1. Finding Transcription Modules from large gene-expression data sets Ned Wingreen – Molecular Biology Morten Kloster, Chao Tang – NEC Laboratories America

2. Outline Introduction – transcription, regulation, gene chips, and transcription modules. Iterative Signature Algorithm (ISA). Advantages of Progressive Iterative Signature Algorithm (PISA). PISA applied to yeast data.

3. Transcription regulation http://doegenomestolife.org

4. Gene chips DNA microarray

5. Gene-expression profile E gc g=1,2,...,N g c=1,2,...,N c But data very noisy…

6. Transcription module C1C1 C2C2 C3C3 Conditions G1G1 G7G7 G2G2 G3G3 G4G4 G5G5 G6G6 Genes TF 1 TF 2 TF 3 TF 4 Transcription factors A Transcription Module: a set of conditions and a set of genes connected by a transcription factor.

7. A gene can be in multiple transcription modules. Conditions Genes c 1 c 2 c 3 … … c m … … c n...... c N c g 1 g 2 g 3. g i. g j. g N g Signature of a transcription module

8. Iterative Signature Algorithm (ISA) Barkai group (2002,2003) Transcription Module (TM) Gene vector and condition vector: Conditions Genes c 1 c 2 c 3 … … c m … … c n...... c N C g 1 g 2 g 3. g i. g j. g N G Thresholding on both genes and conditions reduces noise. Thresholding:

9. Limitations of ISA Lots of spurious modules (millions…). Weak modules may be absorbed by strong ones. ISA does not make use of identified modules to find new ones. c 1 c 2 c 3 … … c m … … c n...... c N c g 1 g 2 g 3. g i. g j. g Ng

10. Progressive Iterative Signature Algorithm (PISA) c 1 c 2 c 3 … … c m … … c n...... c N c g 1 g 2 g 3. g i. g j. g N g

11. Advantages of PISA over ISA Removing found modules reveals “hidden” modules, and reduces noise for unrelated modules. No positive feedback. Improved thresholding for genes. Combines coregulated and counter-regulated genes.

12. Example of PISA vs. ISA TF 1 TF 2 G1G1 G2G2 AB

13. The gene-score threshold Goal: less than one gene included in the module by mistake. Require: threshold that is insensitive to (unknown) module size. Gene scores along the condition vector for some module

14. Eliminating false modules For scrambled data, preliminary modules either have few genes or few contributing conditions. True positives

15. PISA applied to yeast data Applied PISA to a dataset containing almost all available microarray data for S. cerevisiae: >6000 genes, ~1000 conditions. Found ~140 different modules, including all “good” modules found by ISA. Found some unknown modules. Found many “good” small modules that ISA could not find / separate from the spurious modules. ~2600 genes in at least one module, ~900 genes in more than module.

16. Some modules found by PISA

17. Example: Zinc module ZRT1 YNL254C INO1ZAP1 YOL154W ADH4 ZRT3ZRT2 YOR387C ZRT1 ZAP1 ZRT2 YNL254C YOL154W ZRT3 ADH4 RAD27 ZRC1 … Lyons et al., PNAS 97, 7957-7962 (2000) ZAP1-regulated genes during zinc starvation. Zinc module found by PISA

18. Comparison with other databases “Gold standard”: Gene Ontology (Genome Res. 11, 1425-1433 (2001)) Database A: Immunoprecipitation (Lee et al., Science 298, 799-804 (2002)) Database B: Comparative genomics (Kellis et al., Nature 423, 241-254 (2003))

19. anticorrelated correlated Oxidative stress response(69) De novo purine biosyn (32) Lysine biosyn (11) Biotin syn & transport (6) Arg biosyn (6) aa biosyn (96) Oxidative stress response (69) aryl alcohol dehydrogenase (6) proteolysis (27) trehalose & hexose metabolism/conversion (21) COS genes (11) heat shock (52) repair of disulfide bonds (26) Mating genes for type a (15) Mating type a signaling genes (6) Mating (110) Mating factors/receptors: a/  difference (26) rRNA processing (117) Ribosomal proteins (126) Histone (19) Fatty acid syn ++ (22) Cell cycle G2/M (31) Cell cycle M/G1 (35) Cell cycle G1/S (66) Correlations

20. Summary Data from gene chips can be used to identify transcription modules (TMs). Iterative approach (ISA) is promising. PISA improves on ISA by taking out found TMs. –PISA also improves gene thresholding, avoids positive feedback, and improves signal to noise by grouping coregulated and counter-regulated genes. –PISA very effective for finding “secondary modules”. http://cn.arxiv.org/abs/q-bio/0311017

21. Future Directions Input to experiment: –new modules and new genes in old modules. –what kinds of experiments give the most informative data? Improve PISA: –better pre/post-processing of data. Apply PISA to other organisms. Combine PISA with other data (experimental, bioinformatic) to systematically identify TMs, and reconstruct the transcription network.

22. De novo purine biosynthesis Number of genes: 32 Average number of contributing conditions: 14.6 Consistency: 0.59 Best ISA overlap: 0.59 at t G =5.0; frequency 16

23. Galactose induced genes Number of genes: 23 Average number of contributing conditions: 18.1 Consistency: 0.55 Best ISA overlap: 0.74 at t G =3.2; frequency 686

24. Hexose transporters Number of genes: 10 Average number of contributing conditions: 33.7 Consistency: 0.59 Best ISA overlap: 0.6 at t G =3.8; frequency 41

25. Peroxide shock Number of genes: 69 Average number of contributing conditions: 23.9 Consistency: 0.50 Best ISA overlap: 0.34 at t G =3.4; frequency (1)

26. Implementation of PISA Normalization of gene-expression data Iterative algorithm to find preliminary modules (modified ISA) –avoiding positive feedback –gene-score threshold Orthogonalization Finding consistent modules

27. Normalization of expression data Gene-score matrix E G : Condition-score matrix E C : removes reference-condition bias normalizes total RNA levels makes gene scores comparable  makes condition scores comparable

28. Iterative algorithm: modified ISA (mISA) Start with a random set of genes G I. Produce condition-score vector s C. Produce gene-score vector s G, using “leave-one-out” scoring to avoid positive feedback. From s G, calculate gene vector m G for next iteration.

29. Orthogonalization After finding each converged preliminary module (s G, s C ), remove component along s C from all genes: s1Cs1C s’s’ s2Cs2C

30. Why does scrambled data yield large modules? Long tails of expression data lead to single-condition modules.

31. Finding consistent modules Repeat PISA runs many times (~30). Tabulate preliminary modules. A preliminary module contributes to a module if: –the preliminary module contains > 50% of the genes in the module, –these genes constitute > 20% of the preliminary module. A gene is included in a module if it appears in >50% of the contributing modules, always with the same gene-score sign.

32. Comparison with other databases Gene Ontology (Genome Res. 11, 1425-1433 (2001)) Database A: Immunoprecipitation (Lee et al., Science 298, 799-804 (2002)) Database B: Comparative genomics (Kellis et al., Nature 423, 241-254 (2003)) N g — number of genes in organism m — number of genes in module c — number of genes in GO category n — number of genes in both module and GO category p value:

33. Correlation of modules Conditions Genes c 1 c 2 c 3 … … c m … … c n...... c Nc g 1 g 2 g 3. g i. g j. g Ng