No BSD License  

Highlights from
Quantitative High-Throughput Gene Expression Imaging

5.0

5.0 | 1 rating Rate this file 24 Downloads (last 30 days) File Size: 7.18 MB File ID: #13684
image thumbnail

Quantitative High-Throughput Gene Expression Imaging

by

 

17 Jan 2007 (Updated )

Image Processing for Quantitative Gene Expression Analysis of Drosophila Embryo Images

Editor's Notes:

This file was selected as MATLAB Central Pick of the Week

| Watch this File

File Information
Description

The collection includes several files.

1. The script flyexdemo.m uses the Image Processing and Curve Fitting Toolboxes to process an image of a Drosophila Embryo, stained with fluorescently tagged antibodies that bind to the products of an individual gene. The genes whose products are stained in these images are related to the processes that begin to segment the fly embryo into different sections.

2. The folder html contains a published HTML version of flyexdemo.m.

3. The file flyproc.m is version of the algorithm in flexdemo.m. All graphical output has been removed, and the script has been converted to a function that accepts as input the filename of a Drosophila image, and returns a rotated, cropped image, along with curves fit to the red, green and blue channels. flyproc.m is called by localflyexdemo.m and dctflyexdemo.m.

4. The files localflyexdemo.m and dctflyexdemo.m are scripts that batch-process a number of images using flyproc.m. localflyexdemo.m carries this out on the local computer, dctflyexdemo.m carries out the same operation on a cluster using the Distributed Computing Toolbox. It is instructive to compare the structure of these files to see how to simply convert a batch script into a distributed batch script.

5. The directory reportgen contains a .rpt file that can be used with the MATLAB Report Generator to process the images on a cluster, and automatically generate a PDF report of the results.

All images courtesy of the FlyEx Database, http://flyex.ams.sunysb.edu/flyex and http://urchin.spbcas.ru/flyex. Used by permission.

References:

Poustelnikova E, Pisarev A, Blagov M, Samsonova M, Reinitz J (2004). A database for management of gene expression data _in situ_. _Bioinformatics_ 20:2212-2221. Available from http://flyex.ams.sunysb.edu/FlyEx/Flyex_bioinf.pdf

Myasnikova E, Samsonova M, Kosman D, Reinitz J (2005). Removal of background signal from _in situ_ data on the expression of segmentation genes in _Drosophila_. _Development, Genes and Evolution_ 215(6):320-326.

Myasnikova E, Samsonova A, Samsonova M, Reinitz J (2002). Support vector regression applied to the determination of the developmental age of a _Drosophila_ embryo from its segmentation gene expression patterns.
 _Bioinformatics_ 18:S87-S95.

Myasnikova E, Samsonova A, Kozlov K, Samsonova M, Reinitz J (2001). Registration of the expression patterns of _Drosophila_ segmentation genes by two independent methods. _Bioinformatics_ 17(1):3-12.

Myasnikova E, Kosman D, Reinitz J, Samsonova M (1999). Spatio-temporal registration of the expression patterns of _Drosophila_ segmentation genes. _Seventh International Conference on Intelligent Systems for Molecular Biology_ 195 - 201. Menlo Park, California: AAAI Press.

Kosman D, Reinitz J, Sharp DH (1999). Automated assay of gene expression at cellular resolution. In Altman R, Dunker K, Hunter L, Klein T (_eds._), _Proceedings of the 1998 Pacific Symposium on Biocomputing_ 6-17.

Kosman D, Small S, Reinitz J (1998). Rapid preparation of a panel of polyclonal antibodies to _Drosophila_ segmentation proteins. _Dev Genes Evol_ 208:290 -294.

Required Products Curve Fitting Toolbox
Image Processing Toolbox
MATLAB Report Generator
Parallel Computing Toolbox
MATLAB release MATLAB 7.3 (R2006b)
Tags for This File   Please login to tag files.
Please login to add a comment or rating.
Comments and Ratings (4)
17 Feb 2009 Brett Shoelson

I've used Sam's demo to present high-throughput screening in MATLAB, and am a big fan. This is an extremely well-thought-out demo that describes in great detail a very nice image-processing and curve-fitting workflow. It includes discussions of image exploration and algorithm development, stepping through to automation and batch processing, and ending with a nice implementation of the code in a distributed (cluster) environment. The subject matter is real and relevant. I highly recommend this to anyone who wants to improve his or her understanding of how MATLAB can help streamline an otherwise tedious image processing workflow.

03 Jun 2007 ash m

image processing with matlab

03 Jun 2007 ashkan masomi

matlab+gpr

22 Apr 2007 ramin chagini

image processing with matlab

Contact us