Clonal analysis

import cospar as cs

cs.logging.print_version()
cs.settings.verbosity = 2  # range: 0 (error),1 (warning),2 (info),3 (hint).
cs.settings.set_figure_params(
    format="png", figsize=[4, 3.5], dpi=75, fontsize=14, pointsize=3
)

Running cospar 0.2.0 (python 3.8.12) on 2022-01-30 14:45.

# Each dataset should have its folder to avoid conflicts.
cs.settings.data_path = "data_cospar"
cs.settings.figure_path = "fig_cospar"
cs.hf.set_up_folders()

Load an existing dataset. (If you have pre-processed data, you can load it with cs.hf.read(file_name).)

adata_orig = cs.datasets.hematopoiesis_subsampled()

Show barcode heatmap as aggregated into given fate clusters (defined in adata_orig.obs['state_info'])

selected_times = None
selected_fates = [
    "Ccr7_DC",
    "Mast",
    "Meg",
    "pDC",
    "Eos",
    "Lymphoid",
    "Erythroid",
    "Baso",
    "Neutrophil",
    "Monocyte",
]
celltype_names = ["mDC", "Mast", "Meg", "pDC", "Eos", "Ly", "Ery", "Baso", "Neu", "Mon"]
cs.pl.barcode_heatmap(
    adata_orig,
    selected_times=selected_times,
    selected_fates=selected_fates,
    color_bar=True,
    rename_fates=celltype_names,
    log_transform=False,
    binarize=True,
)

Data saved at adata.uns['barcode_heatmap']

<AxesSubplot:>

Fate coupling in the underlying clonal data, defined in our package as the normalized barcode covariance between cells annotated in different fates.

cs.tl.fate_coupling(
    adata_orig, selected_fates=selected_fates, source="X_clone"
)  # compute the fate coupling
cs.pl.fate_coupling(adata_orig, source="X_clone")  # actually plot the coupling

Results saved as dictionary at adata.uns['fate_coupling_X_clone']

<AxesSubplot:title={'center':'source: X_clone'}>

Fate hierarchy constructed from fate coupling of the underlying clonal data, using the neighbor-joining method.

cs.tl.fate_hierarchy(
    adata_orig, selected_fates=selected_fates, source="X_clone"
)  # compute the fate hierarchy
cs.pl.fate_hierarchy(adata_orig, source="X_clone")  # actually plot the hierarchy

Results saved as dictionary at adata.uns['fate_hierarchy_X_clone']

            /-Lymphoid
         /-|
        |   \-Ccr7_DC
      /-|
     |  |   /-Monocyte
     |   \-|
     |      \-Neutrophil
   /-|
  |  |         /-Baso
  |  |      /-|
  |  |   /-|   \-Mast
  |  |  |  |
--|   \-|   \-Eos
  |     |
  |     |   /-Erythroid
  |      \-|
  |         \-Meg
  |
   \-pDC

Next, we compute the clonal fate bias, -log(Q-value). We calculated a P-value that that a clone is enriched (or depleted) in a fate, using Fisher-Exact test (accounting for clone size). The P-value is then corrected to give a Q-value by Benjamini-Hochberg procedure. The alternative hypothesis options are: {‘two-sided’,’greater’,’less’}. The default is ‘two-sided’.

cs.tl.clonal_fate_bias(
    adata_orig, selected_fate="Monocyte", alternative="two-sided"
)  # compute the fate hierarchy
cs.pl.clonal_fate_bias(adata_orig)  # actually plot the hierarchy

100%|██████████| 500/500 [00:01<00:00, 442.37it/s]

Data saved at adata.uns['clonal_fate_bias']

result = adata_orig.uns["clonal_fate_bias"]
result

	Clone_ID	Clone_size	Q_value	Fate_bias	clonal_fraction_in_target_fate
0	488	58	1.037416e-20	19.984047	0.896552
1	387	37	1.608291e-16	15.793635	0.945946
2	227	45	1.665465e-15	14.778465	0.866667
3	162	42	6.210001e-13	12.206908	0.833333
4	302	112	6.210001e-13	12.206908	0.000000
...	...	...	...	...	...
495	366	9	1.000000e+00	-0.000000	0.222222
496	46	18	1.000000e+00	-0.000000	0.222222
497	408	27	1.000000e+00	-0.000000	0.259259
498	6	4	1.000000e+00	-0.000000	0.250000
499	463	3	1.000000e+00	-0.000000	0.333333

500 rows × 5 columns

Illustrate some most biased clones.

ids = result["Clone_ID"][:2]
cs.pl.clones_on_manifold(
    adata_orig,
    selected_clone_list=ids,
    color_list=["black", "red", "blue"],
    clone_markersize=15,
)