Transcriptomic in allergy–statistical recommendations
Authors: Michal Ordak, Danilo Di Bona, Gaetano Serviddio
Journal: Allergy (2024)

Transcriptomic data analysis focuses on gene expression at the RNA transcript level, enabling detection of expression changes under various biological conditions. It includes analysis of metabolic and signaling pathways, and investigation of gene regulation by transcription factors and other molecular mechanisms. Transcriptomic analysis is crucial for identifying biomarkers in health and disease, and for studying alternative splicing, essential for comprehensive understanding of gene functions in organisms.1 Recently, there has been an increase in articles published in Allergy utilizing such analysis by authors. In 2022, Allergy published an EAACI position paper that comprehensively discusses the application of omics technologies, including transcriptomics, in the study of allergic diseases and asthma, emphasizing their role in understanding pathophysiology and needs in precision medicine.2 Another example from Allergy, published on August 7, 2024, discusses how transcriptomic analysis is used to study the impact of environmental triggers on asthma exacerbation through changes in airway epithelial cells.3 The analysis of transcriptomic data in allergy research is crucial for uncovering gene expression profiles specific to allergic responses and immune reactions. These profiles help identify biomarkers and understand the mechanisms underlying allergic diseases. Advanced statistical methods tailored to the complexity of allergy data enhance the precision of findings. This focus supports personalized medicine approaches by addressing the variability in immune responses and improving treatment strategies. This article presents recommendations for statistical analysis of transcriptomic data, aiming to ensure methodological rigor and credibility in result interpretation. Firstly, a crucial aspect of statistical analysis of transcriptomic data is normalization, aimed at removing technical differences between samples that could affect the accuracy of differential gene expression (DGE) analyses. Discrepancies in sequencing library sizes and gene lengths can significantly influence gene expression counts across samples. For Bulk RNA-Seq data analysis, normalization methods such as upper quartile (UQ), median, relative log expression (RLE), trimmed mean of M-values (TMM), and quantile normalization are recommended. These methods are designed to correct technical variables in genetic and transcriptomic data, enabling precise comparisons of gene expression levels. For single-cell RNA-Seq analysis, these methods are adapted to handle technical variability and generate normalized gene expression matrices. Examples include methods like SCDE, TASC, and MAST, which employ empirical Bayesian frameworks, and methods such as scran, BASiCS, and SCnorm, differing in their approaches to modeling scaling factors. Specialized approaches like BISCUIT iteratively normalize and cluster cells to analyze heterogeneity. Each normalization method has its own strengths and weaknesses. UQ normalization is robust against outliers but may not be suitable for datasets with highly variable expression levels. Median normalization is straightforward and effective for datasets with similar sample profiles, though it may be less accurate with diverse data. RLE and TMM normalization methods both handle technical variations well, with TMM being particularly good at managing extreme value discrepancies. Quantile normalization effectively reduces systematic differences but can sometimes obscure biologically relevant variations. Implementing these methods using popular bioinformatics packages such as edgeR and DESeq2 is crucial for obtaining reliable analysis results. Furthermore, for Bulk RNA-Seq data, consideration of batch effects and other sources of technical variability is recommended, achievable through advanced statistical methods such as ComBat or principal component analysis (PCA). For single-cell RNA-Seq, several methods have been proposed, such as k-nearest neighbor correct effect, utilizing the χ2 test, and mutual nearest neighbor (MNNs), which corrects effects based on shared populations between batches, implemented in scran as mnnCorrect. Researchers are advised to carefully plan the normalization process based on the specifics of their data collection and to select appropriate methods tailored to the study's technical and biological challenges. Bulk RNA-seq data, which aggregates gene expression from multiple cells, can sometimes obscure detailed insights into cellular composition due to its composite nature. While bulk RNA-seq has limitations in precisely determining the proportions of specific cell types, deconvolution techniques and computational models have been developed to estimate cellular proportions and improve our understanding of tissue heterogeneity.1 In DEG analysis of RNA-seq data, it is recommended to employ statistical models that account for specific data distributions such as Poisson and negative binomial, to accurately model read counts and accommodate overdispersion. Generalized linear models and their extensions, such as the two-stage Poisson model and beta-binomial models, are effective in improving the accuracy of statistical tests. Statistical tests such as the likelihood ratio test, exact test are recommended for assessing the significance of gene expression differences. Parametric methods are advised when modeling assumptions are met; however, in cases where assumptions are not met, non-parametric methods such as SAMseq and NOIseq should be considered. Full or empirical