A comprehensive platform for analyzing longitudinal multi-omics data

Vasaikar, Suhas; Savage, Adam K.; Gong, Qiuyu; Swanson, Elliott; Talla, Aarthi; Lord, Cara; Heubeck, Alexander T; Reading, Julian; Graybuck, Lucas T.; Meijer, Paul; Torgerson, Troy R.; Skene, Peter J.; Bumol, Thomas F.; Li, Xiao-jun

doi:10.1038/s41467-023-37432-w

Cited by 12 publications

(3 citation statements)

References 44 publications

(81 reference statements)

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“…We could find peptides for which most of the variance is explained by the cell type ( Figure 2 b). This was already observed in longitudinal multi-omics data using the PALMO framework [27]. The peptides with the highest biological variance are mainly associated with proteins involved in cytoskeleton rearrangements (CORO1A, LMNA, TMSB4X, VIM).…”

Section: Data Exploration Through Analysis Of Variancesupporting

confidence: 60%

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data

Vanderaa,

Gatto

2023

Preprint

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Analysing mass spectrometry (MS)-based single-cell proteomics (SCP) data is challenging. The data analysis must address numerous problems that are inherent to both MS-based proteomics technologies and single-cell experiments. This has led to the development of complex and divergent data processing workflows within the field. In this work, we present “scplainer”, a principled and standardised approach for extracting meaningful insights from SCP data. The approach relies on minimal data processing combined with linear modelling. The approach is a simple yet powerful approach for exploring and interpreting various types of SCP data. scplainer performs variance analysis, differential abundance analysis and component analysis while streamlining the visualization of the results. This thorough exploration enhances our capacity to gain a deeper understanding of the biological processes hidden in the data. Finally, we demonstrate that scplainer corrects for technical variability, and even enables the integration of data sets from different SCP experiments. The approach effectively generates high-quality data that is amenable to perform downstream analyses. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.

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Section: Data Exploration Through Analysis Of Variancesupporting

confidence: 60%

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data

Vanderaa,

Gatto

2023

Preprint

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“…1). These data span 1,285 healthy donors from control arms of three SLE studies and three HD-only studies [33][34][35] . Per cell-type pairwise Pearson correla�ons were calculated separately in each dataset, and only gene-gene correla�ons with r > 0.5 and empirical p-value < 0.01 were retained (see Methods).…”

Section: Resultsmentioning

confidence: 99%

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Bolouri,

Cerosaletti,

Lacy-Hulbert

2024

Preprint

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Comparisons of single-cell RNA-sequencing (scRNA-seq) data from healthy and diseased tissues are widely used to identify cell-type-specific dysregulated genes, pathways, and processes. Accordingly, many sophisticated methods have been developed for this purpose. However, such tools generally require considerable user expertise for optimal performance. Here, we show that unsupervised application of a linearly-decoded Variational Auto Encoder (a generative AI model) to scRNA-seq data recapitulates and extends findings from a seminal recent lupus study and leads to new insights. Thanks to existing software libraries, our approach is straightforward to implement, computationally efficient, methodologically robust, and produces consistent and reproducible results.

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“…As omics data from different species is increasingly accumulated, the evolutionary integration of multiple models will be essential in analyzing genomic divergences between distinct fungal clades. Computational approaches to the processing of large-scale omics data and data integration methods that focus mainly on a single model are continuously being developed ( Winkler, 2020 ; Duruflé and Déjean, 2023 ; Shave et al., 2023 ; Vasaikar et al., 2023 ). In addition, systems biological approaches—especially those that refine networks via statistical modeling for large datasets with a small cohort size—have proved to be useful in the interpretation of omics data ( Culibrk et al., 2016 ; Karahalil, 2016 ; Maghuly et al., 2022 ).…”

Section: Clusters Of Well-studied Models With Big Data To Bridge Larg...mentioning

confidence: 99%

The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics

Wang

Kim

Wang

et al. 2023

Front. Fungal Biol.

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Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom.

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A comprehensive platform for analyzing longitudinal multi-omics data

Cited by 12 publications

References 44 publications

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics

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