4D-BioReconX: a bioinformatic framework for reconstructing 4D spatial transcriptomics atlas and spatiotemporal analyses+
Installation - Quick start - Documentation
4D-BioReconX is a scalable and versatile framework designed to construct a comprehensive four-dimensional (4D) spatial transcriptomics atlas of whole animals at single-cell resolution. This approach enables us to track the intricate spatiotemporal dependencies of morphogenetic gradients and regenerative patterning. It runs based on spatial transcriptomics data, such as Stereo-seq (spatial enhanced resolution omics sequencing) data. Notably, we are still working on the improvement of performance and calculation efficiency.
Please use GitHub issues tracker for reports and discussions of:
- Bug reports
- Document and data issues
- Feature requirements
- ...
4D-BioReconX is in a stage of rapid development so that we will carefully consider all aspects of your proposal. We hope future input will be given by both users and developers.
**To enhance clarity and prevent any potential confusion, we have organized this GitHub repository into a structured file tree, complete with detailed annotation for each code segment, tailored to their respective purposes.
.
├── LICENSE # MIT License
├── OtherModules # Independent analysis modules for specifc requirements. Please see the README in each folder.
│ ├── README.md # General README for OtherModules.
│ ├── blastemadetection # Blastema region detection.
│ │ ├── BlastemaByWound_v2.py # Python script for blastemadetection.
│ │ └── README.md # README for blastemadetection.
│ └── bodystrengthening # Body straightening.
│ ├── README.md # README for bodystrengthening.
│ ├── adjust_APDV_example.ipynb # Jupyter Notebook for bodystrengthening along AP-DV plane.
│ └── adjust_APML_example.ipynb # Jupyter Notebook for bodystrengthening along AP-ML plane.
├── PolarityAnalysis # To investigate spatial patterns of gene expression capture positional gradients, we conducted a qualitative analysis of positional control genes (PCGs) spanning the planarian body axis.
│ ├── README.md # General README for PolarityAnalysis.
│ ├── newpcgmining # Find genes that could be PCGs by calculating and evaluating correlation coefficient values between known PCGs (from published studies) and target genes.
│ │ ├── filter_gene.py # Python script for filtering genes.
│ │ ├── kl_simple.py # Python script for searching new PCGs by KL divergence.
│ │ ├── pearson_simple.py # Python script for searching new PCGs by Pearson correlation coefficient.
│ │ └── spearman_simple.py # Python script for searching new PCGs by Spearman rank correlation coefficient.
│ ├── patternclustering # perform clustering via hdbscan and LogicRegression.
│ │ ├── cluster_change.py # Python script for detecting cluster change with time.
│ │ ├── clustering.py # Python script for hdbscan clustering.
│ │ ├── draw.heatmap.py # Python script for drawing heatmap.
│ │ ├── draw.line.py # Python script for drawing line.
│ │ ├── gen_data.sh # Shell script for preparing data.
│ │ ├── pcg_pattern.py # Python script for computing pattern.
│ │ ├── plot_heatmap.py # Python script for plotting heatmap.
│ │ ├── run_pcgs_oneindv.sh # Shell script for running task individually.
│ │ └── sort_hcluster.py # Python script for sorting clusters.
│ ├── pca # PCG data in PCA space
│ │ ├── pca.py # Python script for pca.
│ │ └── plot_pca.py # Python script for plotting trace in PCA space.
│ ├── pcgprofiling # Gene spatial profiling.
│ │ ├── AP_Profiling_HVG_log_mean.py # Python script for spatial profiling of HVGs along A/P axis.
│ │ └── README.md # README for pcgprofiling.
│ └── util.py # Python script for utility.
├── README.md # General README for 4D-BioReconX.
├── SPCAnalysis # On the basic assumption that a functional unit must be comprised of a group of cells rather than a single cell, and in the case of neoblast cells in planarian dispersedly distributed in space, we used a functional-spatial-proximity-based strategy to define hubs for cells in similar states or with similar functions.
│ ├── README.md # README for SPCAnalysis.
│ ├── __init__.py # Python script for initialization.
│ ├── cell-cellcolocalization # Calculate cell-cell colocation array by KL in 3D coordinate and then generate its MST.
