tmtk - TranSMART data curation toolkit¶
Author: | Jochem Bijlard |
---|---|
Source Code: | https://github.com/thehyve/tmtk/ |
Generated: | Jan 29, 2018 |
License: | GPLv3 |
Version: | 0.3.2 |
Philosophy
A toolkit for ETL curation for the tranSMART data warehouse for translational research.
The TranSMART curation toolkit (tmtk
) aims to provide a language
and set of classes for describing data to be uploaded to tranSMART.
The toolkit can be used to edit and validate studies prior
to loading them with transmart-batch.
- Functionality currently available:
- create a transmart-batch ready study from clinical data files.
- load an existing study and validate its contents.
- edit the transmart concept tree in The Arborist graphical editor.
- create chromosomal region annotation files.
- map HGNC gene symbols to corresponding Entrez gene IDs using mygene.info.
Note
tmtk is a python3
package meant to be run in Jupyter notebooks
. Results
for other setups may vary.
Basic Usage¶
Step 1: Opening a notebook¶
First open a Jupyter Notebook, open a shell and change directory to some place where your data is. Then start the notebook server:
cd /path/to/studies/
jupyter notebook
This should open your browser to Jupyters file browser, create a new notebook for here.
Step 2: Using tmtk¶
# First import the toolkit into your environment
import tmtk
# Then create a <tmtk.Study> object by pointing to study.params of a transmart-batch study
study = tmtk.Study('~/studies/a_tm_batch_ready_study/study.params')
# Or, by using the study wizard on a directory with correctly structured, clinical data files.
# (Visit the transmart-batch documentation to find out what is expected.)
study = tmtk.wizard.create_study('~/studies/dir_with_some_clinical_data_files/')
Now we have loaded the study as a tmtk.Study
object we have some
interesting functions available:
# Check whether transmart-batch will find any issues with the way your study is setup
study.validate_all()
# Graphically manipulate the concept tree in this study by using The Arborist
study.call_boris()
Contents¶
Changelog¶
Version 0.3.2
- More easily extensible validator functionality
- Added multiple validation methods
- Fix issue with namespace cleaner
Version 0.3.1
- Replaced deprecated pandas functionality
- More reliably start batch job
Version 0.3.0
- Create studies from TraIT data templates, see Data templates.
- Create fully randomized studies of any size:
tmtk.toolbox.RandomStudy
. - Load data right from Jupyter using transmart-batch, with progress bars!! Also works in
as a command line tool
transmart-batch
. - Set name and id from the main study object.
Version 0.2.2
- Minor bug fix for Arborist installation
Version 0.2.1
- The Arborist is now implemented as a Jupyter Notebook extension
- Metadata tags are automatically sorted in Arborist.
Version 0.2.0
- Create and apply tree templates in Arborist
- Improved interaction with metadata tags in Arborist
- Resolved issues with the validator
- R is now an optional dependency
User examples¶
These examples have been extracted from Jupyter Notebooks.
Create study from clinical data.¶
tmtk has a wizard that can be used to quickly go from clinical data files to a study object. The main goal of this functionality is to reduce the barrier of setting up all transmart-batch specific files (i.e. parameter files, column mapping and word mapping files).
The way to use it is to call tmtk.wizard.create_study(path)
, where
path points a directory with clinical data files.
Note: clinical datafiles have to be in a format that is accepted by transmart-batch.
Here we will create a study from these two files:
import os
files_dir = './studies/wizard/'
os.listdir(files_dir)
['Cell-line_clinical.txt', 'Cell-line_NHTMP.txt']
# Load the toolkit
import tmtk
# Create a study object by running the wizard
study = tmtk.wizard.create_study('./studies/wizard/')
##### Please select your clinical datafiles #####
- 0. /home/vlad-the-impaler/tmtk/studies/wizard/Cell-line_clinical.txt
- 1. /home/vlad-the-impaler/tmtk/studies/wizard/Cell-line_NHTMP.txt
Pick number: 0
Selected files: ['Cell-line_clinical.txt']
Pick number: 1
Selected files: ['Cell-line_clinical.txt', 'Cell-line_NHTMP.txt']
Pick number:
✅ Adding 'Cell-line_clinical.txt' as clinical datafile to study.
✅ Adding 'Cell-line_NHTMP.txt' as clinical datafile to study.
The wizard walked us through some of the options for the study we want
to create. Our new study is a public study with STUDY_ID==WIZARD
and
you can pick an appropriate name by setting the study.study_name = 'Ur a wizard harry'
.
None of the clinical params have been set, so tmtk
will use default names for the column and word mapping file. Next the
datafiles have been loaded and the column mapping object has been
created to include the data files.
