Original: P REDICTING SINGLE - CELL PERTURBATION RESPONSES FOR UNSEEN DRUGS
Revised: P REDICTING SINGLE - CELL PERTURBATION RESPONSES FOR UNSEEN DRUGS

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Revised: Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.

Original: However, scaling highthroughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments.
Revised: However, scaling highthroughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments.

Original: Thus, transferring information from routinely performed bulk RNA-seq HTS is required to enrich single-cell data meaningfully.
Revised: Thus, transferring information from routinely performed bulk RNA-seq HTS is required to enrich single-cell data meaningfully.

Original: We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs.
Revised: We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs.

Original: We combine the model with a transfer learning scheme and demonstrate how training on existing bulk RNA-seq HTS datasets can improve generalisation performance.
Revised: We combine the model with a transfer learning scheme and demonstrate how training on existing bulk RNA-seq HTS datasets can improve generalisation performance.

Original: Better generalisation reduces the need for extensive and costly screens at single-cell resolution.
Revised: Better generalisation reduces the need for extensive and costly screens at single-cell resolution.

Original: We envision that our proposed method will facilitate more efﬁcient experiment designs through its ability to generate insilico hypotheses, ultimately accelerating targeted drug discovery.
Revised: We envision that our proposed method will facilitate more efﬁcient experiment designs through its ability to generate insilico hypotheses, ultimately accelerating targeted drug discovery.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Recent advances in single-cell methods allowed the simultaneous analysis of millions of cells, increasing depth and resolution to explore cellular heterogeneity (Sikkema et al., 2022; Han et al., 2020).
Revised: Recent advances in single-cell methods allowed the simultaneous analysis of millions of cells, increasing depth and resolution to explore cellular heterogeneity (Sikkema et al., 2022; Han et al., 2020).

Original: With single-cell RNA sequencing (scRNA-seq) and high-throughput screens (HTSs) one can now study the impact of different perturbations, i.e., drug-dosage combinations, on the transcriptome at cellular resolution (Yofe et al., 2020; Norman et al., 2019).
Revised: With single-cell RNA sequencing (scRNA-seq) and high-throughput screens (HTSs) one can now study the impact of different perturbations, i.e., drug-dosage combinations, on the transcriptome at cellular resolution (Yofe et al., 2020; Norman et al., 2019).

Original: Unlike conventional HTSs, scRNAseq HTSs can identify subtle changes in gene expression and cellular heterogeneity, constituting a cornerstone for pharmaceutics and drug discovery (Srivatsan et al., 2020).
Revised: Unlike conventional HTSs, scRNAseq HTSs can identify subtle changes in gene expression and cellular heterogeneity, constituting a cornerstone for pharmaceutics and drug discovery (Srivatsan et al., 2020).

Original: Nevertheless, these newly introduced sample multiplexing techniques (McGinnis et al., 2019; Stoeckius et al., 2018; Gehring et al., 2018) require expensive library preparation and do not scale to screen thousands of distinct molecules.
Revised: Nevertheless, these newly introduced sample multiplexing techniques (McGinnis et al., 2019; Stoeckius et al., 2018; Gehring et al., 2018) require expensive library preparation and do not scale to screen thousands of distinct molecules.

Original: Even in its most cost-effective version, nuclear hashing, the acquired datasets contain no more than 200 different drugs (Srivatsan et al., 2020).
Revised: Even in its most cost-effective version, nuclear hashing, the acquired datasets contain no more than 200 different drugs (Srivatsan et al., 2020).

Original: Consequently, computational methods are required to address the limited exploration power of existing experimental methods and discover promising therapeutic drug candidates.
Revised: Consequently, computational methods are required to address the limited exploration power of existing experimental methods and discover promising therapeutic drug candidates.

Original: Suitable methods should predict the response to unobserved (combinations of) perturbations.
Revised: Suitable methods should predict the response to unobserved (combinations of) perturbations.

Original: Increasing in difﬁculty, such tasks may include inter- and extrapolation of dosage values, the generalisation to unobserved (combinations of) drug-covariates (e.g., cell-type), or predictions for unseen drugs.
Revised: Increasing in difﬁculty, such tasks may include inter- and extrapolation of dosage values, the generalisation to unobserved (combinations of) drug-covariates (e.g., cell-type), or predictions for unseen drugs.

Original: In terms of medical impact, the prediction of unobserved perturbations may be the most desirable, for example for drug repurposing.
Revised: In terms of medical impact, the prediction of unobserved perturbations may be the most desirable, for example for drug repurposing.

Original: At the same time, it requires the model to properly capture complex chemical interactions within multiple distinct cellular contexts.
Revised: At the same time, it requires the model to properly capture complex chemical interactions within multiple distinct cellular contexts.

Original: Such generalisation capabilities can not yet be learned from single-cell HTSs alone, as they supposedly do not cover the required breadth of chemical interactions.
Revised: Such generalisation capabilities can not yet be learned from single-cell HTSs alone, as they supposedly do not cover the required breadth of chemical interactions.

Original: In this work, we leverage information across datasets to alleviate this issue.
Revised: In this work, we leverage information across datasets to alleviate this issue.

Original: We propose a new model that generalises previous work on Fader Networks by Lample et al.
Revised: We propose a new model that generalises previous work on Fader Networks by Lample et al.

Original: (2017) and the CPA model by Lotfollahi et al.
Revised: (2017) and the CPA model by Lotfollahi et al.

Original: (2021) to the challenging scenario of generating counterfactual predictions for unseen compounds.
Revised: (2021) to the challenging scenario of generating counterfactual predictions for unseen compounds.

Original: Our method is as ﬂexible and interpretable as CPA but further enables us to leverage lower resolution but higher throughput assays, such as bulk RNA-seq
Revised: Our method is as ﬂexible and interpretable as CPA but further enables us to leverage lower resolution but higher throughput assays, such as bulk RNA-seq

Original: HTSs, to improve the model’s generalisation performance on single-cell data (Amodio et al., 2021).
Revised: HTSs, to improve the model’s generalisation performance on single-cell data (Amodio et al., 2021).

Original: Our main contributions are:
Revised: Our main contributions are:

Original: We introduce chemCPA, a model that incorporates knowledge about the compounds’ structure, enabling the prediction of drug perturbations at a single-cell level from molecular representations.
Revised: We introduce chemCPA, a model that incorporates knowledge about the compounds’ structure, enabling the prediction of drug perturbations at a single-cell level from molecular representations.