│ │ ├── CellColocation.py # Python script for cell-cellcolocalization.
│ │ └── README.md # README for cell-cellcolocalization.
│ ├── digitallineagetracing # Lineage similarity calculation.
│ │ ├── README.md # README for digitallineagetracing.
│ │ ├── s01.CellCor.py # Python script for calulating the simularity array for each pair of cells between thoes two datasets and saving the closest cell in another dataset for every cells.
│ │ ├── s02.get_number_anno.py # Python script for calculating the mapping number of cells for all clusters.
│ │ ├── s03.filter.py # Python script for filtering weak connections.
│ │ └── s04.Sankey_plot.ipynb # Jupyter Notebook for plotting the final sankey plot.
│ ├── meta-nicheanalysis # Calculating surrounding cell component.
│ │ ├── calculate-niches-on-coords.ipynb # Jupyter Notebook for meta-nicheanalysis.
│ │ ├── demo.txt # Demo data.
│ │ └── niche.py # Python script for calculating niches.
│ ├── spc-basedccc # SPC-based cell-cell communication analysis.
│ │ ├── CellChatDB.human.interaction.planarian.txt # Modified ligand-receptor database from CellChatDB.
│ │ ├── NICHES_3D.R # R package for NICHES analysis.
│ │ └── single_cell_cross_talk.R # R package for CellChat analysis.
│ └── spcclustering # Spatial Proximity based Clustering.
│ ├── README.md # README for spcclustering.
│ └── cell2spc.py # Python script for spcclustering.
├── WACCA #
│ ├── 3dmeshreconstruction # Mesh generating pipeline.
│ │ ├── README.md # README for 3dmeshreconstruction.
│ │ ├── gen_stacked_tif.py # Python script for generating stacked slices.
│ │ └── reset_obj.py # Python script for resetting object.
│ ├── README.md # General README for WACCA
│ ├── cellsegmentation # Cell segmentation based on ssDNA.
│ │ ├── __pycache__ # pycache files.
│ │ │ ├── objseg.cpython-311.pyc
│ │ │ ├── objseg.cpython-312.pyc
│ │ │ └── objseg.cpython-38.pyc
│ │ ├── blur_detect.py # Python script for blurred region detection.
│ │ ├── cell-segmente-on-ssDNA.ipynb # Jupyter Notebook for cellsegmentation.
│ │ ├── cellsegment.py # Python script for cellsegmentation.
│ │ ├── clahe_enhance.py # Python script for enhancing image contrast by CLAHE.
│ │ ├── default.cppipe # CellProfiler script.
│ │ ├── demo.tif # Demo figure.
│ │ ├── objseg.py # Python script for image processing such as coloring.
│ │ └── segarr.py # Python script for image masking.
│ ├── datapreprocessing # Data preprocessing.
│ │ ├── GEM3D_toolkit.py # Python script for main entries.
│ │ ├── README.md # README for datapreprocessing.
│ │ ├── affine_gem.py # Python script for affine transformation for GEM files.
│ │ ├── affine_h5ad.py # Python script for affine transformation for H5AD files.
│ │ ├── affine_ssdna.py # Python script for affine transformation for ssDNA image files.
│ │ ├── affine_txt.py # Python script for affine transformation for txt files.
│ │ ├── apply_cells.py # Python script for applying transformation to cells.
│ │ ├── apply_registration.py # Python script for applying registration.
│ │ ├── chop_gem.py # Python script for chopping GEM files.
│ │ ├── chop_image.py # Python script for chopping images.
│ │ ├── chop_paste.py # Python script for chopping and pasting.
│ │ ├── draw_heatmap.py # Python script for drawing heatmap.
│ │ ├── gem_to_h5ad.py # Python script for converting GEM to H5AD.
│ │ ├── gem_xy.py # Python script for GEM files.
│ │ ├── h5ad_dataframe.py # Python script for H5AD files.
│ │ ├── image_blend.py # Python script for blending images.
│ │ ├── mask_gem.py # Python script for masking GEM files.
│ │ ├── mask_h5ad.py # Python script for masking H5AD files.
│ │ ├── merge_h5ad.py # Python script for merging H5AD files.
│ │ ├── save_miscdf.py # Python script for saving checking.
