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SIGMA2: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes.

Chari R, Coe BP, Wedseltoft C, Benetti M, Wilson IM, Vucic EA, MacAulay C, Ng RT, Lam WL - BMC Bioinformatics (2008)

Bottom Line: However, software packages to handle, analyze, and visualize data from these multiple 'omics disciplines have not been adequately developed.Multi-dimensional datasets can be simultaneously visualized and analyzed with respect to each dimension, allowing combinatorial integration of the different assays belonging to the different 'omics.The identification of genes altered at multiple levels such as copy number, loss of heterozygosity (LOH), DNA methylation and the detection of consequential changes in gene expression can be concertedly performed, establishing SIGMA2 as a novel tool to facilitate the high throughput systems biology analysis of cancer.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca

ABSTRACT

Background: High throughput microarray technologies have afforded the investigation of genomes, epigenomes, and transcriptomes at unprecedented resolution. However, software packages to handle, analyze, and visualize data from these multiple 'omics disciplines have not been adequately developed.

Results: Here, we present SIGMA2, a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes. Multi-dimensional datasets can be simultaneously visualized and analyzed with respect to each dimension, allowing combinatorial integration of the different assays belonging to the different 'omics.

Conclusion: The identification of genes altered at multiple levels such as copy number, loss of heterozygosity (LOH), DNA methylation and the detection of consequential changes in gene expression can be concertedly performed, establishing SIGMA2 as a novel tool to facilitate the high throughput systems biology analysis of cancer.

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Related in: MedlinePlus

A two-group two dimensional comparison of 37 NSCLC and 16 SCLC cancer cell lines. First, segmentation analysis is performed to delineate gains and losses in each sample. Next, a statistical comparison of the distribution of gains and losses between the two groups is done using the Fisher's exact test. (A) Using the interactive search, one of the regions of difference identified is on chromosome 7, with a NSCLC and SCLC sample aligned next to each other. The NSCLC has a clear segmental gain of that region, with the SCLC not having the gain. The right-most graph is a frequency plot summary of two sample sets (NSCLC and SCLC). NSCLC is color-coded in red while SCLC in green, and the overlap appears in yellow. The frequency of chromosome arm 7p gain is higher in the red group. (B) A heatmap is shown representing 15 NSCLC and 15 SCLC gene expression profiles, of the specific genes in the region highlighted in yellow. (C) When examining gene expression data of EGFR specifically, a gene in this region, we can see that the expression is drastically higher in NSCLC vs. SCLC, as predicted by the higher frequency of gain in NSCLC vs. SCLC of that region. Gene expression data are represented as log2 of the normalized intensities.
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Figure 7: A two-group two dimensional comparison of 37 NSCLC and 16 SCLC cancer cell lines. First, segmentation analysis is performed to delineate gains and losses in each sample. Next, a statistical comparison of the distribution of gains and losses between the two groups is done using the Fisher's exact test. (A) Using the interactive search, one of the regions of difference identified is on chromosome 7, with a NSCLC and SCLC sample aligned next to each other. The NSCLC has a clear segmental gain of that region, with the SCLC not having the gain. The right-most graph is a frequency plot summary of two sample sets (NSCLC and SCLC). NSCLC is color-coded in red while SCLC in green, and the overlap appears in yellow. The frequency of chromosome arm 7p gain is higher in the red group. (B) A heatmap is shown representing 15 NSCLC and 15 SCLC gene expression profiles, of the specific genes in the region highlighted in yellow. (C) When examining gene expression data of EGFR specifically, a gene in this region, we can see that the expression is drastically higher in NSCLC vs. SCLC, as predicted by the higher frequency of gain in NSCLC vs. SCLC of that region. Gene expression data are represented as log2 of the normalized intensities.

Mentions: In addition to single group analysis, two-dimensional genome/transcriptome analysis can be applied to two-group comparison analysis. For example, if patterns of copy number alterations are compared between two groups and a particular region is more frequently gained in one group than another, the expression data can subsequently compared between the groups of sample to determine if there is an association between gene dosage and gene expression. That is, we would expect the group with more frequent copy number gain to have higher expression than the other group. Notably, this functionality does not require both copy number and expression data to exist for the same sample, but allows the user to select an independent dataset for expression data comparisons (Figure 7).


