Visualization of proteomics data using R and Bioconductor

Abstract

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

“…This approach is advantageous as distinct data sets may have similar summary statistics but markedly different distributions of data points. 48,49 The full distributions allow subpopulations of ions to be identified, which can be key to optimizing LC-MS/MS performance. Additionally, these distributions can be conditioned on common ions, allowing for a more principled comparison, as discussed in the Results section.…”

In Silico Based Analysis of CKD Expressions Data in Correlation with Diabetes Mellitus Unveils Biomarker Gene

“…For visualizing of chips affyqcreport package was downloaded from Bioconductor [19]. Starting from library affy package and readaffy function read the 22 samples from directories celfiles names save as in the same directories having all plot described below.…”

Cardiovascular proteomics in the era of big data: experimental and computational advances

“…A modular and open-source software development paradigm, where individual software functionalities can interoperate via common interfaces and standards, helps ensure that new software can dovetail with existing ones with ease, and that software development may continue following inactivity from the original research team. Examples of such frameworks include the GalaxyP proteomics extension [43], and the proteomics packages within the R/BioConductor framework [44]. …”