Next we will run the validator and find out that some files cannot be found. This is expected as these objects are only in memory and not yet on disk.
study.validate_all(5)
⚠ No valid file found on disk for /home/vlad-the-impaler/tmtk/studies/wizard/clinical/word_mapping_file.txt, creating dataframe.
Validating params file at clinical
❌ WORD_MAP_FILE=word_mapping_file.txt cannot be found.
❌ COLUMN_MAP_FILE=column_mapping_file.txt cannot be found.
Detected parameter WORD_MAP_FILE=word_mapping_file.txt.
Detected parameter COLUMN_MAP_FILE=column_mapping_file.txt.
Validating params file at study
Detected parameter TOP_NODE=\Public Studies\You're a wizard Harry\.
Detected parameter STUDY_ID=WIZARD.
Detected parameter SECURITY_REQUIRED=N.
Of course, we want to write our study to disk so it can be loaded with transmart-batch.
study = study.write_to('~/studies/my_new_study')
Writing file to /home/vlad-the-impaler/studies/my_new_study/clinical/clinical.params
Writing file to /home/vlad-the-impaler/studies/my_new_study/study.params
Writing file to /home/vlad-the-impaler/studies/my_new_study/clinical/column_mapping_file.txt
Writing file to /home/vlad-the-impaler/studies/my_new_study/clinical/Cell-line_clinical.txt
Writing file to /home/vlad-the-impaler/studies/my_new_study/clinical/word_mapping_file.txt
Writing file to /home/vlad-the-impaler/studies/my_new_study/clinical/Cell-line_NHTMP.txt
Next you can use the TranSMART Arborist to modify the concept tree or
use tmtk to load to transmart if you’ve set your $TMBATCH_HOME
, see Using transmart-batch from Jupyter.
TranSMART Arborist¶
GUI editor for the concept tree.¶
First load the toolkit.
import tmtk
Create a study object by entering a “study.params” file.
study = tmtk.Study('../studies/valid_study/study.params')
To verify the study object is compatible with transmart-batch for loading you can run the validator
study.validate_all()
Validating Tags:
❌ Tags (2) found that cannot map to tree: (1. Cell line characteristics∕1. Cell lines∕Age and 1. Cell line characteristics∕1. Cell lines∕Gender). You might want to call_boris() to fix them.
We will ignore this issue for now as this will be fixed automatically when calling the Arborist GUI.
The GUI allows a user to interactively edit all aspects of TranSMART’s concept tree, this include:
- Concept Paths from the clinical column mapping.
- Word mapping from clinical data files.
- High dimensional paths from subject sample mapping files.
- Meta data tags
# In a Jupyter Notebook, this brings up the interactive concept tree editor.
study.call_boris()
Once returned from The Arborist to Jupyter environment we can write the updated files to disk. You can then run transmart-batch on that study to load it into your tranSMART instance.
study.write_to('~/studies/updated_study')
Collaboration with non technical users.¶
Though using Jupyter Notebooks is great for technical users, less technical domain experts might quickly feel discouraged. To allow for collaboration with these users we will upload this concept tree to a running Boris as a Service webserver. This will allow others to make refinements to the concept tree.
study.publish_to_baas('arborist-test-trait.thehyve.net')
Once the study is updated in BaaS, we can update the local files by copying the url for the latest tree into this command.
study.update_from_baas('arborist-test-trait.thehyve.net/trees/valid-study/3/~edit')
Using transmart-batch from Jupyter¶
Using tmtk you can load data to transmart right from Jupyter. For this to work you need to download and build transmart-batch, if you want to do this see the transmart-batch github.
Once you’ve done that you need to set an environment variable to the path of the github repository. The easiest way to do this is to add the following to your ~/.bash_profile:
export $TMBATCH_HOME=/home/path/to/transmart-batch
Next make sure to create a batchdb.property file with an appropriate name in the $TMBATCH_HOME
directory. tmtk will look for any *.property file and allow you run transmart-batch with that property
file from many objects. An examples of a good names are production.properties or test-environment.properties.
Next you will be able to do something like this:
study.load_to.production()
API Description¶
Study class¶
-
class
tmtk.
Study
(study_params_path=None, minimal=False)[source]¶ Bases:
tmtk.utils.validate.ValidateMixin
Describes an entire TranSMART study. This is the main object used in tmtk. Studies can be initialized by pointing to a study.params file. This study has to be structured according to specification for transmart-batch.
>>> import tmtk >>> study = tmtk.Study('./studies/valid_study/study.params')
This will create the study object which can be used as a starting point for custom curation or directly in The Arborist.
