# Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for
Adversarial Multi-source Domain Adaptation

Wei Duan1 Hui Liu1,∗
1College of Computer and Information Engineering, Nanjing Tech University,
Nanjing, 211816, Jiangsu, China.
<EMAIL_ADDRESS>
###### Abstract

The development of single-cell sequencing technology had promoted the
generation of a large amount of single-cell transcriptional profiles,
providing valuable opportunities to explore drug-resistant cell subpopulations
in a tumor. However, the drug sensitivity data in single-cell level is still
scarce to date, pressing an urgent and highly challenging task for
computational prediction of the drug sensitivity to individual cells. This
paper proposed scAdaDrug, a multi-source adaptive weighting model to predict
single-cell drug sensitivity. We used an autoencoder to extract domain-
invariant features related to drug sensitivity from multiple source domains by
exploiting adversarial domain adaptation. Especially, we introduced an
adaptive weight generator to produce importance-aware and mutual independent
weights, which could adaptively modulate the embedding of each sample in
dimension-level for both source and target domains. Extensive experimental
results showed that our model achieved state-of-the-art performance in
predicting drug sensitivity on sinle-cell datasets, as well as on cell line
and patient datasets.

##  1 Introduction

The intrinsic and acquired resistance to drugs was the main cause of failure
in tumor therapy. Despite the fact that routine drug treatment can effectively
eliminate the majority of malignant cells, a small subset of tumor cells would
survive and continue to proliferate, ultimately leading to tumor recurrence
and progression. In recent years, the advent of single-cell RNA-sequencing
(scRNA-seq) technology had generated a wealth of single-cell transcriptional
data, providing valuable opportunity to explore the intratumoral
heterogeneity. However, there remains a significant gap in our knowledge
regarding drug response at the single-cell level, pressing a challenging but
urgent task to predict the drug sensitivity of individual cells.

Domain adaptation, which enables the knowledge transfer from source domains to
target domains, had achieved remarkable success in the fields of computer
vision and natural language processing. In recent years, domain adaptation had
gradually been applied to the field of biomedicine, such as domain transfer
from cancer cell lines to individual cells or patients. For instance, He et
al. proposed the CODE-AE model he2022context , which was based on domain
separation network bousmalis2016domain  to extract shared features between
cell lines and patients. This model was trained on cell line drug sensitivity
data and then used to predict the drug response for tumor patients. Chawla et
al. developed Precily chawla2022gene  that integrated signaling pathways and
drug features to predict drug responses in vitro and in vivo. Inspired by the
Fader network lample2017fader  and the compositional perturbation autoencoder
(CPA) lotfollahi2023predicting , Hetzel et al. proposed ChemCPA
hetzel2022predicting  to combine chemical property and drug-induced expression
profiles to infer transcriptional responses to unseen drug perturbations.
Other models, such as DeepCE pham2021deep  and GEARS roohani2022gears , have
been developed to predict drug-induced transcriptional profiles.

Due to the limited availability of single-cell drug response data, some
studies had leveraged domain adaptation between bulk RNA-seq (source domain)
and scRNA-seq (target domain) data to predict drug sensitivity of individual
cells. For example, Chen et al. proposed scDEAL chen2022deep  to align the
bulk RNA-seq and scRNA-seq features by minimizing the maximum mean discrepancy
(MMD) gretton2012kernel  in the latent space, so that the classifier trained
on cell line drug sensitivities can be transferred to predict single-cell drug
sensitivity. Zheng et al. developed SCAD zheng2023enabling , which was based
on adversarial domain adaptation, to learn drug-gene signatures from the GDSC
dataset yang2012genomics  for inferring drug sensitivity in single cells.
However, previous models were based on knowledge transfer from a single source
domain, which limited their ability to learn about the essential mechanism of
diverse types of cells responding to drug treatment. Multi-source domain
adaptation (MDA) often involves transferring knowledge from multiple source
domains to target domain sun2015survey ; sun2011two ; duan2012exploiting ;
schweikert2008empirical . For instance, Zhao et al. introduced a multi-source
domain adaptation (MDDA) method zhao2020multi  that minimized the empirical
Wasserstein distance between source and target domains, leading to high
performance in image classification tasks. Pei et al. proposed a method called
multi-adversarial domain adaptation (MADA) pei2018multi , which focused on
class-level alignment of different data distributions using multiple domain
discriminators. Additionally, Fu et al. proposed PFSA fu2021partial  to
address the category shift problem where the numbers and types of categories
in the source and target domains were inconsistent in practice.

