1 in the latent dimension, and the inverse for decoding layers.
Revised: A more specific description of the Janus architecture can be found in Appendix.

Original: Anecdotally, we find that embeddings are not particularly sensitive to hidden layer sizes, and defaultselections are often sufficient.

Original: 2.2 Decoupling modal contributions to cell type in single cell CITE-seq data
Revised: 2.2 Decoupling modal contributions to cell state in single cell CITE-seq data

Original: Janus autoencoders are natural tools for analyzing multiomic datasets.
Revised: Janus autoencoders can be used naturally to analyze multiomics data in a modality-aware fashion, without intensive preprocessing or integration.

Original: With each measurementmodality mapped to a different latent dimension, it is possible to infer modality-specific contributionsto cell state based on visualization of the latent space.
Revised: With each measurement modality mapped to a different latent dimension, it is possible to infer modality-specific contributions to cell state based on visualization of the latent space.

Original: We demonstrate a proof-of-concept using apublically available CITE-seq [14] dataset  quantifying the transcripts and surface proteins of 5,000peripheral blood mononuclear cells (PBMCs)
Revised: We demonstrate a proof-of-concept using a publically available CITE-seq [14] dataset simultaneously quantifying the transcripts and surface proteins of 5, peripheral blood mononuclear cells (PBMCs)

Original: In line with prior analyses of multiomic PBMC datasets [3], we first determined putative coarse celltype clusters based on multiplexed Leiden clustering. As expected, visualization of the dataset usinga traditional 2D UMAP preserves putative cell type clusters (Fig. 2a). However, in this visualization,it is not possible to deconvolve the contributions of the two modalities. Such deconvolution is possible when each reduced dimension contains information from onlyone modality.
Revised: Using Janus autoencoders, we visualize this data such that each axis corresponds to information from a single modality (Fig. 1b).

Original: A oneSENSE plot, where UMAP reductions to a single dimension are performedseparately for RNA measurements and protein measurements, satisfies this criteria. However, wefind that oneSENSE is unable to preserve putative cell type clusters (Fig. 2b), indicating inaccurateembeddings. With visualization by Janus autoencoding, each axis is determined using only information froma single modality, and putative cell clusters are preserved, indicating accurate embeddings (Fig.2c). Based on the Janus autoencoder visualization, we observe that the RNA measurements containa large amount of cell type information, and that protein measurements can be used to resolvetranscriptomically similar cell types.
Revised: Qualitatively, Leiden clusters are preserved, satisfying an initial sanity check for accurate embeddings.

Original: This is in line with observations made in a recent integrativeanalysis of other multiomic PBMC datasets [9].
Revised: When a similar visualization is generated using oneSENSE, this sanity check is failed (Appendix Fig. 2b).

Original: Additionally, the Janus autoencoder shows distinct populations of PBMCs within coarse cell typeclusters.
Revised: Furthermore, the Janus visualization shows distinct populations of PBMCs within coarse Leiden clusters.

Original: The putative monocyte cluster (shown in blue) diverges into two populations in the Janus
Revised: For example, the cluster shown in green diverges into two sub-clusters along the RNA axis.

Original: [Figure] autoencoder visualization, separated only on the protein dimension, suggesting that cells with similartranscriptomic state may exhibit different surface marker profiles due to  the exogenously inducedcell surface protein remodeling or post-tranlational modifications. Similarly, the CD8+ memory T cell cluster (shown in green) diverges into two sub-clusters along the RNA axis in the Janusautoencoder visualization, indicating thatcells with similar surface marker signatures could havedistinct transcriptomic states.
Revised: Based on established cell type markers [15], we determined that this cluster corresponds to CD8+ T-cells, and the sub-clusters correspond to CD8+ memory and naive T-cells, differentiated by transcriptomic state (Appendix Fig 3).

Original: It has been demonstrated that mRNA levels in cells and tissues do not  predict protein abundance [15].
Revised: It is well known mRNA levels in cells and tissues do not fully predict protein abundance [16].

Original: In this initial demonstration, we show that Janus antoencoders could be used to decipher relationshipsbetween transcriptome and proteome in multi-omics data.
Revised: In this initial demonstration, we show that Janus antoencoders could be used to decipher relationships between transcriptome and proteome in multi-omics data.

Original: 2.3 Regressing out cell cycle effects in mass cytometry data
Revised: 2.3 Regressing out cell cycle effects in mass cytometry data

Original: It is known that experimentally meaningful single cell variation is often obscured by confoundingfactors such as cell cycle stage and cell size [13].
Revised: Experimentally meaningful single cell variation is often obscured by confounding factors such as cell cycle stage and cell size [13].

