Biostatistics Role in Microarray Analysis

8/3/2019 Biostatistics Role in Microarray Analysis

1/44


2/44


3/44

Define the elemental concept of microarrays.

Describe the utility of the analysis of microarrays. Describe the different sources of variability among the

analysis of microarrays.

Describe the linear technique used to normalize

microarray data.

Describe the role of statistics in the normalization

techniques described today.

Describe the different transformation techniques

mentioned today.

Distinguish between the different transformationtechniques described today

Describe the different pairwise comparisons techniques

used to test for independence among genes.


4/44

Layman's term:

A DNA microarray (also commonly known as genechip, DNA chip, or biochip) is a collection of

microscopic DNA spots attached to a solid surface.

Scientists use DNA microarrays to measure theexpression levels of large numbers of genessimultaneously or to genotype multiple regions of

a genome. http://www.sciencedaily.com/articles/d/dna_microarray.

htm
http://www.sciencedaily.com/articles/d/dna_microarray.htmhttp://www.sciencedaily.com/articles/d/dna_microarray.htmhttp://www.sciencedaily.com/articles/d/dna_microarray.htmhttp://www.sciencedaily.com/articles/d/dna_microarray.htmhttp://www.sciencedaily.com/articles/d/dna_microarray.htmhttp://www.sciencedaily.com/articles/d/dna_microarray.htm


5/44


6/44


7/44

Which genes are related.

Which genes causes a certain disease.

What subcategories of disease X are there.

How certain can we be about this.

Dont expect it to fix bad data!


8/44

Microarray data are inherently highly variable. YOU are measuring mRNA levels

Some of this variability is relevant since itcorresponds to the differential expression ofgenes.

Unfortunately, a large portion of undesirablebiases are introduced during the many technicalsteps of the experimental procedure.


9/44

Biological variability

RNA extraction

Probe labeling Ex: dye differences

Printing Ex: print-order, plate-order, clone variation

Hybridization Ex: temperature, time, mixing technique

Human

Ex: variation between lab researchers Scanning

Ex: laser & detector, chemistry of the fluorescent label

Image analysis Ex: identification, quantification, background methods


10/44

Raw Exploration

Normalization

Logarithmic Transformation (adjustment of variances)

M vs. A plot (rotation of logarithmic transformation)

This method adjust the median of differences to 0.

Background Transformation (RMA background approach usedfor linear scenarios) (to minimize the noise in the observed

plot)

Averaging normalization techniques

After normalization of all of the spots in the microarraychip, we average them to obtain a more stable masterslide.

Establish the cutting points

Nave approach (Establish cut off points by logs ratios)

Justifiable approach (Establish cut off points by T-statistic)


11/44

Statistical analysis For eachgene iwe have the hypothesis test:

Null (neutral) hypothesis H0,i: Mi = 0 Alternative hypothesis H1,i: Mi 0

Post-hoc pairwise comparisons

Minimize false positives


12/44


13/44

At first, your data would probably be like this:

Large numbers are very heavy to workwith, so we need a more suitable way to

play with them

Observed data (R,G):

R= signal in red channel

G= signal in greenchannel


14/44


15/44

Not be confused with the normalization in statistical procedures in

which the purpose is to make the data distribution to a normal orGaussian distribution.

Normalization of microarray data is aimed to correct for the systematicmeasurement errors and bias in the observed data.

The process of normalization can be classified into linear and non linearnormalization.

Linear= is applied to selected genes or global ones. The process is quite suitablefor consistent data.

Non-linear= is highly precise for data at extreme values, but requires a gene setfor reference.

The purpose of both methods is to bring each image in the microarraydata to same average brightness using statistical modeling.


16/44

Expectation: Most genes are non-differentially expressed

i.e. most of the data points should be around M=0.

Idea: Do various exploratory plots to see if this assumption is met. For example, M vs A, spatial plots, density & boxplots plots, print-

order plots etc.

Result: We commonly observe something like this: Measured value= real value +systematic errors+noise

Correction: If so, normalizethe data to get rid of errors &noise: Corrected value= real value +systematic errors+noise


17/44


18/44

Logarithmic Transformation

Why Log2??...

log2R=log2G


19/44

M vs. A is basically arotation of the log2R vs.log2G scatter plot.

Now the quantity of

interest, i.e. the foldchange, is contained inone variable, namely M!

Transformed data (M,A): M = log2(R) - log2(G) (log ratio)

A = [log2(R) + log2(G)] (logintensity)


20/44

R vs. G log(R) vs. log(G) M vs AR=red channel signalG=green channel signal

M= log2(R/G)aka log-ratio

A = log2(RG)aka log-intensity


21/44


22/44

It stands for Robust Multichip Average (Irizarry, 2003)

More robust than the Lowess (aka Loess) technique.

Mostly used in Affymetrix microarray data.

It is biologically sound to assume that fluorescenceintensities from a microarray experiment are composed

of both signal and noise, and that the noise isomnipresent throughout the entire signal distribution.

A convolution model of a signal distribution and a noise

distribution is a good choice in such a situation.


23/44

Convolution model is a mathematicaloperation on two functions fand g, producing athird function that is typically viewed as a modifiedversion of one of the original functions.

