Python Programming for Biology: Bioinformatics and Beyond

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.