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REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.

Zhu M, Liu CC, Cheng C - BMC Genomics (2013)

Bottom Line: REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets.When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer.Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.

ABSTRACT

Background: Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.

Results: Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

Conclusions: REACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

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REACTIN algorithm identifies significant activity difference of ER alpha (Haib_T47d_Eralphaa_Gen1h) in the Hess dataset. (a) Genes with higher t-scores (ER+ vs ER-) are more likely to be regulated by ER alpha. Genes are sorted in a decreasing order according to their t-scores (ER+ vs ER-). The –log10(P-value) is calculated by TIP, indicating the probability of a gene is bound by ER alpha in Haib_T47d_Eraphaa_Gen1h ChIP-seq data. The green lines indicates ER alpha target genes identified by peak-based method; (b) The correlation between the t-scores of genes and TF binding scores calculated by TIP; (c) The foreground and background functions for Haib_T47d_Eraphaa_Gen1h binding profile. The foreground and background functions are defined in Formula (xx) and (xx). Note the maximum deviation is obtained at the 18.9% percentile of all genes. (d) GSEA results for the ER alpha target gene sets defined by peak-based method (the green lines in (a)). Note that it cannot detect the activity difference of ER alpha between ER+ and ER- samples.
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Figure 3: REACTIN algorithm identifies significant activity difference of ER alpha (Haib_T47d_Eralphaa_Gen1h) in the Hess dataset. (a) Genes with higher t-scores (ER+ vs ER-) are more likely to be regulated by ER alpha. Genes are sorted in a decreasing order according to their t-scores (ER+ vs ER-). The –log10(P-value) is calculated by TIP, indicating the probability of a gene is bound by ER alpha in Haib_T47d_Eraphaa_Gen1h ChIP-seq data. The green lines indicates ER alpha target genes identified by peak-based method; (b) The correlation between the t-scores of genes and TF binding scores calculated by TIP; (c) The foreground and background functions for Haib_T47d_Eraphaa_Gen1h binding profile. The foreground and background functions are defined in Formula (xx) and (xx). Note the maximum deviation is obtained at the 18.9% percentile of all genes. (d) GSEA results for the ER alpha target gene sets defined by peak-based method (the green lines in (a)). Note that it cannot detect the activity difference of ER alpha between ER+ and ER- samples.

Mentions: In Figure 3, we use Haib_T47d_Eralphaa_Gen1h in the Hess dataset as the example to show how the REACTIN algorithm identifies its activity difference. As shown in Figure 3a, when genes are sorted in the decreasing order of their t-scores (ER+ versus ER- samples in Hess dataset), genes with higher binding possibilities (grey lines with larger –log10 (P-value)) as calculated by TIP [31] are more likely to have higher t-scores (i.e. biased to the left side). This indicates that target genes of ER alpha are more likely to have higher expression levels in ER+ than in ER- samples, implying the regulatory activity difference of ER alpha. In fact, the t-score of genes is significantly correlated with TF binding scores calculated by TIP with a correlation Rho=0.186 (Figure 3b). REACTIN captures such a relationship by comparing a foreground function with a background function as shown in Figure 3c. The maximum deviation of the two functions reflects the activity difference of ER alpha between ER+ and ER- samples (See “Methods” for details).


REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.

Zhu M, Liu CC, Cheng C - BMC Genomics (2013)

REACTIN algorithm identifies significant activity difference of ER alpha (Haib_T47d_Eralphaa_Gen1h) in the Hess dataset. (a) Genes with higher t-scores (ER+ vs ER-) are more likely to be regulated by ER alpha. Genes are sorted in a decreasing order according to their t-scores (ER+ vs ER-). The –log10(P-value) is calculated by TIP, indicating the probability of a gene is bound by ER alpha in Haib_T47d_Eraphaa_Gen1h ChIP-seq data. The green lines indicates ER alpha target genes identified by peak-based method; (b) The correlation between the t-scores of genes and TF binding scores calculated by TIP; (c) The foreground and background functions for Haib_T47d_Eraphaa_Gen1h binding profile. The foreground and background functions are defined in Formula (xx) and (xx). Note the maximum deviation is obtained at the 18.9% percentile of all genes. (d) GSEA results for the ER alpha target gene sets defined by peak-based method (the green lines in (a)). Note that it cannot detect the activity difference of ER alpha between ER+ and ER- samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750236&req=5

Figure 3: REACTIN algorithm identifies significant activity difference of ER alpha (Haib_T47d_Eralphaa_Gen1h) in the Hess dataset. (a) Genes with higher t-scores (ER+ vs ER-) are more likely to be regulated by ER alpha. Genes are sorted in a decreasing order according to their t-scores (ER+ vs ER-). The –log10(P-value) is calculated by TIP, indicating the probability of a gene is bound by ER alpha in Haib_T47d_Eraphaa_Gen1h ChIP-seq data. The green lines indicates ER alpha target genes identified by peak-based method; (b) The correlation between the t-scores of genes and TF binding scores calculated by TIP; (c) The foreground and background functions for Haib_T47d_Eraphaa_Gen1h binding profile. The foreground and background functions are defined in Formula (xx) and (xx). Note the maximum deviation is obtained at the 18.9% percentile of all genes. (d) GSEA results for the ER alpha target gene sets defined by peak-based method (the green lines in (a)). Note that it cannot detect the activity difference of ER alpha between ER+ and ER- samples.
Mentions: In Figure 3, we use Haib_T47d_Eralphaa_Gen1h in the Hess dataset as the example to show how the REACTIN algorithm identifies its activity difference. As shown in Figure 3a, when genes are sorted in the decreasing order of their t-scores (ER+ versus ER- samples in Hess dataset), genes with higher binding possibilities (grey lines with larger –log10 (P-value)) as calculated by TIP [31] are more likely to have higher t-scores (i.e. biased to the left side). This indicates that target genes of ER alpha are more likely to have higher expression levels in ER+ than in ER- samples, implying the regulatory activity difference of ER alpha. In fact, the t-score of genes is significantly correlated with TF binding scores calculated by TIP with a correlation Rho=0.186 (Figure 3b). REACTIN captures such a relationship by comparing a foreground function with a background function as shown in Figure 3c. The maximum deviation of the two functions reflects the activity difference of ER alpha between ER+ and ER- samples (See “Methods” for details).

Bottom Line: REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets.When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer.Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.

ABSTRACT

Background: Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.

Results: Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

Conclusions: REACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

Show MeSH
Related in: MedlinePlus