Original: Using Janus autoencoders, it is possible to mappotential confounding features to a different latent dimension than other markers of interest, allowingone to visualize the effects of and regress out confounding features in single cell datasets.
Revised: Using Janus autoencoders, it is possible to map potential confounding features to a different latent dimension than other markers of interest, allowing one to visualize the effects of and regress out confounding features in single cell datasets.

Original: We demonstrate a proof of concept using a mass cytometry dataset which profiles 4 proteins withprominent cell cycle dependence in addition to 31 intracellular phosphorylation sites in a monocytecell line (THP1) [13].
Revised: We demonstrate a proof of concept using a mass cytometry dataset which profiles 4 proteins with prominent cell cycle dependence in addition to 31 intracellular phosphorylation sites in a monocyte cell line (THP1) [13].

Original: As observed in prior work, non-cell-cycle aware analyses, such as visualizationwith 2D UMAP, erroneously indicate the presence of multiple cell clusters due to the differential cellcycle states of analyzed cells (Fig 3a).
Revised: As observed in prior work, non-cell-cycle aware analyses, such as visualization with 2D UMAP, erroneously indicate the presence of multiple cell clusters due to the differential cell cycle states of analyzed cells (Appendix Fig 4a).

Original: By analyzing cell-cycle dependent markers independently from the phosphorylation panel, it ispossible to deconvolve cell cycle effects from experimentally meaningful differences in cell state.
Revised: By analyzing cell-cycle dependent markers independently from the phosphorylation panel, it is possible to deconvolve cell cycle effects from experimentally meaningful differences in cell state.

Original: When applying oneSENSE with cell cycle markers mapped to one dimension and standard targetsmapped to another dimension, we see thatalmost all single cell variation is attributable to cell cycle(Fig 3b). However, the oneSENSE plot contains cluster-like structures, rather than the ground-truthcontinuum expected based on prior work [13].
Revised: When visualized with Janus autoencoders, it is clear that cell state is relatively homogeneous when standard phosphorylation targets are decoupled from cell-cycle dependent targets, with the exception of cells in M-phase (Appendix Fig 4c), in line with prior analyses [13].

Original: Additionally, theoneSENSE plot fails to recover anaccurate pseudotime ordering of cells, as approximated by phospho-RB expression.
Revised: Furthermore, the Janus autoencoder visualization faithfully reconstructs the pseudotime ordering of the cells in their cell cycle state (Fig 1c).

Original: When visualized with Janus autoencoders, it is clear that cell state is relatively homogeneous whenstandard phosphorylation targets are decoupled from cell-cycle dependent targets, with the exceptionof cells in M-phase, in line with prior analyses [13].
Revised: In comparison to an analogous plot generated using oneSENSE, cell cycle trajectory is captured much more effectively: the Janus autoencoder has Spearman rank correlation of > 0 . 99 with a pseudotime estimate, compared to ∼ 0 .

Original: Furthermore, the Janus autoencoder visualizationfaithfully reconstructs the pseudotime ordering of the cells in their cell cycle state (Fig. 3c).
Revised: 88 for the analogous oneSENSE plot.

Original: Thisanalysis indicates that Janus autoencoders enable directly analyzing the effects of confounding factorsin mass cytometry data without intensive data correction or normalization.[Figure]
Revised: This analysis indicates that Janus autoencoders enable directly analyzing the effects of confounding factors in mass cytometry data without intensive data correction or normalization.

Original:  Discussion
Revised:  Discussion

Original: In this work, we have introduced Janus autoencoders, a novel neural network architecture forjointly learning multiple one dimensional embeddings of high dimensional data.
Revised: In this work, we have introduced Janus autoencoders, a novel neural network architecture for jointly learning multiple one dimensional embeddings of high dimensional data.

Original: Through initialdemonstrations for modality-aware cell typing in multiomic  data and cell cycle normalization in masscytometry data, we have shown the potential general utility of Janus autoencoders for interpretablevisualization of single cell data.In both single-cell analysis problems, we observe that Janus autoencoders are able to capture theexpected qualitative trends in cell state, while introducing meaning to the dimensions in visualizations. In the analysis of CITE-seq profiling of PBMCs, Janus autoencoders are able to capture cell typeclusters while revealing the distinct contributions of the protein and RNA modalities. In the analysisof cell cycle effects in mass cytometry data, Janus autoencoders are able to reconstruct the pseudotimeordering of cells, while regressing out cell cycle dependent single cell variation.
Revised: Through initial demonstrations for modality-aware clustering in multiomic sequencing data and cell cycle normalization in mass cytometry data, we have shown the potential general utility of Janus autoencoders for interpretable visualization of single cell data.

