GV Exercise.8
Find genes which expression correlates (anti-) with one given marker
[ Main_Page | Genevestigator_training | Analyze_public_microarray_data_using_Genevestigator | GV Exercise.7 ]
last edit: October 31, 2014
Contents
Aim
Find probes that show expression profiles correlated with that of a given probe (gene). Probes showing similar expression profiles across conditions could be co-regulated and therefore be part of a shared biological process. The principle of guilt by association is often used to discover genes related by some biological function or process.
Find other genes similar in expression to one of the ABA-1h probe from the former exercise (ASG4 259432_at aka AT1G01520)
In the context of ALL Ath experiments
- Use the ABA signature list and set the Sample selection back to Ath.All and uncheck all probes above 259432_at to have this probe first checked in the list
try it first
- note that hovering onto the probe shows more information
try it first
- start the coexpression tool and search for 25 correlated probes in the perturbation context
try it first
- run the tool for positive correlations
try it first
In the context of the AT-00110 experiment
To improve correlation results, we need to focus on conditions that are more related to each other. In this example, we choose the easy way by taking the experiment than was used to build the signature, In real cases, you will chose samples based on other GV result as seen in former exercises.
- switch to the AT-00110 sample group and run the tool for positive correlations
try it first
- run the tool for negative correlations
try it first
Continue the analysis outside of Genevestigator
- export this data to table in order to use it elsewhere
positive correlations
- run the tool with anti-correlations to discover inverse profiles and export the data in excel format
negative correlations
- perform functional enrichment of the two lists in Plaza, Plant GSEA, or any tools that accepts gene lists (and/or correlation coefficients). To do so, please proceed as shown in GV_Exercise.4#Perform_GSEA_with_the_Plant_GSEA_web-portal.
There seems to be a possibility to transform Pearson correlation coefficients into p-values [1]. The problem is to define the sample size - all genome! or sample size itself (25)
Download the exercise files
Try it by yourself before expanding on the right!
References:
- ↑ Soper, D.S. (2014). p-Value Calculator for Correlation Coefficients [Software]. Available from http://www.danielsoper.com/statcalc
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