14. References

14.2. Statistical Tools

Code related to the ANOVA analysis to find associations between drug IC50s and genomic features

class MultipleTesting(method=None)[source]

This class eases the computation of multiple testing corrections

The method implemented so far are based on statsmodels or a local implementation of qvalue method.

method name Description
bonferroni one-step correction
sidak one-step correction
holm-sidak step down method using Sidak adjustments
holm step down method using Bonferroni adjustments
simes-hochberg step up method (independent)
hommel close method based on Simes tests (non negative)
fdr_bh FDR Benjamini-Hochberg (non-negative)
fdr_by FDR Benjamini-Yekutieli (negative)
fdr_tsbky FDR 2-stage Benjamini-Krieger-Yekutieli non negative
frd_tsbh FDR 2-stage Benjamini-Hochberg’ non-negative
fdr same as fdr_bh
qvalue see QValue class

See also

gdsctools.qvalue.

Constructor

Parameters:method – default to fdr that is the FDR Benjamini-Hochberg correction.
get_corrected_pvalues(pvalues, method=None)[source]

Return corrected pvalues

Parameters:
  • pvalues (list) – list or array of pvalues to correct.
  • method – use the one defined in the constructor by default but can be overwritten here
method

get/set method

plot_comparison(pvalues, methods=None)[source]

Simple plot to compare the pvalues correction methods

from gdsctools.stats import MultipleTesting
mt = MultipleTesting()
pvalues = [1e-10, 9.5e-2, 2.2e-1, 3.6e-1, 5e-1, 6e-1,8e-1,9.6e-1]
mt.plot_comparison(pvalues,
    methods=['fdr_bh', 'qvalue', 'bonferroni', 'fdr_tsbh'])

(Source code, png, hires.png, pdf)

_images/references-2.png

Note

in that example, the qvalue and FDR are identical, but this is not true in general.

valid_methods = None

set of valid methods

cohens(x, y)[source]

Effect size metric through Cohen’s d metric

Parameters:
  • x – first vector
  • y – second vector
Returns:

absolute effect size value

The Cohen’s effect size d is defined as the difference between two means divided by a standard deviation of the data.

d = \frac{\bar{x}_1 - \bar{x}_2}{s}

For two independent samples, the pooled standard deviation is used instead, which is defined as:

s = \sqrt{  \frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2} }

A Cohen’s d is frequently used in estimating sample sizes for statistical testing: a lower d value indicates the necessity of larger sample sizes, and vice versa.

Note

we return the absolute value

References:https://en.wikipedia.org/wiki/Effect_size
signed_effects(df)[source]

Implementation of qvalue estimate

Author: Thomas Cokelaer

class QValue(pv, lambdas=None, pi0=None, df=3, method='smoother', smooth_log_pi0=False, verbose=True)[source]

Compute Q-value for a given set of P-values

Constructor

The q-value of a test measures the proportion of false positives incurred (called the false discovery rate or FDR) when that particular test is called significant.

Parameters:
  • pv – A vector of p-values (only necessary input)
  • lambdas – The value of the tuning parameter to estimate pi_0. Must be in [0,1). Can be a single value or a range of values. If none, the default is a range from 0 to 0.9 with a step size of 0.05 (inluding 0 and 0.9)
  • method – Either “smoother” or “bootstrap”; the method for automatically choosing tuning parameter in the estimation of pi_0, the proportion of true null hypotheses. Only smoother implemented for now.
  • df – Number of degrees-of-freedom to use when estimating pi_0 with a smoother (default to 3 i.e., cubic interpolation.)
  • pi0 (float) – if None, it’s estimated as suggested in Storey and Tibshirani, 2003. May be provided, which is convenient for testing.
  • smooth_log_pi0 – If True and ‘pi0_method’ = “smoother”, pi_0 will be estimated by applying a smoother to a scatterplot of log pi_0 rather than just pi_0

Note

Estimation of pi0 differs slightly from the one given in R (about 0.3%) due to smoothing.spline function differences between R and SciPy.

If no options are selected, then the method used to estimate pi_0 is the smoother method described in Storey and Tibshirani (2003). The bootstrap method is described in Storey, Taylor & Siegmund (2004) but not implemented yet.

estimate_pi0(pi0)[source]

Estimate pi0 based on the pvalues

qvalue()[source]

Return the qvalues using pvalues stored in pv attribute

14.3. Readers

14.3.1. IC50, Genomic Features, Drug Decode

IO functionalities

Provides readers to read the following formats

class IC50(filename, v18=False)[source]

Reader of IC50 data set

This input matrix must be a comman-separated value (CSV) or tab-separated value file (TSV).

The matrix must have a header and at least 2 columns. If the number of rows is not sufficient, analysis may not be possible.

The header must have a column called “COSMIC_ID” or “COSMIC ID”. This column will be used as indices (row names). All other columns will be considered as input data.

The column “COSMIC_ID” contains the cosmic identifiers (cell line). The other columns should be filled with the IC50s corresponding to a pair of COSMIC identifiers and Drug. Nothing prevents you to fill the file with data that have other meaning (e.g. AUC).

If at least one column starts with Drug_, all other columns will be ignored. This was implemented for back compatibility.

The order of the columns is not important.

Here is a simple example of a valid TSV file:

COSMIC_ID   Drug_1_IC50 Drug_20_IC50
111111      0.5         0.8
222222      1           2

A test file is provided in the gdsctools package:

from gdsctools import ic50_test

You can read it using this class and plot information as follows:

from gdsctools import IC50, ic50_test
r = IC50(ic50_test)
r.plot_ic50_count()

(Source code, png, hires.png, pdf)

_images/references-3.png

You can get basic information using the print function:

>>> from gdsctools import IC50, ic50_test
>>> r = IC50(ic50_test)
>>> print(r)
Number of drugs: 11
Number of cell lines: 988
Percentage of NA 0.206569746043

You can get the drug identifiers as follows:

r.drugIds

and set the drugs, which means other will be removed:

r.drugsIds = [1, 1000]

Changed in version 0.9.10: The column COSMIC ID should now be COSMIC_ID. Previous name is deprecated but still accepted.

Constructor

Parameters:filename – input filename of IC50s. May also be an instance of IC50 or a valid dataframe. The data is stored as a dataframe in the attribute called df. Input file may be gzipped
copy()[source]
cosmic_name = 'COSMIC_ID'
drop_drugs(drugs)[source]

drop a drug or a list of drugs

drugIds

list the drug identifier name or select sub set

drug_name_to_int(name)[source]
get_ic50()[source]

Return all ic50 as a list

hist(bins=20, **kargs)[source]

Histogram of the measured IC50

Parameters:
  • bins – binning of the histogram
  • kargs – any argument accepted by pylab.hist function.
Returns:

all measured IC50

from gdsctools import IC50, ic50_test
r = IC50(ic50_test)
r.hist()

(Source code, png, hires.png, pdf)

_images/references-4.png
plot_ic50_count(**kargs)[source]

Plots the fraction of valid/measured IC50 per drug

Parameters:kargs – any valid parameters accepted by pylab.plot function.
Returns:the fraction of valid/measured IC50 per drug
class GenomicFeatures(filename=None, empty_tissue_name='UNDEFINED')[source]

Read Matrix with Genomic Features

These are the compulsary column names required (note the spaces):

  • ‘COSMIC_ID’
  • ‘TISSUE_FACTOR’
  • ‘MSI_FACTOR’

If one of the following column is found, it is removed (deprecated):

- 'SAMPLE_NAME'
- 'Sample Name'
- 'CELL_LINE'

and features can be also encoded with the following convention:

  • columns ending in “_mut” to encode a gene mutation (e.g., BRAF_mut)
  • columns starting with “gain_cna”
  • columns starting with “loss_cna”

Those columns will be removed:

  • starting with Drug_, which are supposibly from the IC50 matrix
>>> from gdsctools import GenomicFeatures
>>> gf = GenomicFeatures()
>>> print(gf)
Genomic features distribution
Number of unique tissues 27
Number of unique features 677 with
- Mutation: 270
- CNA (gain): 116
- CNA (loss): 291

Changed in version 0.9.10: The header’s columns’ names have changed to be more consistant. Previous names are deprecated but still accepted.

