Introduction to Bioinformatics Microarrays2: Microarray Data Normalisation

1
Introduction to Bioinformatics Microarrays2
Microarray Data Normalisation

• Course 341
• Department of Computing
• Imperial College, London
• Moustafa Ghanem

2
Lecture Overview

• Background and Motivation
• Introduction
• Microarray experiments and microarray data
  analysis
• Sources of variability
• Experimental design
• Normalisation Examples
• Probe intensity values
• Two colour arrays
• Positive controls
• Spatial normalisation within array
• Between array normalisation
• Normalisation Methods
• Total intensity normalisation
• Scaling and centring
• Linear regression
• MA plots and Lowess

3
BackgroundMicroarrays
A Microarray works by exploiting the ability of
mRNA molecule to hybridize to its complementary
DNA probe The mRNA molecules in a target
biological sample are labelled using a
fluorescent dye and applied to the array The
fluorescent label enables the detection of which
probes have hybridised (presence) via the light
emitted from the probe.

• A Microarray is a device detects the presence and
  abundance of labelled nucleic acids in a
  biological sample.
• The Microarray consists of a solid surface onto
  which known DNA molecules have been chemically
  bonded at special locations.
• Each array location is typically known as a probe
  and contains many replicates of the same
  molecule.
• The molecules in each array location are
  carefully chosen so as to hybridise only with
  mRNA molecules corresponding to a single gene.

4
BackgroundMicroarray Data Analysis
Biological question
Sample Attributes
Experimental design Platform Choice
16-bit TIFF Files
Microarray experiment
(Rspot, Rbkg), (Gspot, Gbkg)
Image analysis
Normalization
Clustering
Statistical Analysis
Data Mining
Pattern Discovery
Classification
Biological verification and interpretation
5
Motivation

• Data generated from Microarray experiments are
  inherently highly variable.
• First, there is the Law of Large Numbers
• Any measurement of thousands of values will find
  some large differences due to chance (normal
  distribution)
• However, the average gene does not change its
  expression across experiments
• Must have replication (e.g. different patients
  different experiments) and statistics to show
  that measured differences are real.
• Second, there are also Systematic Sources of
  Variability
• e.g. Errors is scanning microarray images,
  differences between properties of Cy3 and Cy5
  channels, etc
• Must have systematic methods for addressing such
  errors.

6
Motivation

• Normalisation is a general term for a collection
  of methods that are directed at reasoning about
  and resolving the systematic errors and bias
  introduced by microarray experimental platforms
• Normalisation methods stand in contrast with the
  data analysis methods described in other lectures
  (e.g. differential gene expression analysis,
  classification and clustering).
• Our overall aim is to be able to quantify
  measured/calculated variability, differentials
  and similarity
• Are they biologically significant or just side
  effects of the experimental platforms and
  conditions?

7
IntroductionSources of Microarray Data
Variability
The measured gene expression in any experiment
includes true gene expression,together with
contributions from many sources of variability

• There are several levels of variability in
  measured gene expression of a feature.
• At the highest level, there is biological
  variability in the population from which the
  sample derives.
• At an experimental level, there is
• variability between preparations and labelling of
  the sample,
• variability between hybridisations of the same
  sample to different arrays, and
• variability between the signal on replicate
  features on the same array.

8
IntroductionSources of Microarray Data
Variability
There are many standard experimental protocols
that biologists need to follow when conducting
their experiments to minimize variability

• Population Variation
• Whose mRNA are we using? May need to test
  different samples in parallel.
• May need many replicates to study biological
  variation
• Sample Treatment
• Experimental conditions
• Tissue preparation
• Target Preparation
• RNA isolation need to use use identical amounts
  of tissue, identical extraction methods use
  minimum number of steps measure amount of RNA
  and normalize concentration
• Labelling need to account for and measure
  incorporation of label and normalize samples to
  same concentration
• Amount Need to add same amount of label to each
  hybridization

9
IntroductionSources of Microarray Data
Variability
Oligos reduce variability of probes compared to
PCR products. In-situ synthesis standardises
probe production and produces better spot quality
and reduces errors in image acquisition

• Arrays
• Same sample may be hybridized to different arrays
  in different labs
• PCR products probes prepared through
  amplification directly from cells, must add same
  amount of product to each spot on filter
• Uniformity of spotting must use arraying tool
  for filter arrays or robot for microarrays.
• Treatment and handling of filters or slides
• Hybridization and washing
• Time long hybridization ensures that
  hybridization goes to completion.
• Temperature most hybridisations performed
  between 45 and 65 oc
• Data acquisition
• Image acquisition
• Spot and background detection

10
IntroductionBiological and Technical Variability

• Biological Variability variation between
  individuals in the population and is independent
  of the microarray process itself
• Population variability can be measured with pilot
  studies
• Technical Variability is dependent on the
  microarray process itself.
• Technical variability is measured in calibration
  experiments.
• In good experiments, technical variation should
  be much less than biological variation

11
IntroductionExperimental Design

• Tree representation of replicate experiments
• The first level is at the level of biological
  replicates
• This is followed by two independent mRNA
  extractions, and reciprocal Cy3 and Cy5 labelling
• Finally on each array, each probe is printed in
  triplicate.
• In this example, each data point in the
  experiment is replicated a total of 24 times.
• Furthermore, in each microarray experiment, each
  gene (each probe or probe set) is really a
  separate experiment in its own right

12
IntroductionConducting Good Experiments
13
IntroductionGene Expression Matrices
14
Normalisation Examples Probe Intensity Value
Typical Problem Usually more variability at low
intensity