│ │ ├── slice_dataframe.py # Python script for converting dataframe.
│ │ ├── split_gem.py # Python script for spliting GEM files.
│ │ └── trakEM2_to_affine.py # Python script for alignment using trakEM2.
│ ├── mirrorregistration # the mirro pipeline aim to registrate ssDNA image to GEM coordinate system with minimum registration error.
│ │ ├── MIRROR.py # Python script for mirrorregistration.
│ │ ├── README.md # README for mirrorregistration.
│ │ ├── gem_to_gemc.py # Python script for converting GEM to GEMC.
│ │ ├── gemc_to_h5ad.py # Python script for converting GEMC to H5AD.
│ │ ├── prepare_registration_heatmap.py # Python script for preparing gene heatmap for registration.
│ │ ├── prepare_registration_ssdna.py # Python script for preparing ssDNA images for registration.
│ │ ├── second_registration.py # Python script for second-round registration.
│ │ └── slice_dataframe.py # Python script for converting dataframe.
│ └── seamalignment # seam pipeline aims to align and merge serial slices (each slice in an H5AD file) into one 3D atlas (one H5AD file with aligned 3D.
│ ├── README.md # README for seamalignment.
│ ├── SEAM.py # Python script for seamalignment.
│ ├── apply_alignment.py # Python script for applying alignment to all coordinates.
│ ├── get_xml_matrix.py # Python script for obtaining transformation matrix.
│ └── prepare_alignment_image.py # Python script for preparing images for 3D alignment.
├── docs # Files for readthedocs.
│ ├── Makefile
│ ├── make.bat
│ ├── requirements.txt
│ └── source
│ ├── Tutorials
│ │ ├── BIO.rst
│ │ ├── CCC.rst
│ │ ├── CellCellColocation3D.rst
│ │ ├── Meta-Niche.rst
│ │ ├── Other.rst
│ │ ├── PCG.rst
│ │ ├── Preprocess.rst
│ │ ├── SPC.rst
│ │ ├── adjust_APDV_example.ipynb
│ │ ├── adjust_APML_example.ipynb
│ │ ├── assign_blastema_region.rst
│ │ ├── body_straightening.rst
│ │ ├── calculate-niches-on-coords.ipynb
│ │ ├── cell-segmentation.rst
│ │ ├── cell-segmente-on-ssDNA.ipynb
│ │ ├── clustering.rst
│ │ ├── data-preprocess.rst
│ │ ├── digital-lineage-s1-s3.rst
│ │ ├── digital-lineage-s4.rst
│ │ ├── digital-lineage.rst
│ │ ├── gene_profiling.rst
│ │ ├── index.rst
│ │ ├── meshgen.rst
│ │ ├── mining.rst
│ │ ├── mirror.rst
│ │ ├── pca.rst
│ │ ├── s04.Sankey_plot.ipynb
│ │ └── seam.rst
│ ├── _static
│ │ ├── 4D-BioReconX_workflow_v1.0.0.jpg
│ │ ├── assign_blastema_region_2d.png
│ │ ├── assign_blastema_region_aligned.png
│ │ ├── assign_blastema_region_final.png
│ │ ├── assign_blastema_region_raw.png
│ │ ├── assign_blastema_region_workflow.png
│ │ ├── fig.png
│ │ ├── gene_profiling_workflow.png
│ │ ├── meshgen_workflow.png
│ │ ├── mirror_ipo.png
│ │ ├── mirror_workflow.png
│ │ ├── sankey-01.png
│ │ ├── sankey-02.png
│ │ ├── seam_ipo.png
│ │ ├── seam_workflow.png
│ │ ├── spc_annotation.png
│ │ ├── spc_compare.jpg
│ │ ├── spc_leiden.png
│ │ └── spc_leiden.spc.png
│ ├── conf.py
│ ├── content
│ │ ├── 00_Installation.rst
│ │ ├── 01_Basic_Usage.rst
│ │ └── 03_References.rst
│ └── index.rst
├── planarian manuscript # Detailed usage of this framework in the planarian manuscript.
│ └── mappingtoplanarian.jpg # Mapping relations.
├── pyproject.toml # Readthedocs building.
└── requirements.txt # Installation requirements.