Changed in version 0.9.15: If a tissue is empty, it is replaced by UNDEFINED. We also strip the spaces to make sure there is “THIS” and “THIS ” are the same.

Constructor

If no file is provided, using the default file provided in the package that is made of 1001 cell lines times 680 features.

Parameters:empty_tissue_name (str) – if a tissue name is let empty, replace it with this string.
colnames = {'media': 'MEDIA_FACTOR', 'cosmic': 'COSMIC_ID', 'tissue': 'TISSUE_FACTOR', 'msi': 'MSI_FACTOR'}
compress_identical_features()[source]

Merge duplicated columns/features

Columns duplicated are merged as follows. Fhe first column is kept, others are dropped but to keep track of those dropped, the column name is renamed by concatenating the columns’s names. The separator is a double underscore.

gf = GenomicFeatures()
gf.compress_identical_features()
# You can now access to the column as follows (arbitrary example)
gf.df['ARHGAP26_mut__G3BP2_mut']
drop_tissue_in(tissues)[source]

Drop tissues from the list

Parameters:tissues (list) – a list of tissues to drop. If you have only one tissue, can be provided as a string. Since rows are removed some features (columns) may now be empty (all zeros). If so, those columns are dropped (except for the special columns (e.g, MSI).
features

return list of features

fill_media_factor()[source]

Given the COSMIC identifiers, fills the MEDIA_FACTOR column

If already populated, replaced by new content.

get_TCGA()[source]
keep_tissue_in(tissues)[source]

Drop tissues not in the list

Parameters:tissues (list) – a list of tissues to keep. If you have only one tissue, can be provided as a string. Since rows are removed some features (columns) may now be empty (all zeros). If so, those columns are dropped (except for the special columns (e.g, MSI).
plot(shadow=True, explode=True, fontsize=12)[source]

Histogram of the tissues found

from gdsctools import GenomicFeatures
gf = GenomicFeatures() # use the default file
gf.plot()

(Source code)

shift
tissues

return list of tissues

unique_tissues

return set of tissues

class Reader(data=None)[source]

Convenience base class to read CSV or TSV files (using extension)

Constructor

This class takes only one input parameter, however, it may be a filename, or a dataframe or an instance of Reader itself. This means than children classes such as IC50 can also be used as input as long as a dataframe named df can be found.

Parameters:data – a filename in CSV or TSV format with format specified by child class (see e.g. IC50), or a valid dataframe, or an instance of Reader.

The input can be a filename either in CSV (comma separated values) or TSV (tabular separated values). The extension will be used to interpret the content, so please be consistent in the naming of the file extensions.

>>> from gdsctools import Reader, ic50_test
>>> r = Reader(ic50_test.filename) # this is a CSV file
>>> len(r.df)   # number of rows
988
>>> len(r)      # number of elements
11856

Note that Reader is a base class and more sophisticated readers are available. for example, the IC50 would be better to read this IC50 data set.

The data has been stored in a data frame in the df attribute.

The dataframe of the object itself can be used as an input to create an new instance:

>>> from gdsctools import Reader, ic50_test
>>> r = Reader(ic50_test.filename, sep="\t")
>>> r2 = Reader(r) # here r.df is simply copied into r2
>>> r == r2
True

It is sometimes convenient to create an empty Reader that will be populated later on:

>>> r = Reader()
>>> len(r)
0

More advanced readers (e.g. IC50) can also be used as input as long as they have a df attribute:

>>> from gdsctools import Reader, ic50_test
>>> ic = IC50(ic50_test)
>>> r = Reader(ic)
check()[source]

Checking the format of the matrix

Currently, only checks that there is no duplicated column names

header = None

if populated, can be used to check validity of a header

read_data(filename)[source]
to_csv(filename, sep=', ', index=False, reset_index=True)[source]

Save data into a CSV file without indices

class DrugDecode(filename=None)[source]

Reads a “drug decode” file

The format must be comma-separated file. There are 3 compulsary columns called DRUG_ID, DRUG_NAME and DRUG_TARGET. Here is an example:

DRUG_ID     ,DRUG_NAME   ,DRUG_TARGET
999         ,Erlotinib   ,EGFR
1039        ,SL 0101-1   ,"RSK, AURKB, PIM3"

TSV file may also work out of the box. If a column name called ‘PUTATIVE_TARGET’ is found, it is renamed ‘DRUG_TARGET’ to be compatible with earlier formats.

In addition, 3 extra columns may be provided:

- PUBCHEM_ID
- WEBRELEASE
- OWNED_BY

The OWNED_BY and WEBRELEASE may be required to create packages for each company. If those columns are not provided, the internal dataframe is filled with None.

Note that older version of identifiers such as:

Drug_950_IC50

are transformed as proper ID that is (in this case), just the number:

950

Then, the data is accessible as a dataframe, the index being the DRUG_ID column:

data = DrugDecode('DRUG_DECODE.csv')
data.df.iloc[999]

Note

the DRUG_ID column must be made of integer

Constructor

check()[source]
companies
drugIds

return list of drug identifiers

drug_annotations(df)[source]

Populate the drug_name and drug_target field if possible

Parameters:df – input dataframe as given by e.g., anova_one_drug()
Return df:same as input but with the FDR column populated
drug_names
drug_targets
get_info()[source]
get_name(drug_id)[source]
get_public_and_one_company(company)[source]

Return drugs that belong to a specific company and public drugs

get_target(drug_id)[source]
is_public(drug_id)[source]

14.3.2. OmniBEM related

OmniBEM functions

class OmniBEMBuilder(genomic_alteration)[source]

Utility to create GenomicFeatures instance from BEM data

Starting from an example provided in GDSCTools (test_omnibem_genomic_alteration.csv.gz), here is the code to get a data structure compatible with the GenomicFeature, which can then be used as input to the ANOVA class.

