Expression Angler

If this program proves useful to your research, please cite Wilkins et al. (2009). Thank you!

This program will calculate the correlation coefficients for expression for all gene expression vectors as compared to the one for the Poplar probeset number you enter below. A file will be created containing the expression data and the correlation coefficients that meet your cut-off criterion, or specified number of hits. The data sets used by this tool come from the Bio-Array Resource. Poplar gene expression data for ~61413 Populus balsamifera genes generated using the Affymetrix GeneChip Poplar Genome Array. Click here for instructions in a new window

Click here for instructions in a new window

• Based on diverse Perl modules. Runs locally on the BAR.
• Simply enter the Poplar probeset ID that you are interested in into the box below,
• and if desired different Pearson correlation coefficient (r-value) cutoffs. Click here for info on the Pearson correlation coefficient in a new window, or see the bottom of the page

Click here for info on the Pearson correlation coefficient in a new window, or see the bottom of the page

• Then click Submit and wait about 1 minute.

Calculate Pearson Correlation Coefficient of each expression vector to the one belonging to:
(enter Poplar probeset ID, e.g. PtpAffx.222760.1.S1_x_at)

Select an r-value cutoff range:

Or, regardless of r-value just return:

the top	25, 50 or 100 hits
or bottom	25, 50 or 100 hits (potentially anti-correlated)

Choose the number of best (or worst) correlated genes you'd like to see or optionally enter a lower and upper r-value. The highest the r-value can be is 1, and that means that two vectors are a perfect match. Zero is no match, and -1 is a perfect anti-correlation, i.e. the expression response is exactly opposite to that of your gene of interest. The default lower r-value cutoff is 0.75, and the default upper r-value cutoff is 1.00. A tighter r-value range will result in smaller output files.

Search in

or upload your own file to angle in:
See an example file here. Click here for instructions in a new window

If you want to select various experiments in any of these data sets in which to angle, or to design your own bait, go to the Subselect and Custom Bait Page . To use subselection feature with your own data set, perform one round of angling from this page - upload your own file, of course, and be sure to note its identifier on the output page - then go to then Subselect and Custom Bait Page. We will store you data file, with an anonymous identifier, for one day only.

Calculation of Pearson correlation coefficient, r, for two gene expression vectors (series of values over a given number of samples):

Note that the Pearson correlation coefficient effectively normalizes the magnitude of the expression vector. Thus the Expression Angler program will identify those genes which respond in a similar manner. That is, genes which have a relatively moderate expression pattern in, say, Sample 1 and a high relative expression level in Sample 2, and a low relative expression level in Sample 3, and so on, will be identified as similar (will be scored with a higher r-value), even if the expression levels are dramatically different. For the two expression vectors for Genes X and Y above, the Pearson correlation coefficient is one, that is, the genes are responding identically! You may wish to manually examine the expression levels on the output...