def filter_columns(self, draw_hist=False, savefig=None, perc_zero=75, by_mean=True, silent=False): """ Call filtering function, to remove artefactual columns in a given Hi-C matrix. This function will detect columns with very low interaction counts; columns passing through a cell with no interaction in the diagonal; and columns with NaN values (in this case NaN will be replaced by zero in the original Hi-C data matrix). Filtered out columns will be stored in the dictionary Experiment._zeros. :param False draw_hist: shows the distribution of mean values by column the polynomial fit, and the cut applied. :param None savefig: path to a file where to save the image generated; if None, the image will be shown using matplotlib GUI (the extension of the file name will determine the desired format). :param 75 perc_zero: maximum percentage of cells with no interactions allowed. :param True by_mean: filter columns by mean column value using :func:`pytadbit.utils.hic_filtering.filter_by_mean` function """ self.bads = filter_by_zero_count(self, perc_zero, silent=silent) if by_mean: self.bads.update(filter_by_mean( self, draw_hist=draw_hist, silent=silent, savefig=savefig, bads=self.bads)) if not silent: print 'Found %d of %d columnswith poor signal' % (len(self.bads), len(self))
def filter_columns(self, draw_hist=False, savefig=None): """ Call filtering function, to remove artefactual columns in a given Hi-C matrix. This function will detect columns with very low interaction counts; columns passing through a cell with no interaction in the diagonal; and columns with NaN values (in this case NaN will be replaced by zero in the original Hi-C data matrix). Filtered out columns will be stored in the dictionary Experiment._zeros. :param False draw_hist: shows the distribution of mean values by column the polynomial fit, and the cut applied. :param None savefig: path to a file where to save the image generated; if None, the image will be shown using matplotlib GUI (the extension of the file name will determine the desired format). """ self.bads = filter_by_mean(self, draw_hist=draw_hist, savefig=savefig)
def filter_columns(self, draw_hist=False, savefig=None, perc_zero=75): """ Call filtering function, to remove artefactual columns in a given Hi-C matrix. This function will detect columns with very low interaction counts; columns passing through a cell with no interaction in the diagonal; and columns with NaN values (in this case NaN will be replaced by zero in the original Hi-C data matrix). Filtered out columns will be stored in the dictionary Experiment._zeros. :param False draw_hist: shows the distribution of mean values by column the polynomial fit, and the cut applied. :param None savefig: path to a file where to save the image generated; if None, the image will be shown using matplotlib GUI (the extension of the file name will determine the desired format). :param 75 perc_zero: maximum percentage of cells with no interactions allowed. """ self.bads = filter_by_zero_count(self, perc_zero, silent=False) self.bads.update(filter_by_mean(self, draw_hist=draw_hist, savefig=savefig, bads=self.bads))