See the constructor for the header format.

from gdsctools import gdsctools_data, OmniBEMBuilder, GenomicFeatures

input_data = gdsctools_data("test_omnibem_genomic_alterations.csv.gz")
bem = OmniBEMBuilder(input_data)

# You may filter the data for instance to keep only a set of genes.
# Here, we keep everything
gene_list = bem.df.GENE.unique()
bem.filter_by_gene_list(gene_list)

# Finally, create a MoBEM dataframe
mobem = bem.get_mobem()

# You may filter with other functions(e.g., to keep only Methylation
# features
bem.filter_by_type_list(["Methylation"])
mobem = bem.get_mobem()

# Then, let us create a dataframe that is compatible with
# GenomicFeature. We just need to make sure the columns are correct
mobem[[x for x in mobem.columns if x!="SAMPLE"]]
gf = GenomicFeatures(mobem[[x for x in mobem.columns if x!="SAMPLE"]])

# Now, we can perform an ANOVA analysis:
from gdsctools import ANOVA, ic50_test
an = ANOVA(ic50_test, gf)
results = an.anova_all()
results.volcano()

(Source code, png, hires.png, pdf)

_images/references-6.png

Note

The underlying data is stored in the attribute df.

Constructor

Parameters:genomic_alteration (str) – a filename in CSV format (gzipped or not). The format is explained here below.

The input must be a 5-columns CSV file with the following columns:

COSMIC_ID: an integer
TISSUE_TYPE: e.g. Methylation,
SAMPLE: this should correspond to the COSMID_ID
TYPE: Methylation,
GENE: gene name
IDENTIFIER: required for now but may be removed (rows can be
    used as identifier indeed

Warning

If GENE is set to NA, we drop it. In the resources shown in the example here above, this corresponds to 380 rows (out of 60703). Similarly, if TISSUE is NA, we also drop these rows that is about 3000 rows.

filter_by_cosmic_list(cosmic_list)[source]

Filter the data by cosmic identifers

Parameters:cosmic (list) – the cosmic identifiers to be kept. The data is updated in place.
filter_by_gene_list(genes, minimum_gene=3)[source]

Keeps only required genes

Parameters:
  • genes – a list of genes or a filename (a CSV file with a column named GENE).
  • minimum_gene – genes with not enough entries are removed (defaults to 3)
filter_by_sample_list(sample_list)[source]

Filter the data by sample name

Parameters:tissue (list) – the samples to be kept. The data is updated in place.
filter_by_tissue_list(tissue_list)[source]

Filter the data by tissue

Parameters:tissue (list) – the tissues to be kept. The data is update in place.
filter_by_type_list(type_list)[source]

Filter the data by type

Parameters:tissue (list) – the types to be kept. The data is update in place. Here are some examples of types: Point.mutation, Amplification, Deletion, Methylation.
get_genomic_features()[source]
get_mobem()[source]

Return a dataframe compatible with ANOVA analysis

The returned object can be read by GenomicFeatures.

get_significant_genes(N=20)[source]

Return most present genes

Parameters:N (int) – the maximum number of genes to return
Returns:list of genes with the number of occurences

The genes returned by be used to filter the data:

genes = bem.get_significant_genes(N=20)
bem.filter_by_gene_list(genes.index)
plot_alterations_per_cellline(fontsize=10, width=0.9)[source]

Plot number of alterations

from gdsctools import *
data = gdsctools_data("test_omnibem_genomic_alterations.csv.gz")
bem = OmniBEMBuilder(data)
bem.filter_by_gene_list(gdsctools_data("test_omnibem_genes.txt"))
bem.plot_alterations_per_cellline()

(Source code, png, hires.png, pdf)

_images/references-7.png
plot_number_alteration_by_tissue(fontsize=10, width=0.9)[source]

Plot number of alterations

from gdsctools import *
data = gdsctools_data("test_omnibem_genomic_alterations.csv.gz")
bem = OmniBEMBuilder(data)
bem.filter_by_gene_list(gdsctools_data("test_omnibem_genes.txt"))
bem.plot_number_alteration_by_tissue()

(Source code, png, hires.png, pdf)

_images/references-8.png

This module provides an interface to download and filter GDSC1000 data, as published by Iorio et al (2016).

Author: Elisabeth D. Chen Date: 2016-06-17

class GDSC1000(annotate=False, data_folder_name='./data/gdsc_1000_data/')[source]

Help data retrieval from GDSC website (version v17)

from gdsctools import GDSC1000
data = GDSC1000()
data.download_data()

Next time, starting GDSCTools in the same directory, just type:

data = GDSCTools()
data.load_data()

CNA (e.g., cna_df), Methylation and gene variants are downloaded. IC50 as well and a set of annotations. There are combined in genomic_df.

The genomic_df contains the cna_df, methylation_df and variant_df data. The three latter contains in common the CELL_LINE, ALTERATION_TYPE, IDENTIFIER and TISSUE_FACTOR.

ALTERATION_TYPE can be GENETIC_VARIATION, METHYLATION, DELETION / AMPLIFICATION variant_df also has the COSMIC_ID. The annotation_df gives mapping between identifiers and gene names.

download_data() Download and load data in memory
load_data([annotation]) If CSV files are already downloaded, just load them
make_matrix([min_recurrence]) Return a dataframe compatible with ANOVA analysis

With this class, you may (1) download the data, (2) annotate or filter the data and (3) create a genomic matrix.

To load the data, either download it from the website using download_data() (downloads data from cancerrxgene.org loading methylation, cna, variant and cell lines datasets). It extract relevant columns, re-names column names and saves as csv files in the data_folder_name directory.

If you have already downloaded the data, you may just load it using load_data(). This function also annotates the data with gene information.

Then, you filter the data with one of the filter methods:

filter_by_cell_line([ "AsPC-1", "U-2-OS", "MDA-MB-231"... ] )
filter_by_cosmic_id([ 948121, 2839818, ...] )
filter_by_gene(["ATM", "TP53", ...])
filter_by_recurrence(min_recurrence = 3 )
filter_by_tissue_type([ "BRCA", "COAD/READ", ... ] )
filter_by_alteration_type(["METHYLATION", "AMPLIFICATION",
                                "DELETION", "GENE_VARIANT" ] )

Finally, you can create the genomic matrix using make_matrix().

You can also look at the unique regions for agiven data set. For instance, methylation dataframe has about 2,000 regions but only 338 are unique:

len(data.methylation_df.groupby('IDENTIFIER').groups)

constructor

Parameters:
  • annotate (bool) –
  • data_folder_name (str) –
annotate_all()[source]

Annotates dataframes after read_annotation

download_data()[source]

Download and load data in memory

filter_by_alteration_type(type_list=None)[source]
filter_by_cell_line(cell_line_list=None)[source]
filter_by_cosmic_id(cosmic_list=None)[source]
filter_by_gene(gene_list=None)[source]
filter_by_recurrence(min_recurrence=None)[source]
filter_by_tissue_type(tissue_list=None)[source]
get_cna_info()[source]

Return dataframe with number of genes per unique CNA identifier

get_genomic_info()[source]

Return information about the genomic dataframe

The returned dataframes contains two columns: the number of unique cosmic identifiers and features for each type of alterations.

get_methylation_info()[source]

Return dataframe with number of genes per unique methylation identifier

load_data(annotation=True)[source]

If CSV files are already downloaded, just load them

make_matrix(min_recurrence=None)[source]

Return a dataframe compatible with ANOVA analysis

reset_genomic_data()[source]

14.4. Visualisation

14.4.1. Volcano plot

Volcano plot utilities

The VolcanoANOVA is used in the creation of the report but may be used by a usr in a Python shell.

class VolcanoANOVA(data, sep='t', settings=None)[source]

Utilities related to volcano plots

This class is used in gdsctools.anova but can also be used independently as in the example below.

from gdsctools import ANOVA, ic50_test, VolcanoANOVA
an = ANOVA(ic50_test)

# retrict analysis to a tissue to speed up computation
an.set_cancer_type('lung_NSCLC')

# Perform the entire analysis
results = an.anova_all()

# Plot volcano plot of pvalues versus signed effect size
v = VolcanoANOVA(results)
v.volcano_plot_all()

(Source code, png, hires.png, pdf)

_images/references-9.png

Note

Within an IPython shell, you should be able to click on a circle and the title will be updated with the name of the drug/feature and FDR value.

