Added RunMetabolism() for single-cell metabolism pathway scoring with support for AUCell, GSVA, ssGSEA, and optional VISION, and MetabolismPlot() for visualization
RunMetabolism() now uses scMetabolism KEGG / Reactome metabolism pathway definitions to identify metabolism-related terms, then rebuilds pathway gene sets from updated PrepareDB() annotations, enabling cached database updates and cross-species metabolism scoring. Related issue #146 (@mengxu98).
Added RunDecontX() and integrated optional decontX ambient RNA decontamination into RunCellQC(), including contamination metadata, optional decontaminated assay output, and threshold-based QC filtering. Related issue #147 (@mengxu98).
fix:
FeatureStatPlot(): Fixed duplicated X/Y axis titles and main title when stack = TRUE with theme_args or title. Stack assembly now strips axis and plot titles from non-edge panels and draws a single shared ylab/title via gtable; theme styling from theme_args (e.g. axis.title.y, title/plot.title) is applied to these shared grobs. Related issue #145 (@PanSX-Dr).
docs:
Updated the README badge/logo.
data:
Removed the example datasets ifnb_sub, ref_scHCL, and ref_scZCL.
ListDB(): Now supports multiple species in the species parameter simultaneously and adds Species and DB columns to the output data frame for clearer identification.
configure_apple_silicon_env() (Python function): Added OpenMP compatibility handling on macOS arm64 by prepending environment lib paths to DYLD_FALLBACK_LIBRARY_PATH/DYLD_LIBRARY_PATH and preloading libomp.dylib/libiomp5.dylib via ctypes.CDLL(..., RTLD_GLOBAL) to reduce scanpy/python-igraph import conflicts.
scVI_integrate(): Unified parameter naming by renaming num_threads to cores for consistency across integration functions.
Added find_neighbors_and_clusters() and run_nonlinear_reduction() helper functions to unify operations across integration functions and reduce redundant code.
TF database now uses AnimalTFDB4 as the data source.
MP database - the Web Archive URL year is now dynamically determined from the current date, and the archive snapshot date for all MP-related file downloads (VOC_MammalianPhenotype.rpt, MGI_Gene_Model_Coord.rpt, MGI_GenePheno.rpt) is extracted from the server index page to ensure consistent versioning.
hTFtarget data download URL has been changed from "http://bioinfo.life.hust.edu.cn/static/hTFtarget/file_download/tf-target-infomation.txt" to "https://guolab.wchscu.cn/static/hTFtarget/file_download/tf-target-infomation.txt".
CSPA data download URL has been changed from "https://wlab.ethz.ch/cspa/data/S1_File.xlsx" to "https://raw.githubusercontent.com/mengxu98/CSPA/main/S1_File.xlsx".
Updated argument naming/style from LIGER_dims_use to liger_dims_use, and removed legacy quantile-normalization parameter compatibility mapping (ref_dataset), keeping reference as the supported interface.
Optimized the installation of some Python packages on Apple Silicon devices.
docs:
Updated README example code and visualizations. After regenerating figures, the package size was reduced by ~3 MB.
scop 0.8.4
fix:
FeatureStatPlot() / ExpressionStatPlot(): Fixed box and violin x-axis misalignment when add_box = TRUE with split.by. Groups with fewer than 2 observations are now filtered before violin density estimation (with a warning), and the violin layer uses a consistent position_dodge(width = 0.9) to match the boxplot. Related issue #123 (@oranges7).
scop 0.8.3
feat:
RunDynamicFeatures(): Added PreTSA method for dynamic feature fitting. The PreTSA algorithm in scop has been re implemented to support parallelization for higher performance. Original research: PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns. Use fit_method = "pretsa" for B-spline-based piecewise truncated spline analysis; fit_method = "gam" (default) keeps generalized additive models. PreTSA supports knot (0 or "auto") and max_knot_allowed when knot = "auto". Relate issue #133.
CellDimPlot() and FeatureDimPlot(): Added legend.title parameter (default NULL) to control the legend title. When NULL, default titles are used (e.g. group name for CellDimPlot, feature or empty for FeatureDimPlot).
ExpressionStatPlot() and FeatureStatPlot(): Added legend.title parameter (default NULL) for single-legend plots. When NULL, the default title (e.g. keynm or feature/group name) is used. FeatureStatPlot() forwards legend.title to ExpressionStatPlot().
DynamicHeatmap() / heatmap_enrichment(): Fixed incorrect db handling when using custom TERM2GENE/TERM2NAME. Enrichment results with Database = "custom" could be incorrectly filtered by default db values (e.g. "GO_BP"), causing false “No term enriched using the threshold” warnings even when enrichment succeeded. Relate issue #133 (@1228849000).
scop 0.8.2
feat:
Add the DEtestPlot() function, which calls the original VolcanoPlot() and adds two plot types, Manhattan and Ring, controlled by the plot_type parameter (c("volcano", "manhattan", "ring"), default "volcano"). Add standalone functions DEtestManhattanPlot() and DEtestRingPlot() for direct use. Relate issue #121 (@ericavalentini).