Original: We benchmark different molecule encoding networks for their applicability in the presented perturbation context.
Revised: We benchmark different molecule encoding networks for their applicability in the presented perturbation context.

Original: Finally, we propose and evaluate a transfer learning scheme to leverage HTS bulk RNA-seq data in the setting of both identical and different gene sets between the source (bulk) and target (single-cell) datasets.
Revised: Finally, we propose and evaluate a transfer learning scheme to leverage HTS bulk RNA-seq data in the setting of both identical and different gene sets between the source (bulk) and target (single-cell) datasets.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Over the past years, deep learning (DL) has become an essential tool for analysis (Lopez et al., 2020) and interpretation (Rybakov et al., 2020) of scRNA-seq data.
Revised: Over the past years, deep learning (DL) has become an essential tool for analysis (Lopez et al., 2020) and interpretation (Rybakov et al., 2020) of scRNA-seq data.

Original: Representation learning in particular, has been useful not only for identifying cellular heterogeneity and integration (Gayoso et al., 2022), or mapping query to reference datasets (Lotfollahi et al., 2022), but also in the context of modelling single-cell perturbation responses (Ramp ´ a ˇ sek et al., 2019; Seninge et al., 2021; Lotfollahi et al., 2019;
Revised: Representation learning in particular, has been useful not only for identifying cellular heterogeneity and integration (Gayoso et al., 2022), or mapping query to reference datasets (Lotfollahi et al., 2022), but also in the context of modelling single-cell perturbation responses (Ramp ´ a ˇ sek et al., 2019; Seninge et al., 2021; Lotfollahi et al., 2019;

Original: Ji et al., 2021).
Revised: Ji et al., 2021).

Original: Unlike linear models (Dixit et al., 2016; Kamimoto et al., 2020) or mechanistic approaches (Fr ¨ ohlich et al., 2018; Yuan et al., 2021), DL is suited to capture non-linear cell-type-speciﬁc responses and easily scales to genome-wide measurements.
Revised: Unlike linear models (Dixit et al., 2016; Kamimoto et al., 2020) or mechanistic approaches (Fr¨ohlich et al., 2018; Yuan et al., 2021), DL is suited to capture non-linear cell-type-speciﬁc responses and easily scales to genome-wide measurements.

Original: Recently, Lotfollahi et al.
Revised: Recently, Lotfollahi et al.

Original: (2021) introduced the computational perturbation auto-encoder (CPA) for modelling perturbations on scRNA-seq data.
Revised: (2021) introduced the computational perturbation auto-encoder (CPA) for modelling perturbations on scRNA-seq data.

Original: CPA does not generalise to unseen compounds, hindering its application to virtual screening of drugs not yet measured via scRNA-seq data, which is required for effective drug discovery.
Revised: CPA does not generalise to unseen compounds, hindering its application to virtual screening of drugs not yet measured via scRNA-seq data, which is required for effective drug discovery.

Original: For bulk RNA-seq data, on the other hand, several methods have been proposed to predict gene expression proﬁles for de novo chemicals (Pham et al., 2021; Zhu et al., 2021; Umarov et al., 2021).
Revised: For bulk RNA-seq data, on the other hand, several methods have been proposed to predict gene expression proﬁles for de novo chemicals (Pham et al., 2021; Zhu et al., 2021; Umarov et al., 2021).

Original: Crucially, the L1000 dataset, introduced by the LINCS programme (Subramanian et al., 2017), greatly facilitated such advances on phenotype-based compound screening.
Revised: Crucially, the L1000 dataset, introduced by the LINCS programme (Subramanian et al., 2017), greatly facilitated such advances on phenotype-based compound screening.

Original: However, it remains unclear how to translate these approaches to single-cell datasets that include signiﬁcantly fewer compounds and, in many cases, rely on different gene sets.
Revised: However, it remains unclear how to translate these approaches to single-cell datasets that include signiﬁcantly fewer compounds and, in many cases, rely on different gene sets.

Original:  C HEMICAL C OMPOSITIONAL P ERTURBATION A UTOENCODER
Revised:  C HEMICAL C OMPOSITIONAL P ERTURBATION A UTOENCODER

Original: We consider a dataset D
Revised: We consider a dataset D

Original: = { ( x i , y i ) } Ni =1 = { ( x i , ( d i , s i , c i ))
Revised: = { ( x i , y i ) } Ni =1 = { ( x i , ( d i , s i , c i ))

Original: } Ni =1 , where x i ∈ R n describes the n -dimensional gene expression and y i an attribute set.
Revised: } Ni =1 , where x i ∈ R n describes the n -dimensional gene expression and y i an attribute set.

Original: For scRNA-seq perturbation data, we usually consider the drug and dosage attributes, d i ∈ { drugs in D} and s i ∈ R , respectively, and the cellline c i of cell i .
Revised: For scRNA-seq perturbation data, we usually consider the drug and dosage attributes, d i ∈ { drugs in D} and s i ∈ R , respectively, and the cellline c i of cell i .

Original: Note that this set of attributes Y depends on the available data and could be extended to covariates such as patient, or species.
Revised: Note that this set of attributes Y depends on the available data and could be extended to covariates such as patient, or species.

Original: A possible approach to predicting counterfactual combinations is to encode a cell’s gene expression x i invariantly from its attributes y i as a latent vector z i , called the basal state.
Revised: A possible approach to predicting counterfactual combinations is to encode a cell’s gene expression x i invariantly from its attributes y i as a latent vector z i , called the basal state.

Original: Afterwards, z i can be combined with z attribute to encode any attribute combination y (cid:48)
Revised: Afterwards, z i can be combined with z attribute to encode any attribute combination y (cid:48)

Original: i (cid:54) =
Revised: i (cid:54) =

Original: y i , and decoded back to a gene expression state
Revised: y i , and decoded back to a gene expression state

Original: ˆ x i that corresponds to this new set of chosen attributes.
Revised: ˆ x i that corresponds to this new set of chosen attributes.

Original: To this end, we divide our model into three parts: (1) the gene expression encoder and decoder, (2) the attribute embedders, and (3) the adversarial classiﬁers, see Figure 1 for an illustration.
Revised: To this end, we divide our model into three parts: (1) the gene expression encoder and decoder, (2) the attribute embedders, and (3) the adversarial classiﬁers, see Figure 1 for an illustration.

Original: 3. G ENE ENCODER AND DECODER
Revised: 3. G ENE ENCODER AND DECODER

Original: Following Lotfollahi et al.
Revised: Following Lotfollahi et al.