Inspired by multi-source domain adaptation, we proposed scAdaDrug to predict
single-cell drug sensitivity. Specifically, we considered the bulk RNA-seq of
multiple cell lines to constitute the multiple source domains, with the scRNA-
seq of single cells serving as the target domain. By introducing an adaptive
weight generator, we produced a importance-aware weight vector that captured
the degree of relevance in dimension level between each source domain and
target domain. This approach allowed us to extract highly relevant feature
specific to each source domain sample and target domain sample under specific
drug treatment. Furthermore, through adversarial learning, we learned domain-
invariant features related to drug sensitivity, enabling our model to
generalize its predictive capabilities to drug responses in target domain.

The main contributions of this study are as follows:
1\. We are the first to apply multi-source domain adaptation (MDA) to the task
of single-cell drug sensitivity prediction. To transfer knowledge between the
label-rich source domains and label-lack target domain, we conducted domain
adaptation through adversarial learning to capture domain-variant features .
2\. Considering the difference of importance when transferring knowledge from
multiple source domains, we devised an adaptive weight generator that could
produce a importance-aware weight vector to capture element-wise relevance
between each source domain and target domain.
3\. To avoid information redundancy among multiple source domains, we imposed
conditional independence constraints on the generated weights, allowing our
model to capture causally independent features from multiple source domains in
then alignment to target domain.
4\. Our extensive experimental results demonstrated that the proposed model
achieved state-of-the-art performance in transfer knowledge from in vitro cell
lines to both single cells and patients for predict drug sensitivity.

##  2 Related Works

###  2.1 Multi-source Domain Adaptation

Multi-source domain adaptation (MDA) often involves transferring knowledge
between multiple source domains and target domains. Previous MDA methods
primarily utilized shallow models sun2015survey  to learn latent features from
different domains sun2011two duan2012exploiting  or incorporate pre-trained
source classifiers schweikert2008empirical  to transfer knowledge. However,
recent advancements have shifted towards deep learning-based approaches. For
instance, Zhao et al. introduced a multi-source domain adaptation (MDDA)
zhao2020multi  that minimized the empirical Wasserstein distance between
source and target domains, leading to high performance in image classification
tasks. Pei et al. proposed a method called multi-adversarial domain adaptation
(MADA) pei2018multi  to achieve class-level alignment of different data
distributions using multiple domain discriminators. Additionally, Fu et al.
proposed PFSA fu2021partial  to address the category shift problem where the
number and type of categories in the source and target domains are
inconsistent in practice.

###  2.2 Transfer learning-based Drug Response Prediction

Several studies have made progress in applying transfer learning to predict
patient drug responses. He et al. proposed the CODE-AE model he2022context
that is based on domain separation network bousmalis2016domain  to extract
shared features between cell lines and patients. It was trained using cell
line drug sensitivity data and then used to predict the drug response for
tumor patients. Chawla et al. developed Precily chawla2022gene  that
integrated signaling pathway and drug feature to predict drug responses in
vitro and in vivo. Inspired by the Fader network lample2017fader  and the
compositional perturbation autoencoder (CPA) lotfollahi2021learning , Hetzel
et al. proposed ChemCPA hetzel2022predicting , which combined chemical
property and drug-induced expression profiles to infer transcriptional
responses to unseen drug perturbations.

Due to the limited availability of single-cell drug response data, some
studies have leveraged domain adaptation between bulk RNA-seq (source domain)
and scRNA-seq (target domain) data to predict drug sensitivity of individual
cells. For example, Chen et al. proposed scDEAL chen2022deep , which aligned
the bulk RNA-seq and scRNA-seq features by minimizing the maximum mean
discrepancy (MMD) gretton2012kernel  in the latent space. This allowed the
classifier trained on cell line sensitivity can be transferred to predict
single-cell drug sensitivity. Zheng et al. developed SCAD zheng2023enabling ,
which is based on adversarial domain adaptation, to learn drug-gene signatures
from the GDSC dataset yang2012genomics  for inferring drug sensitivity in
single cells.

