Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

Abstract

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Keywords: Biomarker discovery; Data integration; Multi-omics; Multivariate data analysis; R shiny application; Storability; Sugar beet.

Fig. 1

The Holomics workflow. To make use of all the functionality provided by Holomics, a certain workflow should be followed. (1) Input datasets: first, the datasets are uploaded where an eventual pre-filtering/transformation step takes place. (2) Single-omics analysis: afterwards, the single-omics analysis is performed, where key features are identified and the datasets are reduced accordingly. (3) Reduced datasets: the single-omics feature selection process is resulting in reduced datasets. (4) Multi-omics analysis: with these reduced datasets (or with the input datasets from step 1), the multi-omics analyses are applied to identify correlations between 2-n datasets

Fig. 2

Example of the sPLS tuning effect using heatmaps. (s)PLS analysis and the tuning process were performed with two microbiomics datasets (ITS and 16S). A Result of the (s)PLS analysis using the two mixMC pre-processed, PLS-DA-filtered and within the PLS analysis standardized datasets, ITS (119 features) as dataset X of the analysis and 16S (40 features) as dataset Y. The analysis was performed using canonical mode and four components. The heatmap visualizes the correlations between the features of the two datasets. B Result of the (s)PLS analysis after the tuning process, which reduced the ITS dataset down to 10 features and the 16S dataset to 25 features. Additionally, the ideal number of components is 1. The heatmap shows the correlations between the features of the two reduced datasets. Note: Feature names were removed from the heatmaps

Fig. 3

Showcase of DIABLO tuning effect using Circos plots. Usage of the DIABLO analysis and tuning process with a metabolomics, a transcriptomics and two microbiomics datasets (16S and ITS). A Result of the DIABLO analysis using the four untuned, PLS-DA-filtered and within the DIABLO analyzes standardized datasets, metabolomics (23 features), transcriptomics (16 features), 16S (40 features) and ITS (119 features), and nine components. The Circos plot shows the correlations between the features of the four untuned datasets using an absolute cutoff value of 0.8. B Result of the DIABLO analysis after the tuning process, which reduced the metabolomics dataset to 10 features, transcriptomics to 10 features, 16S to 10 features and ITS to 50 features. Additionally, the ideal number of components is 1. The Circos plot shows the correlations between the features of the four reduced datasets using an absolute cutoff value of 0.8

Fig. 4

Sampling scheme of the case study input datasets. The following four varieties were included: two (V1, V6) with good storability (less sucrose loss, marked in green) and two (V2, V5) with increased sucrose loss after storage (purple coloring). After harvest, the sugar beets were stored in a semi-controlled environment for 12–13 weeks as previously described [51]. From three individuals per variety, samples for microbiomics (16S rRNA and ITS amplicon sequencing) were taken from the adhering soil, the peel, the tissue at the periphery and the tissue of the center of the beet root. For transcriptomic (T) and metabolomic (M) analyses, a disc was cut from the beet root, from which blocks were extracted and of which the outer first centimeter was removed [51]. Designed by Tatjana Hirschmugl

Fig. 5

Contribution of component one for each of the single-omics datasets (A–C) after PLS-DA filtering. Green-colored features were associated with good storability, and purple-colored features were associated with bad storability

Fig. 6

Loading plots (A) for metabolomics (left) and transcriptomics (right) and a heatmap (B) of pairwise analysis explaining the abundance of metabolites via transcriptomics

Fig. 7

Heatmaps of pairwise microbiomics analysis. First analysis (A) explained bacterial microbiomics (16S dataset) with fungal microbiomics (ITS dataset), and the second analysis was done vice-versa (B)

Fig. 8

Circos plot from the DIABLO analysis indicating the positive and negative correlations among the features of all four omics datasets in the first component. For each feature, its ’expression’ with regard to the storability is presented as a continuous line: the green-colored line reflects the abundance of the feature in well storable varieties, whereas the purple-colored line shows the abundance in the badly storable ones

Fig. 9

The R shiny application Holomics offers an easy-to-use and practical solution for multi-omics data integration and analysis