Original: (2021), our model is based on an encoder-decoder architecture combined with adversarial training.
Revised: (2021), our model is based on an encoder-decoder architecture combined with adversarial training.

Original: The encoding network E θ : R n → R l is a multi-layer perceptron (MLP) with parameters θ that maps a measured gene expression state x i ∈ R n to its l -dimensional latent

Original: vector z i =

Original: E θ ( x i ) .

Original: Through adversarial classiﬁers, z i is trained to not contain any information about its attributes y i .

Original: We assume an additive structure of the perturbation response in the latent space, i.e., for a new set of independently chosen attributes we compute the new latent state as z (cid:48)

Original: i = z i + z attribute . The decoder D ψ : R l → R 2 n is also an MLP that takes z (cid:48)
Revised: The encoding network E θ : R n → R l is a multi-layer perceptron (MLP)

Original: i as input and computes the component-wise means µ = D µψ ( z (cid:48) ) and variances σ 2 = D σ 2 ψ ( z (cid:48) ) of normal distributions describing the attribute dependent gene expression state.

Original: During training, we use a Gaussian likelihood reconstruction loss:

Original: [Equation]
Revised: [Equation]

Original: where (cid:15) =
Revised: where (cid:15) =

Original: 10 − 6 is used for stability and max is evaluated component-wise.
Revised: 10 − 6 is used for stability and max is evaluated component-wise.

Original: Next, we provide intuition about how we can meaningfully interpret latent space arithmetics and how we encode drug and cell-line attributes.
Revised: Next, we provide intuition about how we can meaningfully interpret latent space arithmetics and how we encode drug and cell-line attributes.

Original: 3. A TTRIBUTE EMBEDDING
Revised: 3. A TTRIBUTE EMBEDDING

Original: Due to their different nature, we encode the drug attributes and cell-line information separately.
Revised: Due to their different nature, we encode the drug attributes and cell-line information separately.

Original: For the cell-lines we use the same approach as Lotfollahi et al.
Revised: For the cell-lines we use the same approach as Lotfollahi et al.

Original: (2021), where we optimised a d -dim latent representation z c for each cell-line c .
Revised: (2021), where we optimised a d -dim latent representation z c for each cell-line c .

Original: For the drugs, we train an embedding network that maps molecular representations—such as its graph or SMILES representation—and the used dosage to its latent perturbation state.
Revised: For the drugs, we train an embedding network that maps molecular representations—such as its graph or SMILES representation—and the used dosage to its latent perturbation state.

Original: The drug perturbation network P ϕ consists of the molecule encoder, the dosage scaler, and the perturbation encoder, see Figure 1 (2).
Revised: The drug perturbation network P ϕ consists of the molecule encoder, the dosage scaler, and the perturbation encoder, see Figure 1 (2).

Original: First, the molecule encoder G : G → R m encodes the molecule representation g i ∈ G as a ﬁxed size embedding h drug ∈ R m .
Revised: First, the molecule encoder G : G → R m encodes the molecule representation g i ∈ G as a ﬁxed size embedding h drug ∈ R m .

Original: Next, the dosage scaler S :
Revised: Next, the dosage scaler S :

Original: R m +

Revised: R m +

Original: → R maps h drug together with s i to the scaled dosage value
Revised: → R maps h drug together with s i to the scaled dosage value

Original: ˆ s i .
Revised: ˆ s i .

Original: In a ﬁnal step, the perturbation encoder
Revised: In a ﬁnal step, the perturbation encoder

Original: M : R m → R l takes the scaled molecular embedding ˆ s i h
Revised: M : R m → R l takes the scaled molecular embedding ˆ s i h

Original: d i as input and generates the ﬁnal drug perturbation z d i ∈ R l .
Revised: d i as input and generates the ﬁnal drug perturbation z d i ∈ R l .

Original: Put together, we end up with
Revised: Put together, we end up with

Original: [Equation]
Revised: [Equation]

Original: The molecule encoder G can be any encoding network that maps molecular representations to ﬁxedsize embeddings.
Revised: The molecule encoder G can be any encoding network that maps molecular representations to ﬁxedsize embeddings.

Original: Due to the limited number of drugs available in scRNA-seq HTSs, we rely on different pretrained encoding models.
Revised: Due to the limited number of drugs available in scRNA-seq HTSs, we rely on different pretrained encoding models.

Original: In this work, we include GROVER (Rong et al., 2020), a larger transformer-based model, pretrained on tasks ranging from molecule reconstruction to property prediction, JT-VAE (Jin et al., 2018), an autoencoding model acting on commonly occurring graph-level motifs, as well as three simpler models, a GCN (Kipf & Welling, 2016), an MPNN (Gilmer et al., 2017), and a weave model (Kearnes et al., 2016), which are all pretrained and were taken from DGL-LifeSci (Li et al., 2021).
Revised: In this work, we include GROVER (Rong et al., 2020), a larger transformer-based model, pretrained on tasks ranging from molecule reconstruction to property prediction, JT-VAE (Jin et al., 2018), an autoencoding model acting on commonly occurring graph-level motifs, as well as three simpler models, a GCN (Kipf & Welling, 2016), an MPNN (Gilmer et al., 2017), and a weave model (Kearnes et al., 2016), which are all pretrained and were taken from DGL-LifeSci (Li et al., 2021).

Original: As a baseline, we also include RDKit ﬁngerprints, a hash-based embedding that is easily available for any molecular graph (Landrum, 2016).
Revised: As a baseline, we also include RDKit ﬁngerprints, a hash-based embedding that is easily available for any molecular graph (Landrum, 2016).

Original: These design decisions allow us to also generate drug perturbations for molecules that have not been experimentally measured ( d / ∈ D ) in an interpretable and semantically meaningful way by computing the latent attribute vector as
Revised: These design decisions allow us to also generate drug perturbations for molecules that have not been experimentally measured ( d / ∈ D ) in an interpretable and semantically meaningful way by computing the latent attribute vector as

Original: [Equation]
Revised: [Equation]

Original: Intuitively, we want z (cid:48)
Revised: Intuitively, we want z (cid:48)

Original: i to be decoded to a (distribution over) transcriptome(s) that would result from applying the perturbations encoded by z d i ,
Revised: i to be decoded to a (distribution over) transcriptome(s) that would result from applying the perturbations encoded by z d i ,

Original: z c i to the “basal state” of x i , i.e., as if no perturbations were made, which we aim to encode by z i .
Revised: z c i to the “basal state” of x i , i.e., as if no perturbations were made, which we aim to encode by z i .

