Data and Statistical Code: Divergence of Human and Mouse

Brain Transcriptome Highlights Alzheimer Disease Pathways

Miller, J.A., Horvath, S., Geschwind, D.H.

Email Addresses

General questions:                 jeremymiller@ucla.edudhg@ucla.edu
Statistical correspondence:  shorvath@mednet.ucla.edu


ABSTRACT

This web site includes data and statistical code for the paper:

* JA Miller, S Horvath, DH Geschwind (2010) Divergence of human and mouse brain
transcriptome highlights Alzheimer disease pathways. PNAS. 2010 June 25; Epub ahead of print.

Link to paper: PNAS Webpage

Contents

Gene by eigengene tables

      Human network

      Mouse network

      Orthology conversion table for converting between mouse and human gene symbols

Real Data Analysis

      R-code and Statistical Analysis

             Code documents (zipped): contains word (recommended) and pdf versions.

      Download the following R function files, which contain several R functions needed for the analysis.

             Network R Functions

             Other necessary functions for meta-analysis

      Pre-processed microarray data sets

             Data sets (zipped): format of data explained in code documents

Other material regarding weighted gene co-expression network analysis

             Weighted Gene Co-Expression Network Page

             The weighted gene co-expression network analysis method is described in Theory Paper 1: Zhang and Horvath (2005)

             For a more mathematical description of weighted gene co-expression networks, see: Dong and Horvath (2007, 2008)

 


2010-03-23

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Please send your suggestions and comments to: shorvath@mednet.ucla.edu