##  3 Method

###  3.1 Problem Definition

Suppose that there are $N$ source domains $S_{1}$, $S_{2}$, …,$S_{K}$ and one
target domain $T$. In the unsupervised multi-source domain adaptation
scenario, $S_{1}$, $S_{2}$,…,$S_{K}$ are labeled and $T$ is unlabeled.
Formally, we assume that there exist $N_{S_{K}}$ labeled cell lines
$D_{S_{k}}=\left.\\{(x_{i}^{S_{k}},y_{i}^{S_{k}})\\}_{i=1}^{N_{S_{k}}}\right.$
included in source domain $S_{k}$ ($k$=1,…,$K$), and $N_{T}$ unlabeled single
cells $D_{T}=\\{x_{i}^{T}\\}_{i=1}^{N_{T}}$ included in target domain, where
$x_{i}^{S_{k}}$ is the bulk transcriptional profile of $i$-th cell line from
the $k$-th source domain $S_{k}$, $y_{i}^{S_{k}}$ referred to the
corresponding label (sensitive or resistant) upon specific drug treatment, and
$x_{i}^{T}$ represents the single-cell transcriptional profile of $i$-th cell
from the target domain without label. An input sample consists of a tuple of
source domain samples and their true labels and a target domain sample. For
instance, $i$-th input is denoted by
$(x_{i}^{S_{1}},..,x_{i}^{S_{K}},y_{i}^{S_{1}},..y_{i}^{S_{K}},x_{i}^{T})$.

###  3.2 scAdaDrug framework

We propose a novel method, referrted to as scAdaDrug, which leverages the
multi-source domain adaptation to predict drug sensitivity at single-cell
level. In this context, we consider the bulk RNA-seq data of multiple cell
lines of the same cancer type as the multi-source domains, and scRNA-seq data
of the same cancer type as the target domain. A shared autoencoder is employed
to extract the features for both source and target domains. Specifically, we
introduce an adaptive weight generator to produce an importance-aware vector
for each source domain, which could capture element-wise relevance between
source and target domains. Meanwhile, we required the adaptively generated
weights were conditionally independent so as to eliminate information
redundancy between multiple domain sources. Therefore, the adaptively
generated weights allow for fine-grained and nonredundant knowledge transfer
from source domains to target domain. Furthermore, we incorporated adversarial
learning into our framework to learn domain-level invariant features related
to drug response. The designed model enables to generalize well across
different domains, thereby improving its predictive performance for drug
sensitivity at the single-cell level.

Figure 1: Illustrative flowchart of the proposed scAdaDrug framework. It
consists of four components: autoencoder-based feature extractor, adaptive
weight generator, adversarial domain discriminator and drug sensitivity
predictor.

###  3.3 Autoencoder architecture

We employ a shared autoencoder to extract features from expression profiles
for both multiple source domains and target domain. Taking as input the source
domain sample $x_{i}^{S_{k}}$ and target domain sample $x_{i}^{T}$, the
encoder $E_{\theta}$ maps them to the embeddings
$h_{i}^{S_{k}}=E_{\theta}(x_{i}^{S_{k}})$ and
$h_{i}^{T}=E_{\theta}(x_{i}^{T})$ in the latent space. The decoder os defined
as $G_{\varphi}$ that converts the embedding to output space. Our
encoder/decoder networks are fully or densely connected neural networks with
rectified linear unit (ReLU) activation function, $\theta$ and $\varphi$ are
the learnable parameters of the encoder and decoder. We require that the
encoder converts the input expression profiles into low-dimensional but
informative representations in the latent space, while the decoder tries to
reconstruct the input samples as much as possible. We optimize the parameters
by minimizing the reconstruction loss as below:

