While loading bigWig files path from ChIP-atlas using bindome.datasets.chipatlas, a normalization constant has to be applied to loaded bigWig files before generating the final object, based on the formula described here formula.
https://github.com/inutano/chip-atlas/issues/84
Tasks
[ ] Using requests / bs4 / soup snippets in starpy/notebooks/bam, add a function to bindome.datasets.chipatlas that adjusts the counts into integers, using the factors indicated on the website e.g. def _convert_bw_to_raw_counts
[ ] Implement a method called get_bigWig, that similar to bindome.datasets.chipatlas downloads (if not local) and retrieves the bigWig file that is requested. A parm adjust would convert the counts from floating point to integer, based on the formula indicated in the chipatlas issues adjust_counts=True.
This notebooks indicates logic for extracting the n_reads total and the fraction of duplicates, based on the suggestion from ChIP-atlas. However, when verifying the estimated total number of counts, the fraction seems to be ~20-30x higher than expected, due to a factor that has to be inspected e.g. 1e6, or million of mapped reads.
https://github.com/theislab/starpy/blob/main/notebooks/bam/00_load_bam.ipynb
Description
While loading bigWig files path from ChIP-atlas using
bindome.datasets.chipatlas
, a normalization constant has to be applied to loaded bigWig files before generating the final object, based on the formula described here formula. https://github.com/inutano/chip-atlas/issues/84Tasks
def _convert_bw_to_raw_counts
get_bigWig
, that similar tobindome.datasets.chipatlas
downloads (if not local) and retrieves the bigWig file that is requested. A parmadjust
would convert the counts from floating point to integer, based on the formula indicated in the chipatlas issuesadjust_counts=True
.