Differential expression visualization (DEtestPlot(), VolcanoPlot(), DEtestManhattanPlot(), DEtestRingPlot()): added res parameter to accept existing DE results (data.frame). When res is provided, srt is ignored. Data processing supports: (1) group1 or cluster column for grouped plots; (2) no grouping column for a single panel; (3) gene names from row names when gene column is missing. Relate issue #129 (@mengxu98).
PrepareEnv(): Update version parameter to specify the Python version of the conda environment. Default is "3.11-1" on Windows and "3.10-1" on macOS and Unix. Relate issue #103 (@PanSX-Dr).
RunNMF(): Add the cores parameter for RunNMF() and optimize the printed message.
Removed the setting in Python functions that prevents drawing functions from causing R crashes.
fix:
RunPalantir(): Fixed unused plot_format parameter error. The parameter is now properly excluded from arguments passed to Python functions. Relate issue #126 (@christinejay990202-dev).
scop 0.8.1
fix:
FeatureHeatmap(): Fixed group_palcolor when a named vector is passed: the function previously used only the first color for all groups because group_palcolor[[1]] on a vector returns a single element. Now when group.by has length 1, a vector is automatically wrapped as a list; when within_groups = TRUE, group_palcolor is expanded in line with group_palette.
GroupHeatmap(): Same group_palcolor fix as FeatureHeatmap(): support for named-vector input and correct expansion when within_groups = TRUE.
RunMonocle2(): New function for performing Monocle2 trajectory analysis with support for various dimensionality reduction methods (DDRTree, ICA, tSNE, SimplePPT, L1-graph, SGL-tree). Uses the fixed version of monocle2 from mengxu98/monocle.
RunMonocle3(): New function for performing Monocle3 trajectory analysis with support for cell ordering, trajectory learning, and pseudotime computation.
RunCytoTRACE(): New function for running CytoTRACE 2 analysis to predict cellular potency scores and categories (Differentiated, Unipotent, Oligopotent, Multipotent, Pluripotent, Totipotent) with support for human and mouse species.
RunPalantir(): Fixed unused plot_format parameter error. The parameter is now properly excluded from arguments passed to Python functions. Relate issue #114 (@Moonerss).
RunSCVELO(): Fixed PAGA computation error by replacing scv.tl.paga with sc.tl.paga (scanpy implementation) for better stability. The function now uses the same PAGA implementation as RunPAGA() function.
RunCellRank(): Fixed GPCCA Schur decomposition error by adding fallback mechanism. When brandts method fails with “subspace_angles” error, the function automatically tries krylov method. If both methods fail, it automatically switches to CFLARE estimator for more robust computation.
RunCellRank(): Fixed recover_dynamics error by ensuring velocity_graph and velocity_graph_neg are properly set before calling scv.tl.recover_dynamics() for latent time computation.
feat:
adata_to_srt(): Removed automatic removal of “X_” prefix from dimensionality reduction names in obsm keys. The function now preserves original reduction names as they are stored in AnnData objects.
data:
Reducing the size of pancreas_sub example dataset.
scop 0.7.7
feat:
adata_to_srt(): Enhanced to support multiple AnnData object types including Python AnnData objects (from scanpy/reticulate), R6 AnnData objects from the anndata package (AnnDataR6), and R6 AnnData objects from the anndataR package (InMemoryAnnData). Added internal helper functions get_adata_element() and get_adata_names() for better compatibility. Relate issues #67 (@lisch7), #91 (@mengxu98) and commit91#issuecomment.
fix:
RunDEtest(): Fixed error when comparing one cluster against multiple clusters using group1 and group2 parameters. Relate issue #111 (@zhaoxiaoyan9225).
AnnotateFeatures(): Fixed bug where the function would fail when processing GTF file annotations due to column name matching issues during data naming. The function now correctly handles column name intersections when merging annotation data.
scop 0.7.6
feat:
RunDM(): Added automatic PCA-based dimensionality reduction when using many features (>1000) to speed up diffusion map computation. The npcs parameter can be used to control the number of principal components used for pre-processing.
RunDM(): Added automatic PCA-based dimensionality reduction when using many features (>1000) to speed up diffusion map computation. The npcs parameter can be used to control the number of principal components used for pre-processing.
scop 0.7.3
feat:
RunCellTypist(): New function for cell type annotation using the CellTypist method.
CellTypistModels(): New function for downloading and managing CellTypist pre-trained models.
scop 0.7.2
feat:
RunCellRank(): Performance optimizations and code improvements.
Added comprehensive environment variable configuration to prevent crashes when calling Python functions, including setting thread limits for OMP, OPENBLAS, MKL, NUMBA, and other libraries. This improves stability on all platforms, especially Apple silicon Macs.
Added accept_conda_tos() function to automatically accept conda Terms of Service for required channels, improving the conda environment setup process.
Fixed conda Terms of Service acceptance issue in PrepareEnv(). The function now automatically accepts conda Terms of Service for required channels, eliminating the need for manual acceptance. This addresses the issue reported in #85.
PrepareSCExplorer(): Fixed package version dependency issues with shiny and bslib compatibility. The function now properly handles bslib theme configuration to work with both shiny 1.6.0 and 1.7.0+, addressing compatibility errors reported in #87.
refactor:
Improved code formatting and consistency across multiple functions.
