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Gallery

Base coverage

Plot the base coverage of a single row of your dataframe.

DOCSTRING: base_coverage

seismic_graph.study.Study.base_coverage(*args, **kwargs)

Plot the base coverage of a single row of your dataframe.

Parameters:

sample (str, optional) – Selects this sample. Defaults to None.
reference (str, optional) – Selects this reference. Defaults to None.
section (str, optional) – Selects this section. Defaults to full.
cluster (str, optional) – Selects this cluster. Defaults to pop_avg.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Compare mutation profiles

Plot the mutation fraction of multiple mutation profiles.

DOCSTRING: compare_mutation_profiles

seismic_graph.study.Study.compare_mutation_profiles(*args, **kwargs)

Plot the mutation fraction of multiple mutation profiles.

Parameters:

max_plots – maximum number of plots to show.
max_axis – maximum value of the x and y axis. If None, the maximum value of the data will be used if above 0.15, otherwise 0.15.
sample (list, str, optional) – Filter rows by sample (a list of samples or just a sample). Defaults to None.
reference (list, str, optional) – Filter rows by reference (a list of references or just a reference). Defaults to None.
section (list, str, optional) – Filter rows by section (a list of sections or just a section). Defaults to None.
cluster (list, str, optional) – Filter rows by cluster (a list of clusters or just a cluster). Defaults to None.
normalize (bool, optional) – Fit one sample to the other to normalize the mutation fractions. Defaults to False.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Correlation by refs between samples

Plot the correlation between mutation profiles of multiple samples, for each reference.

DOCSTRING: correlation_by_refs_between_samples

seismic_graph.study.Study.correlation_by_refs_between_samples(*args, **kwargs)

Plot the correlation between mutation profiles of multiple samples, for each reference.

Parameters:

sample (list, str, optional) – Filter rows by sample (use exactly two samples). Defaults to None.
reference (list, str, optional) – Filter rows by reference (a list of references or just a reference). Defaults to None.
section (list, str, optional) – Filter rows by section (a list of sections or just a section). Defaults to None.
cluster (list, str, optional) – Filter rows by cluster (a list of clusters or just a cluster). Defaults to None.
normalize (bool, optional) – Fit one sample to the other to normalize the mutation fractions. Defaults to False.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Experimental variable across samples

Plot a given experimental variable vs Mutation fraction across samples for a given reference and section.

DOCSTRING: experimental_variable_across_samples

seismic_graph.study.Study.experimental_variable_across_samples(*args, **kwargs)

Plot a given experimental variable vs Mutation fraction across samples for a given reference and section.

Parameters:

experimental_variable (str) – Name of the experimental variable to plot.
models (List[str], optional) – Models to fit on the data using scipy.optimize.curve_fit. Under the form 'lambda x, a, b: a*x+b' where x is the variable. Defaults to [].
sample (list, str, optional) – Filter rows by sample (a list of samples or just a sample). Defaults to None.
reference (list, str, optional) – Filter rows by reference (a list of references or just a reference). Defaults to None.
section (list, str, optional) – Filter rows by section (a list of sections or just a section). Defaults to None.
cluster (list, str, optional) – Filter rows by cluster (a list of clusters or just a cluster). Defaults to None.
normalize (bool, optional) – Fit one sample to the other to normalize the mutation fractions. Defaults to False.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Mutation fraction

Plot the mutation rates as histograms.

DOCSTRING: mutation_fraction

seismic_graph.study.Study.mutation_fraction(*args, **kwargs)

Plot the mutation rates as histograms.

Parameters:

show_ci (bool, optional) – Show confidence intervals. Defaults to True.
sample (str, optional) – Selects this sample. Defaults to None.
reference (str, optional) – Selects this reference. Defaults to None.
section (str, optional) – Selects this section. Defaults to full.
cluster (str, optional) – Selects this cluster. Defaults to pop_avg.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Mutation fraction delta

Plot the Mutation fraction difference between two mutation profiles.

