Abstract
SummaryCompared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data.Availability and implementationPANDA-view is freely available at https://sourceforge.net/projects/panda-view/.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
In the new era of life-omics, quantitative proteomics is becoming wide-spread with the rapid developments of high-resolution mass spectrometers (MS) and superior experiment strategies (Schubert et al, 2017)
Once a file is chosen, all its column names will be shown in a wizard graphical user interface (GUI)
When reading extremely large files, multi threads will be automatically started and the uploaded data can be displayed in the GUI in dynamic real time to avoid potential halt or crash
Summary
In the new era of life-omics, quantitative proteomics is becoming wide-spread with the rapid developments of high-resolution mass spectrometers (MS) and superior experiment strategies (Schubert et al, 2017). To break the barrier between -omics data (especially the quantitative proteomics data) and the hidden biological/medical discoveries, we present an easy-to-use and light-weight tool, named PANDA-view, for statistical analysis and visualization of -omics data. PANDA-view can be compatible with other -omics tools by reading their results in comma-separated value (CSV) or tabdelimited text file format. It includes comprehensive methods for data normalization, imputation, DEP statistical test, unsupervised. It can provide a multilevel representation (from protein to MS spectrum) for the quantification results of PANDA (Chang et al, 2018)
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