CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq

System Requirements

CLEAR is built to run on any system with Anaconda Python (see: https://www.anaconda.com/download/) properly installed.

The following packages should additionally be installed:

• matplotlib
• numpy

Processing samples from Human genome requires approximately 1GB of RAM, but this can vary based on the size of the reference genome and complexity of the coverage map.

Installation

1. Ensure that python is properly installed and available on the system path.
2. Clone the CLEAR repository into a working folder for installation.
3. Retreive needed reference files in NCBI table format

• Example download locations:
  • NCBI RefSeq GRCh38: http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/ncbiRefSeq.txt.gz
  • NCBI RefSeq GRCm38: http://hgdownload.cse.ucsc.edu/goldenPath/mm10/database/ncbiRefSeq.txt.gz

4. Ensure that reference files are uncompressed.

Creating BED Coverage Maps

1. Use an external tool such as BedTools to create a .bed file of coverages from each aligned sample to be interrogated.

Running CLEAR

For each .bed file, do the following:

1. Run python make_dat.py [Name of reference file] [Name of the .bed file from above], which will generate a file ending in .dat with the mu callings.
2. Run python fitter.py [Name of dat file generated in (1)] which will print the passing transcripts to the terminal - This can be printed to a file using python fitter.py [Name of dat file generated in (1)] > file_name

Grouping Transcript Lists

For each transcript name file produced above, run the grouper.py command as follows: python grouper.py [name of file 1] [name of file 2] [name of...]

You can also add the --require-samples [#] parameter, where [#] is the number of samples a transcript must apper in to be included in the output. This can be used to relax the "passing in all samples" requirement used in the manuscript.

Wrapper script

See wrapper.sh for a complete wrapper for running the steps outlined above (bash wrapper.sh).

Generating Visualization Violin Plots

1. Open the folder with all previously-generated .dat files.
2. Run python make_violin_plots.py to create a file CLEAR_violins.pdf containing violin plots of all samples in the folder.

Example Case

An example case containing 6 cells' data from Zeisel et al. [1], allowing you to test your installation. To run, simply run cd example & bash run_example.sh. The expected results are in the result folder

[1] Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 2015 Mar 6;347(6226):1138-42. PMID: 25700174x