$\mathcal{L}_{reco}=\frac{1}{N_{T}}\sum_{j=1}^{N_{T}}\|G_{\varphi}(h_{i}^{T})-x_{i}^{T}\|^{2})\\\
+\sum_{k=1}^{K}(\frac{1}{N_{S_{k}}}\sum_{i=1}^{N_{S_{k}}}\|G_{\varphi}(h_{i}^{S_{k}})-x_{i}^{S_{k}}\|^{2})$
(1)

###  3.4 Adaptive weight generator

Most multi-source domain adaptation methods assume that the source domains
have uniform importance in transferring knowledge, neglecting the inherent
difference in their contribution to target domain tasks. Inspired by MDDA
zhao2020multi , we realize that different source domains have varying levels
of importance in the context of domain transfer. To capture the correlation of
features between source and target domain in dimension level, we introduce an
adaptive weight generator (AWG) to produce importance-aware vectors, which
assigns dimension-wise weights to the embeddings of the source and target
samples. The AWG is a two-layer fully-connected network, which takes as input
the embeddings of source and target samples and automatically learns to
produce the weight vector representing the importance of different dimensions.
Formally, let $F_{\omega}$ represent the function of AWG parametered by
$\omega$, we have

$v_{i}^{S_{k}}=F_{\omega}(|h_{i}^{T}-h_{i}^{S_{k}}|)$ (2)

in which $v_{i}^{S_{k}}$ is the generated weight for the sample $i$ of source
domain $S_{k}$ paired to target domain sample $j$ in a mini-batch, and
$|\cdot|$ represents the abs function. The subtract of $h_{i}^{T}$ by
$h_{i}^{S_{k}}$ represents the similarity between the source and target
samples, which is motivated by PFSA study fu2021partial . After the generation
of $v_{ij}^{S_{k}}$, we weight the embedding $h_{i}^{S_{k}}$ as below:

$z_{i}^{S_{k}}=h_{i}^{S_{k}}\odot v_{i}^{S_{k}}$ (3)

where $\odot$ represents the element-wise product. For the target sample, we
compute the mean weight vector regarding $K$ source domains as
$\bar{v}_{i}=\frac{1}{K}\sum_{k=1}^{K}v_{i}^{S_{k}}$, and then apply it to the
target sample embedding by element-wise product.

Meanwhile, to avoid information redundancy in transferring knowledge from
source domain to target domain, we also impose conditional independence
constraint on the adaptively generated weights. Specifically, we require that
the weight vectors of different source domain should be orthogonal to each
other. Let $C$ be the correlation matrix whose the element
$C_{jk}$=$corr(v_{i}^{S_{j}},v_{i}^{S_{k}})$ ($j,k$=$1..K$) is the correlation
coefficient of the weight vectors regarding source domain $S_{j}$ and $S_{k}$,
we define the following loss function:

$\mathcal{L}_{ind}=\frac{1}{2}||C-I||_{F}^{2}$ (4)

where $I$ is the identity matrix, and $F$ denotes the Frobenius norm. The
minimization of $\mathcal{L}_{ind}$ make the non-diagonal elements of $C$
close to 0, i.e., enforcing the generated weight vectors to be jointly
independent.

It is worth noting that our method differs from PFSA fu2021partial  in at
least two aspects. First, instead of using uniform weight vector for all
source domain features, our method assigns unique feature weighting vector to
each individual source domain sample. This approach implicates sample-level
weighting scheme that effectively underscores the significant dimensions
within the latent feature space. Second, we introduce the conditional
independence constraint on the adaptively generated weights to minimize
redundant information among source domain features, thus allowing us to
establish the explicit connection for transferring knowledge from source to
target domain.

###  3.5 Adversarial domain adaptation

For adversarial domain adaptation, a domain discriminator is introduced to
distinguish the domain origin (source or target) of the features. The feature
extractor aims to produce features that confuses the domain discriminator,
while the domain discriminator strives to correctly identify the domain of the
features. Through adversarial training, the feature extractor learns to
produce domain-invariant representations, while still preserving sufficient
discriminative information for the main task. For this purpose, we assign a
domain label of 1 to the source domain features and a label of 0 to target
domain features. Denote by $L_{adv}$ the domain discrimination loss, the
domain discriminator is trained to minimize the binary-cross entropy (BCE)
loss as below

$\mathcal{L}_{adv}=-\sum_{k=1}^{K}\sum_{i=1}^{M}(y_{D}\log
D(z_{i})+(1-y_{D})\log(1-D(z_{i})))$ (5)

where $y_{D}$ is the domain label assigned to feature $z_{i}$, $D(z_{i})$
represents the predicted domain label by the domain discriminator, and $M$ is
the total number of samples from both source and target domains included in a
mini-batch.

