Learning Principal Graphs with DDRTree

Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.

An R implementation of the DDRTree algorithm for learning principal graphs


DDRTree 0.0.0 Series NEWS

Version 0.1.5

BUGFIXES o Fixed an problem where DDRTre would return different results on repeated runs given the same inputs. The problem was actually in DDRTree in two places: kmeans and irlba. We now call irlba with deterministically initialized eigenvectors and kmeans with deterministically selected rows of the input.

Version 0.1.4

BUGFIXES o Fixed a build error triggered by recent versions of GCC using the C++14 standard

Reference manual

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0.1.5 by Xiaojie Qiu, 2 years ago

Browse source code at https://github.com/cran/DDRTree

Authors: Xiaojie Qiu , Cole Trapnell , Qi Mao , Li Wang

Documentation:   PDF Manual  

Artistic License 2.0 license

Imports Rcpp

Depends on irlba

Linking to Rcpp, RcppEigen, BH

System requirements: C++11

See at CRAN