SLAS Discovery - Special Issue on Advances in MALDI Mass Spectrometry for Drug Discovery - Volume 22 Issue 10 - December 2017 - 1247

1247

Jagadeesan and Ekström
simplify, visualize, and analyze the data, while retaining the
relationships among the different samples/parameters.10
While there are many software options for analysis of raw
MS data, there are fewer alternatives allowing for differential analysis. There are some devoted vendor-specific software tools for analysis of HTS MS data, for example, the
RapidFire Integrator software; alternatively, more general
tools, such as R,11 SPSS, and Matlab12 packages, can be
used. In the case of MS-based proteomic datasets, there are
a great variety of open-source libraries and frameworks
available, such as OpenMS, Proteowizard, MsInspect, and
Bioconductor.13 The statistical analysis is of utmost importance for HTS data, and here DanteR14 and InfernoRDN15
can provide a user-friendly graphical interface to the R
framework for normalization, ANOVA, and various other
multivariate analysis techniques. Perseus,16 a part of the
MaxQuant package, also provides statistical analysis tools
that are similar to those of DanteR. However, most of these
tools are streamlined for data generated by ESI liquid chromatography (LC)-MS or require a certain knowledge of bioinformatics and sometimes even programming skills.
Outside of bacteria typing software, there are a lack of
software tools that cover HTS-scale MALDI data visualization and analysis. In this article, we present MALDIViz, a
comprehensive informatics tool for MALDI HTS data that
can facilitate visualization, statistical analysis, and highquality image and data export. MALDIViz is an R-Shinybased web application, accessible independently of the
operating system and without the need to install the program locally. The functionality of MALDIViz application is
demonstrated by analysis of in-house-generated screening
data from an investigation of different solid-phase extraction (SPE) materials17 and a publicly available cancer dataset.18 The MALDIViz application is accessible through
https://jkkishore85.shinyapps.io/maldiviz/.

Materials and Methods
Built-In Analysis Functions
MALDIViz incorporates algorithms for multidimensional
visualization, statistical analysis, and creation of publicationquality images. It has been implemented entirely in R11 (R
version 3.2.3) and developed using Rstudio and the Shiny
framework. Rstudio is an open-source interface for the development of R applications, and Shiny is a package that allows
the creation of web applications directly from R.
MALDIViz uses several R packages internally. The
shinydashboard package has been crucial for the design of
the MALDIViz application. This package provides a theme
on top of Shiny, allowing the creation of visually attractive
dashboards. Shinydashboard was used to create interface
items, such as the side menu, and facilitates the user's navigation through the different tabs. Other R packages, data.

table,19 reshape,20 and plyr,21 are used for basic data manipulation, for example, filtering and grouping. The rgl,22 plotly,23
and ggplot224 packages are used for the generation of plots.
The d3heatmap and pheatmap25 packages are used for drawing heatmaps. The MALDIquant26 package was implemented
for MALDI data preprocessing, and the pcamethod27 package for principal component analysis (PCA) calculations.

Datasets
Dataset 1: Solid-Phase Extraction Screening Dataset. This dataset comes from a screening experiment with different molecularly imprinted SPE phases developed for the selective
enrichment of a proteotypic peptide from the protein ProGRP, a biomarker used for small-cell lung cancer diagnosis.
Briefly, six different SPE phases in duplicate were examined
using four different SPE conditions using a 384-plate-based
micro-SPE platform. Thereafter, the collected eluents were
analyzed in triplicate by MALDI-MS.17
Dataset 2: Cancer Dataset. This is a publicly available dataset from a MALDI-based sample profiling publication
using gold nanoparticles in order to separate the proteins
and peptides in human serum. The protocol described in this
work divides each sample into two subsamples: pellet and
supernatant. The MALDI spectra of both subsamples are
grouped by their corresponding conditions using threedimensional (3D) PCA. The dataset is composed of MALDI
spectra from five patients with lymphoma, five patients
with myeloma, and two healthy donors.18

Data Visualization and Analysis Pipeline
In MALDIViz, users can browse through the visualization
and analysis steps by going to each of the tabs in the left
side panel. All tabs work in a similar fashion; for example,
changing the settings in the left panel will automatically
renew the image or table on the right. When moving from
one tab to another, all settings are saved automatically.
The menu has been divided into four basic groups, each
containing the operations the program can perform: "Data
Import," "Spectra View," "Analysis - Comparison," and
"Analysis - Classification" (Fig. 1).
Data Import. Users can directly load datasets from any file
that stores tabular data, including flat files (either CSV or
tab-delimited text files) and Microsoft Excel files (.csv), or
similar formats. The functionality can be tested by loading
the predefined datasets, available in the online app. Input
data for analysis can be any data table (cross-tab) containing columns of mass and intensities, with additional columns corresponding to informational metadata from each
experiment, such as type of separation phase, buffer condition, replicate details, or clinical outcomes. In an interactive

https://jkkishore85.shinyapps.io/maldiviz/

Table of Contents for the Digital Edition of SLAS Discovery - Special Issue on Advances in MALDI Mass Spectrometry for Drug Discovery - Volume 22 Issue 10 - December 2017