Microarray statistical validation and functional annotation
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Microarray statistical validation and functional annotation. Microarrays. DNA microarray technology is an high throughput method for gaining information on gene function.

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Microarrays l.jpg
Microarrays

  • DNA microarray technology is an high throughput method for gaining information on gene function.

  • Microarray technology is based on the availability of gene sequences arrayed on a solid surface and it allows parallel expression analysis of thousands of genes.


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Microarrays

  • Microarray can be a valuable tool

    • to define transcriptional signatures bound to a pathological condition

    • to rule out molecular mechanisms tightly bound to transcription

  • Since our actual knowledge on genes function in high eukaryotes is quite limited

    • Microarray analysis frequently does not imply a final answer to a biological problem but allows the discovery of new research paths which let to explore it by a different perspective


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Microarrays

  • A gold standard methodology to identify, with high sensitivity and precision, “biologically meaningful” differentially expressed genes is not yet available.

    • Therefore, various approaches are under development to optimize the extraction of data linked to the “biology” of the problem under study.


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Microarrays

  • The principal steps of a microarray analysis are:

    • Gene intensity measurements and data normalization.

    • Statistical validation of differential expression.

    • Functional data mining.


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Microarrays

  • Statistical validation usually implies the selection from the user of statistical significance parameters.

  • For example:

    • SAM (Significance Analysis of Microarrays) always requires the input of a “delta” value which defines the threshold of false positive in the validated dataset.

  • If the stringency of the statistical validation is too high biologically meaningful genes can be lost making more difficult to role out functional correlations between the differentially expressed genes.

  • If the stringency of the statistical validation is too loose the increase of false positives creates background noise from which is difficult to extract trustful functional correlations between the differentially expressed genes.




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Microarrays

  • Statistical validation implies the selection from the user of statistical significance parameters.

  • For example:

    • SAM (Significance Analysis of Microarrays) requires the definition of a “delta” value which defines the threshold of false positive in the validated dataset.

    • When Fisher’s test is used the definition of a threshold value is even more hard.



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Microarrays

  • It is important to remark that:

    • A statistical validation not always implies the selection of the most “biologically” meaningful dataset

  • Therefore we are trying to integrate “biologically” important parameters, as Gene ontology, in the statistical validation.


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Microarrays

  • Gene Ontology (GO) is a dynamic controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing.

  • GO might help to link differentially expressed genes to specific functional classes.


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Microarrays

  • Molecular Function:

    the tasks performed by individual gene, products; examples are transcription factor and DNA helicase.


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Microarrays

  • Biological Process:

    broad biological goals, such as mitosis or purine metabolism, that are accomplished by ordered assemblies of molecular functions


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Microarrays

  • Cellular Component:

    subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and origin recognition complex


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Microarrays

  • Recently has been shown that:

    • There is a strong instability of the size and overlap of the gene lists that result from varying gene selection methods.

(Hosack et al, Genome Biology 2003, 4:P4)


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Microarrays

The percentage of genes overlapping

in any two lists was highly variable,

and ranged from 7% to 60%.

(Hosack et al, Genome Biology 2003, 4:P4)


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Microarrays

  • In spite of this striking variation:

    • The top five biological biologically themes linked to the data sets are the same.

    • This evidence suggests that the conversion of genes to themes favour the "biological result" of the experiment to be determined despite substantial differences in gene list content resulting from the use of various normalization, gene intensity and statistical selection methods.

(Hosack et al, Genome Biology 2003, 4:P4)


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Microarrays

(Hosack et al, Genome Biology 2003, 4:P4)


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Microarrays

  • Integrating GO in statistical validation:

    • The number of GO classes are counted in the data set under statistical validation.

    • SAM analyses are performed using various delta parameters.

    • The GO classes present in the statistically validated subsets are counted.

    • The presence of enrichment of GO classes in the SAM validated sets is evaluated using a binomial test corrected for Type I errors.

      • A score for each GO class is generated performing the log2(p-value * % hits)

    • The SAM subset showing the best compromise between number of enriched GO classes and number of HITs for each class is selected for further studies


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22 wks

10 wks

Atypical hyperplasia and in situ carcinomas

Cured mammary gland

22 wks

Lobular carcinoma

CONCORDANT MORPHOLOGIC AND GENE EXPRESSION DATA SHOW THAT A VACCINE FREEZES HER-2/neu PRENEOPLASTIC LESIONS

(Quaglino et al submitted)


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log2(p-value * %HITs)

Microarrays


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Microarrays

  • We observed that:

    • simple statistical validation and statistical validation mediated by GO classes analysis have strong overlap.

  • However, some interesting differentially expressed genes can be only detected using GO mediated statistical validation.


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-3.0

1:1

3.0

a

e

b

c

d

Ig-linked immuno response

common to simple statistical analysis

and GO-mediated statistical validation

Cell-linked immuno response

specific of GO-mediated

statistical validation


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(d)

Subsets of SAM validated genes

(SSVG)

(h)

(c)

min(AMs specific

p-value)

SAM

program

(g)

Patser

program

No

(e)

(n)

(b)

Run SAM with

at least 3 different

threshold?

Selected SSVG

(f)

Consensus

program

Alignment matrices (AMs)

(a)

Yes

Starting dataset

(SD)

(i)

(l)

Any

AM is over-represented

in SSVG?

Patser

program

Filtering by

AMs specific

P-value

(m)

We also observed that the previously described approach can also be used to improve data mining related to the transcriptional signature present in co-regulated gene

No

Discard


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