Legend and color conventions:
 The green circles indicate significant hits that are resistant while reds show sensitive hits. Circles are colored if there are below the FDR_threshold AND below the pvalue_threshold AND if the signed effect size is above the effect_threshold.

Constructor

Parameters:
  • data – an ANOVAResults instance or a dataframe with the proper columns names (see below)
  • settings – an instance of ANOVASettings

Expected column names to be found if a filename is provided:

ANOVA_FEATURE_pval
ANOVA_FEATURE_FDR
FEATURE_delta_MEAN_IC50
FEATURE_IC50_effect_size
N_FEATURE_pos
N_FEATURE_pos
FEATURE
DRUG_ID

If the plotting is too slow, you can use the selector() to prune the results (most of the data are noise and overlap on the middle bottom area of the plot with little information.

savefig(filename, size_inches=(10, 10))[source]
selector(df, Nbest=1000, Nrandom=1000, inplace=False)[source]

Select only the first N best rows and N random ones

Sometimes, there are tens of thousands of associations and future analysis will include more features and drugs. Plotting volcano plots should therefore be fast and scalable. Here, we provide a naive way of speeding up the plotting by selecting only a subset of the data made of Nbest+Nrandom associations.

Parameters:
  • df – the input dataframe with ANOVAResults
  • Nbest (int) – how many of the most significant association should be kept
  • Nrandom (int) – on top of the Nbest significant association, set how many other randomly chosen associations are to be kept.
Returns:

pruned dataframe

volcano_plot_all()[source]

Create an overall volcano plot for all associations

This method saves the picture in a PNG file named volcano_all.png.

volcano_plot_all_drugs()[source]

Create a volcano plot for each drug and save in PNG files

Each filename is set to volcano_<drug identifier>.png

volcano_plot_all_features()[source]

Create a volcano plot for each feature and save in PNG files

Each filename is set to volcano_<feature name>.png

volcano_plot_one_drug(drug_id)[source]

Volcano plot for one drug (all genomic features)

Parameters:drug_id – a valid drug identifier to be found in the results
volcano_plot_one_feature(feature)[source]

Volcano plot for one feature (all drugs)

Parameters:feature – a valid feature name to be found in the results
class VolcanoANOVAJS(data, sep='t', settings=None)[source]
get_fdr_ypos()[source]
render_all()[source]
render_drug(name)[source]
render_feature(name)[source]

14.4.2. Boxplot and beeswarm

boxswarm(data, names=None, vert=True, widths=0.5, **kwargs)[source]

Plot boxplot with all points as circles.

This function is a wrapper of BoxSwarm

Parameters:
  • data – a dataframe. Each column is a data set from which a boxplot is created.
  • names
  • vert – orientation of the boxplots
  • widths – widths of the boxes
  • kargs – any argument accepted by BoxSwarm

See BoxSwarm documentation for details

class BoxSwarm(data, names=None, fontsize=20, hold=False, title='', lw=2, colors=['lightgrey', 'blue'])[source]

Simple beeswarm plot (boxplot + dots for each data point)

from pylab import randn
from gdsctools.boxswarm import BoxSwarm
b = BoxSwarm({'a':randn(100), 'b':randn(20)+2})
b.plot(vert=False)

(Source code, png, hires.png, pdf)

_images/references-10.png

Note

could use pybeeswarm, which is a proper implementation of beeswarm.

Constructor

Param:

a list of list (not same size) or a dictionary of lists

Parameters:
  • data
  • names
  • fontsize
  • hold
  • title
  • lw – width of lines
  • colors – loop over the list of colors provided to fill boxplots
  • **kargs
beeswarm(data, position, ratio=2.0)[source]

Naive plotting of the data points

We assume gaussian distribution so we expect fewers dots far from the mean/median. We’d like those dots to be close to the axes. conversely, we expect lots of dots centered around the mean, in which case, we’d like them to be spread in the box. We uniformly distribute position using

X = X + \dfrac{ U()-0.5 }{ratio} \times factor

but the factor is based on an arctan function:

factor = 1 - \arctan( \dfrac{X - \mu }{\pi/2})

The farther the data is from the mean \mu, the closest it is to the axes that goes through the box.

plot(vert=True, alpha=0.4, widths=0.5, **kwargs)[source]

Plot the boxplots and dots

Code related to the ANOVA analysis to find associations between drug IC50s and genomic features

class BoxPlots(odof, fontsize=20, savefig=False, directory='.')[source]

Box plot for a given association of drug versus genomic feature

from gdsctools import ANOVA, ic50_test
from gdsctools.boxplots import BoxPlots

gdsc = ANOVA(ic50_test)

# Perform the entire analysis
odof = gdsc._get_one_drug_one_feature_data(1047, 'TP53_mut')

# Plot volcano plot of pvalues versus signed effect size
bx = BoxPlots(odof)
bx.boxplot_association()

(Source code, png, hires.png, pdf)

_images/references-11.png

If the gdsc analysis has the MSI factor and tissue factor on, then additional plots can be created using boxplot_pancan().

Note that odof in the example above is a dictionary with the following keys:

  • drug_name
  • feature_name
  • masked_tissue: a dataframe with cosmic ids as index and 1 column of tissues names.
  • Y: list with the IC50s
  • masked_features: a dataframe with cosmic ids as index and 1 column of masked feature (1/0)
  • masked_msi: same as masked_features
  • negatives: subset of the IC50s corresponding to positive feature
  • positives: subst of the IC50s corresponding to negative feature

Constructor

boxplot_association(fignum=1)[source]

Boxplot of the association (negative versus positive)

Parameters:fignum – number of the figure
boxplot_pancan(mode, fignum=1, title_prefix='')[source]

Create boxplot related to the MSI factor or Tissue factor

Parameters:mode – either set to msi or tissue
directory = None

directory where to save the figure.

fontsize = None

fontsize for the plots

lw = None

linewidth used in the plots

odof = None

dictionary as returned by ANOVA._get_one_drug_one_feature_data

savefig = None

boolean to save figure

class BoxPlotsJS(odof, fontsize=20, savefig=False, directory='.')[source]
get_html_association()[source]
get_html_media()[source]
get_html_msi()[source]
get_html_tissue()[source]

14.5. reports

Base classes to create HTML reports easily

class ReportMain(filename='index.html', directory='report', overwrite=True, verbose=True, template_filename='index.html', mode=None, init_report=True)[source]

A base class to create HTML pages

This Report class holds the CSS and HTML layout and will ease the creation of new reports and HTML pages. For instance, it will add a footer and header automatically, save files in the proper directory, create the directory if it is missing, copy CSS and JS files in the directory automatically.

from gdsctools import Report
r = Report()
r.add_section('Example with some text', 'Example' )
r.report(onweb=True)

Note

For developers the original CSS and JS files are stored in the share/data directory.