Bayesian approaches, as implemented in ShrinkSeq and baySeq, can further enhance the accuracy of analysis. Monte Carlo methods provide a powerful statistical approach to address distribution-related issues in transcriptomics data. These methods use random sampling to approximate complex distributions, allowing for more accurate modeling of gene expression variability and enhancing the robustness of statistical inferences.1, 4-6 In the case of single-cell RNA-Seq, methods for dimensionality reduction and feature selection are crucial. Dimensionality reduction techniques such as PCA, t-distributed Stochastic Neighbor Embedding, Laplacian eigenmaps, and Independent Component Analysis (ICA) allow for projecting gene expression data into a lower-dimensional space, facilitating visualization and analysis of the data. Recently, methods like uniform manifold approximation and projection (UMAP) and scvis were developed to reduce the dimensions of scRNA-seq data, with UMAP proving to be faster, more reproducible, and better at organizing cell clusters than other techniques. Conversely, feature selection methods, such as identifying highly variable genes (HVG), enable the removal of biologically irrelevant genes, thereby improving the precision of analysis and reducing computation time. However, correlations between gene expressions should not be used solely for this purpose, as they can provide valuable insights into gene interactions and regulatory mechanisms. These approaches are essential for effectively utilizing single-cell RNA-seq data in cell biology studies.1, 7, 8 The next recommendation pertains to testing thousands of genes, where it is crucial to control both the overall Type I error rate and achieve the desired statistical power. It is recommended to use methods for multiple comparison correction, such as controlling the family-wise error rate (FWER) and false discovery rate (FDR). FWER methods, including Bonferroni, Holm, and Hochberg, provide strong error control but may be more conservative. Conversely, FDR-based methods, such as the Benjamini-Hochberg approach, are less conservative and enhance test power, which is particularly advantageous when testing a large number of hypotheses simultaneously. Furthermore, it is important to consider that expression profiles of individual genes are often related, which many traditional methods assume to be independent. The Westfall-Young error-correction method addresses this issue by adjusting for dependencies among genes, providing a more accurate control of FWER. This method involves resampling procedures that account for the correlation structure within the data, thereby reducing the likelihood of false positives in the presence of gene–gene dependencies. Additionally, considering Bayesian FDR (q-values) can improve the interpretation of statistical results and better control false discoveries. Implementing these recommendations enhances the credibility and accuracy of obtained results, minimizing the risk of false discoveries.1, 9-11 Another recommendation concerns the application of machine learning methods. Recommended approaches include k-means clustering, which identifies cell clusters based on similarities in gene expression; hierarchical clustering, which explores hierarchical structure in data; and density-based methods capable of identifying clusters of high-density based on gene expression patterns. Additionally, graph-based clustering is useful for detecting strong intercellular interactions inferred from expression data. Feature selection, such as identifying HVG, is essential for eliminating biologically irrelevant genes and reducing the impact of technical noise. K-means clustering is simple and efficient for identifying cell clusters based on gene expression similarities but may struggle with non-spherical clusters. Hierarchical clustering provides a comprehensive exploration of data structure but can be computationally intensive for large datasets. Density-based methods effectively identify high-density clusters but may fail with varying densities. Graph-based clustering is powerful for detecting strong intercellular interactions but can be sensitive to noise in the data. Feature selection, such as identifying HVG, is essential for reducing technical noise and focusing on biologically relevant genes.12 Table 1 provides a summary of methods for transcriptomic data analysis (Table 1). Bulk RNA-Seq: Single-cell RNA-Seq: Specialized approaches: Bulk RNA-Seq: Single-cell RNA-Seq: Statistical models: Parametric and non-parametric methods: Monte Carlo methods General techniques: Newer methods: Family-Wise Error Rate (FWER): False Discovery Rate (FDR): Error correction for gene dependencies: In conclusion, the integration of advanced statistical methods and machine learning techniques into transcriptomic data analysis is vital for enhancing our understanding of allergic diseases. By addressing specific challenges related to allergy research, such as cellular composition and gene expression variability, researchers can achieve more precise and meaningful results. Continued advancements in these methodologies will further support personalized medicine approaches, improving diagnostics and treatment strategies for allergy patients. Future research should focus on refining these techniques and exploring novel approaches to better capture the complexities of allergic responses. All authors approved the final version of the manuscript as submitted and agreed to be accountable for all aspects of the work. None. None. The authors declare that there are no conflicts of interest for this work. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.