16
Array data
Contents
Multiplexed experiments
Microarrays
Handling array data
Reading array data
Importing text matrices
Extracting array image data
The ‘
Microarray
’ class
Exporting array data
Value normalisation
Changing array channels
Array analysis
Differences and similarities
Hierarchical clustering
Multiplexed experiments
In many areas of biological and medical science, as new techniques and machinery are
developed there is a tendency to record ever increasing amounts of data. A notable
example of this is comes with ‘next-generation’ DNA sequencing, which we discuss
further in
Chapter 17
. In general though, with high-throughput methods the idea is to
perform many small experiments, of the same design, in parallel. When we simultaneously
detect the outcome of many assays the procedure can be described as being multiplexed.
This not only has speed advantages but can also reduce costs and improve consistency
between experiments. And naturally, to handle large numbers of experimental assays it is
important to use computers for the processing and analysis of data.
A multitude of modern techniques involve parallel experiments, including the detection
of potential drug compounds, RNA molecules, antibodies and protein crystals, to name
only a few. However, in this chapter we do not have space to cover the informatics of lots
of specific techniques, so instead we cover general themes, such as data organisation,
normalisation and comparison. Also, all of the examples will be based on the notion of the
experimental data being arranged as a rectangular array, which in turn is often a
consequence of the physical manner in which the assays were performed and detected, on
some form of regular grid.
Although there has been a recent trend to use the R programming language for working
with array-based assays, Python together with its NumPy and SciPy libraries is naturally
more than capable. In a change from much of this book, where we describe code that is
simply based on Python functions, here we will use an object-oriented framework. Hence
we create a ‘Microarray’ class that will tie together experimental data and various
functions that operate on it. The ideas behind this approach are discussed in
Chapters 7
,
8
and
15
. Though unlike
Chapter 15
, where we have a relatively complicated hierarchical
data model involving several classes, here we describe only a single class to organise
things into a helpful construct.
Do'stlaringiz bilan baham: |