This repo records the notebooks that are used in the off-cloud analysis of the targeted APA study of TCGA samples.

Notebooks that related to result reproduction are mostly in analysis-notebooks. For details, please see the README.md inside each folder.

Citation

<a href=https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4903-7 , target=_blank> Xue Z, Warren RL, Gibb EA, MacMillan D, Wong J, Chiu R, et al. Recurrent tumor-specific regulation of alternative polyadenylation of cancer-related genes. BMC Genomics. 2018;19:536

Run codes

Most of the codes are run in jupyter notebooks format. Most of the packages used are listed in eda_import.py. Some extra include pysam, etc.

To get started, you may not need all the packages. I used Miniconda to manage python environments. Try getting start with

conda create -p venv -y python=3 pandas jupyter matplotlib scikit-learn
source activate venv

To run jupyter notebook server

jupyter notebook --no-browser --ip 0.0.0.0

Data

The data used as input to the off-cloud analysis are produced by on-cloud run with tasrkleat. All (i.e. 10K-TCGA-sample run) are generated with tasrkleat-v0.1 except the benchmark part which used tasrkleat-0.1.1 with adjustment in the reads2genome alignment step. The reads2genome alignment step doesn't affect the results of KLEAT, but may have an effect for the other tools.

Q&A

If you have any question, please ask by opening a new issue.