pyXLMS.plotting package

Submodules

pyXLMS.plotting.plot_score_distribution module

pyXLMS.plotting.plot_score_distribution.plot_score_distribution(

data: List[Dict[str, Any]],

bins: int = 25,

density: bool = False,

colors: List[str] = ['#00a087', '#3c5488', '#e64b35'],

title: str = 'Target and Decoy Score Distribution',

figsize: Tuple[float, float] = (16.0, 9.0),

filename_prefix: str | None = None,

) → Tuple[Figure, Any]

Plot the score distribution for a set of crosslink-spectrum-matches or crosslinks.

Plot the target-target, target-decoy, and decoy-decoy score distribution as a histogram for a set of crosslink-spectrum-matches or crosslinks.

Parameters:

• data (list of dict of str, any) – A list of crosslink-spectrum-matches or crosslinks.

• bins (int, default = 25) – The number of equal-width bins in the histogram.

• density (bool, default = False) – If True, draw and return a probability density: each bin will display the bin’s raw count divided by the total number of counts and the bin width, so that the area under the histogram integrates to 1.

• colors (list of str, default = ["#00a087", "#3c5488", "#e64b35"]) – Colors of the histogram lines.

• title (str, default = "Target and Decoy Score Distribution") – The title of the histogram.

• figsize (tuple of float, float, default = (16.0, 9.0)) – Width, height in inches.

• filename_prefix (str, or None) – If given, plot will be saved with and without title in .png and .svg format with the given prefix.

Returns:

The created figure and axis from matplotlib.pyplot.subplots().

Return type:

tuple of matplotlib.figure.Figure, any

Raises:

• TypeError – If a wrong data type is provided.

• ValueError – If parameter data does not contain any crosslink-spectrum-matches or crosslinks.

• ValueError – If attribute ‘score’, ‘alpha_decoy’, or ‘beta_decoy’ is not available for any of the data.

Examples

>>> from pyXLMS import parser
>>> from pyXLMS import plotting
>>> pr = parser.read_msannika("data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.xlsx")
>>> csms = pr["crosslink-spectrum-matches"]
>>> fig, ax = plotting.plot_score_distribution(csms)

pyXLMS.plotting.plot_venn_diagram module

pyXLMS.plotting.plot_venn_diagram.plot_venn_diagram(

data_1: List[Dict[str, Any]],

data_2: List[Dict[str, Any]],

data_3: List[Dict[str, Any]] | None = None,

by: Literal['peptide', 'protein'] = 'peptide',

labels: List[str] = ['Set 1', 'Set 2', 'Set 3'],

colors: List[str] = ['#4361EE', '#4CC9F0', '#F72585'],

alpha: float = 0.6,

contour: bool = False,

linewidth: float = 0.5,

title: str = 'Venn Diagram',

figsize: Tuple[float, float] = (10.0, 10.0),

filename_prefix: str | None = None,

) → Tuple[Figure, Any]

Plot the venn diagram for two or three sets of crosslink-spectrum-matches or crosslinks.

Plot the venn diagram for two or three sets of crosslink-spectrum-matches or crosslinks. Overlaps are calculated by either looking at peptide sequence and crosslink position in the peptide using parameter by = “peptide” or by looking at protein crosslink position by using parameter by = “protein”.

Parameters:

• data_1 (list of dict of str, any) – A list of crosslink-spectrum-matches or crosslinks.

• data_2 (list of dict of str, any) – A list of crosslink-spectrum-matches or crosslinks.

• data_3 (list of dict of str, any, or None, default = None) – Optionally, a third list of crosslink-spectrum-matches or crosslinks.

• by (str, one of "peptide" or "protein") – If peptide or protein crosslink position should be used for determining if a crosslink-spectrum-match or crosslink is unique.

• labels (List[str], default = ["Set 1", "Set 2", "Set 3"]) – List of labels for the sets.

• colors (List[str], default = ["#4361EE", "#4CC9F0", "#F72585"]) – List of valid colors to use for the venn circles.

• alpha (float, default = 0.6) – Color opacity.

• contour (bool, default = False) – If a contour should be drawn around venn circles.

• linewidth (float, default = 0.5) – Linewidth of the contour.

• title (str, default = "Venn Diagram") – Title of the venn diagram.

• figsize (tuple of float, float, default = (10.0, 10.0)) – Width, height in inches.