The idea is that you create sections (text + title) that you add little by little in your HTML documents. Then, you create the report. The report will add a footer, header, table of contents before the sections. The text of a section can contain any HTML document.

Constructor

Parameters:
  • filename – default to index.html
  • directory – defaults to report
  • overwrite – default to True
  • verbose – default to True
  • dependencies – add the dependencies table at the end of the document if True.
  • mode (str) – if none, report have a structure that contains the following directories: OUTPUT, INPUT, js, css, images, code. Otherwise, if mode is set to ‘summary’, only the following directories are created: js, css, images
create_report(onweb=True)[source]
show()[source]

Opens a tab in a browser to see the document

14.6. Data-related

class TCGA[source]

A dictionary-like container to access to TCGA cancer types keywords

>>> from gdsctools import TCGA
>>> tt = TCGA()
>>> tt.ACC
'Adrenocortical Carcinoma'
TCGA_GDSC1000 = ['BLCA', 'BRCA', 'COREAD', 'DLBC', 'ESCA', 'GBM', 'HNSC', 'KIRC', 'LAML', 'LGG', 'LIHC', 'LUAD', 'LUSC', 'OV', 'PAAD', 'SKCM', 'STAD', 'THCA']

TCGA keys used in GDSC1000

class Tissues[source]

List of tissues included in various analysis

Contains tissues included e.g in v17,v18

Data sets provided with GDSCTools

The datasets may be for testing purpose:

  • ic50_test
  • drug_test
  • cosmic_builder_test

or informative:

  • cancer_cell_lines
  • cosmic_info

or used in analysis:

  • genomic_features_test
  • ic50_v17: IC50s for 1001 cell lines
  • gf_v17: dataset with genomic features for 1001 cell lines and 680 features (mutation, CNA)
  • ic50_v5
  • gf_v5
class Data(filename=None, description='No description', authors='GDSC consortium')[source]

A convenience class to hold information about a dataset

Each Data instance contains information about :

  1. the file location (filename)
  2. the data description (description)
  3. the authors (authors)

But the data is not stored and users must read the data set using their own tools.

authors = None

list of authors (string)

description = None

a short description (string)

filename = None

where is located the data set (full path)

location
class COSMICFetcher(filename=None)[source]

Utility to download a flat file about cosmic identier and build a small dataframe with cosmic identifiers and their diseases

The original flat file is downloaded from ftp.expasy.org/databases and contains records as follows:

ID         Identifier (cell line name)     Once; starts an entry
AC         Accession (CVCL_xxxx)           Once
SY         Synonyms                        Optional; once
DR         Cross-references                Optional; once or more
RX         References identifiers          Optional: once or more
WW         Web pages                       Optional; once or more
CC         Comments                        Optional; once or more
DI         Diseases                        Optional; once or more
OX         Species of origin               Once or more
HI         Hierarchy                       Optional; once or more
OI         Originate from same individual  Optional; once or more
SX         Sex (gender) of cell            Optional; once
CA         Category                        Optional; once

We keep only records with COSMIC cross references. From those records, we keep ID, AC, CA, DI (Disease) and the cosmic identifier.

The resulting dataframe can then be accessed in the df attribute.

>>> from gdsctools.cosmictools import COSMICFetcher
>>> cf = COSMICFetcher() # this may take a while to download the file
>>> cf.df.loc[0]
ID                                       OS-A
AC                                  CVCL_0C23
CA                           Cancer cell line
COSMIC_ID                             2239090
Disease      C4917; Small cell lung carcinoma
Name: 0, dtype: object

Constructor

Parameters:filename (str) – If not provided, download file from expasy.org and store it in data. Otherwise, if filename is provided, reads a local file. Format should be the same as the file downloaded from expasy
class COSMICInfo[source]

Retrieve information about cell line included in GDSC1000

This file reads a GDSCTools dataset gdsctools.datasets.cosmic_info. Its content is stored in df.

In corresponds to Table S1E (List cell line samples with data availability and annotations across the different omics

The method get() retrieves information contained in the dataframe df. Provide a known cosmic identifier as follows:

>>> from gdsctools import COSMICInfo
>>> c = COSMICInfo()
>>> c.get(909907, 'SAMPLE_NAME')
'ZR-75-30'

or get all available field as follows:

>>> c.get(909907)
SAMPLE_NAME           ZR-75-30
SEQ                          1
CNA                          1
EXP                          1
MET                          1
DRUG_SCR                     1
GDSC_description_1      breast
GDSC_description_2      breast
Study_Abbreviation        BRCA
MMR                      MSI-L
SCREEN_MEDIUM                R
GROWTH_PROPERTIES     Adherent
Name: 909907, dtype: object

Note

there are only 1000 cell lines in the df. Additional cell lines may be retrieve using COSMICFetcher

If a cosmic identifier is not found, the returned object has the same structure as above but with all fields set to False.

constructor

df = None

dataframe with all information

get(identifier, colname=None)[source]
Parameters:
  • identifier (int) – a cosmic identifiers. Possible values are stored in df.index attribute
  • colname – specific field.
Returns:

if colname is not provided, returns a time series for the identifier with all available fields. Otherwise, returns a specific field.

on_web(identifier)[source]

Open a tab related to the COSMIC identifier (in your browser)

14.7. Logistics

Sets of miscellaneous tools

class Logistic(xmid, scale, Asym=1, N=9, increase=False)[source]

Simple logistic class to see the curve implied by xmid/scale parameters

from gdsctools.logistics import Logistic
from pylab import legend
tl = Logistic(2, 1)
tl.plot()
tl.scale = 4
tl.plot(hold=True)
legend(['scale=1', 'scale=4'])

(Source code, png, hires.png, pdf)

_images/references-12.png

The X values are set automatically given the number of data points N and the minimum and maximum X values. By default a sensible values for the minimun and maximum x values are guessed based on scale parameter but one can set the xmin and xmax

from gdsctools.logistics import Logistic
from pylab import legend
tl = Logistic(2,1)
tl.plot()
tl.xmin= -4
tl.xmax = 4
tl.N = 40
tl.plot(hold=True)
legend(['default', 'user defined'])

(Source code, png, hires.png, pdf)

_images/references-13.png

Constructor

Parameters:
  • xmid – the first logistic function parameter
  • scale – the second logistic function parameter
  • Asym – Amplitude of the function
  • N – number of data point
  • increase – increasing or decreasing function.
  • xmin – starting range of x-values
  • xmax – ending range of x-values

if increase is False:

L(x) = \frac{Asym}{1 + exp ((xmid-X)/scale)}

if increase is True

L(x) = 1 - \frac{Asym}{1 + exp ((xmid-X)/scale)}

Changing xmin, xmax or N does change the content of X. You can change X directly.

