Release date: 2023-06-20



  • Updated matplotlib dependency from <=3.2.2 to >=3.4.0



Release date: 2023-06-16



  • apply_filter changed to filter_variants in io.load()

  • SubcloneTree and SubcloneTreeGraph classes are renamed to Tree and TreeGraph respectively.

  • show_plot to return_plot in dna.Dna.group_by_genotype()

  • plots.heatmap.Heatmap splits the vertical and horizontal lines on the main heatmap into two traces.

  • The default value of vaf_het in dna.Dna.filter_variants() changed from 35 to 30.

  • Flattened sample.Sample.heatmap`() option has been removed. A more customizable version is available under the sample.Sample.signaturemap() function.

  • The constant - constants.COLORS to have unique values.

    • The grey values at the 10th, 20th, 30th.. positions were modified to be unique

    • The black (#000000) value was moved from the 20th position to the last position


  • Get indexes maintains the order as per find_list when there are duplicates in the find_list and order_using_find_list is True.

  • DANN score in the variants subclone table is shown correctly for saved h5 files.

  • Overlapping of text in phylogeny trees.

  • Error in multiprocessing when fetching gene_names for CNV by adding a max_workers parameter and using threads instead of processes.

  • Missing clone is ignored when finding ADO sisters.


  • Functions to convert legacy loom files to h5 files - io._loom_to_h5, io._update_file

  • Functions to read data from csv files - io._merge_files, io._cnv_raw_counts, io._protein_raw_counts

  • Function to merge h5 files - io._merge

  • show_plot from protein.Protein.normalize_reads(). The same plot can be created in plotly using algorithms.nsp.NSP.plot()

  • show_plot from protein.Protein.get_signal_profile(). The same plot can be created in plotly using algorithms.nsp.ExpressionProfile.plot()

  • protein.Protein.get_signal_profile function. It can be executed using if needed.

  • protein.Protein.get_scaling_factor function. It can be executed using algorithms.nsp.NSP.scaling_factor() if needed