Improving Experimental Outcomes in Kinome Microarrays Through Quality Control
Date
2023-01-23
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0003-1991-5385
Type
Thesis
Degree Level
Masters
Abstract
Peptide microarrays consisting of defined phosphorylation target sites are an effective approach for highthroughput
analysis of cellular kinase (kinome) activity. Kinome peptide arrays are highly customizable
and do not require species-specific reagents to measure kinase activity, making them amenable for kinome
analysis in any species. However, the data emerging from experiments with kinome peptide arrays exhibit a
large amount of variability. To mitigate this issue, we introduce PIIKA 2.5 to expand upon existing software
by providing three important quality control features in an aim to increase the accuracy and consistency
of kinome results, which often suffer due to the aforementioned variability. The first feature concerns the
size of the virtual circle drawn around each probe in microarray analysis software (spot size). This circle
creates the boundary between pixels interpreted as foreground signal and pixels interpreted as background
signal. In this thesis, it is shown that too large of a spot size creates abnormal data characteristics, such as
high skewness (the asymmetry of the distribution of the data), that can alter downstream results. Here, a
feature is presented that alerts users to the existence of improper spot size and informs them of the need to
perform a manual alignment to enhance the quality of the raw intensity data, based on the skewness of the
data as determined by examination of the mean and median of each dataset. The second feature uses interarray
comparisons to identify outlier arrays that sometimes emerge as a consequence of technical or unknown
issues. The work shown in this thesis indicates that the removal of said outlier arrays improves downstream
analysis and interpretation. The third feature is a new background correction method, background scaling.
Here, it is demonstrated to sharply reduce spatial biases in comparison to the most popular background
correction method, background subtraction. Collectively, the modifications presented in PIIKA 2.5 allow
users to identify low-quality data, improve clustering of treatment groups, reduce unintended effects, and
enhance reproducibility in kinome analysis. The web-based and stand-alone versions of PIIKA 2.5 are freely
accessible at http://saphire.usask.ca/saphire/piika.
Description
Keywords
kinome, kinase, phosphorylation, peptide array, quality control
Citation
Degree
Master of Science (M.Sc.)
Department
Computer Science
Program
Computer Science