Welcome to brooklyn’s documentation!

Brooklyn

Brooklyn plot for single cell/nuclei RNA sequencing data. This package enables a Pearson pairwise (ie., many to many) gene comparison and outputs co-expression patterns of genes across the genome. It generates a Brooklyn plot for visualization. More about this in the documentation below.

Links

• Documentation

• Source code

• Report an issue

• Project page on PyPI

Citation

• Installation
  • Welcome to installation protocol of Brooklyn
• User guide
  • Parameters
  • CLI - Example usage
  • How to build required input files, such as gene list, h5ad and biomart annotations?
  • Need assistance/have issues:
  • Resources
• Tutorial notebook - TTN
  • Import the original .h5ad file here. Chose the appropriate file location.
  • Obtain the observations (obs) list of the h5ad file.
  • Determine all of the cell types available in the .h5ad file. Determining the options in any obs can be done as similar as for cell_type.
  • Obtain a single cell type from the h5ad file from a single ‘obs’. Or use the option below to add a single cell type bases on multiple delimiters.
  • Establish the shape of the subset matrix and compare it to the original matrix. The onecell_h5ad have fewer rows.
  • Create the subsetted h5ad file with the specific cell type - delimited by cell type and any other qualifiers needed.
  • Dataset is a variable obtained from the biomart annotation needed for the Brooklyn plot.
  • Converting raw expression count data and converting to a numpy array.
  • This demonstrates what the array values look like.
  • This indicates how many genes are in the array (length), which can be important for generating the Brooklyn plot. Ideally, the original h5ad array does not limit the number of genes from the original sequencing.
  • This code generates mean values for each gene (ENSG ID), from raw expression data, and appends this to gene annotations from biomart.
  • This resets the index and adds a name for the first column (ENSG ID).
  • This command exports a csv file that can be used to pick (automated or manually), genes spread across the whole genome for Brooklyn plots.
  • This part of the code removes the top 3,500 genes by raw xMean, then sorts by chromosome position and generates gene lists of all 3,500 genes and 350 interspersed genes to generate the Brooklyn plot needed for the next python step.
• MIT License