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# Applying SCALEX scRNA-Seq Data Integration for Precise Alzheimer's Disease Biomarker Discovery

Aristidis G. Vrahatis(✉), Konstantinos Lazaros, Petros Paplomatas, Marios G. Krokidis, Themis Exarchos, and Panagiotis Vlamos

Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
aris.vrahatis@ionio.gr

**Abstract.** Alzheimer's disease is a complex and devastating neurological disorder, and there is a pressing need to discover effective treatments. Analyses in specific brain regions can provide valuable insights into the underlying pathology of the disease. Molecular biology techniques, such as single-cell analysis, can offer an in-depth analysis of cellular-level changes in the brain in Alzheimer's disease. However, integrating single-cell RNA sequencing (scRNA-seq) data from different studies can be challenging due to batch effects, which can lead to spurious results. The dominant approach for addressing this issue is SCALEX, an online single-cell data integration method that projects heterogeneous datasets into a common cell-embedding space. In this paper, we highlight the impact of SCALEX in detecting accurate biomarkers in the context of significant genes that are leading scRNA-seq data. We demonstrate the pitfalls of traditional data integration methods and show the protection offered by SCALEX in preserving the biological heterogeneity of the sample while minimizing the impact of technical artifacts introduced by batch effects. Our results show that integrating scRNA-seq data with SCALEX can lead to more accurate biomarkers and significant genes, offering insights into the underlying pathology of Alzheimer's disease. These findings have important implications for the development of new therapies and treatments for this devastating disease.

**Keywords:** scRNA-seq integration · SCALEX · Biomarkers · Alzheimer's Disease

# 1 Introduction

## 1.1 Single-Cell Technologies for Alzheimer's Disease

Alzheimer's disease is a neurodegenerative disorder that affects millions of people worldwide, particularly in the aging population. The disease is characterized by the accumulation of amyloid-beta plaques and tau protein tangles in the brain, leading to progressive cognitive impairment and memory loss. Despite decades of research, there is still no
effective cure for Alzheimer's disease, and current treatments only offer limited symptomatic relief. One of the major challenges in developing effective treatments is the complexity and heterogeneity of the disease, which involves multiple biological pathways and cell types.

Single-cell RNA sequencing (scRNA-seq) [16, 20] has emerged as a powerful tool for understanding the heterogeneity of Alzheimer's disease [4, 19] and identifying potential biomarkers and therapeutic targets. By analyzing gene expression in individual cells, scRNA-seq can reveal the diversity of cell types and their molecular signatures in the brain, including changes associated with disease progression. This technique allows us for a more detailed understanding of the function and dysfunction of different cell types in Alzheimer's disease. By analyzing gene expression at the single-cell level, researchers can gain insights into why certain cells are more vulnerable to the disease, and how the disease progresses over time. Proper data analysis of scRNA-Seq and snRNA-Seq (single nucleus RNA sequencing) data can significantly enhance our understanding of Alzheimer's disease and aid in the development of new therapies.

However, scRNA-seq data is complex and noisy, and requires careful data integration [14, 18] and analysis to identify reliable biomarkers and understand disease mechanisms. Advanced methods such as SCALEX [13] can help to overcome technical variability and improve the accuracy of biomarker identification.

## 1.2 Single-Cell Data Integration – A Major Challenge

Single-cell data integration is the process of combining multiple datasets obtained from single-cell experiments to gain a more comprehensive understanding of the biological system under investigation. With the increasing availability of high-throughput single-cell technologies, there is a need to integrate data from multiple sources to identify novel biological insights and reduce technical and biological variability. There are several challenges associated with single-cell data integration, including batch effects, differences in experimental protocols, and technical variability.

Batch effects are a type of technical variability that can arise in single-cell experiments when samples are processed at different times or using different protocols. In single-cell RNA sequencing (scRNA-seq) experiments, batch effects can arise due to differences in library preparation, sequencing, and cell capture. For example, if cells are isolated and processed at different times, there may be differences in the efficiency of cell capture or differences in the quality of the RNA extracted, which can result in batch effects. Similarly, if different sequencing platforms or protocols are used, there may be differences in read depth, sequencing errors, or gene detection rates that can also lead to batch effects.

