QC and normalisation of microarray experiments - BiGCaT

QC and normalisation
of microarray experiments
Lars Eijssen
POT course toxicogenomics 22-02-2010

Contents
• Background on quality control (QC)
– Examples based on two channel data sets
• Background on further data preprocessing
• Application of a Genepattern QC module for Affymetrix data
– settings
– illustration on data sets
– interpretation of outcome
• Preprocessing Affymetrix data using a Genepattern module
– settings
• Introduction to the afternoon session and the data set to be used
2

Quality Control

One and two channel arrays
• In this course, we will focus on gene expression arrays
• Specific details of QC and normalisation methods are
different for one and two channel arrays
• The principles, however, are similar
• I will cover the principles first and specific application
to Affymetrix data (using a predefined workflow) later
4

QC
• Quality control is an important part of data processing
• It includes two levels: array level and spot level
• Array level: discard abberant arrays
– Low quality material
– Failed hybridisation
– Too low or high overall intensity
• Spot level: discard spots (or regions of spots) that are
abberant
– Stains on the array
– Specific spots not fullfilling quality criteria
5

Examples
• To illustrate some QC aspects to consider, I will show
examples from output of our in-house developed
workflow ArrayQC (taken from different data sets)
– This workflow has been developed to handle one and two
channel array experiments, with predefined settings for
Agilent/Genepix data
– Later I will show QC results for Affymetrix chips in more
detail
6

Global array QC
Foreground intensity
Background intensity
7

Spot specific QC
Red background intensity
Green background intensity
8

Red number of pixels
Green number of pixels
9

Red foreground intensity
Green foreground intensity
10

Spot selection
• Based on aspects as shown in the previous images, one
can determine which spots to include and exclude from
further analysis
• Example criteria
– Spot size (number of pixels) above threshold
– Sufficient spot uniformity
– No saturation
– Above intensity threshold
• For two channel arrays this may involve considering
both channels together; this may be complicated
11

Filtering spots (e.g. low intensity)
• Before filtering
difference between channels
intensity
• After filtering
• Note that low
intensity spots are
much more subject to
noise!
12

Further data preprocessing

Data analysis overview
Microarray scans
Image analysis
•Background correction
•Normalisation
Raw data
Quality control
Further preprocessing
Normalised data
Statistical analysis
List of regulated genes
Pattern analysis
Pathway analysis
Literature data
Untreated (control)
Exposed to compound
14
Results
Slide based on a slide from J. Pennings, RIVM, NL

Background correction
• Background signal needs to be corrected for
– For example signal of remaining non-hybridised mRNA
• Three types of background
– Overall slide background
• Can be corrected for by subtracting mean background, or by
subtracting mean of empty spots
– Local slide background
• Same as previous, but per slide region
– Specific background
• For example cross-hybridization, can be corrected for by mismatch
probes (in case of Affymetrix chips)
15

Normalisation
• After discarding bad arrays and spots, remaining influences due
to any differences related to the procedure followed need to be
corrected for as much as possible
• Between-slide normalisation: correct for experimental differences
between slides
– E.g. one may have an overall higher signal due to differences in
hybridisation
• Within-slide normalisation: correct for within slide variations
– By applying normalisation per region, per spot group etc.
• For two-channel arrays: between-channel normalisation
16

Normalisation procedures
• Scale intensities to the same mean/median value for all slides
– Only if just a small fraction of genes is expected to be changed (not
always the case!)
• Normalize based on values for housekeeping genes…
– Genes that are assumed to have same expression in all samples)
…or spikes
– Unique transcripts added in known concentrations
• Normalise dependent on intensity or on most similar spots
(LOESS normalisation)
• Force distribution of intensities to be the same for all slides
– Quantile normalization
17

Log-transformation
• Generally, for one channel arrays the intensities are first 2 logtransformed.
• After logging and normalisation one can compute the difference
in means (‘logFC’) between several experimental groups.
– The difference is much easier handled statistically
– Also the distribution of the logged intensities is more ‘normal’ than on
the original scale
– 2^logFC corresponds to the ratio on the original scale
• The same procedure is taken to compute Cy5/Cy3 ratios for two
channel arrays; the ratio is computed as 2^(2log (Cy3) - 2log (Cy5))
18

Why log two-color ratio data? (2 channel example)
• This ‘spreads out’ the data
• And offers symmetry
• And makes subsequent statistical analysis easier
• ‘raw’ ratio
½
1 2
• log ratio
2
log of:
½
1 2
19

Red and green foreground intensity
20

LogFC values after LOESS normalisation
For two channel
arrays, it is relevant
to check whether
effects cancel out
between channels
21