Original: Since training examples have already been perturbed, we next describe how we “strip z i from the attribute information” to obtain a basal state representation.
Revised: Since training examples have already been perturbed, we next describe how we “strip z i from the attribute information” to obtain a basal state representation.

Original: 3. A DVERSARIAL CLASSIFIERS FOR INVARIANT BASAL STATES
Revised: 3. A DVERSARIAL CLASSIFIERS FOR INVARIANT BASAL STATES

Original: To generate invariant basal states and produce disentangled representations z i ,
Revised: To generate invariant basal states and produce disentangled representations z i ,

Original: z d i , and z c i , we use adversarial classiﬁers A drug φ and A cov φ .
Revised: z d i , and z c i , we use adversarial classiﬁers A drug φ and A cov φ .

Original: Both adversary networks A jφ : R l → R N j take the latent basal state z i as input and aim to predict the drug that has been applied to example i as well as its cell-line c i .
Revised: Both adversary networks A jφ : R l → R N j take the latent basal state z i as input and aim to predict the drug that has been applied to example i as well as its cell-line c i .

Original: While these classiﬁers are trained to improve classiﬁcation performance, we also add the classiﬁcation loss with a reversed sign to the training objective for the encoder E θ .
Revised: While these classiﬁers are trained to improve classiﬁcation performance, we also add the classiﬁcation loss with a reversed sign to the training objective for the encoder E θ .

Original: Hence, the encoder attempts to produce a latent representation
Revised: Hence, the encoder attempts to produce a latent representation

Original: z i which contains no information about the attributes .
Revised: z i which contains no information about the attributes .

Original: We use the cross-entropy loss for both classiﬁers
Revised: We use the cross-entropy loss for both classiﬁers

Original: [Equation]
Revised: [Equation]

Original: and [Equation] To improve the robustness of the adversarial classiﬁers to measurement noise of gene expressions, we also add a gradient penalty (Gulrajani et al., 2017) to their objective:
Revised: To improve the robustness of the adversarial classiﬁers to measurement noise of gene expressions, we also add a gradient penalty (Gulrajani et al., 2017) to their objective:

Original: [Equation]
Revised: [Equation]

Original: In practice, we alternate SGD update steps between the following competing objectives
Revised: In practice, we alternate SGD update steps between the following competing objectives

Original: [Equation]
Revised: [Equation]

Original: where λ dis balances the importance of good reconstruction against the encoder E θ ’s constraint to generate disentangled basal states
Revised: where λ dis balances the importance of good reconstruction against the encoder E θ ’s constraint to generate disentangled basal states

Original: z i .
Revised: z i .

Original: The gradient penalty is weigthed with λ pen .
Revised: The gradient penalty is weigthed with λ pen .

Original:  D ATASETS AND TRANSFER LEARNING
Revised:  D ATASETS AND TRANSFER LEARNING

Original: We use the sci-Plex3 (Srivatsan et al., 2020) and the L1000 (Subramanian et al., 2017) datasets for the single-cell and bulk experiments, respectively.
Revised: We use the sci-Plex3 (Srivatsan et al., 2020) and the L1000 (Subramanian et al., 2017) datasets for the single-cell and bulk experiments, respectively.

Original: To obtain pretrained chemCPA models, different molecule encoders G are benchmarked in the context of perturbation screens on the L1000 dataset.
Revised: To obtain pretrained chemCPA models, different molecule encoders G are benchmarked in the context of perturbation screens on the L1000 dataset.

Original: The sci-Plex3 data is used for the main experiments concerning chemCPA’s ability to generalise to unseen drugs.
Revised: The sci-Plex3 data is used for the main experiments concerning chemCPA’s ability to generalise to unseen drugs.

Original: The L1000 data contains about 1 .
Revised: The L1000 data contains about 1 .

Original: 3 million bulk RNA-seq observations for 978 different genes.
Revised: 3 million bulk RNA-seq observations for 978 different genes.

Original: It includes measurements for almost 20 k different drugs, some of which are FDA-approved, while others are synthetic compounds with no proven effect on any disease.
Revised: It includes measurements for almost 20 k different drugs, some of which are FDA-approved, while others are synthetic compounds with no proven effect on any disease.

Original: The large size of the L1000 data allows to explore a more diverse space of molecules and, therefore, makes it ideal for pretraining.
Revised: The large size of the L1000 data allows to explore a more diverse space of molecules and, therefore, makes it ideal for pretraining.

Original: The sci-Plex3 data is similar in size and contains measurements for 649 , 340 cells across 7561 drugsensitive genes.
Revised: The sci-Plex3 data is similar in size and contains measurements for 649 , 340 cells across 7561 drugsensitive genes.

Original: On three human cancer cell lines—A549, MCF7, and K562—single-compound perturbations for 188 drugs at four different dosages are examined.
Revised: On three human cancer cell lines—A549, MCF7, and K562—single-compound perturbations for 188 drugs at four different dosages are examined.

Original: Note that all cell lines and about 150 compounds overlap with the L1000 data.
Revised: Note that all cell lines and about 150 compounds overlap with the L1000 data.

Original: In addition, Srivatsan et al.
Revised: In addition, Srivatsan et al.

Original: (2020) assigned to all compounds one of 19 different modes of action (MoA) or pathways.
Revised: (2020) assigned to all compounds one of 19 different modes of action (MoA) or pathways.

Original: These allow us to assess the embedding quality of different perturbation networks P ϕ .
Revised: These allow us to assess the embedding quality of different perturbation networks P ϕ .

Original: As the decoder network D ψ assumes normally distributed data, the dataset was ﬁrst normalised and then log( x + 1) -transformed.
Revised: As the decoder network D ψ assumes normally distributed data, the dataset was ﬁrst normalised and then log( x + 1) -transformed.

Original: After preprocessing, we were left with 840 , 677 (across 17 ,compounds) and 581 , 776 observations for the L1000 and sci-Plex data, respectively.
Revised: After preprocessing, we were left with 840 , 677 (across 17 ,compounds) and 581 , 776 observations for the L1000 and sci-Plex data, respectively.

Original: Depending on the experiment, we further reduced the number of genes included in the single-cell data.
Revised: Depending on the experiment, we further reduced the number of genes included in the single-cell data.

