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CoViD-19 Meta-Analysis of Plasma Proteins (CoViMAPP) app

Version 2 2023-11-09, 13:45
Version 1 2023-08-04, 09:34
online resource
posted on 2023-11-09, 13:45 authored by Haris BabacicHaris Babacic, Nidhi Sharma, Janne Lehtiö, Jonas Klingström, Maria Pernemalm

CoViMAPP (CoViD-19 Meta-Analysis of Plasma Proteins) is a meta-analysis resource of plasma proteins' alterations in COVID-19. It includes summary estimates obtained from published studies of plasma proteome profiling in COVID-19 by unbiased mass-spectrometry proteomics, identified through a systematic review. For more details refer to the manuscript "Comprehensive proteomics and meta-analysis of COVID-19 host response", by Babačić et al. (2023). If using CoViMAPP, please cite the manuscript.

This resource is divided into several sections:

  • Systematic review. A summary table of all the studies that have been identified in a systematic review and included in the meta-analysis.
  • MA Studies: Methods. A summary table of the mass-spectrometry methods used in the studies that have been included in the meta-analysis.
  • Protein SMD. Shows the results from a meta-analysis on standardised mean differences (SMD) for a selected protein, comparing COVID-19 patients to SARS-CoV-2 PCR-negative controls. You can find forest and funnel plots of SMD per selected protein, along with summary tables of mean, standard deviation, and number of participants per group from each study.
  • SMD Summary. A comparison of summary SMD estimates in relation to heterogeneity () of all the proteins included in the meta-analysis. Here you can download the meta-analysis results for all the analysed proteins, i.e., proteins reported in at least two studies.
  • Protein SROC. Shows the Summary Receiver Operating Characteristic (SROC) curves for selected protein, along with summarised mean Sensitivity, Specificity, and Diagnostic Odds Ratio (DOR), estimated with the bivariate model by Reitsma et al. (2005), using a generalised linear mixed modelling approach. In addition, we estimated the preference of the study-specific ROC curves for sensitivity and specificity with the Doebbler & Holling approach (2015), and provided univariate summary DOR estimates.
  • SROC Summary. A comparison of mean sensitivity and mean specificity estimates from the bivariate model for all the proteins included in the meta-analysis, along with a comparison of DOR and heterogeneity estimates from the univariate model. Here you can download the meta-analysis results of all the analysed proteins.


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