Preprocessing Affymetrix data
• For Affymetrix we have more spots (probes) for one
single transcript (probeset)
– these must be summarised into one value
• Well-known methods for preprocessing Affymetrix
chips
– MAS5.0 (uses mismatch intensities)
– RMA (Robust Multiarray Average, does not use mismatches)
• Includes both background correction and (quantile) normalisation
– GCRMA (like RMA, but also takes into account GC content)
– dChip (model-based)
– For exonST en geneST arrays, only RMA can be used (another option is
PLIER, error-model)
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A QC module in Genepattern

The Affymetrix QC workflow
• For QC, you will be using Genepattern module
MADMAXArrayQualityAnalysis
that connects to a dedicated R server in Wageningen
– The module was called NuGOArrayQualityAnalysis before
• Now I first explain the options you can select, and how
to interpret the outcome
• The images are taken from other data sets than the one
you will be using
24

QC: what do the options mean?
1. Provide a zip file that
contains all CEL files
2. Provide email address
3. Select a normalisation
method
• RMA, gcRMA, VSN, or
MAS5
4. Select a distance measure
• Pearson, Spearman,
Euclidean
5. Select a clustering method
• McQuitty, Ward,
average, centroid,
complete, median, single
6. Select the number of
images per page
7. All further questions are
yes/no questions to
decide which output to
receive (all ‘yes’ by
default)
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Quality Control Overview plot
Default criteria:
Percentage present within 10%
Background within 20 units
Scaling factors
within 3-fold from the average
GAPDH 3’/5’ ≤ 1.25
Actin 3’/5’ ≤ 3
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Image plot
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FitPLM weights image
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FitPLM residuals image
29

Density plot
30

Box- and whisker plot
31

Relative Log Expression plot
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Normalised Unscaled Standard Error plot
33

RNA digestion plot
34

MA plot
logFC=
=average
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MA plot
36

Correlation plot/heatmap
37

Clustering dendrogram
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A data preprocessing module in Genepattern

Data preprocessing
• For data preprocessing we will use a Genepattern
module again, NuGOExpressionFileCreator
• This module performs:
– background correction
– normalisation
– summarisation (the combination of all probe signals that
belong to the same reporter into one value for that probe set)
• The outcome is a table of preprocessed data, that can
be used for further (statistical) analysis
– this will be discussed tomorrow
40

NuGOExpressionFileCreator
41
• Provide a zip file that
contains all CEL files
• Select the CDF file to be
used (next slide)
• Select a normalisation
method
– RMA, gcRMA, MAS5,
or dChip
• Do you want to perform
quantile normalisation
(RMA, gcRMA)
• Do you want to perform
background correction
(RMA)
• Some more options for
normalisation
• Some fields to select
options for intput/output
files

Intermezzo: custom CDF files
• Affymetrix provides annotations files for their probesets (CDF file)
• When these get outdated, one can of course update probeset
annotations
• But it may be even better to disassemble these sets into the separate
probes, reannotate probes, and assemble into new probesets
(different ones)
• This is exactly what custom CDF files do
• Note that reassembled probesets do not contain the same number
of probes anymore
42

Intermezzo: BrainArray CDF files 1
• Reannotation based on one of several genome databases
• IDs are created as follows: ID from the gene the probeset refers
to followed by ‘_at’ to ressemble an Affymetrix ID
• When using these annotations in other tools, you have to remove
the ‘_at’ additions, in order to get recognisable IDs
• Note that when using Entrez gene this means that the ID is
composed of a number (Entrez gene ID) followed by ‘_at’, and
as such looks exactly like a normal Affymetrix ID, but IT IS
NOT
1
http://arrayanalysis.mbni.med.umich.edu/arrayanalysis.html
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The afternoon session and the data set

The afternoon session
• In the afternoon session, you will be performing QC
and further data preprocessing yourself
• You will follow a stepwise guide available online
– http://www.bigcat.unimaas.nl/wiki/index.php/PET_course
• You will use an Affymetrix data set and make use of the
Genepattern QC and data preprocessing workflows
discussed
45

Short description of the data set (1)
• Microarray experiments have to be uploaded to online
repositories such as Gene Expression Omnibus (GEO,
NCBI) or ArrayExpress (AE, EBI) upon publication
• We will use a published dataset available from AE
46

Short description of the data set (2)
• The course Wiki contains instructions on how to proceed
• This data set is taken from Ezendam et al., 2004
• Hexachlorobenzene (HCB) is a persistent pollutant, that is toxic
for liver, neurons and the reproductive and immune systems
• In this study, Brown Norway rats were fed a diet supplemented
with HCB of 0, 150, or 450 mg/kg
• Spleen, mesenteric lymph nodes (MLN), thymus, blood, liver,
and kidney were analyzed using the Affymetrix rat RGU-34A
GeneChip microarray
– 13-17 arrays per tissue, max 6 per concentration
• We will be primarily considering the liver data
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