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Bi-correlation clustering algorithm for determining a set of co- regulated genes BIOINFORMATICS vol. 25 no.21 2009 Anindya Bhattacharya and Rajat K. De.

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Presentation on theme: "Bi-correlation clustering algorithm for determining a set of co- regulated genes BIOINFORMATICS vol. 25 no.21 2009 Anindya Bhattacharya and Rajat K. De."— Presentation transcript:

1 Bi-correlation clustering algorithm for determining a set of co- regulated genes BIOINFORMATICS vol. 25 no.21 2009 Anindya Bhattacharya and Rajat K. De

2 Outline  Introduction  Bi-correlation clustering algorithm (BCCA)  Results  Conclusion

3 Introduction  Biclustering  Performs simultaneous grouping on genes and conditions of a dataset to determine subgroups of genes that exhibit similar behavior over a subset of experimental condition.  A new correlation-based biclustering algorithm called bi-correlation clustering algorithm (BCCA)  Produce a diverse set of biclusters of co-regulated genes  All the genes in a bicluster have a similar change of expression pattern over the subset of samples.

4 Introduction  Cluster analysis  Most cluster analysis try to find group of genes that remains co-expressed through all experimental conditions.  In reality, genes tends to be co-regulated and thus co-expressed under only a few experimental conditions.

5 Bi-correlation clustering algorithm  Notation  A set of n genes  Each gene has m expression values  For each gene g i there is an m-dimensional vector, there is the j-th expression value of g i.  A set of m microarry experiments (measurements)  n genes will have to be grouped into K overlapping biclusters

6 Bi-correlation clustering algorithm  Bicluster:  A bicluster can be defined as a subset of genes possesing a similar behavior over a subset of experiments  Represented as  A bicluster contains a subset of genes and a subset of experiments where each gene in is correlated with a correlation valued greater than or equal to specified threshold, with all other genes in over the measurements in.

7 Bi-correlation clustering algorithm  BCCA  Use person correlation coefficient for measuring similarity between expression patterns of two genes and.

8 Bi-correlation clustering algorithm  Step 1:  The set of bicluster S is initialized to NULL and number of bicluster Bicount is initialized to 0  Step 2A  BCCA generate a bicluster (C) for each pair of genes in a dataset under a set of conditions  For each pair of genes.BCCA creates a bicluster, where and.

9 Bi-correlation clustering algorithm  In step 2C:  For a pair of genes in C, if then a sample is detected from C, deletion of which caused maximum increase in correlation value between and.  If being a threshold, the sample is deleted from. otherwise, C is discarded.  Deletion of a measurement for which genes differ in expression value the most will result in the highest increase in correlation value.  BCCA deletes one measurement at a time from.

10 Bi-correlation clustering algorithm  In step 2D(a):  Other genes from, which satisfy the definition of a bicluster are included in C for its augmentation.  In step 2D(b):  Whether present bicluster C has been found. If it is so then we do not to include C, otherwise, C is considered as a new bicluster.

11 Bi-correlation clustering algorithm


13 Results  Datasets  We demonstrate the affectiveness of BCCA in determining a set of co-regulated genes (i.e. the genes having common transcription factors) and functionally enriched clusters (and atributes) on five dataset

14 Results  Variation with respect to threshold  Plot of YCCD dataset : Average number of functionally enriched attributes (computed using P-values) versus correlation threshold value

15 Results  Follow a guideline on this value from a previous study by Allocco et al. (2004) which has concluded that if two genes have a correlation between their expression profiles >0.84 then therre is >50% chance of being bounded by a common transcription factor.

16 Results  By locating common transcription factors  At first, we only consider those biclusters that have less than or equal to 50 genes.  Use a software TOUCAN 2 (Aerts et al., 2005) for performance comparison by extracting information on the number of transcription factors present in proximal promoters of all the genes in a single bicluster.  Presence of common transcription factors in the promoter regions of a set of genes is a good evidence toward co-regulation.

17 Results


19 Sequences of all the five genes found in a bicluster generated by BCCA from SPTD dataset. Any transcription factor may be found present in more than one location in upstream region.

20 Results  Functional enrichment :  P-value  The functional enrichment of each GO category in each of the bicluster  employed the software Funcassociate (Berriz et al., 2003).  P-value represents the probability of observing the number of genes from a specific GO functional category within each cluster.  A low P-value indicates that the genes belonging to the enriched functional categories are biologically significant in the corresponding clusters.

21 Results  P-value of a functional category  Suppose we have total population of N genes, in which M has a particular annotation.  If we observe x genes with that annotation, in a sample of n genes, then we can calculate the probability of that observation.  The probability of seeing x or more genes with an annotation, out of n, given that M in the population of N have that annotation

22 Results  Only functional categories with are reported.  Analysis of the 10 biclusters obtained for the YCCD, the highly enriched category in bicluster Bicluster 1 is the ‘ribosome’ with P-value of

23 Results


25 Conclusion  BCCA is able to find a group of genes that show similar pattern of variation in their expression profiles over a subset of measurements.  Better than other biclustering algorithm:  Find higher number of common transcription factors of a set of gene in a bicluster  More functionally enriched

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