Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Minor corrections #259

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Minor corrections #259

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions R/clusterSamples.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ colSds <- function(x, ...) {
# x matrix, each row is a sample.
# method only support "pearson"
# abs logical. If use absolute value for correlation to distance.
# diag logical. Inlcude diagnol value or not
# diag logical. Include diagonal value or not
# upper output upper panel or lower panel.
.dist.cor=function(x, method="pearson", abs=TRUE, diag=FALSE, upper=FALSE)
{
Expand Down Expand Up @@ -334,10 +334,10 @@ setMethod("clusterSamples", "methylBase",
#' to be plotted.
#' @param transpose if TRUE (default) percent methylation matrix will be
#' transposed, this is equivalent to doing PCA on variables that are
#' regions/bases. The resulting plot will location of samples in the new
#' coordinate system if FALSE the variables for the matrix will be samples
#' and the resulting plot whill show how each sample (variable)
#' contributes to the principle component.the samples that are highly
#' regions/bases. The resulting plot will locate samples in the new
#' coordinate system. If FALSE the variables for the matrix will be samples
#' and the resulting plot will show how each sample (variable)
#' contributes to the principle component. The samples that are highly
#' correlated should have similar contributions to the principal components.
#' @param sd.filter If \code{TRUE}, the bases/regions with low variation will
#' be discarded prior to PCA (default:TRUE)
Expand Down