PALMO: 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, Xiaojun

doi:10.1101/2022.10.17.512585

Cited by 4 publications

(6 citation statements)

References 44 publications

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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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“…PALMO can also be installed in R or RStudio as an R package in CRAN. Source code can also be found at Zenodo 42…”

Section: Data Analysis and Visualizationmentioning

confidence: 99%

A comprehensive platform for analyzing longitudinal multi-omics data

et al. 2023

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Longitudinal bulk and single-cell omics data is increasingly generated for biological and clinical research but is challenging to analyze due to its many intrinsic types of variations. We present PALMO (https://github.com/aifimmunology/PALMO), a platform that contains five analytical modules to examine longitudinal bulk and single-cell multi-omics data from multiple perspectives, including decomposition of sources of variations within the data, collection of stable or variable features across timepoints and participants, identification of up- or down-regulated markers across timepoints of individual participants, and investigation on samples of same participants for possible outlier events. We have tested PALMO performance on a complex longitudinal multi-omics dataset of five data modalities on the same samples and six external datasets of diverse background. Both PALMO and our longitudinal multi-omics dataset can be valuable resources to the scientific community.

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“…The p150 and p180 kits allow simultaneous quantification of 163 and 188 metabolites, respectively 46 . Only metabolites meeting the following QC criteria 47,48 were selected: (1) overlap between p150 and p180; (2) no missing values; (3) at least 50% of measured sample values are equal to or above the LOD of corresponding plates; (4) median relative standard deviation (RSD) of QC samples < 25%; (5) Spearman correlation coefficients between the KORA F4 (remeasured, p180) and F4 (original, p150) > 0.5. After QC procedures, the metabolites values were further normalized using TIGER (Technical variation elImination with ensemble learninG architEctuRe) 46 with its default setting to remove the plate effects.…”

Section: Kora Datasetsmentioning

confidence: 99%

“…The rapid advancement of omics technologies has enabled researchers to obtain high-dimensional datasets across multiple modalities, providing unprecedented insights into various diseases 1 . Each view contributes a partial or entirely independent perspective on complex biological systems 2 .…”

Section: Introductionmentioning

confidence: 99%

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Han,

Yu,

Shi

et al. 2023

Preprint

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Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology that is instrumental to advancements in personalized healthcare. However, the common occurrence of incomplete view makes inference tasks difficult, and there is a lack of tailored methods for solving this critical issue. Here, we introduce LEOPARD, an approach specifically designed to complete missing views for multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers temporal knowledge to omics-specific content to complete missing views. Compared to generic imputation methods, LEOPARD gives the most robust and reliable results across three longitudinal proteomics and metabolomics datasets. LEOPARD-imputed data also achieve the closest performance to observed data in our regression and classification analyses. Our work takes the first step towards a principled and generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal changes within omics data.

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PALMO: a comprehensive platform for analyzing longitudinal multi-omics data

Cited by 4 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

A comprehensive platform for analyzing longitudinal multi-omics data

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

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