N

set/get the number points

X

get/set of the x-values

Y

Getter for Y-values

plot(hold=False)[source]

Plot the logistic function

scale
xmax

set/get the maximum x range

xmin

set/get the minimum x range

class LogisticMatchedFiltering(xmid, scale, N=9)[source]

Experimental class to identify parameters of a noisy Logistic function

This class implements two methods to identify the parameters (xmid and scale) of a logistic function.

Note One constraint is to define the x-values.

from gdsctools import logistics
import pylab

mf = logistics.LogisticMatchedFiltering(1,2)
mf.scan(pylab.linspace(-3,3, 10), pylab.linspace(0,5,10))

(Source code)

constructor

Parameters:
  • xmid
  • scale
  • N
N
X

get/set the X values the logistic signal

get_snr(template, show=True)[source]
objective_function(xmid, scale)[source]
optimise(guess=[1, 1])[source]
plot(results, coords, best, xmid_range, scale_range)[source]
scale

get/set the scale value of the logistic signal

scan(xmid_range, scale_range, method='mf', show=True)[source]
set_noisy_data(sigma=0.1)[source]
xmid

get/set the xmid value of the logistic signal

14.8. Regression Analysis

14.8.1. Common Regression class

class Regression(ic50, genomic_features=None, verbose=False)[source]

Base class for all Regression analysis

In the gdsctools.anova.ANOVA case, the regression is based on the OLS method and is computed for a given drug and a given feature (ODOF). Then, the analysis is repeated across all features for a given drug (ODAF) and finally extended to all drugs (ADAF). So, there is one test for each combination of drug and feature.

Here, all features for a given drug are taken together to perform a Regression analysis (ODAF). The regression algorithm implemented so far are:

  • Ridge
  • Lasso
  • ElasticNet
  • LassoLars

Based on tools from the scikit-learn library.

Constructor

Parameters:
  • ic50 – an IC50 file
  • genomic_features – a genomic feature file

see Data Format and Readers for help on the input data formats.

boxplot(drug_name, model, n=5, minimum_match_per_combo=10, bx_vert=True, bx_alpha=0.5, verbose=False, max_label_length=40)[source]

Boxplot plot of the most important features.

Parameters:
  • n (int) – maximum most important features to be shown (in term of coefficient/weight used in the model)
  • minimum_match_per_combo (int) – minimum number of alterations required for a feature to be shown
# assuming there is a drug ID = 29
r = GDSCLasso()
model = r.get_best_model(29)
r.boxplot(29, model)

In addition, we also show the wild type case where the total number of mutations is 0.

check_randomness(drug_name, kfolds=10, N=10, progress=False, nbins=40, show=True, **kargs)[source]

Compute Bayes factor between NULL model and best model fitted N times

Parameters:
  • drug_name
  • kfolds
  • N (int) – optimise NULL models and real model N times
  • progress
  • nbins
  • show

Bayes factor:

S = sum([s>r for s,r in zip(scores, random_scores)])
proba = S / len(scores)
bayes_factor = 1. / (1-proba)

Interpretation for values of the Bayes factor according to Kass and Raftery (1995).

Interpretation B(1,2)
Very strong support for 1 < 0.0067
Strong support 1 0.0067 to 0.05
Positive support for 1 0.05 to .33
Weak support for 1 0.33 to 1
No support for either model 1
Weak support for 2 1 to 3
Positive support for 2 3 to 20
Strong support for 2 20 to 150
Very strong support for 2 > 150
references: http://www.socsci.uci.edu/~mdlee/LodewyckxEtAl2009.pdf
http://www.aarondefazio.com/adefazio-bayesfactor-guide.pdf
dendogram_coefficients(stacked=False, show=True, cmap='terrain')[source]

shows the coefficient of each optimised model for each drug

This works for demonstration and small data sets.

fit(drug_name, alpha=1, l1_ratio=0.5, kfolds=10, show=True, tol=0.001, normalize=False, shuffle=False, perturbation=0.01, randomize_Y=False)[source]

Run Elastic Net with a cross validation for one value of alpha

Parameters:
  • drug_name – the drug to analyse
  • alpha (float) – note that theis alpha parameter corresponds to the lambda parameter in glmnet R package.
  • l1_ratio (float) – This is the lasso penalty parameter. Note that in scikit-learn, the l1_ratio correspond to the alpha parameter in glmnet R package. l1_ratio set to 0.5 means that there is a weight equivalent for the Lasso and Ridge effects.
  • kfolds (int) – defaults to 10
  • shuffle – shuffle the indices in the KFold
Returns:

kfolds scores for each fold. The score is the pearson correlation.

Note

l1_ratio < 0.01 is not reliable unless sequence of alpha is provided.

Note

alpha = 0 correspond to an OLS analysis

get_best_model(drug_name, kfolds=10, alphas=None, l1_ratio=0.5)[source]

Return best model fitted using a CV

Parameters:
  • drug_name
  • kfolds
  • alphas
  • l1_ratio
plot_importance(drug_name, model=None, fontsize=11, max_label_length=35, orientation='vertical')[source]

Plot the absolute weights found by a fittd model.

Parameters:
  • drug_name (str) –
  • model – a model
  • fontsize (int) – (defaults to 11)
  • max_label_length – 35 by default
  • orientation – orientation of the plot (vertical or horizontal)
Returns:

the dataframe with the weights (may be empty)

Note

if no weights are different from zeros, no plots are created.

plot_weight(drug_name, model=None, fontsize=12, figsize=(10, 7), max_label_length=20, Nmax=40)[source]

Plot the elastic net weights

Parameters:
  • drug_name – the drug identifier
  • alpha
  • l1_ratio

Large alpha values will have a more stringent effects on the weigths and select only some of them or maybe none. Conversely, setting alphas to zero will keep all weights.

from gdsctools import *
ic = IC50(gdsctools_data("IC50_v5.csv.gz"))
gf = GenomicFeatures(gdsctools_data("genomic_features_v5.csv.gz"))
en = GDSCElasticNet(ic, gf)
model = en.get_model(alpha=0.01)
en.plot_weight(1047, model=model)

(Source code, png, hires.png, pdf)

_images/references-15.png
runCV(drug_name, l1_ratio=0.5, alphas=None, kfolds=10, verbose=True, shuffle=True, randomize_Y=False, **kargs)[source]

Perform the Cross validation to get the best alpha parameter.

Returns:an instance of RegressionCVResults that contains alpha parameter and Pearson correlation value.
tune_alpha(drug_name, alphas=None, N=80, l1_ratio=0.5, kfolds=10, show=True, shuffle=False, alpha_range=[-2.8, 0.1], randomize_Y=False)[source]

Interactive tuning of the model (alpha).

This is much faster than plot_cindex() but much slower than runCV().

from gdsctools import *
ic = IC50(gdsctools_data("IC50_v5.csv.gz"))
gf = GenomicFeatures(gdsctools_data("genomic_features_v5.csv.gz"))

en = GDSCElasticNet(ic, gf)

en.tune_alpha(1047, N=40, l1_ratio=0.1)

(Source code, png, hires.png, pdf)

_images/references-16.png

14.8.2. Lasso

class GDSCLasso(ic50, genomic_features=None, verbose=False)[source]

See as Regression

get_model(alpha=1, l1_ratio=None, **kargs)[source]

14.8.3. ElasticNet

class GDSCElasticNet(ic50, genomic_features=None, verbose=False, set_media_factor=False)[source]

Variant of ANOVA that handle the association at the drug level using an ElasticNet analysis of the IC50 vs Feature matrices.

