Gallery
Base coverage
Plot the base coverage of a single row of your dataframe.
DOCSTRING: base_coverage
- dreem.draw.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 tofull
.cluster (
str, optional
) – Selects this cluster. Defaults topop_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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Compare mutation profiles
Plot the mutation fraction of multiple mutation profiles.
DOCSTRING: compare_mutation_profiles
- dreem.draw.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.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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
DeltaG vs sub rate
Plot the Mutation fraction of each paired-expected base of the ROI for each reference of a sample, w.r.t the deltaG estimation.
DOCSTRING: deltaG_vs_sub_rate
- dreem.draw.study.Study.deltaG_vs_sub_rate(*args, **kwargs)
Plot the Mutation fraction of each paired-expected base of the ROI for each reference of a sample, w.r.t the deltaG estimation.
- Parameters
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'
wherex
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.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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
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
- dreem.draw.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'
wherex
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.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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Mutation fraction
Plot the mutation rates as histograms.
DOCSTRING: mutation_fraction
- dreem.draw.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 tofull
.cluster (
str, optional
) – Selects this cluster. Defaults topop_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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Mutation fraction delta
Plot the Mutation fraction difference between two mutation profiles.
DOCSTRING: mutation_fraction_delta
- dreem.draw.study.Study.mutation_fraction_delta(*args, **kwargs)
Plot the Mutation fraction difference between two mutation profiles.
- Parameters
sample1 – sample of the first mutation profile.
sample2 – sample of the second mutation profile.
reference1 – reference of the first mutation profile.
reference2 – reference of the second mutation profile.
section1 – section of the first mutation profile.
section2 – section of the second mutation profile.
cluster1 – cluster of the first mutation profile.
cluster2 – cluster of the second mutation profile.
base_index1 – base index of the first mutation profile.
base_index2 – base index of the second mutation profile.
base_type1 – base type of the first mutation profile.
base_type2 – base type of the second mutation profile.
base_pairing1 – base pairing of the first mutation profile.
base_pairing2 – base pairing of the second mutation profile.
- Returns
{‘fig’: a plotly figure, ‘data’: a pandas dataframe}
- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Mutation fraction identity
Plot the mutation rates as histograms.
DOCSTRING: mutation_fraction_identity
- dreem.draw.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 tofull
.cluster (
str, optional
) – Selects this cluster. Defaults topop_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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Mutation per read per reference
Plot the number of mutations per read per reference as an histogram.
DOCSTRING: mutation_per_read_per_reference
- dreem.draw.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 tofull
.cluster (
str, optional
) – Selects this cluster. Defaults topop_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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Mutations in barcodes
Plot the number of mutations in the barcode per read of a sample as an histogram.
DOCSTRING: mutations_in_barcodes
- dreem.draw.study.Study.mutations_in_barcodes(*args, **kwargs)
Plot the number of mutations in the barcode per read of a 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.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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
Mutations per read per sample
Plot the number of mutations per read per sample as an histogram.
DOCSTRING: mutations_per_read_per_sample
- dreem.draw.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.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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.
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
- dreem.draw.study.Study.num_aligned_reads_per_reference_frequency_distribution(*args, **kwargs)
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.
- 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.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 withflank==flank_1
.
- Returns
{
'fig'
: a plotly figure,'data'
: a pandas dataframe}- Return type
to_html (str, optional): File name to save the figure as a html.
to_png (str, optional): File name to save the figure as a png.