Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2014 Nov 18;4:7097. doi: 10.1038/srep07097

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

Copyright © 2014, Macmillan Publishers Limited. All rights reserved

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

PMC Copyright notice

(a) Suppose target RNA expression (y_i_,t,d) in sample t of cancer type d is a function of DNA methylation (), copy number () and miRNA regulation (). (b) The expression change across samples for the target RNA is modelled as the response variable in a multivariate linear regression framework using the input variables as indicted above. (c) The resulting linear coefficient indicate the corresponding interaction between miRNA k and target gene i of cancer type d and are transformed into z-scores, which are then subsequently subjected to local false discovery rate (locfdr) estimation¹⁸. (d) The joint posteriors for the recurrent interactions given the z-scores are inferred by empirical Bayes using the probabilistic quantities obtained from the locfdr procedure above.

Inline graphic — (a) Suppose target RNA expression (y_i_,t,d) in sample t of cancer type d is a function of DNA methylation (), copy number () and miRNA regulation (). (b) The expression change across samples for the target RNA is modelled as the response variable in a multivariate linear regression framework using the input variables as indicted above. (c) The resulting linear coefficient indicate the corresponding interaction between miRNA k and target gene i of cancer type d and are transformed into z-scores, which are then subsequently subjected to local false discovery rate (locfdr) estimation¹⁸. (d) The joint posteriors for the recurrent interactions given the z-scores are inferred by empirical Bayes using the probabilistic quantities obtained from the locfdr procedure above.