• The raw intensities of signal from each spot on
  the array are not directly comparable. Depending
  on the types of experiments done, a number of
  different approaches to normalization may be
  needed. Not all types of normalization are
  appropriate in all experiments. Some experiments
  may use more than one type of normalization.
• Reasonable Assumption intensities of fluorescent
  molecules reflect the abundance of the mRNA
  molecules generally true but could be
  problematic
• Example
• intensity of gene A spot is 100 units in
  normal-tissue array
• intensity of gene A spot is 50 units in
  cancer-tissue array
• Conclusion gene As expression level in normal
  issue is significantly higher than in cancer
  tissue

15
Normalisation Examples Probe Intensity Value
Images showing examples of how background
intensity can be calculated

• Problem? What if the overall background intensity
  of the normal-tissue array is 95 units while the
  background intensity of cancer-tissue array is 10
  units?
• Solutions
• Subtract background intensity value
• Take ratio of spot intensity to background
  intensity (preferable)
• In both cases have to decide where to measure
  background intensity (e.g. local to spot or
  globally per chip)
• In general, There could be many factors
  contributing to the background intensity of a
  microarray chip
• To compare microarray data across different
  chips, data (intensity levels) need to be
  normalized to the same level

16
Normalisation Examples Two Colour Arrays

• Reasonable Assumption For two colour arrays, in
  a self self hybridization, we expect for each
  spot Red Green
• Problem This is not necessarily true due to
  labelling effects, chemistry (dye properties),
  scanner properties, etc
• Dye Bias in Two-channel microarrays Intensity in
  one channel may be higher than the other
• Solutions
• Dye swapping experiments in first replicate
  label control with red and experiment with green
  in second replicate swap colours
• Calibration Experiments (Self vs. self
  Hybridisation)label same extract with both
  colours and calculate variation

17
Normalisation Examples Two Colour Arrays

• Error correction

y ax
y x

• possible ways of correction
• dividing all x by a 2. multiplying all y by a
  
• Can easily be extended when regression line is y
  axb

18
Normalisation ExamplesRatio of Signal to
Positive Control
How does this approach compare to the affymetrix
PM/MM probes?

• Problem Is there any cross hybridisation?
• Solution It is often useful to spike the
  labelling reaction with some foreign RNA or DNA
  that is not normally in the RNA population.
• The signal  si for gene i would therefore be raw
  counts gi divided by the median of the counts for
  the vector spots.

19
Normalisation ExamplesRatio of Signal to
Positive Control

• Normalization of signal for each gene to a ratio
  makes it possible to compare ratios between
  experiments, provided that the spiked controls
  are the same in all experiments.
• Normalization to a positive control is typically
  used in single-label experiments. Comparison of
  one experiment to another can either be done by
  plotting signal si  directly on a graph, or
  signals from two experiments can be converted
  into a ratio, usually by choosing one treatment
  as a control.
• For example, in a time course, a 0 hour time
  point might be chosen, and signal from all other
  time points divided by the signal for the 0 hour
  time point, to give a ratio.

20
Normalisation ExamplesSpatial variation within
array

• Problem Signal varies according to spot location
• Particularly corners Less hybridization solution
• Also because of print-tip group of robot
• Solutions
• Calculate ratio to mean or total intensity value
• Use Locally Weighted Regression (Lowess)
• Use Block-Block Lowess

21
Normalisation ExamplesBetween Array Normalisation

• Assumption the overall intensities across two
  arrays should be similar
• Problem Not always the case
• Solution1 Ensure that data points in the
  two-intensity coordinate system should be roughly
  centered around the diagonal

Solution2 Use total intensity normalization for
large number
22
Normalisation MethodsBetween Array Normalisation

• Mean/Median centering mean/median intensity of
  every chip brought to same level
• Total intensity normalization scaling factor
  determined by summing intensities
• Spiked-control, housekeeping normalization
  (Positive Controls)

23
Normalisation MethodsCentring and Scaling

• Data is scaled to ensure that the means and the
  standard deviations of all of the distributions
  are equal. For each measurement on the array,
  subtract the mean measurement of the array and
  divide by the standard deviation. Following
  centring, the mean measurements on each array
  will be zero, and the standard deviation will be
  1

24
Normalisation MethodsNormalized ratios usually
expressed as logs

• To facilitate easier mathematical handling of the
  data, as well as comparisons over a wide range of
  expression levels, ratios are usually expressed
  as logs.
• For example, if a gene is expressed at 4 times
  the level in the control than in the mutant, log2
  (1/4) -2. A log ratio of 0 is therefore
  indicative of a gene whose expression is the same
  in both conditions or treatments.

Rg
Ratio Tg
Gg
Rg
log2
Log Ratio log2(Tg)
Gg
25
Normalisation MethodsRegression Normalisation

• Regression normalization
• Fit the linear regression model y ax b
• Test the significance of the intercept b. Fit a
  linear regression without b if it is
  insignificant.
• Transform the data
• Problem assumption may not hold due to nonlinear
  trend

26
Normalisation MethodsFrom Scatter Plot to MA Plot

• Instead of plotting the two intensity values
  against one another (Scatter plot), it is common
  to use an MA plot
• M log2(R/G) ratio of two intensities
• A log2SQRT(RG) ½ log2(RG) mean log
  intensity of two values

27
Normalisation MethodsLowess Normalization

• Locally Weighted Least Square Regression
• Assumption Variation in data is intensity
  dependent
• Smoothes the intensity function
• Lowess is typically applied to M-A plots

28
Summary

• Normalisation used to identify if variation is
  due to experimental conditions.
• Typical sources of variation are
• Population, Sample, Target, Array (Probe),
  Hybridisation, Data Acquisition
• Different Normalisation Examples
• Probe intensity values
• Two colour arrays
• Positive controls
• Spatial normalisation within array
• Between array normalisation
• Common Normalisation Methods
• Mainly scaling factors and regression