Original: In Section 5.2.1, we subsetted both datasets to the same 977 genes found via ensemble gene annotations.
Revised: In Section 5.2.1, we subsetted both datasets to the same 977 genes found via ensemble gene annotations.

Original: In
Revised: In

Original: Section 5.2.2, we hypothesize that more than the L1000 genes are required to capture the variability within the single-cell data.
Revised: Section 5.2.2, we hypothesize that more than the L1000 genes are required to capture the variability within the single-cell data.

Original: To assess whether pretraining on L1000 is still beneﬁcial in this scenario, we included 1023 highly variable genes (HVGs) for the sci-Plex3 data.
Revised: To assess whether pretraining on L1000 is still beneﬁcial in this scenario, we included 1023 highly variable genes (HVGs) for the sci-Plex3 data.

Original: That is, we considergenes in total.
Revised: That is, we considergenes in total.

Original: For the extended gene set, we perform a version of architecture surgery to match the data dimension (977 → 2000) with the models’ dimensions.
Revised: For the extended gene set, we perform a version of architecture surgery to match the data dimension (977 → 2000) with the models’ dimensions.

Original: This is realised by additional linear input and output layers for the encoder E θ and decoder D ψ , respectively.
Revised: This is realised by additional linear input and output layers for the encoder E θ and decoder D ψ , respectively.

Original: Moreover, as the computational cost is negligible compared to the generation cost of the perturbation datasets, we did not freeze any weights during the ﬁne-tuning on single-cell data.
Revised: Moreover, as the computational cost is negligible compared to the generation cost of the perturbation datasets, we did not freeze any weights during the ﬁne-tuning on single-cell data.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: Throughout our experiments, we use the coefﬁcient of determination r 2 as the main performance metric.
Revised: Throughout our experiments, we use the coefﬁcient of determination r 2 as the main performance metric.

Original: This score is computed between the actual measurements and the counterfactual predictions on all genes and the 50 most differentially expressed genes (DEGs) per compound.
Revised: This score is computed between the actual measurements and the counterfactual predictions on all genes and the 50 most differentially expressed genes (DEGs) per compound.

Original: Considering all genes is necessary to evaluate the background and general decoder performance.
Revised: Considering all genes is necessary to evaluate the background and general decoder performance.

Original: However, the resulting r 2 -scores can get inﬂated since many genes stay similar to their controls.
Revised: However, the resulting r 2 -scores can get inﬂated since many genes stay similar to their controls.

Original: In contrast, the
Revised: In contrast, the

Original: DEGs capture the differential signal which reﬂect a drug’s effect.
Revised: DEGs capture the differential signal which reﬂect a drug’s effect.

Original: For the counterfactual prediction, chemCPA encodes only control observations—treated with DMSO—across all cell lines and combines the resulting basal states with the respective attribute encoding.
Revised: For the counterfactual prediction, chemCPA encodes only control observations—treated with DMSO—across all cell lines and combines the resulting basal states with the respective attribute encoding.

Original: Note that poor disentanglement will automatically lead to low scores due to computing the test score on the counterfactual prediction.
Revised: Note that poor disentanglement will automatically lead to low scores due to computing the test score on the counterfactual prediction.

Original: For a fair comparison, it is essential to account for the disentanglement scores of a trained model.
Revised: For a fair comparison, it is essential to account for the disentanglement scores of a trained model.

Original: An entangled model would confound the drug and cell line effects in the basal state, effectively becoming a standard autoencoder.
Revised: An entangled model would confound the drug and cell line effects in the basal state, effectively becoming a standard autoencoder.

Original: In order to classify the degree of disentanglement, we train a separate MLP with four layers over 400 epochs and compute the prediction accuracy given the basal state.
Revised: In order to classify the degree of disentanglement, we train a separate MLP with four layers over 400 epochs and compute the prediction accuracy given the basal state.

Original: This way we obtain the disentanglement score for both drugs and cell lines.
Revised: This way we obtain the disentanglement score for both drugs and cell lines.

Original: We start with the experiment on the L1000 dataset and then report our results on the sci-Plex3 data.
Revised: We start with the experiment on the L1000 dataset and then report our results on the sci-Plex3 data.

Original: 5. B ENCHMARKING DRUG MOLECULE ENCODERS ON THE L1000 DATASET
Revised: 5. B ENCHMARKING DRUG MOLECULE ENCODERS ON THE L1000 DATASET

Original: Enabled by the ﬂexibility of the molecule encoder G , we investigated what impact the architecture choice has on the performance of chemCPA.
Revised: Enabled by the ﬂexibility of the molecule encoder G , we investigated what impact the architecture choice has on the performance of chemCPA.

Original: For this, we compared multiple pretrained graph-based models whose weights were frozen during the training.
Revised: For this, we compared multiple pretrained graph-based models whose weights were frozen during the training.

Original: Next to predeﬁned RDKit ﬁngerprints, we included a GCN, MPNN, weave model, GROVER model, and a JT-VAE.
Revised: Next to predeﬁned RDKit ﬁngerprints, we included a GCN, MPNN, weave model, GROVER model, and a JT-VAE.

Original: The optimal disentanglement scores correspond to the most abundant drug and cell line ratios, which amounts to < 1% and ∼ 2% on the L1000 data, respectively.
Revised: The optimal disentanglement scores correspond to the most abundant drug and cell line ratios, which amounts to < 1% and ∼ 2% on the L1000 data, respectively.

Original: Since no model reaches perfect scores, we decided to ﬁrst subselect on perturbation disentanglement scores < 20% and then compared the ﬁve best-disentangled models concerning the cell lines.
Revised: Since no model reaches perfect scores, we decided to ﬁrst subselect on perturbation disentanglement scores < 20% and then compared the ﬁve best-disentangled models concerning the cell lines.

Original: The encoder’s E θ , decoder’s D ψ , and adversary networks’ A jφ width and depth parameters were optimised with the RDKit features, and their best combination was chosen and ﬁxed throughout the comparison.
Revised: The encoder’s E θ , decoder’s D ψ , and adversary networks’ A jφ width and depth parameters were optimised with the RDKit features, and their best combination was chosen and ﬁxed throughout the comparison.

Original: The dimensionality of the latent space is always 32.
Revised: The dimensionality of the latent space is always 32.

Original: Table 1 summarises the results of this experiment.
Revised: 1 summarises the results of this experiment.

Original: The weave model performs much worse than the others, achieving an r 2 -score of only 65 ± 8 on DEGs.
Revised: The weave model performs much worse than the others, achieving an r 2 -score of only 65 ± 8 on DEGs.

