Package: CellODE 1.0.0

CellODE: Cellular Dynamics Inference Using Neural ODE

An R implementation for single-cell trajectory inference using Variational Autoencoder (VAE) and Neural Ordinary Differential Equations (Neural ODE). CellODE automatically infers cellular dynamics from single-cell RNA sequencing data, providing pseudotime estimation, latent space representation, and vector field analysis. The package is designed for seamless integration with Seurat objects and supports both Seurat V4 and V5.

Authors:Zaoqu Liu [aut, cre]

CellODE_1.0.0.tar.gz
CellODE_1.0.0.zip(r-4.7)CellODE_1.0.0.zip(r-4.6)CellODE_1.0.0.zip(r-4.5)
CellODE_1.0.0.tgz(r-4.6-x86_64)CellODE_1.0.0.tgz(r-4.6-arm64)CellODE_1.0.0.tgz(r-4.5-x86_64)CellODE_1.0.0.tgz(r-4.5-arm64)
CellODE_1.0.0.tar.gz(r-4.7-arm64)CellODE_1.0.0.tar.gz(r-4.7-x86_64)CellODE_1.0.0.tar.gz(r-4.6-arm64)CellODE_1.0.0.tar.gz(r-4.6-x86_64)
CellODE_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CellODE/json (API)

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

Bug tracker:https://github.com/zaoqu-liu/cellode/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.30 score 4 scripts 23 exports 32 dependencies

Last updated from:d163c8cf85 (on main). Checks:8 NOTE, 2 OK, 3 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE237
linux-devel-x86_64NOTE254
source / vignettesOK295
linux-release-arm64NOTE221
linux-release-x86_64NOTE241
macos-release-arm64NOTE126
macos-release-x86_64NOTE379
macos-oldrel-arm64FAIL82
macos-oldrel-x86_64FAIL189
windows-develNOTE202
windows-releaseNOTE208
windows-oldrelFAIL104
wasm-releaseOK172

Exports:cosine_similarityextract_expressionl2_normload_modellog_nblog_zinbMakeDatasetnormal_klodeintplot_lossplot_pseudotimeplot_vector_fieldpredict_latentsppredict_ltsp_from_timepredict_timepredict_vector_fieldreverse_timesplit_datasplit_indexTNODETrainervector_field_embeddingvector_field_embedding_grid

Dependencies:bitbit64callrclicorocpp11descfarverggplot2gluegtableisobandjsonlitelabelinglatticelifecyclemagrittrMatrixprocessxpsR6RColorBrewerRcppRcppArmadillorlangS7safetensorsscalestorchvctrsviridisLitewithr

Advanced Usage and Best Practices

Zaoqu Liu

Rendered fromadvanced-usage.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2026-01-25
Started: 2026-01-25

Algorithm Theory: Neural ODE for Cellular Dynamics

Zaoqu Liu

Rendered fromalgorithm-theory.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2026-01-25
Started: 2026-01-25

Getting Started with CellODE

Zaoqu Liu

Rendered fromCellODE-quickstart.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2026-01-25
Started: 2026-01-25

Vector Field Analysis and Visualization

Zaoqu Liu

Rendered fromvector-field-analysis.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2026-01-25
Started: 2026-01-25