Batch effects can manifest in various ways in scRNA-seq data, such as differences in the expression of certain genes, the presence of artificial clusters or cell populations, or shifts in the distribution of gene expression levels. To overcome batch effects, several methods have been developed to adjust for batch effects, such as batch correction algorithms, which adjust the data to remove any technical variation associated with the batches. These methods aim to remove batch effects while preserving biological variation, allowing for more accurate biological interpretations and comparisons across
samples. Various methods have been developed for data integration [15], such as Seurat [9], Scanpy [11], Harmony [7], and Conos [10].

Meanwhile, in single-cell RNA sequencing (scRNA-seq) experiments, identifying biomarkers through dominant genes is a common task [5]. However, if the scRNA-seq data is not integrated properly, the identified biomarkers may be inaccurate and biased due to the batch effect, which is a technical problem that can arise from processing samples at different times or using different protocols. Batch effects can introduce technical variability that affects the gene expression levels of cells and can lead to the identification of false positive or false negative biomarkers. Therefore, if the integration of single-cell data is not performed correctly, the identified biomarkers may be misleading and not biologically meaningful.

In other words, a bad scRNA data integration can lead to the identification of phe-
notypically accurate biomarkers that are actually derived from the wrong data and not
biologically meaningful, due to the presence of batch effects. Thus, it is essential to
perform proper data integration to remove batch effects and ensure the accurate iden-
tification of biomarkers from scRNA-seq data. This involves assessing the quality of
the data, applying appropriate normalization techniques, and employing reliable data
integration methods that can overcome batch effects.

If the integration of single-cell data is not performed correctly, it can lead to erroneous conclusions and misinterpretations of the underlying biology. For example, if batch effects are not properly corrected, it may appear that certain cell types are present in one dataset but not in another, leading to false discoveries or missed opportunities for identifying important cell types.

Additionally, improper data normalization can lead to an overrepresentation of certain genes or features in downstream analyses, potentially biasing the results. In extreme cases, bad data integration can lead to incorrect biological interpretations and wasted resources. Therefore, it is crucial to carefully assess the quality of the data, perform rigorous normalization and feature selection, and choose appropriate data integration methods to ensure reliable and accurate results. Collaboration with experts in bioinformatics and single-cell data analysis can also be helpful in ensuring that the data integration is performed optimally.

**2 Methodology – Exploring the Implications of SCALEX**
**Integration**

## 2.1 SCALEX

SCALEX is a single-cell data integration method that uses a VAE (Variational Auto
Encoder) [12] framework to project single-cell data onto a low-dimensional space. The
key innovation in SCALEX is the use of an asymmetric autoencoder that inputs batch
information only to the decoder, not the encoder. This allows the encoder to learn a
batch-invariant representation of the data, while the decoder can capture batch-specific
variation. The use of a DSBN layer in the decoder further helps to release the encoder
from the burden of capturing batch-specific variation, allowing it to focus on capturing
the biological signal.
To improve the performance of SCALEX, the authors also employed a mini-batching strategy that samples data from all batches simultaneously, which more closely follows the overall distribution of the input dataset. This strategy includes a Batch Normalization layer in the encoder that adjusts the deviation of each mini-batch and aligns them to the overall input distribution. By using this mini-batching strategy, SCALEX can integrate single-cell data in an online manner, without the need for retraining on new data.

To evaluate the performance of SCALEX, the authors compared it to several test-variants. One variant included an encoder with batch labels as input, which showed similar integration performance to the full SCALEX, but was not capable of integrating data in an online manner. Another variant removed the DSBN layer from the decoder, which resulted in a drop in integration performance. Finally, a variant that used a regular autoencoder instead of a VAE performed the worst among all variants.

Overall, the SCALEX methodology demonstrates an effective approach for online integration of heterogeneous single-cell data using a VAE framework and innovative design choices, such as the asymmetric autoencoder, DSBN layer, and mini-batching strategy. By avoiding the inclusion of batch information in the encoder, SCALEX can learn a batch-invariant representation of the data, allowing for more accurate integration of single-cell data.

## 2.2 Application to Single-Cell RNA-Seq Data

We applied our method to the scREAD dataset [1], a single-cell RNA-seq database for Alzheimer's disease that includes 23 human and mouse control cell atlases based on 17 scRNA-seq and snRNA-seq datasets. These datasets cover 10 brain regions, two genders, and different ages, totaling 713,640 cells that were redefined into 73 datasets based on species, gender, brain region, disease or control, and age. We focused on the superior frontal gyrus and integrated three datasets with a total of 93,000 cells, including 40,000 healthy and 50,000 diseased cells across five different cell types (macrophages, astrocytes, microglia, etc.).