###  3.6 Drug response predictor

The drug response of cell lines to specific drug exposure is used to train the
source domain predictor. As the drug sensitivity is measured by the half
maximal inhibitory concentration (IC50), we categorize the IC50 to binary
labels, namely responsive or non-responsive (resistant). The binarization
threshold is selected as the average AUC value of all available cell-line drug
sensitivity for drugs tested he2022context . The predictor $P$ takes as input
the weighted features of cell lines to predict their labels. Therefore, the
classification loss $\mathcal{L}_{pred}$ is defined as below:

$\mathcal{L}_{pred}=\sum_{k=1}^{K}(\sum_{i=1}^{N_{S_{k}}}\|y_{i}^{S_{k}}-P_{\phi}(z_{i}^{S_{k}})\|^{2})$
(6)

in which $\phi$ denotes the parameters of the predictor. Once the drug
response predictor has been trained on source domain labels, the domain-
invariant features learned by adversarial domain adaptation allow to predict
drug responses in single cells (the target domain) effectively.

Finally, the total loss function is defined as the sum of the four loss terms
as below:

$\mathcal{L}(\theta,\varphi,\omega,\phi)=\mathcal{L}_{reco}+\mathcal{L}_{ind}+\mathcal{L}_{adv}+\mathcal{L}_{pred}$
(7)

in which the parameters are optimized during the minimization of the loss
function.

##  4 Experiments

###  4.1 Data resources

The source domain dataset was retrieved from the GDSC database
gretton2012kernel , which provided drug sensitivities of diverse cell lines
gretton2012kernel . Drug sensitivity was evaluated using half maximal
inhibitory concentration (IC50) and area under the dose-response curve (AUC)
measurements. For each drug, the AUC scores obtained from bulk cell lines were
binarized to determine whether the drug was sensitive or resistant to the cell
line, utilizing the same approach as scDEAL chen2022deep . Additionally, the
bulk RNA-seq data (RMA-normalized basal expression profiles) of the cell lines
was also retrieved from GDSC.

The target domain data comprised single-cell RNA-seq drug sensitivity and
patient drug response data. The single-cell RNA-seq data was obtained from the
National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus
(GEO). The patient drug response data was retrieved from TCGA repository
hutter2018cancer , which included patient transcriptomic data and clinical
drug response information.

###  4.2 Performance evaluation on single-cell drug sensitivity

Our primary objective is to predict drug responses at the single-cell level,
which would facilitate to identify drug-resistant cell subpopulations. The
source domain cell line data was obtained from the GDSC database, while the
target domain single-cell data were sourced from GEO under accession GSE149215
and GSE108383. The GSE149215 dataset comprised transcriptional profiles and
drug response IC50 values of 1,393 single cells from PC9 cell line after
Etoposide treatment. The GSE108383 dataset included transcriptional profiles
and drug response IC50 values of 108 single cells from the A375 cell line and
197 single cells from 451Lu cell line after PLX4720 treatment. We selected the
highly variable genes shared by both cell line and single-cell transcriptional
profiles as input to our model. The details of the source and target domain
datasets with common drugs were listed in Table 1.

Table 1: Details of the source domain (bulk RNA-seq) and target domain (scRNA-seq) datasets regarding drug sensitivity Domain | Dataset | Drug | Cell line | Sensitive | Resistant | Genes | HVgenes
---|---|---|---|---|---|---|---
Source | GDSC | Etoposide | Pan-Cancer | 53 | 811 | 9,738 | 2,668
GDSC | PLX4720 | Pan-Cancer | 88 | 746 | 11,937 | 2,856/2,873
Target | GSE149215 | Etoposide | PC9 | 629 | 764 | 9,738 | 2,668
GSE108383 | PLX4720 | 451Lu | 84 | 113 | 1,1937 | 2,856
GSE108383 | PLX4720 | A375 | 62 | 46 | 11,937 | 2,873
Figure 2: AUROC curves increase with training epoch on two single-cell drug
sensitivity datasets GSE108383 (Etoposide) and GSE149215 (PLX4720).