Original: While the GCN disentangles well, it cannot keep up with the predictive performance of the JT-VAE and GROVER models.
Revised: While the GCN disentangles well, it cannot keep up with the predictive performance of the JT-VAE and GROVER models.

Original: Therefore,we decided to use the best runs—based on the validation score—for GROVER and JT-VAE as initialisation for ﬁne-tuning on the single-cell data. Due to its simplicity and good performance, we also include RDKit features. [Table] [Table]
Revised: Therefore,

Original: [Table]
Revised: [Table]

Original:  in μ M 0.
Revised: 10 Dosage in μ M 0.

Original: E [ r 2 ]
Revised: E [ r 2 ]

Original: o n a ll g e n e s0.
Revised: o n a ll g e n e s

Original: [Table]
Revised: [Figure]

Original: Dosage

Original: [Table]
Revised: [Figure]

Original:  M 0.[Table]
Revised: 10 Dosage in μ M 0.

Original: [Table]
Revised: [Figure]

Original: E [ r 2 ] o n D E G s
Revised: E [ r 2 ] o n [Figure]

Original: Model type

Original: [Table]
Revised: [Table]

Original: the DEGs, more than 50% of the predictions have an r 2 score > 0 .
Revised: DEGs, more than 50% of the predictions have an r 2 score > 0 .

Original: 59 while the baseline’s median is slightly above 0 .
Revised: 59 while the baseline’s median is slightly above 0 .

Revised: we decided to use the best runs—based on the validation score—for GROVER and JT-VAE as initialisation for ﬁne-tuning on the single-cell data.

Revised: Due to its simplicity and good performance, we also include RDKit features.

Original: 5. A PPLYING CHEM CPA TO SINGLE - CELL DATA
Revised: 5. A PPLYING CHEM CPA TO SINGLE - CELL DATA

Original: For the application to the sci-Plex3 data, we considered two settings.
Revised: For the application to the sci-Plex3 data, we considered two settings.

Original: The ﬁrst setting ignores the technological difference between bulk and single-cell, and the same 977 genes from the L1000 data are used for ﬁne-tuning.
Revised: The ﬁrst setting ignores the technological difference between bulk and single-cell, and the same 977 genes from the L1000 data are used for ﬁne-tuning.

Original: The second setting accounts for the variance of single-cell data resulting mostly from its HVGs.
Revised: The second setting accounts for the variance of single-cell data resulting mostly from its HVGs.

Original: We therefore extend the single-cell gene set with 1023 HVGs.
Revised: We therefore extend the single-cell gene set with 1023 HVGs.

Original: Note that on this larger gene set the 50 DEGs per compound become a subset of the HVGs, preventing ﬁne-tuned models from leveraging learned bulk expression directly.
Revised: Note that on this larger gene set the 50 DEGs per compound become a subset of the HVGs, preventing ﬁne-tuned models from leveraging learned bulk expression directly.

Original: We excluded nine drugs from the training and validation for the data split.
Revised: We excluded nine drugs from the training and validation for the data split.

Original: These drugs mostly belong to three MoA—epigenetic regulation, tyrosine kinase signalling, and cell cycle regulation— as these were reported among the most effective drugs in the original publication (Srivatsan et al., 2020).
Revised: These drugs mostly belong to three MoA—epigenetic regulation, tyrosine kinase signalling, and cell cycle regulation— as these were reported among the most effective drugs in the original publication (Srivatsan et al., 2020).

Original: We used 4% of the data across all dosages, 10 nM, 100 nM, 1 µ M, and 10 µ M, for the validation.
Revised: We used 4% of the data across all dosages, 10 nM, 100 nM, 1 µ M, and 10 µ M, for the validation.

Original: The drug and cell line thresholds at which we consider a model disentangled were set to 7% (optimal is 3% ) and 70% (optimal is 51% ) respectively.
Revised: The drug and cell line thresholds at which we consider a model disentangled were set to 7% (optimal is 3% ) and 70% (optimal is 51% ) respectively.

Original: Across all runs, we observed that pretraining on L1000 leads to better covariate invariance scores: (88 ± 16)% for non-pretrained models and (79 ± 16)% for ﬁne-tuned models.
Revised: Across all runs, we observed that pretraining on L1000 leads to better covariate invariance scores: (88 ± 16)% for non-pretrained models and (79 ± 16)% for ﬁne-tuned models.

Original: For the generalisation comparison, we selected the best performing model according to its validation performance for each of the three chemCPA versions: RDKit, GROVER, and JT-VAE. 
Revised: For the generalisation comparison, we selected the best performing model according to its validation performance for each of the three chemCPA versions: RDKit, GROVER, and JT-VAE. [Table] [Table]

Original: 5.2. G ENERALISATION TO UNSEEN DRUGS IN THE SETTING OF SHARED GENE SETS
Revised: G ENERALISATION TO UNSEEN DRUGS IN THE SETTING OF SHARED GENE SETS

Original: Table 2 shows the test performance of chemCPA for the nine unseen drugs across the three cell lines in the ﬁrst scenario with identical genes.
Revised: Table 2 shows the test performance of chemCPA for the nine unseen drugs across the three cell lines in the ﬁrst scenario with identical genes.

Original: The reported baseline performance reﬂects the case when the model predicts the gene expression of a control cell, ignoring the drug’s inﬂuence.
Revised: The reported baseline performance reﬂects the case when the model predicts the gene expression of a control cell, ignoring the drug’s inﬂuence.

Original: This choice allows to estimate the difference between the treated cell’s gene expression and their control[Table] (unperturbed) version.
Revised: This choice allows to estimate the difference between the treated cell’s gene expression and their control (unperturbed) version.

Original: The ﬁne-tuned models consistently outperform the baseline and their nonpretrained version at the highest dose with RDKit achieving the highest median score on DEGs.
Revised: The ﬁne-tuned models consistently outperform the baseline and their nonpretrained version at the highest dose with RDKit achieving the highest median score on DEGs.

Original: Figure 2 illustrates that the drugs have a very subtle to no effect at low dosages, which is reﬂected by the high baseline scores.
Revised: Figure 2 illustrates that the drugs have a very subtle to no effect at low dosages, which is reﬂected by the high baseline scores.

Original: However, we see how the drug encoding, here GROVER, improves the prediction for high drug dosages.
Revised: However, we see how the drug encoding, here GROVER, improves the prediction for high drug dosages.