Enhanced Python functions in inst/python/functions.py with better error handling and message formatting.
docs:
Updated documentation for multiple functions to reflect code improvements.
scop 0.6.2
feat:
CellChatPlot(): Adjusted the size of saved figures for better file size optimization.
docs:
Updated README.md to remove references to Monocle2 and Monocle3 (deprecated functions).
scop 0.6.1
feat:
PrepareEnv(): Improved message formatting and simplified log output for better user experience.
Added get_conda_envs_dir() helper function to centralize conda environment directory retrieval.
integration_scop(): Enhanced integration_method parameter definition with explicit method list for better code clarity.
refactor:
Moved exist_python_pkgs() function to check_package.R for better code organization.
Replaced direct conda_info()$envs_dirs[1] calls with get_conda_envs_dir() helper function for consistency.
Updated documentation across multiple functions to reflect code refactoring.
Improved code organization and maintainability.
scop 0.5.5
fix:
Fixed VelocityPlot() function error in plot_type = "grid" mode: replaced vectorized arrow length with fixed-length arrows (using mean length) to resolve vapply() error that occurred when grid::arrow() received a vector instead of a single value, see #72, #74.
Fixed SingleCellExperiment object creation in RunScmap() and RunSingleR() functions: changed from coercing SummarizedExperiment to directly constructing SingleCellExperiment objects.
scop 0.5.3
feat:
PrepareDB(): Changed default Ensembl_version parameter from 103 to NULL for more flexible version handling.
Added Python version log_message() for Python-based functions (RunSCVELO(), RunPAGA(), RunPalantir(), RunCellRank(), RunWOT()) and added verbose parameter inheritance and improved message formatting using cli-style formatting.
refactor:
Delete harmonizomeapi.py file.
Move scop_analysis.py into a single functions.py file in inst/python/ for better code organization and maintainability.
docs:
Improved parameter documentation consistency.
scop 0.5.1
docs:
Improved reference formatting and consistency across multiple functions.
Enhanced documentation clarity and readability.
scop 0.5.0
feat:
RunCellChat(): New function to perform CellChat analysis for investigating cell-to-cell communication with support for human, mouse, and zebrafish species.
CellChatPlot(): New function to visualize CellChat analysis results with various plot types and customization options.
Multiple integration functions: Improved error messages and message formatting for better user experience.
deps:
Added CellChat package dependency with remote repository jinworks/CellChat.
docs:
Updated README.md with improved code formatting and examples.
Enhanced documentation for cell communication analysis functions.
Improved error messages and user guidance across integration functions.
refactor:
Removed some example figures to optimize package installation size.
scop 0.4.0
feat:
RunProportionTest(): New function to perform Monte-carlo permutation test for quantifying cell proportion differences between conditions.
ProportionTestPlot(): New function to generate proportion test plots with customizable significance thresholds and visualization options.
Multiple Python-based functions: add \dontrun{} blocks for Github workfolw checking.
docs:
Added comprehensive documentation for new proportion testing functions.
Enhanced example documentation across multiple functions.
Updated package documentation and examples.
scop 0.3.4
docs:
Updated workflow examples and function documentation.
scop 0.3.3
feat:
Multiple functions: Improved parameter documentation formatting and consistency across the package.
scop 0.3.2
feat:
GetFeaturesData() and AddFeaturesData(): Enhanced argument clarity, added input validation, and standardized return values for Seurat, Assay, and Assay5 objects.
Improved error message formatting using cli-style formatting.
Simplified variable assignments and improved readability.
docs:
Comprehensive documentation updates across multiple functions including AnnotateFeatures, CellDimPlot, CellStatPlot, FeatureStatPlot, GroupHeatmap, RunCellQC, and others.
Improved parameter descriptions and function clarity.
scop 0.3.1
feat:
EnrichmentPlot() and GSEAPlot(): Removed conditional font face styling (face = ifelse() logic) for better text rendering consistency. Set the default value of lineheight from 0.5 to 0.7.
Updated check_r() function for improved package checking functionality.
Updated reexports functionality.
docs:
Updated documentation formatting and consistency.
scop 0.3.0
feat:
Fixed segmentation faults and R crashes on M-series MacBook when running Python functions.
RunPAGA(): Enhanced with M-series MacBook detection and automatic environment configuration.
RunSCVELO(): Added ARM64-specific optimizations to prevent crashes and ensure stable execution.
RunCellRank(): Implemented M-series compatibility with proper NUMBA configuration.
RunPalantir(): Added ARM64 support with single-threaded execution mode.
RunWOT(): Enhanced with M-series MacBook environment variable settings.
RunTriMap(): Added M-series MacBook compatibility for dimensionality reduction.
Added remove_python() function for easy remove Python packages.
fix:
Corrected an issue in py_to_r2() function (intrinsic function), which ensures that Python-dependent functions like RunPAGA() and RunSCVELO() function run correctly.
scop 0.1.9
feat:
Update CellScoring() and AddModuleScore2() functions. Now, new parameters cores and verbose have been added.