(a) Two source domains (b) Three source domains

Figure 3: ROC curves and AUC values achieved by our method using two and three
source domains.

To benchmark the performance of scAdaDrug, we conducted performance comparison
with SCAD zheng2023enabling , which has been evaluated on the same dataset.
Due to the significant imbalance in the number of sensitive and resistant cell
lines in the source domains, we tried both weighted sampling and SMOTE
sampling to alleviate the influence of class imbalance during the model
training process. This upsampling strategy was also used by SCAD
zheng2023enabling , thereby allowing for objective performance comparisons.
Simultaneously, we sought to investigate the impact of varying the number of
source domains on the predictive performance in target domains. For this
purpose, we constructed two datasets that consisted of two and three domain
sources to train our model for performance evaluation. As shown in Table 2,
our model significantly outperformed SCAD in predicting single-cell drug
sensitivity across two drug treatments. Notably, our method consistently
delivered superior performance regardless of the upsampling strategy and
number of source domains. In particular, when utilizing two source domains
with SMOTE upsampling, our model achieved the best performance. The increasing
trends of AUROC values corresponding to drug-induced resistance and
sensitivity by Etoposide and PLX4720 were depicted in Fig. 2. Furthermore,
Fig. 3(a-b) illustrated the ROC curves achieved by our method when using two
and three source domains. It can be found that two source domains resulted in
approximately 2% better performance on both drug-induced datasets.

Table 2: AUROC values achieved by scAdaDrug and SCAD on single-cell drug sensitivity datasets Method | scAdaDrug (2 source domains) | scAdaDrug (3 source domains) | SCAD
---|---|---|---
Drugs | Weight | Smote | Weight | Smote | Weight | Smote
Etoposide | 0.717 | 0.733 | 0.702 | 0.717 | 0.583 | 0.671
PLX4720(451Lu) | 0.536 | 0.557 | 0.488 | 0.529 | 0.508 | 0.504
PLX4720(A375) | 0.867 | 0.987 | 0.874 | 0.969 | 0.780 | 0.937
Average | 0.707 | 0.759 | 0.688 | 0.738 | 0.624 | 0.704

###  4.3 Multiple independent datasets verified model generalizability

To further evaluate the generalizability of scAdaDrug in predicting single-
cell drug sensitivity, we utilized five public scRNA-seq datasets to assess
the drug response prediction performance. These datasets encompassed four
distinct drugs: Cisplatin, Gefitinib, Docetaxel and Erlotinib, with the
details summarized in Table 3. Also, we compared our method to scDEAL
chen2022deep , which was evaluated on the same datasets and also employed
SMOTE sampling to address class imbalance problem. As depicted in Fig. 4, the
experimental results indicated that scAdaDrug consistently delivered high
performance in predicting single-cell drug responses across all five datasets.
Both the AUROC and AUPR values of our method were mostly superior to those of
scDEAL on all five datasets. These experimental results strongly validated the
generalizability of our method to different drug-induced single-cell
sensitivity predictions.

Table 3: Five independent single-cell drug sensitivity dataset for performance evaluation Drug | GEO accession | Sensitive cells | Resistant cells | Species | Cancer type
---|---|---|---|---|---
Cisplatin-1 | GSE117872 | 361 | 187 | Human | Oral squamous cell carcinomas
Cisplatin-2 | GSE117872 | 403 | 165 | Human | Oral squamous cell carcinomas
Gefitinib | GSE112274 | 37 | 470 | Human | Lung cancer
Docetaxel | GSE140440 | 162 | 162 | Human | Prostate Cancer
Erlotinib | GSE149383 | 1,001 | 1,325 | Human | Lung cancer

(a) AUROC (b) AUPR

Figure 4: Comparison of AUROC and AUPR values between scAdaDrug and scDEAL on
five single-cell drug sensitivity datasets.