Original: Compared to oneSENSE,a similar approach utilizing 1D embeddings to profile the relationshipsbetween categorized sets of features, Janus autoencoders qualitatively demonstrate a greater capacityto capture cell type and pseudotime information in both initial demonstrations. Cell typing, pseudotime inference, and confounding effect regression are common single cell dataanalysis tasks, each typically done using a distinct computational tool. In this work, we have shownthat Janus autoencoders can perform each of these tasks, without explicit optimization for any specifictask. This indicates that Janus autoencoders and similar approaches could potentially serve as generaltools for nonlinear analyses of single cell data.
Revised: Compared to oneSENSE, the existing state-of-the-art, Janus autoencoders appear to more effectively capture cell type information, regress out cell cycle effects, and preserve pseudotime ordering.

Original: Janus autoencoders are subject to some of the same intrinsic shortcomings as other single cell datavisualization techniques.
Revised: Janus autoencoders are subject to some of the same intrinsic shortcomings as other single cell data visualization techniques.

Original: In particular, for most high-dimensional datasets, it is highly unclear ifreduction to a low dimensional space is mathematically justifiable.
Revised: In particular, for most high-dimensional datasets, it is highly unclear if reduction to a low dimensional space is mathematically justifiable.

Original: In future work, we plan to developgeneral metrics of embedding quality that can serve as a heuristic for the validity of visualizationsproduced by Janus autoencoders.
Revised: In future work, we plan to develop general metrics of embedding quality that can serve as a heuristic for the validity of visualizations produced by Janus autoencoders.

Original: Janus autoencoders currently have not been formulated in a probabilistic setting.

Original: In future work, itwould be compelling to create variational analogs of Janus autoencoders, such that identified latentspaces can be interpreted through the lens of Bayesian inference.

Original: Such formalization could enablequantitative exploration of embeddings, rather than qualitative assessments based on visualizations.

Original:  Acknowledgements
Revised:  Acknowledgements

Original: We thank Alan Amin and Tatiana Brailovskaya for fruitful discussions.
Revised: We thank Alan Amin and Tatiana Brailovskaya for fruitful discussions.

Revised: We thank anonymous reviewers for thoughtful criticism.

Original: We acknowledge the Caltech Summer Undergraduate Fellowship (SURF) Program and Wyss Institute Molecular Robotics Initiative(MRI) for funding.
Revised: We acknowledge the Caltech Summer Undergraduate Fellowship (SURF) Program and Wyss Institute Molecular Robotics Initiative (MRI) for funding.

Original: We thank the Jupyter Project for maintaining open-source computational tools.
Revised: We thank the Jupyter Project for maintaining open-source computational tools.

Revised:  Appendix 5.1 Network architecture details

Revised: Informally, for the case of a two dimensional latent space, Janus architectures contain two encoder modules computing

Revised: [Equation]

Revised: in which x 1 , x 2 are two disjoint subsets of elements in the input vector x , and z 1 , z 2 are the latent dimensions to which they are mapped.

Revised: These two latent dimensions are decoded with three modules, computing

Revised: [Equation]

Revised: in which | ˆ x | = | x 1 | + | x 2 | .

Revised: In the case of a Janus autoencoder with 2 latent dimensions, where the input X is split into disjoint categories X 1 , X 2 , with reconstructions ˆ X, ˆ X 1 , ˆ X 2 , we use a weighted mean-squared error loss function

Revised: [Equation]

Revised: where the decoder weights w 1 , w 2 , w 3 are user-defined parameters which must be tuned for each specific application.

Revised: In initial demonstrations, we have used three hidden layers for each of the encoders and decoders, generally following the rule of thumb that each successive hidden encoding layer should decrease in size by a factor of 2 until enforced to be 1 in the latent dimension, and the inverse for decoding layers.

Revised: While this architecture is theoretically generalizable to larger latent spaces, we have thus far explored only 2 dimensional latent spaces.

Revised: 5.2 CITE-seq analysis details

Revised: We used MUON [3] to preprocess data by filtering cells based on read count and percentage of mitochondrial reads, log transforming RNA counts, and selecting highly variable genes.

Revised: In line with the MUON documentation, we performed multiplexed Leiden clustering.

Revised: We generated a conventional 2D UMAP plot, preconditioned on 20 principal components (Fig 2a).

Revised: We find that a OneSENSE plot of the data partitioned by modality does not preserve Leiden clusters, indicating that cell type information is not faithfully represented (Fig 2b).

Revised: As seen in Fig , the Leiden cluster shown in green in Fig 2c contains cells with high CD8 protein expression.

Revised: Through differential gene expression analysis, we found that the left subcluster has high expression of CCL5, a marker of memory T cells [15].

Revised: As such, we propose that the Janus autoencoder has differentiated CD8 positive cells into memory and naive cells, a finer visualization of cell type than shown in the 2D UMAP 2a.

Revised: We note that while this Leiden cluster is split in the

Revised: One-SENSE plot, it diverges on the protein axis rather than the RNA axis, and thus cannot indicate the same distinction in CD8 positive subtypes.

Revised: [Figure]

Revised: [Figure]

Revised: [Figure]