To the best of our knowledge, this is the first machine learning study to include such a large dataset from the superior frontal gyrus of humans with Alzheimer's disease, integrating several scRNA-seq studies. Our goal was to demonstrate the heterogeneity of significant genes arising without and with SCALEX integration. We compared the results obtained using traditional data integration methods (harmonization) with SCALEX in terms of biomarker detection. Our observations showed that different genes were identified as potential biomarkers using traditional data integration methods compared to SCALEX. This emphasizes the importance of proper data integration, as traditional methods can lead to the identification of wrong gene biomarkers.

More specifically, we employ UMAP [17] to visualize the SCALEX integration in two dimensions and compare its performance against the simple concatenation method for harmonized data provided by the dataset authors. Additionally, we evaluate two state-of-the-art data integration techniques, Scanorama [8] and BBKNN (Batch Balanced KNN) [6]. The 2D visualizations highlight the effectiveness of SCALEX in comparison to the other methods, demonstrating its superior capabilities in data integration. Assessing these visualizations in terms of clustering, we evaluate their performance using the silhouette index, yielding values of 0.7948, 0.2706, 0.3398, and 0.3517 for SCALEX,
Concatenation, Scanorama, and BBKNN, respectively. The high score achieved by SCALEX is reflected in the denser clusters observed in Fig. 1, further emphasizing its superior performance in data integration.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** This UMAP embeddings of our scRNA-seq dataset are displayed, showcasing four different integration methods: the original concatenated integration provided by the dataset authors (top left), SCALEX integration (top right), BBKNN integration (bottom left), and Scanorama integration (bottom right). The UMAP embeddings are color-coded based on cell types. We observe that SCALEX generates denser clusters for each cell type, demonstrating its superior performance in terms of both batch effect removal and preservation of biologically meaningful information.</figcaption></figure>

We also evaluated the effectiveness of SCALEX in identifying the most relevant genes, or potential biomarkers, for Alzheimer's disease research compared to the classical concatenation method. By comparing the significant gene detection performance of the two approaches using XGBoost [2] and ANOVA [3] feature importance methods (see Fig. 2), we aim to demonstrate the benefits of using SCALEX for integrating single-cell RNA sequencing (scRNA-seq) data. The Fig. 2 illustrates the degree to which potentially important disease biomarkers are lost when using the classical concatenation method as compared to the SCALEX approach, emphasizing the advantages of employing SCALEX for a more accurate identification of significant genes in Alzheimer's disease research.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** This figure presents a comparison of significant gene detection between SCALEX and concatenated data integration approaches, utilizing both XGBoost (orange line) and ANOVA (blue line) feature importance methods. The x-axis represents the top 2000 features ranked by importance, while the y-axis indicates the number of non-shared genes between the top features of the concatenated and SCALEX-integrated data. For instance, in the XGBoost plot, at X = 400, Y = 154, which implies that among the top 400 features identified by XGBoost, there are 154 non-shared genes between the two datasets. The graphs highlight the degree to which potentially important disease biomarkers are lost when using the classical concatenation method as compared to the SCALEX approach, for both XGBoost and ANOVA feature selection methods, thereby emphasizing the advantages of employing SCALEX for a more accurate identification of significant genes.</figcaption></figure>

Note here that XGBoost (eXtreme Gradient Boosting) is a popular machine learning algorithm based on decision trees and gradient boosting. In the context of gene expression data, XGBoost can be used to rank the importance of different genes based on their contribution to the predictive accuracy of the model. The XGBoost variable importance score is calculated by considering the frequency and impact of a gene in the decision trees built during the learning process. Highly ranked genes based on their variable importance scores are considered more crucial for the classification or prediction task, providing insights into potential biomarkers and significant genes. Also, analysis of variance (ANOVA) is a statistical test used to compare the means of two or more groups. In the context of differentially expressed genes, ANOVA can be applied to assess the significance of gene expression differences across multiple conditions or groups. The feature importance using ANOVA is based on the F-statistic, which measures the ratio of the between-group variance to the within-group variance. Genes with
higher F-statistics indicate a larger expression difference across groups, suggesting their potential importance as biomarkers or significant genes.

