1 / 2

Significance Curves

Significance Curves. Cluster Generation Calls. Ward Clustering (where X.csv contains peptide hit counts across ORFs/fractions for GMPA score significant ORFs for a given metal) X <- read.csv(“X.csv", head=TRUE, row.names=1) X[X>1]<-1 d <- dist(X) hc <- hclust(d, "ward")

asa
Download Presentation

Significance Curves

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. Significance Curves

  2. Cluster Generation Calls • Ward Clustering (where X.csv contains peptide hit counts across ORFs/fractions for GMPA score significant ORFs for a given metal) • X <- read.csv(“X.csv", head=TRUE, row.names=1) • X[X>1]<-1 • d <- dist(X) • hc <- hclust(d, "ward") • cutreeHybrid (through cutreeDynamic wrapper) • cutreeDynamic(hc, cutHeight = NULL, minClusterSize = 6, method = "hybrid", deepSplit = 3, pamStage = TRUE, distM = as.matrix(d), maxDistToLabel = 0, verbose = 0); Note: cutreeHybrid as called will in some cases return clusters or unlabeled groupings smaller than “minClusterSize” (e.g. see Co clustering) as explained in supplementary material to Langfelder et al.

More Related