Plotting STRING Score Distribution for Inter-Links

from pyXLMS import __version__
 
print(f"Installed pyXLMS version: {__version__}")

    Installed pyXLMS version: 1.8.7

from pyXLMS import parser
from pyXLMS import plotting

All plotting functionality is available via the plotting submodule. We also import the parser submodule here for reading result files.

parser_result = parser.read(
    [
        "../../data/ms_annika/Nucleus_Rep1_CSMs.parquet",
        "../../data/ms_annika/Nucleus_Rep1_Crosslinks.parquet",
    ],
    engine="Custom",
    crosslinker="DSBSO",
)

    Reading CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████████████████| 3770/3770 [00:00<00:00, 5529.58it/s]
    Reading crosslinks...: 100%|██████████████████████████████████████████████████████████████████████████████████| 2937/2937 [00:00<00:00, 10651.60it/s]

We read crosslink-spectrum-matches (CSMs) and crosslinks using the generic parser  from two .parquet files. These files contain CSMs and crosslinks validated for 1% estimated CSM/crosslink-level FDR respectively with data from human K562 nuclei from this study .

fig, ax = plotting.plot_string_score_distribution(
    data=parser_result["crosslinks"],
    organism="Homo sapiens",
    figsize=(7.0, 4.0),
    filename_prefix="string_score_dist_xls",
)

    Mapped 466 of 471 proteins (98.93842887473461%) to STRING IDs.
    Annotating STRING scores for inter-links...: 100%|█████████████████████████████████████████████████████████████| 382/382 [00:00<00:00, 190968.31it/s]

We can plot the STRING  score distribution for our inter-link crosslinks by passing the crosslinks as the first argument. Because this function internally calls the STRING API we also need to specify an organism, e.g. in this case "Homo sapiens". You can also specify the taxon identifier instead of the common name, which is actually the preferred way of passing the organism. You can find the taxon identifier of your organism here . The default figure size is 16 by 9 inches and does not need to be set explicitly, we just used a smaller one here for demonstration purposes. The filename_prefix parameter is also optional, if it is given the plot is saved four times: once without the title in .png and .svg format, and once with the title in .png and .svg format.

fig, ax = plotting.plot_string_score_distribution(
    data=parser_result["crosslink-spectrum-matches"],
    organism=9606,
    figsize=(7.0, 4.0),
    filename_prefix="string_score_dist_csms",
)

    Mapped 518 of 525 proteins (98.66666666666667%) to STRING IDs.
    Annotating STRING scores for inter-links...: 100%|█████████████████████████████████████████████████████████████| 478/478 [00:00<00:00, 239159.88it/s]

We can of course also create the same plot for our CSMs. This time we also use the taxon identifier 9606 instead of the common name Homo sapiens.

fig, ax = plotting.plot_string_score_distribution(
    data=parser_result["crosslinks"],
    plot_type="hist",
    figsize=(7.0, 4.0),
)

We can also plot the STRING score distribution as a histogram for target-target, target-decoy, and decoy-decoy hits by setting plot_type="hist". Note that this time we also did not need to specify the organism because the crosslinks were already annotated with STRING scores from the first function call. In general you can omit the organism if your data has been annotated with STRING scores previously, e.g. using transform.annotate_string_scores(). Since we did not specify a filename_prefix the plot is not saved to disk. There are also other parameters that can be set to tune your plot like density and colors, you can read more about all the possible parameters here: docs.