As compared to the GDSCRidge and GDSCLasso

Here is an example on how to perform the analysis, which is similar to the ANOVA API:

from gdsctools import GDSCElasticNet, gdsctools_data, IC50, GenomicFeatures
ic50 = IC50(gdsctools_data("IC50_v5.csv.gz"))
gf = GenomicFeatures(gdsctools_data("genomic_features_v5.csv.gz"))

en = GDSCElasticNet(ic50, gf)
en.enetpath_vs_enet(1047)

(Source code, png, hires.png, pdf)

_images/references-17.png

For more information about the input data sets please see ANOVA, readers

Constructor

Parameters:
  • IC50 (DataFrame) – a dataframe with the IC50. Rows should be the COSMIC identifiers and columns should be the Drug names (or identifiers)
  • features – another dataframe with rows as in the IC50 matrix and columns as features. The first 3 columns must be named specifically to hold tissues, MSI (see format).
  • verbose – verbosity in “WARNING”, “ERROR”, “DEBUG”, “INFO”
enetpath_vs_enet(drug_name, alphas=None, l1_ratio=0.5, nfeat=5, max_iter=1000, tol=0.0001, selection='cyclic', fit_intercept=False)[source]

#if X is not scaled, the enetpath and ElasticNet will give slightly # different results #if X is scale using:

from sklearn import preprocessing
xscaled = preprocessing.scale(X)
xscaled = pd.DataFrame(xscaled, columns=X.columns)
get_model(alpha=1, l1_ratio=0.5, tol=0.001, normalize=False, **kargs)[source]
plot_cindex(drug_name, alphas, l1_ratio=0.5, kfolds=10, hold=False)[source]

Tune alpha parameter using concordance index

This is longish and performs the following task. For a set of alpha (list), run the elastic net analysis for a given l1_ratio with kfolds. For each alpha, get the CIndex and find the CINdex for which the errors are minimum.

Warning

this is a bit longish (300 seconds for 10 folds and 80 alphas) on GDSCv5 data set.

14.8.4. Ridge

class GDSCRidge(ic50, genomic_features=None, verbose=False)[source]

Same as Regression

get_model(alpha=1, l1_ratio=None, **kargs)[source]

14.8.5. Report

Code related to the Regression analysis to find associations between drug IC50s and genomic features

class RegressionReport(method, directory='.', verbose=True, image_dir='images', data_dir='data', config={'boxplot_n': '?'})[source]

Class used to interpret the results and create final HTML report

Constructor

Parameters:
  • method – Method used in the regression analysis (lasso, elasticnet, ridge)
  • results
create_html_drug()[source]

report for each individual drug

create_html_main(onweb=False)[source]

Create HTML main document (summary)

14.9. Data Packages

class IC50Cluster(ic50, ratio_threshold=10, verbose=True, cluster=True)[source]

Utility to cluster columns that correspond to the same drug ID

From GDSC v18 data sets onwards, DRUG identifiers may be duplicated to account for different drug concentration. This is not recommended since we’d rather use unique identifier for different experiments but to account for this feature, the IC50Cluster will rename the columns and transform the data as follows.

Consider the case of the DRUG 1211. It appears 3 times in the original data:

Drug_1211_0.15625_IC50
Drug_1211_1_IC50
Drug_1211_10_IC50

Actually, there are about 15 such cases even though in general there are only 2 duplicates:

    DRUG_ID   1   2    3  total  icommon       ratio
21     1782  47  47  NaN     47      47  100.000000
20     1510  45  45  NaN     45      45  100.000000
19     1211  48  47  4.0     50      46   92.000000
18     1208  48  47  NaN     50      45   90.000000
16     1032  43  47  NaN     50      40   80.000000
17     1207  38  47  NaN     50      35   70.000000
13      231   2  39  NaN     39       2    5.128205
14      232  45   2  NaN     45       2    4.444444
10      226   2  45  NaN     45       2    4.444444
12      230   2  46  NaN     46       2    4.347826
15      238   2  46  NaN     46       2    4.347826
0       206   2  46  NaN     46       2    4.347826
1       211  46   2  NaN     46       2    4.347826
9       224   2  46  NaN     46       2    4.347826
8       223   2  46  NaN     46       2    4.347826
7       221   2  46  NaN     46       2    4.347826
6       217   2  46  NaN     46       2    4.347826
5       216   2  46  NaN     46       2    4.347826
4       215   2  46  NaN     46       2    4.347826
3       214   2  46  NaN     46       2    4.347826
2       213   2  46  NaN     46       2    4.347826
11      229   2  46  NaN     46       2    4.347826

The clustering works as follows. If the ratio of drugs in common between several concentrations is large, then they are studied independently. Otherwise they are merged.

In the final dataframe, the columns names are transformed into unique identifiers like in the IC50 class by removing the Drug_ prefix and ``_conc_IC50 suffix.

The mapping contains the mapping between new and old identifiers.

See also

cleanup() method.

constructor

Parameters:
  • ic50
  • ratio_threshold (int) –
  • verbose (bool) –
  • cluster (bool) – may be useful to not cluster the data for testing or debugging
cleanup(offset=10000)[source]

Rename the columns into unique identifiers

Parameters:offset (int) – if duplicated, add the offset

The mapping contains the mapping, which should be used to update the decoder file.

cluster()[source]
duplicated
to_cluster
class GDSC(ic50, drug_decode, genomic_feature_pattern='GF_*csv', main_directory='tissue_packages', verbose=True)[source]

Wrapper of the ANOVA class and reports to analyse all TCGA Tissues and companies automatically while creating summary HTML pages.

First, one need to provide an unique IC50 file. Second, the DrugDecode file (see DrugDecode) must be provided with the DRUG identifiers and their corresponding names. Third, a set of genomic feature files must be provided for each TCGA tissue.

You then create a GDSC instance:

from gdsctools import GDSC
gg = GDSC('IC50_v18.csv', 'DRUG_DECODE.txt',
    genomic_feature_pattern='GF*csv')

At that stage you may want to change the settings, e.g:

gg.settings.FDR_threshold = 20

Then run the analysis:

gg.analysis()

This will launch an ANOVA analysis for each TCGA tissue + PANCAN case if provided. This will also create a data package for each tissue. The data packages are stored in ./tissue_packages directory.

Since all private and public drugs are stored together, the next step is to create data packages for each company:

gg.create_data_packages_for_companies()

you may select a specific one if you wish:

gg.create_data_packages_for_companies(['AZ'])

Finally, create some summary pages:

gg.create_summary_pages()

You entry point is an HTML file called index.html

Constructor

Parameters:
  • ic50 – an IC50 file.
  • drug_decode – an DrugDecode file.
  • genomic_feature_pattern – a glob to a set of GenomicFeature files.
analyse(multicore=None)[source]

Launch ANOVA analysis and creating data package for each tissue.