###  4.4 Evaluation on patients drug responses

To verify the extensible ability of our model, we went further to check
whether our model can generalize from cell lines to patient drug response. For
this purpose, we retrieved the transcriptional profiles and clinical response
data from TCGA repository. The common genes of cell lines and patients were
selected to compute pathway activities, which were taken as input to our
model. The patients received six drug treatments, include Cisplatin,
Docetaxel, 5-fluorouracil, Gemcitabine, Paclitaxel, Sorafenib, were sufficient
for performance evaluation, and these drugs were shared between GDSC cell
lines and TCGA patients. The clinical drug responses of TCGA patients were
derived from clinical metadata, with complete response and partial response
patients categorized as responders and patients with clinically progressive
and stable diseases marked as non-responders.

Our model yields AUPR values of 0.893, 0.753, 0.742, 0.616, 0.689, and 0.846,
in predicting the patient response to the six drug treatment, respectively.
Furthermore, we compared our model to a variety of existing methods in
predicting these TCGA patient response to therein four drugs (5-Fluorouracil,
Cisplatin, Gemcitabine, Sorafenib), whose performance has been reported in
CODE-AE he2022context . The experimental results were shown in Fig. 5, it can
be seen that our model demonstrated better predictive performance than all
other methods. Especially for Cisplatin and Sorafenib, our method achieved
more significant advantage over comparative methods.

Figure 5: Performance comparison of scAdaDrug and other existing methods in
predicting clinical patient responses to four drug treatments.

###  4.5 Ablation study

Table 4: AUROC values achieved by different model variants on two single-cell drug sensitivity datasets | Smote Sampling | Weight Sampling
---|---|---
2 source domains | 3 source domains | 2 source domains | 3 source domains
Drug | Etoposide | PLX4720 | Etoposide | PLX4720 | Etoposide | PLX4720 | Etoposide | PLX4720
w/o MDA | 0.606 | 0.814 | 0.633 | 0.738 | 0.614 | 0.765 | 0.634 | 0.812
w/o IND | 0.674 | 0.819 | 0.669 | 0.763 | 0.656 | 0.789 | 0.652 | 0.852
w/o AWG | 0.706 | 0.872 | 0.675 | 0.834 | 0.638 | 0.792 | 0.643 | 0.752
scAdaDrug | 0.733 | 0.987 | 0.717 | 0.969 | 0.716 | 0.867 | 0.702 | 0.874

To validate the effectiveness of different components included in our model,
we conducted model ablation experiments on two single-cell drug sensitivity
datasets, namely GSE149215 and GSE108383, with different configurations of
upsampling strategy and source domain number. We designed three variants of
our method as below:

  * •

w/o MDA: without multi-source domain adaptation. Only the source domain data
was used to train the autoencoder and predictor, which was then applied to
predict single-cell drug sensitivity.

  * •

w/o IND: without conditional independence constraint on the adaptively
generated weights.

  * •

w/o AWG: without the adaptive weight generator. The source domains were
assumed to contribute equally to domain transfer, and the features extracted
by the autoencoder were aligned by domain adaptation without weight
adjustment.

As shown in Table 4, without the multi-source domain adaptation, the model
performance reduced most severely upon each combination of upsampling strategy
and source domain count. Also, the conditional independence constraint and
adaptive feature weighting contributed greatly to the performance. With these
components integrated, our model achieved consistently superior performance
under different sampling methods and varying numbers of source domains.

##  5 Conclusion

In this paper, we proposed a novel model for predicting drug sensitivity at
the single-cell level, named scAdaDrug. By integrating multi-source domain
adaptation, adaptive feature weighting and adversarial learning methods,
scAdaDrug achieved superior generalizability in predicting drug responses at
the single-cell level. Experimental results demonstrated that our model excels
in predicting drug responses not only for single cells but also for patient
samples. In summary, scAdaDrug highlighted its potential for precision
medicine applications, and would greatly contribute to our better
understanding of drug resistance mechanisms. More generally, our study has
confirmed the correlation between cell lines and patients in response to drug
treatment, providing crucial guidance for the development of personalized
medicine.

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