Phenotyping/Image Analysis
Image analysis is a relatively young sub-field in computational biology but has become an increasingly topic of interest with the arrival of high-throughput microscopy data. We are particulary interested in such data as 'high-resolution gene expression data', in order to obtain and utilize information on the spatiotemporal expression of genes.
1. High throughput extraction and comparison of gene expression patterns from in situ hybridization images.
Daniel L. Mace, Nicole Varnardo, Weiping Zhang, Erwin Frise, and Uwe Ohler
Link to Imaging code
This code includes all the source code (c++, matlab), required to run/reproduce all the results in this paper. See the README.txt in the compressed file for instructions
Link to Java Web Start Application
A preliminary Java Web Start application for comparing in situ hybridization images. Currently only runs under linux, using Java 1.6
(This has been run on 32 and 64 bit version of Ubuntu and Redhat. For 64 bit versions of linux it is necessary to download Java 1.6 update 12 or greater)
Tar file of 1231 significance images (This tar file is 987MB)
Tar file of 200 validation images (This tar file is 153MB)
2. Automatic annotation of spatial expression patterns via sparse Bayesian factor models.
Iulian Pruteanu-Malinici, Daniel L. Mace and Uwe Ohler
Download the manuscript here
Link to information about the dataset used throughout the paper
Link to data and the Matlab/Java code
This code includes all the source code (Matlab, Java), required to run/reproduce all the results in this paper. See the ReadMe.txt in the compressed file for more instructions.
3. Automated annotation of gene expression image sequences via nonparametric factor analysis and conditional random fields.
Iulian Pruteanu-Malinici, William H. Majoros and Uwe Ohler
Download the manuscript here
Link to information about the dataset used throughout the paper
Link to data and the Matlab code
This code includes all the source code (Matlab), required to run/reproduce all the results in this manuscript. See the ReadMe.txt in the compressed file for more instructions.
4. A microfluidic device for automated high throughput live imaging of gene expression.
Busch, W. Moore, B. T. Martsberger, B. Mace, D. L. Twigg, R. W. Jung, J. Pruteanu-Malinici, I. Kennedy, S. J. Fricke, G. K. Clark, R. L. Ohler, Uwe Benfey, P. N
We have developed an imaging pipeline that includes automated tracking of growing roots, automated image acquisition and gene expression quantification.
Download the pipeline here
5. Quantification of transcription factor expression from Arabidopsis images.
Mace DL, Lee JY, Twigg RW, Colinas J, Benfey PN and Ohler U.
This material is based upon work supported by the National Science Foundation under Grant No 0953184 (DBI CAREER award -- Advances in biological informatics).