• filename_prefix (str, or None, default = None) – If given, plot will be saved with and without title in .png and .svg format with the given prefix.

Returns:

The created figure and axis from matplotlib.pyplot.subplots().

Return type:

tuple of matplotlib.figure.Figure, any

Raises:

• TypeError – If a wrong data type is provided.

• TypeError – If parameter by is not one of ‘peptide’ or ‘protein’.

• ValueError – If one of the data parameters does not contain any crosslink-spectrum-matches or crosslinks.

• ValueError – If attribute ‘alpha_proteins’, ‘alpha_proteins_crosslink_positions’, ‘beta_proteins’, or ‘beta_proteins_crosslink_positions’ is not available for any of the data and parameter ‘by’ was set to ‘protein’.

Examples

>>> from pyXLMS import parser
>>> from pyXLMS import plotting
>>> a = parser.read("data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt", engine="MS Annika", crosslinker="DSS")
>>> a = a["crosslink-spectrum-matches"]
>>> b = parser.read("data/maxquant/run1/crosslinkMsms.txt", engine="MaxQuant", crosslinker="DSS")
>>> b = b["crosslink-spectrum-matches"]
>>> fig, ax = plotting.plot_venn_diagram(a, b, labels=["MS Annika", "MaxQuant"], colors=["orange", "blue"])

>>> from pyXLMS import parser
>>> from pyXLMS import plotting
>>> a = parser.read("data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt", engine="MS Annika", crosslinker="DSS")
>>> a = a["crosslink-spectrum-matches"]
>>> b = parser.read("data/maxquant/run1/crosslinkMsms.txt", engine="MaxQuant", crosslinker="DSS")
>>> b = b["crosslink-spectrum-matches"]
>>> c = parser.read("data/plink2/Cas9_plus10_2024.06.20.filtered_cross-linked_spectra.csv", engine="pLink", crosslinker="DSS")
>>> c = c["crosslink-spectrum-matches"]
>>> fig, ax = plotting.plot_venn_diagram(a, b, c, labels=["MS Annika", "MaxQuant", "pLink"], contour=True)

pyXLMS.plotting.plot_venn_diagram.venn(

set_1: Set[Any],

set_2: Set[Any],

set_3: Set[Any] | None = None,

labels: List[str] = ['Set 1', 'Set 2', 'Set 3'],

colors: List[str] = ['#4361EE', '#4CC9F0', '#F72585'],

alpha: float = 0.6,

contour: bool = False,

linewidth: float = 0.5,

title: str = 'Venn Diagram',

figsize: Tuple[float, float] = (10.0, 10.0),

filename_prefix: str | None = None,

) → Tuple[Figure, Any]

Wrapper with pre-set defaults for creating venn diagrams with the matplotlib-venn package.

Wrapper with pre-set defaults for creating venn diagrams with the matplotlib-venn package github.com/konstantint/matplotlib-venn.

Parameters:

• set_1 (set) – First set of the venn diagram.

• set_2 (set) – Second set of the venn diagram.

• set_3 (set, or None, default = None) – If not None a three set venn diagram will be drawn, if None the two set venn diagram of set_1 and set_2 will be drawn.

• labels (List[str], default = ["Set 1", "Set 2", "Set 3"]) – List of labels for the sets.

• colors (List[str], default = ["#4361EE", "#4CC9F0", "#F72585"]) – List of valid colors to use for the venn circles.

• alpha (float, default = 0.6) – Color opacity.

• contour (bool, default = False) – If a contour should be drawn around venn circles.

• linewidth (float, default = 0.5) – Linewidth of the contour.

• title (str, default = "Venn Diagram") – Title of the venn diagram.

• figsize (tuple of float, float, default = (10.0, 10.0)) – Width, height in inches.

• filename_prefix (str, or None, default = None) – If given, plot will be saved with and without title in .png and .svg format with the given prefix.

Returns:

The created figure and axis from matplotlib.pyplot.subplots().

Return type:

tuple of matplotlib.figure.Figure, any

Warns:

RuntimeWarning – If more labels or colors than sets are supplied.

Raises:

IndexError – If less labels or colors than sets are supplied.

