BABELOMICS: Microarray Data Analysis

Transcription

1 BABELOMICS: Microarray Data Analysis Madrid, 21 June 2010 Martina Marbà Bioinformatics and Genomics Department Centro de Investigación Príncipe Felipe (CIPF) (Valencia, Spain)

2 DNA Microarrays Paradigm of High Throughput Technologies Yield concentration measurements for : genes, SNP, exons, mrna... Measure cells in different biological conditions In a genomic scale Allow us conducting biological experiments So... How do they work?

3 Central Dogma of Molecular Biology CentralDogma3 For a cell, at a particular time, thousands of mrna are created and sent out of the nucleus to be translated into proteins. Protein concentration regulates biological systems.

4 DNA Microarrays

5 Biological Sample We want to know which genes are expressed under particular biological conditions. We can extract all mrna molecules that are being translated within the cells and provide an expression level indicator of its concentration in the biological sample.

6 RNA Extraction

7 Labelling the Sample Fluorescent Dye

8 Hybridization

9 Expression Measurement If the fluorescent label is attached to one spot we know that the particular complementary gene transcript was present in our cell sample. The greater the fluorescence the greater the concentration of the transcript.

10 Scanning the Microarray Measuring the intensity of the fluorescence in each spot We get a measurement of the hybridization in each spot of the microarray

11 The Data For each biological sample (individual) We get intensity measurements for thousands of genetic transcripts. The measured intensity is used as an indicator of gene expression _s_at _at _at _s_at _at _at _at _at _s_at _at _at _s_at _x_at _at _s_at _s_at _x_at _at _at _s_at _at _at _at _s_at _at

12 Several Microarrays

13 Data Matrix

14 How to treat the data??

15 Babelomics Pipeline

16 Definitions Babelomics One of the most complete integrated packages of tools for microarray data analysis available over the web A complete suite of web tools for functional analysis of genome-scale experiments

17 Babelomics Web

18 Questions Is there any significant difference in gene expression between tumor and healthy cells? Can we detect group of genes with similar expression profiles? Can we classify new biological samples based on gene expression patterns? Is there any significant functional enrichment in my gene list? Are these genes involved in the same disease?

19 Modules Gene Expression Analysis Normalization Differential Expression Clustering Predictors Functional Analysis FatiGO Fatiscan

20 Gene Expression Analysis Gene Expression Analysis

21 Normalization

22 Differential expression

23 Predictors

24 Clustering

25 Functional Analysis Fuctional Analysis

26 From data to functional interpretation Gene Expression Analysis Microarray Data Differential expression Preprocessing (Normalization, Scaling, ) Tab matrix file Predictors Clustering Genes differentially expressed Predicting genes Genes with same expression patterns Functional Analysis

27 Functional Analysis Babelomics, a suite of web tools for: statistical test, multiple test corrections, blast,... List of genes or ids, ie: differentially expressed genes,... Integrated Biological DB of Functional Annotation (GO, KEGG, InterPro, Ensembl, SwisProt, Transcription Factors, MicroRNA, Cisred, Biocarta, Bioentities Literature)

28 FatiGO

29 FatiGO features Compares two lists of genes. Compares one list of genes against the rest of the genome. One statistical test (Fisher s exact) for each Block of annotation. Multiple testing context. Filtering of annotation is convenient (the less tests the best correction).

30 FatiGO test One Gene List (A) The other list (B) Are this two groups of genes carrying out different biological roles? Biosynthesis 60% Biosynthesis 20% Sporulation Sporulation 20% Genes in group A have significantly to do with biosynthesis, but not with sporulation. Biosynthesis No biosynthesis A % B 2 8 We do this for each GO, mirna, Interpro,...!!!

31 FatiScan

32 FatiScan Interpret a ranked list of genes. There is not need for choosing a cut off. All information is included. One statistical test for each Block of annotation. Multiple testing context (hundreds of annotation). Filtering of annotation is convenient (the less tests the best correction).

33 FatiScan Testing along an ordered list Annotation label A List of genes Index ranking genes according to some biological aspect under study. + A B C Annotation label B Annotation label C Block of genes enriched in the annotation A Database that stores gene class membership information. Annotation C is homogeneously distributed along the list FatiScan searches over the whole ordered list, trying to find runs of functionally related genes. - Block of genes enriched in the annotation B

34 FatiScan results Significant results: Enriched class Annotated genes par GO from each list

35 Babelomics Running an Example...

36 Example Experiment description: Rheumatoid Arthritis and Osteoarthritis 15 Affymetrix (HG U133A), 38,500 genes Human 3 classes: control, osteoarthritis and rheumatoid arthritis

37 Example Rheumatoid Arthritis and Osteoarthritis Analysis: Normalization Differential expression FatiScan

38 Input form Normalization Select your data > browser server > select your data : affy_arthritis Analysis : RMA Job name : normalization_arthritis

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41 Normalization Results

42 Normalization Results

43 Input form Differential expression (ND vs RA) Dataset file name : rma.summary.txt Class : CLASS [ND, RA] Test : t Multiple-test correction : fdr Adjusted p-value : 0.05 Job name : Diffexpr_2class_arthritis

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45 Diff Expression Results

46 Diff Expression Results

47 Input form FatiScan Species : hsa Duplicates management : Never Fisher exact test : Over represented terms in list 2 Organism: Homo sapiens Data bases: GO biological process Job name : fatiscan_bottomlist_arhritis

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49 FatiScan Results

50 Acknowledgements Supervisor: Joaquín Dopazo The GEPAS team: Fatima Al-Shahrour Eva Alloza José Carbonell Ana Conesa Pablo Escobar Francisco García Stefan Göetz Martina Marbà Ignacio Medina Pablo Mínguez David Montaner Luis Pulido Javier Santoyo Joaquín Tárraga