Parameters:
  • onweb (bool) – By default, reports are created but HTML pages not shown. Set to True if you wish to open the HTML pages.
  • multicore – number of cpu to use (1 by default)
companies
create_data_packages_for_companies(companies=None)[source]

Creates a data package for each company found in the DrugDecode file

create_summary_pages()[source]

Create summary pages

Once the main analyis is done (analyse()), and the company packages have been created (create_data_packages_for_companies()), you can run this method that will creade a summary HTML page (index.html) for the tissue, and a similar summary HTML page for the tissues of each company. Finally, an HTML summary page for the companies is also created.

The final tree direcorty looks like:

|-- index.html
|-- company_packages
|   |-- index.html
|   |-- Company1
|   |   |-- Tissue1
|   |   |-- Tissue2
|   |   |-- index.html
|   |-- Company2
|   |   |-- Tissue1
|   |   |-- Tissue2
|   |   |-- index.html
|-- tissue_packages
|   |-- index.html
|   |-- Tissue1
|   |-- Tissue2
tcga

14.10. MISC

Sets of miscellaneous tools

class Savefig(verbose=False)[source]

A simple class to save matploltib figures in the proper place

Note

For developers only

directory

directory where to save figures

savefig(name, size_inches=None, **kargs)[source]

Save a matplotlib figure

Parameters:
  • filename (str) – where to save the figure.
  • kargs – accepts all parameters known by pylab.savefig

Code related to the ANOVA analysis to find associations between drug IC50s and genomic features

class ANOVASettings(**kargs)[source]

All settings used in gdsctools.anova.ANOVA analysis

This class behaves as a dictionary but values for a given key (setting) can be accessed and changed easily like an attribute:

>>> from gdsctools import ANOVASettings
>>> s = ANOVASettings()
>>> s.FDR_threshold
25
>>> s.FDR_threshold = 20

It is the responsability of the users or developers to check the validity of the settings by calling the check() method. Note, however, that this method does not perform exhaustive checks.

Finally, the method to_html() creates an HTML table that can be added to an HTML report.

Note

for developers a key can be changed or accessed to as if it was an attribute. This prevents some functionalities (such as copy() or property) to be used normaly hence the creation of the check() method to check validity of the values rather than using properties.

Here are the current values available.

Name Default Description
include_MSI_factor True Include MSI in the regression
feature_factor_threshold 3 Discard association where a genomic feature has less than 3 positives or 3 negatives values (e.g., 0, 1 or 2)
MSI_factor_threshold 2 Discard association where a MSI count has less than 2 positives or 2 negatives values (e.g., 0, or 1).
analysis_type PANCAN Type of analysis. PANCAN means use all data. Otherwise, you must provide a valid tissue name to be found in the Genomic Feature data set.
pvalue_correction_method fdr Type of p-values correction method used. Could be fdr, qvalue or one accepted by MultipleTesting
pvalue_correction_level True Apply pvalue correction globally. If False, applied to ‘drug_level’ only.
equal_var_ttest True Assume equal variance in the t-test
minimum_nonna_ic50 6 Minimum number of IC50 required to perform an analysis for a given drug.
fontsize 25 Used in some plots for labels
FDR_threshold 25 FDR threshold used in volcano plot and significant hits
pvalue_threshold 0.001 Used to select significant hits see ANOVAReport
directory html_gdsc_anova Directory where images and HTML documents are saved.
savefig False Save the figure or not (PNG format)
effect_threshold 0 Used in the volcano plot. See VolcanoPlot

There are parameters dedicated to the regression method. Note that only regression_formula is effective right now.

Name Default Description
regression_method OLS Regression method amongst OLS. NOT USED YET.
regression_alpha 0.01 Fraction of penalty included. If 0, equivalent to OLS. NOT USED YET.
regression_L1_wt 0.5 Fraction of the penalty given to L1 penalty term. Must be between 0 and 1. If 0, equivalent to Ridge. If 1 equivalent to Lasso. NOT USED YET.
regression_formula auto if auto, use standard regression from GDSCTools (see link_formula) otherwise any valid regression formula can be used.

See also

About the settings or gdsctools.readthedocs.org/en/master/settings.html#filtering decrease the number of significant hits.

check()[source]

Checks the values of the parameters

This may not be exhaustive. Right now, checks

  • MSI factor is boolean.
  • Regression.method in OLS/Ridge/Lasso/ElasticNet
  • FDR thresohld in [0,1]
  • pvalues_threshold in [0,inf[
  • effect_threshold in [0,inf[
  • pvalue_correction_method
  • etc
to_html()[source]

Convert the sets of parameters into a nice HTML table

Small functionalities to retrive chembl/chemspider identifiers based on a drug name

class ChemSpiderSearch(drug_decode)[source]

This class uses ChemSpider and ChEMBL to identify drug name

Warning

this is a draft version in dev mode

c = ChemSpiderSearch()
c.search_in_chemspider()
c.search_from_smile_inchembl()
df = c.find_chembl_ids()

It happens that most of public names can be found and almost none of non-public are found. As expected…

If chemspider, chembl and pubchem are empty, search for the drug name in chemspider.

CHEMSPIDER search:
if no identifier found, the search if DROPPED if 1 identifier found, we keep going using the SMILE identifier If more than 1 identifier found, this is AMBIGUOUS.

If chembl and pubchem, check with unichem If chembl, check smiles If chembl and chemspider, check smiles ?

SMILES are not unique

filling_chembl_pubchem_using_unichem()[source]
find_chembl_ids()[source]
get_chemspider_ids(drug_name)[source]
search_from_smile_inchembl()[source]
search_in_chemspider()[source]

Code related to the ANOVA analysis to find associations between drug IC50s and genomic features

class BaseModels(ic50, genomic_features=None, drug_decode=None, verbose=True, set_media_factor=False)[source]

A Base class for ANOVA / ElaticNet models

Constructor

Parameters:
  • IC50 (DataFrame) – a dataframe with the IC50. Rows should be the COSMIC identifiers and columns should be the Drug names (or identifiers)
  • features – another dataframe with rows as in the IC50 matrix and columns as features. The first 3 columns must be named specifically to hold tissues, MSI (see format).
  • drug_decode – a 3 column CSV file with drug’s name and targets see readers for more information.
  • verbose – verbosity in “WARNING”, “ERROR”, “DEBUG”, “INFO”

The attribute settings contains specific settings related to the analysis or visulation.

cosmicIds

get/set the cosmic identifiers in the IC50 and feature matrices

diagnostics(details=False)[source]

Return dataframe with information about the analysis

drugIds

Get/Set drug identifers

feature_names

Get/Set feature names

init()[source]
read_drug_decode(filename=None)[source]

Read file with the DRUG information

read_settings(settings)[source]

Read settings and update cancer type if set

set_cancer_type(ctype=None)[source]

Select only a set of tissues.

Input IC50 may be PANCAN (several cancer tissues). This function can be used to select a subset of tissues. This function changes the ic50 dataframe and possibly the feature as well if some are not relevant anymore (sum of the column is zero for instance).

settings = None

an instance of ANOVASettings

exception GDSCToolsDuplicatedDrugError(drug_id)[source]
exception GDSCToolsError(value)[source]