Examples

>>> from pyXLMS.plotting import venn
>>> fig, ax = venn({"A", "B", "C"}, {"B", "C", "D", "E", "F"}, labels=["A", "F"], colors=["orange", "blue"])

>>> from pyXLMS.plotting import venn
>>> fig, ax = venn({"A", "B", "C"}, {"B", "C", "D", "E", "F"}, {"F", "G"})

Module contents

pyXLMS.plotting.plot_score_distribution(

data: List[Dict[str, Any]],

bins: int = 25,

density: bool = False,

colors: List[str] = ['#00a087', '#3c5488', '#e64b35'],

title: str = 'Target and Decoy Score Distribution',

figsize: Tuple[float, float] = (16.0, 9.0),

filename_prefix: str | None = None,

) → Tuple[Figure, Any]

Plot the score distribution for a set of crosslink-spectrum-matches or crosslinks.

Plot the target-target, target-decoy, and decoy-decoy score distribution as a histogram for a set of crosslink-spectrum-matches or crosslinks.

Parameters:

• data (list of dict of str, any) – A list of crosslink-spectrum-matches or crosslinks.

• bins (int, default = 25) – The number of equal-width bins in the histogram.

• density (bool, default = False) – If True, draw and return a probability density: each bin will display the bin’s raw count divided by the total number of counts and the bin width, so that the area under the histogram integrates to 1.

• colors (list of str, default = ["#00a087", "#3c5488", "#e64b35"]) – Colors of the histogram lines.

• title (str, default = "Target and Decoy Score Distribution") – The title of the histogram.

• figsize (tuple of float, float, default = (16.0, 9.0)) – Width, height in inches.

• filename_prefix (str, or None) – If given, plot will be saved with and without title in .png and .svg format with the given prefix.

Returns:

The created figure and axis from matplotlib.pyplot.subplots().

Return type:

tuple of matplotlib.figure.Figure, any

Raises:

• TypeError – If a wrong data type is provided.

• ValueError – If parameter data does not contain any crosslink-spectrum-matches or crosslinks.

• ValueError – If attribute ‘score’, ‘alpha_decoy’, or ‘beta_decoy’ is not available for any of the data.

Examples

>>> from pyXLMS import parser
>>> from pyXLMS import plotting
>>> pr = parser.read_msannika("data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.xlsx")
>>> csms = pr["crosslink-spectrum-matches"]
>>> fig, ax = plotting.plot_score_distribution(csms)

pyXLMS.plotting.plot_venn_diagram(

data_1: List[Dict[str, Any]],

data_2: List[Dict[str, Any]],

data_3: List[Dict[str, Any]] | None = None,

by: Literal['peptide', 'protein'] = 'peptide',

labels: List[str] = ['Set 1', 'Set 2', 'Set 3'],

colors: List[str] = ['#4361EE', '#4CC9F0', '#F72585'],

alpha: float = 0.6,

contour: bool = False,

linewidth: float = 0.5,

title: str = 'Venn Diagram',

figsize: Tuple[float, float] = (10.0, 10.0),

filename_prefix: str | None = None,

) → Tuple[Figure, Any]

Plot the venn diagram for two or three sets of crosslink-spectrum-matches or crosslinks.

Plot the venn diagram for two or three sets of crosslink-spectrum-matches or crosslinks. Overlaps are calculated by either looking at peptide sequence and crosslink position in the peptide using parameter by = “peptide” or by looking at protein crosslink position by using parameter by = “protein”.

Parameters:

• data_1 (list of dict of str, any) – A list of crosslink-spectrum-matches or crosslinks.

• data_2 (list of dict of str, any) – A list of crosslink-spectrum-matches or crosslinks.

• data_3 (list of dict of str, any, or None, default = None) – Optionally, a third list of crosslink-spectrum-matches or crosslinks.

• by (str, one of "peptide" or "protein") – If peptide or protein crosslink position should be used for determining if a crosslink-spectrum-match or crosslink is unique.

• labels (List[str], default = ["Set 1", "Set 2", "Set 3"]) – List of labels for the sets.

• colors (List[str], default = ["#4361EE", "#4CC9F0", "#F72585"]) – List of valid colors to use for the venn circles.

• alpha (float, default = 0.6) – Color opacity.

• contour (bool, default = False) – If a contour should be drawn around venn circles.

• linewidth (float, default = 0.5) – Linewidth of the contour.

• title (str, default = "Venn Diagram") – Title of the venn diagram.

• figsize (tuple of float, float, default = (10.0, 10.0)) – Width, height in inches.

• filename_prefix (str, or None, default = None) – If given, plot will be saved with and without title in .png and .svg format with the given prefix.

