Package: treefit 1.0.3.9000

Kouhei Sutou

treefit: The First Software for Quantitative Trajectory Inference

Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.

Authors:Momoko Hayamizu [aut], Kouhei Sutou [aut, cre], Ryohei Suzuki [aut], Hiromi Ishii [aut]

treefit_1.0.3.9000.tar.gz
treefit_1.0.3.9000.zip(r-4.7-any)treefit_1.0.3.9000.zip(r-4.6-any)treefit_1.0.3.9000.zip(r-4.5-any)
treefit_1.0.3.9000.tgz(r-4.6-any)treefit_1.0.3.9000.tgz(r-4.5-any)
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treefit_1.0.3.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
treefit/json (API)

# Install 'treefit' in R:
install.packages('treefit', repos = c('https://hayamizu-lab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hayamizu-lab/treefit-r/issues

On CRAN:

Conda:

4.78 score 3 stars 1 scripts 271 downloads 6 exports 24 dependencies

Last updated from:462101ae5e. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develWARNING194
source / vignettesOK255
linux-releaseWARNING186
macos-releaseWARNING144
macos-oldrelWARNING180
windows-develWARNING105
windows-releaseWARNING86
windows-oldrelWARNING103
wasm-releaseOK155

Exports:generate_2d_n_arms_linked_star_datagenerate_2d_n_arms_star_datagenerate_n_arms_star_dataperturbate_knnperturbate_poissontreefit

Dependencies:clicpp11farverggplot2gluegtableigraphisobandlabelinglatticelifecyclemagrittrMatrixpatchworkpkgconfigpracmaR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Treefit - Working with Seurat
1. Why "quantitative" trajectory inference? | 2. Trajectory inference using Treefit and Seurat | 2.1. Loading the dataset | 2.2. Preprocessing with Seurat | 2.3. Visualization with Seurat | 3. Quantitative trajectory inference with Treefit | 3.1. Performing the Treefit analysis | 3.2. Interpreting the results
Rendered fromworking-with-seurat.Rmdusingknitr::rmarkdown

Last update: 2021-03-20
Started: 2020-02-25

Treefit - Getting started
1. Generating toy data | 1.1. Tree-like toy data | 1.2. Less tree-like, noisy toy data | 2. Analyzing the data | 2.1. Tree-like toy data | 2.2. Less tree-like, noisy toy data | 2.3. Comparing two results | 3. Interpreting the results | 3.1. Estimating the tree-likeness of data | 3.2. Predicting the number of principal paths in the underlying tree
Rendered fromtreefit.Rmdusingknitr::rmarkdown

Last update: 2020-03-04
Started: 2020-02-20