Original: Looking at the predicted phenotype over all genes, the pretrained model has a signiﬁcant advantage over its non-pretrained version, showing a clear improvement over the baseline.
Revised: Looking at the predicted phenotype over all genes, the pretrained model has a signiﬁcant advantage over its non-pretrained version, showing a clear improvement over the baseline.

Original: The results look worse when zoomed in to the DEGs.
Revised: The results look worse when zoomed in to the DEGs.

Original: Nevertheless, also in this scenario, the ﬁne-tuned model can explain gene expression values that must result from the drugs’ inﬂuence.
Revised: Nevertheless, also in this scenario, the ﬁne-tuned model can explain gene expression values that must result from the drugs’ inﬂuence.

Revised: The results for RDKIT and JT-VAE are presented in A.4.

Original: In Figure 3, latent perturbations z d are visualised for the lowest and highest dosage values.
Revised: In Figure 3, latent perturbations z d are visualised for the lowest and highest dosage values.

Original: The drug perturbations do not show a clear grouping for the lowest dosage, indicating a sparse signal.
Revised: The drug perturbations do not show a clear grouping for the lowest dosage, indicating a sparse signal.

Original: In contrast, the stronger response in higher dosages is reﬂected by causing similar drugs with identical MoAs to cluster together, for example, the epigenetic drugs that cause histone deacetylation.
Revised: In contrast, the stronger response in higher dosages is reﬂected by causing similar drugs with identical MoAs to cluster together, for example, the epigenetic drugs that cause histone deacetylation.

Original: 5.2. G ENERALISATION TO UNSEEN DRUGS IN THE SETTING OF EXTENDED GENE SETS
Revised: 5.2. G ENERALISATION TO UNSEEN DRUGS IN THE SETTING OF EXTENDED GENE SETS

Original: The extension to the larger gene set poses an even greater challenge to the chemCPA model.
Revised: The extension to the larger gene set poses an even greater challenge to the chemCPA model.

Original: In
Revised: In

Original: Table 3, we show the same analysis as in Section 5.2.1.
Revised: Table 3, we show the same analysis as in Section 5.2.1.

Original: Strikingly, the advantage of the ﬁne-tuned models translates to this scenario, while the non-pretrained models fail to beat the baseline.
Revised: Strikingly, the advantage of the ﬁne-tuned models translates to this scenario, while the non-pretrained models fail to beat the baseline.

Original: A more comprehensive view for all dosages is shown in Figure 4.
Revised: A more comprehensive view for all dosages is shown in Figure 4 for the GROVER model, see also A.4 for the RDKit and JT-VAE results. [Table] 10 Dosage in μM 0. E [ r 2 ]

Revised: o n a ll g e n e s 0.

Revised: 10 Dosage in μM 0.

Original:  We believe that the poor performance of the non-pretrained model can be attributed to two things.
Revised: E [ r 2 ] o n D E G s [Table] , see also Figure We believe that the poor performance of the non-pretrained model can be attributed to two things.

Original: First, perturbations are subtle, and since we are predicting the whole phenotype, a typical reconstruction loss might not be sufﬁcient to capture the drugs’ effect.
Revised: First, perturbations are subtle, and since we are predicting the whole phenotype, a typical reconstruction loss might not be sufﬁcient to capture the drugs’ effect.

Original: A loss that explicitly accounts for the DEGs might help in that regard.
Revised: A loss that explicitly accounts for the DEGs might help in that regard.

Original: Second, the sci-Plex3 data is the ﬁrst of its kind, and technological noise is still an issue.
Revised: Second, the sci-Plex3 data is the ﬁrst of its kind, and technological noise is still an issue.

Original: When evaluated over the whole training set, the baseline achieves r 2 -scores higher than 65% for more than 96% of the observations.This sparsity holds the [Table]
Revised: When evaluated over the whole training set, the baseline achieves r 2 -scores higher than 65% for more than 96% of the observations.

Original: 10 Dosage in μM 0.

Original: E [ r 2 ]

Original: o n a ll g e n e s 0.

Original: 10 Dosage in μM 0.

Original: E [ r 2 ] o n D E G s [Table] risk of overﬁtting for non-pretrained models, whereas the ﬁne-tuned models are more robust to noise.
Revised: This sparsity holds the risk of overﬁtting for non-pretrained models, whereas the ﬁne-tuned models are more robust to noise.

Original: Perturbation prediction for Dacinostat and Hesperadin for chemCPA across all three celllines for the identical gene set using RDKit as molecule encoder.

Original: In addition, the drug-dose combinations for which the examined cells deviate from the controls’ phenotypes correspond to less than 20% of the compounds in the training set.
Revised: In addition, the drug-dose combinations for which the examined cells deviate from the controls’ phenotypes correspond to less than 20% of the compounds in the training set.

Original: Accordingly, the effective number of drugs for which chemCPA can learn perturbations reduces to fewer than 30 drugs.
Revised: Accordingly, the effective number of drugs for which chemCPA can learn perturbations reduces to fewer than 30 drugs.

Original: This is another indication for the limitation of the sci-Plex3 dataset, preventing us to identify clear differences between the molecule encoders.
Revised: This is another indication for the limitation of the sci-Plex3 dataset, preventing us to identify clear differences between the molecule encoders.

Original: Nevertheless, chemCPA was partly able to successfully predict the response when the baseline failed, see Figure 5 for two examples, and we believe that it will beneﬁt from higher-quality scRNA-seq HTSs in the future.
Revised: Nevertheless, chemCPA was partly able to successfully predict the response when the baseline failed, see Figure 5 for two examples, and we believe that it will beneﬁt from higher-quality scRNA-seq HTSs in the future.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this paper, we introduced chemCPA, a model for predicting cellular phenotypes for unseen drug perturbations by encoding the drugs’ molecular structures.
Revised: In this paper, we introduced chemCPA, a model for predicting cellular phenotypes for unseen drug perturbations by encoding the drugs’ molecular structures.

Original: Enabled by the ﬂexible architecture of chemCPA, we evaluated different molecule encoding networks and found that readily available molecule features such as RDKit ﬁngerprints yield competitive results.
Revised: Enabled by the ﬂexible architecture of chemCPA, we evaluated different molecule encoding networks and found that readily available molecule fea- tures such as RDKit ﬁngerprints yield competitive results.

