Changes in version 0.1.0                        

Initial release of CellOracleR, a complete R implementation of the
CellOracle Python package for in silico gene perturbation analysis.

Core Features

Gene Regulatory Network Inference

  - Ridge regression with bootstrap aggregation (bagging)
  - Cluster-specific or whole-data GRN fitting
  - Configurable regularization strength

Perturbation Simulation

  - Signal propagation through GRN coefficient matrix
  - Support for knockouts, knockdowns, and overexpression
  - Iterative propagation with configurable depth

Cell State Transition Analysis

  - Transition probability estimation from expression correlation
  - Embedding-based velocity calculation
  - Grid-based flow visualization

Motif Analysis

  - Peak-to-gene association
  - TF binding site scanning via TFBSTools/motifmatchr
  - JASPAR motif database integration

Network Analysis

  - Network centrality metrics (degree, betweenness, eigenvector)
  - Hub gene identification
  - Cluster-specific network comparison

Visualization

  - ggplot2-based plotting functions
  - Vector field visualization
  - Expression change heatmaps

Technical Features

  - Seurat V4/V5 compatibility: Automatic detection and handling
  - C++ acceleration: Core computations via Rcpp/RcppArmadillo
  - Parallel computing: future framework for cross-platform
    parallelization
  - Cross-platform: Tested on macOS, Linux, and Windows

Dependencies

Required

  - R (>= 4.0.0)
  - Seurat (>= 4.0.0)
  - R6, Rcpp, RcppArmadillo
  - glmnet, igraph, ggplot2

Optional (for motif analysis)

  - TFBSTools, motifmatchr
  - BSgenome, JASPAR2020

References

Kamimoto K, et al. (2023). Dissecting cell identity via network
inference and in silico gene perturbation. Nature, 614(7949), 742-751.