# 3 Discussion

Molecular biology and transcriptomics have significant potential to contribute to our understanding of Alzheimer's disease (AD). Transcriptomic studies can help identify molecular pathways that are disrupted in AD, and highlight potential therapeutic targets. By analyzing gene expression changes in AD brains compared to healthy controls, researchers can gain insights into the underlying molecular mechanisms of the disease. Transcriptomics can also be used to identify biomarkers for AD, which can aid in early diagnosis and monitoring of disease progression. By analyzing gene expression patterns in blood or cerebrospinal fluid, for example, researchers can identify biomarkers that are associated with the disease.

Moreover, transcriptomic studies can aid in drug development for AD by identifying potential drug targets and predicting drug efficacy. By analyzing the gene expression changes induced by candidate drugs in AD models, researchers can assess their potential therapeutic value. AD is a complex disease that affects multiple cell types in the brain. Transcriptomics can help researchers understand the heterogeneity of cell types and their molecular signatures in AD, which can aid in the development of more targeted therapies. Integrating transcriptomic data with other molecular and clinical data can provide a more comprehensive understanding of AD. For example, integrating transcriptomic data with data from imaging or proteomic studies can help identify key molecular pathways and biomarkers associated with disease progression.

The results of this study showed that traditional data integration methods (harmonization) can lead to the identification of different genes as potential biomarkers compared to the SCALEX method. This suggests that the traditional methods may not accurately account for batch effects and other sources of technical variability, leading to false positive or false negative results in biomarker identification.

In contrast, the SCALEX method, which was specifically designed to account for batch effects and reduce technical variability, identified different genes as potential biomarkers compared to the traditional methods. This demonstrates that proper data integration using advanced methods such as SCALEX can improve the accuracy and reliability of biomarker identification in scRNA-seq datasets.

Moreover, the study focused on the superior frontal gyrus, which is an important brain region affected by Alzheimer's disease. The integration of multiple scRNA-seq datasets allowed for a comprehensive analysis of the heterogeneity of gene expression across different cell types and conditions. The large dataset size also provided a powerful resource for identifying potential biomarkers and understanding disease mechanisms.

Regarding the comparisons for the significant gene detection performance between the SCALEX and concatenated data integration approaches, we observed the potential pitfalls and misleading conclusions that can arise from improper data integration without effectively removing batch effects. Inadequate removal of batch effects can lead to the identification of incorrect pathways and erroneous biomarkers, which can have significant consequences for downstream analyses and the development of targeted therapies.
By emphasizing the importance of employing appropriate data integration methods, such as SCALEX, we aim to minimize these risks and facilitate more accurate research outcomes in Alzheimer's disease studies.

In our comparisons we utilized XGBoost variable importance and ANOVA feature importance since they offer distinct methods for ranking genes based on their relevance in gene expression data. XGBoost focuses on the predictive power of genes in a machine learning context, while ANOVA highlights the statistical differences in gene expression levels across multiple conditions or groups. Combining these two approaches can provide a comprehensive understanding of the most important genes for a particular study, contributing to the discovery of potential biomarkers and key genes involved in disease processes.

## 4 Conclusions

Overall, the discovery of new biomarkers and therapeutic targets for Alzheimer's disease is a pressing need, and scRNA-seq provides a powerful tool for making progress in this area. By integrating large and diverse datasets using advanced methods, researchers can gain new insights into the disease and develop more effective treatments. To ensure the identification of reliable biomarkers, it is crucial to begin with well-preprocessed and clean data as a foundation. In the case of single-cell RNA-seq data, this necessitates effective data integration. SCALEX is a recent tool that has demonstrated remarkable potential in this research area. By applying SCALEX to Alzheimer's disease scRNA-seq data, we showcase the capabilities of this tool and anticipate that it will become a dominant method in the field, paving the way for more accurate and reliable biomarker discovery.

**Acknowledgment.** This research is funded by the European Union and Greece (Partnership Agreement for the Development Framework 2014–2020) under the Regional Operational Programme Ionian Islands 2014–2020, project title: “Study of Clinical trial protocols with biomarkers that define the evolution of non-genetic neurodegenerative diseases- NEUROTRIAL”, project number: 5016089.

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