Original: Applied to single-cell data, we demonstrated how pretraining on bulk HTSs improves chemCPA’s generalisation performance.
Revised: Applied to single-cell data, we demonstrated how pretraining on bulk HTSs improves chemCPA’s generalisation performance.

Original: With new scRNA-seq HTSs, chemCPA can become a powerful aid in the drug screening and drug discovery process. 
Revised: With new scRNA-seq HTSs, chemCPA can become a powerful aid in the drug screening and drug discovery process. A CKNOWLEDGMENTS

Revised: L.H. is thankful for valuable feedback from Fabiola Curion and Carlo De Donno.

Revised: L

Revised: H. acknowledges support by the Helmholtz Association under the joint research school Munich School for

Revised: Data Science - MUDS”.

Original:  A A PPENDIX A. A TTRIBUTE EMBEDDING
Revised: This work was supported by the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036B. F.J T. acknowledges support by the BMBF (01IS18053A) and the Helmholtz Associations Initiative and Networking Fund through Helmholtz AI (ZT-I-PF-5-01) and sparse2big (ZT-I-007) A A PPENDIX A. A TTRIBUTE EMBEDDING

Original: [Table]
Revised: [Table]

Original: A. C OUNTERFACTUAL PREDICTION
Revised: A. C OUNTERFACTUAL PREDICTION

Original: To compute counterfactual predictions, we obtain basal states
Revised: To compute counterfactual predictions, we obtain basal states

Original: z i for all control observations present in the test set.
Revised: z i for all control observations present in the test set.

Original: For each combination of drug, dose, and cell line in the test set, we compute the latent attribute state z attribute and combine it with all z i .
Revised: For each combination of drug, dose, and cell line in the test set, we compute the latent attribute state z attribute and combine it with all z i .

Original: Subsequently, we compute the mean per gene across all predictions and likewise for the real measurements.
Revised: Subsequently, we compute the mean per gene across all predictions and likewise for the real measurements.

Original: As a result, we obtain two n dimensional vectors, where n is the number of genes (977 or 2000), for which we compute the r 2 score.
Revised: As a result, we obtain two n dimensional vectors, where n is the number of genes (977 or 2000), for which we compute the r 2 score.

Original: Taken together, we get one score per combination.
Revised: Taken together, we get one score per combination.

Original: A. A DDITIONAL INFORMATION ON THE L1000 EXPERIMENT
Revised: A. A DDITIONAL INFORMATION ON THE L1000 EXPERIMENT

Original: For infos on the RDKit sweep and resulting best run, see Table 5 and Table 6. 2.
Revised: For infos on the RDKit sweep and resulting best run, see Table 5 and Table 6. 2.

Original: Architectures for best conﬁguration of the perturbation networks P ϕ and adversary classiﬁers are presented in Table 7. 3.
Revised: Architectures for best conﬁguration of the perturbation networks P ϕ and adversary classiﬁers are presented in Table 7. 3.

Original: For details on the performance of the best runs, see Table 8.
Revised: For details on the performance of the best runs, see Table 8.

Original: [Table]
Revised: [Table]

Original: A. 
Revised: A. A DDITIONAL INFORMATION ON THE SCI -P LEX 3 EXPERIMENTS

Original: A DDITIONAL INFORMATION ON THE SCI -P LEX 3 EXPERIMENTS

Original: The optimisation was performed similarly to the presented sweeps in Table 6 and Tablefor the perturbation network and adversary parameters for 10 samples each per category.
Revised: The optimisation was performed similarly to the presented sweeps in Table 6 and Tablefor the perturbation network and adversary parameters for 10 samples each per category.

Original: Boxplot results for RDKit, see Figures 6 and 8, and JT-VAE, see Figures 7 and 9. 3.
Revised: Boxplot results for RDKit, see Figures 6 and 8, and JT-VAE, see Figures 7 and 9. 3.

Original: Paired t-tests were performed for both settings, see Table 10 for the shared gene set and Table 10 for the extended gene set.
Revised: Paired t-tests were performed for both settings, see Table 10 for the shared gene set and Table 10 for the extended gene set.

Original: More examples on the performance with respect to speciﬁc drugs are presented in Figure 10, Figure 11, Figure 12, and Figure 13.
Revised: More examples on the performance with respect to speciﬁc drugs are presented in Figure 10, Figure 11, Figure 12, and Figure 13.

Original: [Table]
Revised: [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 10 Dosage in μM 0.
Revised: 10 Dosage in μM 0.

Original: E [ r 2 ]
Revised: E [ r 2 ]

Original: o n a ll g e n e s 0.
Revised: o n a ll g e n e s 0.

Original: 10 Dosage in μM 0.
Revised: 10 Dosage in μM 0.

Original: E [ r 2 ] o n D E G s
Revised: E [ r 2 ] o n D E G s

Original: Model type pretrained non-pretrained baseline
Revised: Model type pretrained non-pretrained baseline

Original: 10 Dosage in μM 0.
Revised: 10 Dosage in μM 0.

Original: E [ r 2 ]
Revised: E [ r 2 ]

Original: o n a ll g e n e s 0.
Revised: o n a ll g e n e s 0.

Original: 10 Dosage in μM 0.
Revised: 10 Dosage in μM 0.

Original: E [ r 2 ] o n D E G s
Revised: E [ r 2 ] o n D E G s

Original: Model type pretrained non-pretrained baseline
Revised: Model type pretrained non-pretrained baseline

Original: 10 Dosage in μM 0.
Revised: 10 Dosage in μM 0.

Original: E [ r 2 ]
Revised: E [ r 2 ]

Original: o n a ll g e n e s 0.
Revised: o n a ll g e n e s 0.

Original: 10 Dosage in μM 0.
Revised: 10 Dosage in μM 0.

Original: E [ r 2 ] o n D E G s
Revised: E [ r 2 ] o n D E G s

Original: Model type pretrained non-pretrained baseline
Revised: Model type pretrained non-pretrained baseline

Original: 10 Dosage in μ M 0.
Revised: 10 Dosage in μ M 0.

Original: E [ r 2 ]
Revised: E [ r 2 ]

Original: o n a ll g e n e s 0.
Revised: o n a ll g e n e s 0.

Original: 10 Dosage in μ M 0.
Revised: 10 Dosage in μ M 0.

Original: E [ r 2 ] o n D E G s
Revised: E [ r 2 ] o n D E G s

Original: Model type pretrained non-pretrained baseline
Revised: Model type pretrained non-pretrained baseline

Original: [Table] [Table]
Revised: [Table] [Table]