BayesTME¶

A unified statistical framework for spatial transcriptomics.

This package implements BayesTME, a fully Bayesian method for analyzing ST data without needing single-cell RNA-seq (scRNA) reference data.

Contents¶

  • Installation
    • Option 1 (Recommended): Using Docker
    • Option 2: Install Using pip
  • Overview
  • Nextflow Pipeline
  • Output
  • Example Plots
    • cell_num_scatterpie
    • cell_type_counts
    • cell_type_probabilities
    • marker_genes
    • rank_genes_groups
    • stp program
    • stp top genes
  • Data Format
    • Input Format
    • Output Format
  • Reference scRNA
  • Command Line Interface
    • load_spaceranger
    • filter_genes
    • bleeding_correction
    • phenotype_selection
    • deconvolve
    • select_marker_genes
    • spatial_transcriptional_programs
    • Plotting
    • plot_bleeding_correction
    • plot_deconvolution
  • bayestme package
    • Submodules
    • bayestme.bleeding_correction module
    • bayestme.cv_likelihoods module
    • bayestme.data module
    • bayestme.deconvolution module
    • bayestme.svi.deconvolution module
    • bayestme.expression_truth module
    • bayestme.gene_filtering module
    • bayestme.log_config module
    • bayestme.phenotype_selection module
    • bayestme.plot.common module
    • bayestme.plot.deconvolution module
    • bayestme.semi_synthetic_spatial module
    • bayestme.spatial_transcriptional_programs module
    • bayestme.synthetic_data module
    • bayestme.utils module
  • Github

Indices and tables¶

  • Index

  • Module Index

  • Search Page

bayestme

Navigation

  • Installation
  • Overview
  • Nextflow Pipeline
  • Output
  • Example Plots
  • Data Format
  • Reference scRNA
  • Command Line Interface
  • bayestme package
  • Github

Related Topics

  • Documentation overview
    • Next: Installation

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©2022, Haoran Zhang, Miranda V. Hunter, Jacqueline Chou, Jeffrey F. Quinn, Mingyuan Zhou, Richard White, Wesley Tansey. | Powered by Sphinx 7.4.7 & Alabaster 0.7.16 | Page source