SLAS Discovery - Special Issue on Advances in MALDI Mass Spectrometry for Drug Discovery - Volume 22 Issue 10 - December 2017 - 1248

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SLAS Discovery 22(10)
is generated using pheatmap and d3heatmap R packages.
Similar to the boxplot, the grouping of the data is accomplished based on user-supplied metadata, as in Figure 3B.
In the heatmap, the vertical axis represents the samples and
the horizontal axis represents the different masses and the
colors' corresponding to intensities. Annotations based on
the metadata can also be added on top of the heatmap, to
easily identify which groups are separated. Optionally, rows
and/or columns in the heatmap can be clustered based on
user-defined distance and linkage methods.

Figure 1. Outline of the operations that MALDIViz can
perform.

analysis, the user might want to modify the sample groups,
for instance, to include or exclude certain samples. To
enable an efficient sample grouping, the user can change
sample selection based on supplied metadata. This strategy
is easy to use and particularly suited for batch processing. In
the present version of MALDIViz, up to four columns of
metadata can be added by the user. A screenshot from the
web application showing a typical dataset loaded is displayed in Figure 2.
Spectra View. To compare multiple mass spectra from different samples, two-dimensional (2D) visualization is efficient
in showing many data points at the same time. However, it
is sometimes difficult to visualize the data trends when
there are large overlaps. Spectra View provides a 3D plot, as
an optional way to visualize and compare large datasets. In
the 3D plot, in the horizontal axis, x represents m/z; in the
vertical axis, y represents intensity; and in the depth axis, z
represents different samples. This type of 3D plot is very
useful for finding features and patterns across the experiment in one quick glance instead of looking at all the individual spectrum. The user can inspect data values such as
m/z, intensity, and sample details by moving the cursor
along the spectrum. Moving, scaling, and zooming functions are also made as easy and user-friendly as possible,
and most manipulations are accessible by mouse. To show
the 3D plot from a variable viewpoint, the user can zoom
and rotate the view around all axes. Figure 3A shows an
example of the 3D plot provided by MALDIViz.
Analysis - Comparison. The "Analysis - Comparison" window allows for comparison of the experimental data using
grouping based on user-supplied values in the metadata.
Significant differences between groups can be visualized
using the boxplot and heatmap functions. The boxplot provides a visual interpretation of the differences between
groups and a measure of the within-group variation. For
example, SPE material screening data can be plotted with a
grouping based on buffer condition to examine any significant differences due to varying buffer conditions. Heatmap1

Analysis - Classification. The analysis classification view
offers more powerful, but still easy-to-use multivariate statistical analysis tools for data exploration and validation.
Principal Component Analysis. MALDIViz can provide userselectable options for generating a 3D PCA plot (Fig. 4A),
for classification and identification of major sources of
variation in the data. PCA is an unsupervised feature selection technique used for data compression, information
extraction, and preliminary visualization of observations or
samples. The ultimate goal of PCA is to reduce the dimensionality of a multivariate dataset while retaining the variation in the dataset.10,28 By reducing the dimensionality, each
sample (spectrum) can represented by a point in a 2D or 3D
coordinate system (score plot), in which spectra with similar variation characteristics can be clustered together and
the differences between sample groups can be readily
visualized.
PCA creates new variables, the principal components
(PCs), which are linear combinations of the original variables (i.e., m/z values). The first PC (PC1) is the direction
along which the samples show the largest variation. The
second PC (PC2) is the direction uncorrelated to the first
component along which the samples show the largest variation. The first three of the orthogonal and linearly independent new coordinates (PC1, PC2, and PC3) are then used to
visualize the data as dots in a 3D space.29
It can be estimated how much each of the original variables (e.g., m/z values) contributes to each of the new PCs.
These values are called loadings. The loading plots of the
PCA provide information regarding the contribution of each
m/z value to the variance covered by each respective PC.
MALDIViz also offers two types of data preprocessing:
mean centering and variance scaling prior to PCA.30 By
mean centering, an average data spectrum is calculated and
subtracted from each spectrum of the dataset. Without mean
centering, the calculated PC will be the average of the dataset and will not describe the variation in spectral content.
Variance scaling can be used to ensure that all variables
contribute equally during the PCA calculation.
Cluster Analysis. The cluster analysis aims to identify similarities or differences between the different groups in a dataset.

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