1 / 23

Two methods to look for probes with differential expression in KO and WT uninvolved tissue

Two methods to look for probes with differential expression in KO and WT uninvolved tissue.

quail-porter
Download Presentation

Two methods to look for probes with differential expression in KO and WT uninvolved tissue

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Two methods to look for probes with differential expression in KO and WT uninvolved tissue • Statistical: DC, “distinction coefficient”; similar to t-test but includes sign (statistic is positive if “up”, negative if “down). It is also highly influenced by variability within the two sets being compared, so it will score highly genes with similar values within each of the conditions. (eg, A1 = A2, B1 = B2 will be scored highly even if the difference between A and B is relatively minor) • Profile searching: use the DC to select good “seed” profiles and then compute the similarity of every other profile to the average of the seeds. Since we are looking at the whole profile we will miss genes with differential expression and different expression in tumor and MCA-only.

  2. Arrangement for k-means clustering WT KO T Uninv MCA T T Uninv MCA

  3. Statistical method: Distinction coefficient • Compute DC for two groups: A = WT uninvolved tissue, B = KO uninvolved tissue. • DC values range from -50 to 60 (generally anything over 3 is interesting, however i tend to have 3-5 in each group) • Select profiles with either WT > KO in uninvolved tissue, or the reverse. • This does not consider expression in the other samples.

  4. DC-WT>KOuninvolved

  5. DC-upWTuninvolved

  6. DC-KO>WTuninvolved (n=38)

  7. DC-upKO

  8. Seeds used in profile search for KO>WT uninvolved

  9. Profile:KO>WTuninvolved (n=100)

  10. UKU

  11. Profile: anticorrelated (n=13)

  12. antiUKU

  13. Seeds used in profile search for WT>KO uninvolved

  14. Profile:WT>KOuninvolved (n=136)

  15. UWU

  16. Anticorrelated to WT>KO uninvolved (n = 16)

  17. Anti-UWU

  18. i’m interested in the degree to which IPA can find networks in “non-related” genes • Select 36 genes, four from each profile, that have different expression patterns in the data set we are looking at. • Run IPA, and find two networks with a dozen genes each. • Possibly this is a lower bound for interpreting IPA results, eg we should expect to get a network with a dozen genes no matter what the input; • Thus we’d look at the biology of the genes + network to determine what it means rather than on the simple fact that we see a network relating several genes from the input set.

  19. Centroid profiles: n=36, 4 from each cluster

  20. Expression of centroid profiles across all the data

  21. Ingenuity analysis of centroid probe sets.

  22. Possibly these genes are actually biologically related, so... • Open the annotation for the array on netaffx, and select the first 100 genes. • IPA analysis follows....

  23. IPA analysis of first 100 probe sets listed in mouse array on netaffx

More Related