SIGMA2: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes.

Chari R, Coe BP, Wedseltoft C, Benetti M, Wilson IM, Vucic EA, MacAulay C, Ng RT, Lam WL - BMC Bioinformatics (2008)

A two-group two dimensional comparison of 37 NSCLC and 16 SCLC cancer cell lines. First, segmentation analysis is performed to delineate gains and losses in each sample. Next, a statistical comparison of the distribution of gains and losses between the two groups is done using the Fisher's exact test. (A) Using the interactive search, one of the regions of difference identified is on chromosome 7, with a NSCLC and SCLC sample aligned next to each other. The NSCLC has a clear segmental gain of that region, with the SCLC not having the gain. The right-most graph is a frequency plot summary of two sample sets (NSCLC and SCLC). NSCLC is color-coded in red while SCLC in green, and the overlap appears in yellow. The frequency of chromosome arm 7p gain is higher in the red group. (B) A heatmap is shown representing 15 NSCLC and 15 SCLC gene expression profiles, of the specific genes in the region highlighted in yellow. (C) When examining gene expression data of EGFR specifically, a gene in this region, we can see that the expression is drastically higher in NSCLC vs. SCLC, as predicted by the higher frequency of gain in NSCLC vs. SCLC of that region. Gene expression data are represented as log2 of the normalized intensities.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2571113&req=5

Figure 7: A two-group two dimensional comparison of 37 NSCLC and 16 SCLC cancer cell lines. First, segmentation analysis is performed to delineate gains and losses in each sample. Next, a statistical comparison of the distribution of gains and losses between the two groups is done using the Fisher's exact test. (A) Using the interactive search, one of the regions of difference identified is on chromosome 7, with a NSCLC and SCLC sample aligned next to each other. The NSCLC has a clear segmental gain of that region, with the SCLC not having the gain. The right-most graph is a frequency plot summary of two sample sets (NSCLC and SCLC). NSCLC is color-coded in red while SCLC in green, and the overlap appears in yellow. The frequency of chromosome arm 7p gain is higher in the red group. (B) A heatmap is shown representing 15 NSCLC and 15 SCLC gene expression profiles, of the specific genes in the region highlighted in yellow. (C) When examining gene expression data of EGFR specifically, a gene in this region, we can see that the expression is drastically higher in NSCLC vs. SCLC, as predicted by the higher frequency of gain in NSCLC vs. SCLC of that region. Gene expression data are represented as log2 of the normalized intensities.
Mentions: In addition to single group analysis, two-dimensional genome/transcriptome analysis can be applied to two-group comparison analysis. For example, if patterns of copy number alterations are compared between two groups and a particular region is more frequently gained in one group than another, the expression data can subsequently compared between the groups of sample to determine if there is an association between gene dosage and gene expression. That is, we would expect the group with more frequent copy number gain to have higher expression than the other group. Notably, this functionality does not require both copy number and expression data to exist for the same sample, but allows the user to select an independent dataset for expression data comparisons (Figure 7).

Bottom Line: However, software packages to handle, analyze, and visualize data from these multiple 'omics disciplines have not been adequately developed.Multi-dimensional datasets can be simultaneously visualized and analyzed with respect to each dimension, allowing combinatorial integration of the different assays belonging to the different 'omics.The identification of genes altered at multiple levels such as copy number, loss of heterozygosity (LOH), DNA methylation and the detection of consequential changes in gene expression can be concertedly performed, establishing SIGMA2 as a novel tool to facilitate the high throughput systems biology analysis of cancer.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca

ABSTRACT

Background: High throughput microarray technologies have afforded the investigation of genomes, epigenomes, and transcriptomes at unprecedented resolution. However, software packages to handle, analyze, and visualize data from these multiple 'omics disciplines have not been adequately developed.

Results: Here, we present SIGMA2, a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes. Multi-dimensional datasets can be simultaneously visualized and analyzed with respect to each dimension, allowing combinatorial integration of the different assays belonging to the different 'omics.

Conclusion: The identification of genes altered at multiple levels such as copy number, loss of heterozygosity (LOH), DNA methylation and the detection of consequential changes in gene expression can be concertedly performed, establishing SIGMA2 as a novel tool to facilitate the high throughput systems biology analysis of cancer.

Show MeSH
Related in: MedlinePlus