Support for non-H5 inputs

Greetings,

Am very excited to try this approach, but I can’t seem to be able to get our data into it. Our data comes from the InDrops method. I did go through the trouble of passing the data through Seurat/LOOM to generate .h5 files, which unfortunately does not seem compatible with CellBender (see ValueError: blocks must be 2-D error, below).

Is there any chance that you could introduce a more generic/accessible format that could be used as CellBender input? Ultimately, we all start with barcodes and genes. A sparse matrix would be convenient, for example.

Alternatively, if you know of a good way to load inDrops data into CellBender, then that would really make my day!

JP

cellbender:remove-background: Command:
cellbender remove-background --input data.KO_Gene_new.cells.h5ad --output output.h5 --cuda --expected-cells 500 --total-droplets-included 1000 --epochs 100
cellbender:remove-background: 2019-10-22 11:59:42
cellbender:remove-background: Running remove-background
cellbender:remove-background: Loading data from file data.KO_Gene_new.cells.h5ad
cellbender:remove-background: CellRanger v2 format
Traceback (most recent call last):
  File "C:\Users\c\AppData\Local\Continuum\miniconda3\envs\CellBender\Scripts\cellbender-script.py", line 11, in <module>
    load_entry_point('cellbender', 'console_scripts', 'cellbender')()
  File "c:\users\c\cellbender\cellbender\base_cli.py", line 101, in main
    cli_dict[args.tool].run(args)
  File "c:\users\c\cellbender\cellbender\remove_background\cli.py", line 92, in run
    main(args)
  File "c:\users\c\cellbender\cellbender\remove_background\cli.py", line 185, in main
    run_remove_background(args)
  File "c:\users\c\cellbender\cellbender\remove_background\cli.py", line 143, in run_remove_background
    args.low_count_threshold)
  File "c:\users\c\cellbender\cellbender\remove_background\data\dataset.py", line 82, in __init__
    self._load_data()
  File "c:\users\c\cellbender\cellbender\remove_background\data\dataset.py", line 125, in _load_data
    self.data = get_matrix_from_cellranger_h5(self.input_file)
  File "c:\users\c\cellbender\cellbender\remove_background\data\dataset.py", line 874, in get_matrix_from_cellranger_h5
    count_matrix = sp.vstack(csc_list, format='csc')
  File "C:\Users\c\AppData\Local\Continuum\miniconda3\envs\CellBender\lib\site-packages\scipy\sparse\construct.py", line 499, in vstack
    return bmat([[b] for b in blocks], format=format, dtype=dtype)
  File "C:\Users\c\AppData\Local\Continuum\miniconda3\envs\CellBender\lib\site-packages\scipy\sparse\construct.py", line 548, in bmat
    raise ValueError('blocks must be 2-D')
ValueError: blocks must be 2-D

[c5creative]

Thanks to everyone’s feedback!

@sjfleming - With regards to the inDrop data - in our case, we originally used the inDrops pipeline for the data processing/filtering. I can share some output with you (it’s large), but in short, it’s a tab-delimited file with barcodes as rows and genes/features as columns: Since one can provide mtx, I think that is sufficient enough for a generic means to load data.

@achamess - great tip, I went back and reprocessed with STARsolo and now am making progress with CellBender

[achamess]

Just chiming in. I also used STARsolo. I had to manually changes features.tsv to genes.tsv and then it works. Alternatively, one an use DropletUtils to make h5 files. Either way, there is an intermediate step from the outputs of STARsolo to Cellbender. Good tool, btw. Really cleans up my data.