Fluorescent signal

Observed data

Background noise
http://en.wikipedia.org/wiki/Operation_(mathematics)http://en.wikipedia.org/wiki/Operation_(mathematics)


24/44


25/44

The equation of the RMA method, E(Si|Xi=xi) willbe used as the background intensity correction forgene i(it is applied to all genes in the microarrayin order to minimize the noise from the observed

signal).


26/44

Useful when having different segmentation of the samegene.

Combines all segmentation of the same gene into anaverage transformed single unit.

Can apply T test to work out if the mean of data is sameor different between two conditions.

Can apply ANOVA to work out if the mean of data issame or different across two or more conditions.


27/44

normalization

Average slide


28/44


29/44

Nave approach

Establish cut off pointsby logs ratios.

This has to be done postM vs. A transformation &background correction

Top and bottom 0.5 of

the absolute Mvalueshave to be shaven off.


30/44

Justifiable approach Establish cut off points using T-

statistic via Significance Analysisof Microarrays*

For replicated data, i.e.multiple measurements of the

same thing, we trust thisapproach more if the deviation(std.dev.) is small.

T = mean(x) / SE(x) Where

The M axis is the only one tobe transformed by T.

If the deviation is large, we donot trust it that much.(stickwith nave approach)

*R package / Excel Add-In


31/44


32/44

For eachgene iwe have the hypothesistest:

Which genes or groups are (most) differentially

expressed?H0,i: Mi= 0H1,i: Mi 0

=5%

CI= 95%


33/44

Thousands of tests, i.e. each gene is tested

againstH0: T=0.

false positives problems are a serious threat.

need to adjust p-values.

Different adjustment procedures

Pairwise comparisons post-hoc test

Bonferroni (best in linear situations)

Tukey

Sidak

Duncan

Holm


34/44

Multiple tests: a family of tests They compared a list of significant genes

Then family-wise error (FWE) = 0.05

Bonferroni correction: set k=p/m

Where: k= new p-value; p= original ; m= # of posthoc performed.


35/44

To sort and rank data.

To reduce data set of 1000s genes to 10s or

100s (via Averaging NormalizationTechniques).

As a guide in selecting which genes tovalidate more precisely and which no to.


36/44

Filter out bad spots.

Adjust low intensities.

Normalize background noise and raw data. Calculate average ratios and statistical

significance values per gene.

Perform pairwise post hoc comparisons tominimize false positives.


37/44

There are many different statistical significancemetrics.

T-test (P values), SAM (T values), Wilcoxon RST,ANOVA (F-statistics), many more

Just many variations on a theme!

Choose one (or more!) wisely.


38/44

BUT: dont let it make decisions for you!

There will always be false positives. (theres no

post hoc test that can eliminate all!!)

The most accurate tool in validating the results isthe researchers judgment, with the help of the

keen point of view of a biostatistician of course!...


39/44

You need replication and statistics to find realdifferences between genes.

In most cases the nave approach (cutoff points bylog ratios) is notenough.

Cutoff points by t-statisticsis a much wiser decision.

Look out for false positives.

Multiple testing = must adjust the pvalues.


40/44

Dchip

Affymetrix

R

Bioconductor

BRBArray tools (NCI biometric research branch)

Matlab Bioinformatics Toolbox

GeneSpring

Partek


41/44

For further reading regarding the non-linear normalization of

microarrays please visit:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdf
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdfhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC126873/pdf/gb-2002-3-9-research0048.pdf


42/44

1. Good image analysis is essential. Some software are

obsolete and not that good.

2. Normalization is needed. We understand more now

than a few years ago.

3. Use at least the t-statistics to identify differentially

expressed genes. Do not rely exclusively on log-ratios.

4. Multiple testing must be considered for false positives;

adjust yourp-values.

5. Talk to a biostatistician before doing the experiments!They too have a family to feed thanks to your work!.


43/44

Analysis of Microarray Data

Henrik Bengtsson [email protected]

Brown,S. (2009). Microarray Data Analysis. September 8, MMXI.

Retrieved from http://www.docstoc.com/docs/5822653/Microarray-Data-Analysis

Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP.(2003). Summaries of Affymetrix GeneChip probe level data.

Nucleic Acids Res. 31:e15.

The Use of Statistics in Microarray Studies (Dr. Ernst Wit)

http://www.stats.gla.ac.uk/~microarray

Wikipedia. MA plots. September 8, MMXI.

Retrieved from http://en.wikipedia.org/wiki/MA_plot
mailto:[email protected]://www.docstoc.com/docs/5822653/Microarray-Data-Analysishttp://www.docstoc.com/docs/5822653/Microarray-Data-Analysishttp://www.stats.gla.ac.uk/~microarrayhttp://en.wikipedia.org/wiki/MA_plothttp://en.wikipedia.org/wiki/MA_plothttp://www.stats.gla.ac.uk/~microarrayhttp://www.docstoc.com/docs/5822653/Microarray-Data-Analysishttp://www.docstoc.com/docs/5822653/Microarray-Data-Analysishttp://www.docstoc.com/docs/5822653/Microarray-Data-Analysishttp://www.docstoc.com/docs/5822653/Microarray-Data-Analysishttp://www.docstoc.com/docs/5822653/Microarray-Data-Analysismailto:[email protected]


44/44

Date post:	07-Apr-2018
Category:	Documents
Upload:	geronimo-maldonado-martinez
View:	219 times
Download:	0 times

Biostatistics Role in Microarray Analysis

Documents