A BIOINFORMATIC PIPELINE FOR THE ANALYSIS OF PROTEIN MICROARRAY DATA WITH APPLICATIONS TO MALARIA AND LUNG CANCER STUDIES
Embargo until
2022-05-01
Date
2021-04-22
Authors
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Publisher
Johns Hopkins University
Abstract
Bioinformatic pipelines are steps taken to transform data from a raw measurement
to a form that enables direct biological inference. These steps vary across
assays and different methods can have important impacts on downstream
analyses and subsequent inference. While there has been substantial work
on optimizing methods for many types of assays including DNA microarrays,
relatively few methods have been developed and evaluated specifically
for protein microarrays. Due to the high levels of technical variation, and
relative measurements obtained from protein microarray data methods specifically
suited to these assays are especially important to ensure that biological
questions of interest can be directly answered with these data. Here, we
propose a bioinformatic pipeline for protein microarray data that contains
three main steps: a pre-processing pipeline to quantify and address technical
variation, a Bayesian model to produce full posterior distributions of signal,
and ranking methods that use information from full posterior distributions.
In Chapter 2 we use Bland-Altman plots and associated analysis show that
the pre-processing pipeline reduces technical variation in two previously published
data sets that use protein microarrays to investigate lung cancer and
malaria. In Chapter 3 we show that our proposed Bayesian model fits well
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to these same two data sets and produces estimates of signal that are well
suited to downstream inference, specifically to ranking methods that pay
attention to uncertainty. Finally, in Chapter 4 we show how the use of our
bioinformatic pipeline, can impact downstream inference. In particular, using
protein microarray data from a previously published malaria study, we show
how our pipeline identifies potential biomarkers of past malaria infection that
were not identified with previous analysis methods.
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Keywords
bioinformatics, protein microarrays, malaria,