Returns:

The created figure and axis from matplotlib.pyplot.subplots().

Return type:

tuple of matplotlib.figure.Figure, any

Raises:

• TypeError – If a wrong data type is provided.

• TypeError – If parameter by is not one of ‘peptide’ or ‘protein’.

• ValueError – If one of the data parameters does not contain any crosslink-spectrum-matches or crosslinks.

• ValueError – If attribute ‘alpha_proteins’, ‘alpha_proteins_crosslink_positions’, ‘beta_proteins’, or ‘beta_proteins_crosslink_positions’ is not available for any of the data and parameter ‘by’ was set to ‘protein’.

Examples

>>> from pyXLMS import parser
>>> from pyXLMS import plotting
>>> a = parser.read("data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt", engine="MS Annika", crosslinker="DSS")
>>> a = a["crosslink-spectrum-matches"]
>>> b = parser.read("data/maxquant/run1/crosslinkMsms.txt", engine="MaxQuant", crosslinker="DSS")
>>> b = b["crosslink-spectrum-matches"]
>>> fig, ax = plotting.plot_venn_diagram(a, b, labels=["MS Annika", "MaxQuant"], colors=["orange", "blue"])

>>> from pyXLMS import parser
>>> from pyXLMS import plotting
>>> a = parser.read("data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt", engine="MS Annika", crosslinker="DSS")
>>> a = a["crosslink-spectrum-matches"]
>>> b = parser.read("data/maxquant/run1/crosslinkMsms.txt", engine="MaxQuant", crosslinker="DSS")
>>> b = b["crosslink-spectrum-matches"]
>>> c = parser.read("data/plink2/Cas9_plus10_2024.06.20.filtered_cross-linked_spectra.csv", engine="pLink", crosslinker="DSS")
>>> c = c["crosslink-spectrum-matches"]
>>> fig, ax = plotting.plot_venn_diagram(a, b, c, labels=["MS Annika", "MaxQuant", "pLink"], contour=True)

pyXLMS.plotting.venn(

set_1: Set[Any],

set_2: Set[Any],

set_3: Set[Any] | None = None,

labels: List[str] = ['Set 1', 'Set 2', 'Set 3'],

colors: List[str] = ['#4361EE', '#4CC9F0', '#F72585'],

alpha: float = 0.6,

contour: bool = False,

linewidth: float = 0.5,

title: str = 'Venn Diagram',

figsize: Tuple[float, float] = (10.0, 10.0),

filename_prefix: str | None = None,

) → Tuple[Figure, Any]

Wrapper with pre-set defaults for creating venn diagrams with the matplotlib-venn package.

Wrapper with pre-set defaults for creating venn diagrams with the matplotlib-venn package github.com/konstantint/matplotlib-venn.

Parameters:

• set_1 (set) – First set of the venn diagram.

• set_2 (set) – Second set of the venn diagram.

• set_3 (set, or None, default = None) – If not None a three set venn diagram will be drawn, if None the two set venn diagram of set_1 and set_2 will be drawn.

• labels (List[str], default = ["Set 1", "Set 2", "Set 3"]) – List of labels for the sets.

• colors (List[str], default = ["#4361EE", "#4CC9F0", "#F72585"]) – List of valid colors to use for the venn circles.

• alpha (float, default = 0.6) – Color opacity.

• contour (bool, default = False) – If a contour should be drawn around venn circles.

• linewidth (float, default = 0.5) – Linewidth of the contour.

• title (str, default = "Venn Diagram") – Title of the venn diagram.

• figsize (tuple of float, float, default = (10.0, 10.0)) – Width, height in inches.

• filename_prefix (str, or None, default = None) – If given, plot will be saved with and without title in .png and .svg format with the given prefix.

Returns:

The created figure and axis from matplotlib.pyplot.subplots().

Return type:

tuple of matplotlib.figure.Figure, any

Warns:

RuntimeWarning – If more labels or colors than sets are supplied.

Raises:

IndexError – If less labels or colors than sets are supplied.

Examples

>>> from pyXLMS.plotting import venn
>>> fig, ax = venn({"A", "B", "C"}, {"B", "C", "D", "E", "F"}, labels=["A", "F"], colors=["orange", "blue"])

>>> from pyXLMS.plotting import venn
>>> fig, ax = venn({"A", "B", "C"}, {"B", "C", "D", "E", "F"}, {"F", "G"})