DOCSTRING: mutation_fraction_delta

seismic_graph.study.Study.mutation_fraction_delta(*args, **kwargs)

Plot the Mutation fraction difference between two mutation profiles.

Returns:: {‘fig’: a plotly figure, ‘data’: a pandas dataframe}
Return type:: dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Mutation fraction identity

Plot the mutation rates as histograms.

DOCSTRING: mutation_fraction_identity

seismic_graph.study.Study.mutation_fraction_identity(*args, **kwargs)

Plot the mutation rates as histograms.

Parameters:

show_ci (bool, optional) – Show confidence intervals. Defaults to True.
sample (str, optional) – Selects this sample. Defaults to None.
reference (str, optional) – Selects this reference. Defaults to None.
section (str, optional) – Selects this section. Defaults to full.
cluster (str, optional) – Selects this cluster. Defaults to pop_avg.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Mutation per read per reference

Plot the number of mutations per read per reference as an histogram.

DOCSTRING: mutation_per_read_per_reference

seismic_graph.study.Study.mutation_per_read_per_reference(*args, **kwargs)

Plot the number of mutations per read per reference as an histogram.

Parameters:

sample (str, optional) – Selects this sample. Defaults to None.
reference (str, optional) – Selects this reference. Defaults to None.
section (str, optional) – Selects this section. Defaults to full.
cluster (str, optional) – Selects this cluster. Defaults to pop_avg.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Mutations per read per sample

Plot the number of mutations per read per sample as an histogram.

DOCSTRING: mutations_per_read_per_sample

seismic_graph.study.Study.mutations_per_read_per_sample(*args, **kwargs)

Plot the number of mutations per read per sample as an histogram.

Parameters:

sample (list, str, optional) – Filter rows by sample (a list of samples or just a sample). Defaults to None.
reference (list, str, optional) – Filter rows by reference (a list of references or just a reference). Defaults to None.
section (list, str, optional) – Filter rows by section (a list of sections or just a section). Defaults to None.
cluster (list, str, optional) – Filter rows by cluster (a list of clusters or just a cluster). Defaults to None.
normalize (bool, optional) – Fit one sample to the other to normalize the mutation fractions. Defaults to False.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

Num aligned reads per reference frequency distribution

Plot the number of aligned reads per reference as a frequency distribution. x axis is the number of aligned reads per reference, y axis is the count of reference that have this number of aligned reads.

DOCSTRING: num_aligned_reads_per_reference_frequency_distribution

seismic_graph.study.Study.num_aligned_reads_per_reference_frequency_distribution(*args, **kwargs)

Parameters:

sample (list, str, optional) – Filter rows by sample (a list of samples or just a sample). Defaults to None.
reference (list, str, optional) – Filter rows by reference (a list of references or just a reference). Defaults to None.
section (list, str, optional) – Filter rows by section (a list of sections or just a section). Defaults to None.
cluster (list, str, optional) – Filter rows by cluster (a list of clusters or just a cluster). Defaults to None.
normalize (bool, optional) – Fit one sample to the other to normalize the mutation fractions. Defaults to False.
base_index (list, int, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base index, using 1-indexing. Can be a unique sequence in the row’s sequence, a list of indexes or a single index. Gives a Defaults to None.
base_type (list, str, optional) – Filter per-base attributes (sub_rate, sequence, etc) by base type. Defaults to ['A','C','G','T'].
base_pairing (bool, optional) – Filter per-base attributes (sub_rate, sequence, etc) by expected base pairing. True will keep only base pairs, False will keep only non-base pairs. Defaults to None.
**kwargs – Additional arguments to pass to filter rows by. Ex: flank='flank_1' will keep only rows with flank==flank_1.

Returns:

{'fig': a plotly figure, 'data': a pandas dataframe}

Return type:

dict

to_html (str, optional): File name or ‘return’ to save the figure as HTML or return HTML as bytes.

to_png (str, optional): File name or ‘return’ to save the figure as PNG or return PNG as bytes.

to_svg (str, optional): File name or ‘return’ to save the figure as SVG or return SVG as bytes.

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