-
add_metadata
()[source]¶ Create the Tags object for this study. Does nothing if it is already present.
-
all_files
¶ All file objects in this study.
-
annotation_files
¶ All annotation file objects in this study.
-
call_boris
(height=650)[source]¶ Launch The Arborist GUI editor for the concept tree. This starts a Flask webserver in an IFrame when running in a Jupyter Notebook.
While The Arborist is opened, the GIL prevents any other actions. :param height: set the height of the output cell
-
clinical_files
¶ All clinical file objects in this study.
-
concept_tree
¶ ConceptTree object for this study.
-
concept_tree_json
¶ Stringified JSON that is used by JSTree in The Arborist.
-
concept_tree_to_clipboard
()[source]¶ Send stringified JSON that is used by JSTree in The Arborist to clipboard.
-
find_annotation
(platform=None)[source]¶ Search for annotation data with this study and return it.
Parameters: platform – platform id to look for in this study. Returns: an Annotations object or nothing.
-
find_params_for_datatype
(datatypes=None)[source]¶ Search for parameter files within this study object and return them as list.
Parameters: datatypes – single string datatype or list of strings Returns: a list of parameter objects for specific datatype in this study
-
get_objects_with_prop
(prop: <built-in function all>)[source]¶ Search for objects with a certain property.
Parameters: prop – string equal to the property name. Returns: generator for the found objects.
-
high_dim_files
¶ All high dimensional file objects in this study.
-
load_to
¶
-
publish_to_baas
(url, study_name=None, username=None)[source]¶ Publishes a tree on a Boris as a Service instance.
Parameters: - url – url to a instance (e.g. http://transmart-arborist.thehyve.nl/).
- study_name – a nice name.
- username – if no username is given, you will be prompted for one.
Returns: the url that points to the study you’ve just uploaded.
-
sample_mapping_files
¶ All subject sample mapping file objects in this study.
-
study_id
¶ The study ID as it is set in study params.
-
study_name
¶ The study name, extracted from study param TOP_NODE.
-
tag_files
¶
-
update_from_baas
(url, username=None)[source]¶ Give url to a tree in BaaS.
Parameters: - url – url that has both the study and version of a tree in BaaS (e.g. http://transmart-arborist.thehyve.nl/trees/study-name/1/~edit/).
- username – if no username is given, you will be prompted for one.
-
update_from_treefile
(treefile)[source]¶ Give path to a treefile (from Boris as a Service or otherwise) and update the current study to match made changes.
Parameters: treefile – path to a treefile (stringified JSON).
-
validate_all
(verbosity='WARNING')[source]¶ Validate all items in this study.
Parameters: verbosity – only display output of this level and above. Levels: ‘debug’, ‘info’, ‘okay’, ‘warning’, ‘error’, ‘critical’. Default is ‘WARNING’. Returns: True if no errors or critical is encountered.
-
write_to
(root_dir, overwrite=False, return_new=True)[source]¶ Write this study to a new directory on file system.
Parameters: - root_dir – the base directory to write the study to.
- overwrite – set this to True to overwrite existing files.
- return_new – if True load the study object from the new location and return it.
Returns: new study object if return_new == True.
-
Params classes¶
Params Container¶
-
class
tmtk.params.Params.
Params
(study_folder=None)[source]¶ Bases:
tmtk.utils.validate.ValidateMixin
Container class for all params files, called by Study to locate all params files.
-
add_params
(path, parameters=None)[source]¶ Add a new parameter file to the Params object.
Parameters: - path – a path to a parameter file.
- new – if new, create parameter object.
- parameters – add dict here with parameters if you want to create a new parameter file.
-
static
create_params
(path, parameters=None, subdir=None)[source]¶ Create a new parameter file object.
Parameters: - path – a path to a parameter file.
- parameters – add dict here with parameters if you want to create a new parameter file.
- subdir – subdir is used as string representation.
Returns: parameter file object.
-
Base class: ParamsBase¶
-
class
tmtk.params.ParamsBase.
ParamsBase
(path=None, parameters=None, subdir=None, parent=None)[source]¶ Bases:
tmtk.utils.validate.ValidateMixin
Base class for parameter files.
AnnotationParams¶
ClinicalParams¶
HighDimParams¶
-
class
tmtk.params.HighDimParams.
HighDimParams
(path=None, parameters=None, subdir=None, parent=None)[source]¶ Bases:
tmtk.params.ParamsBase.ParamsBase
-
docslink
= 'https://github.com/thehyve/transmart-batch/blob/master/docs/hd-params.md'¶
-
is_viable
()[source]¶ Returns: True if both the datafile and map file are located, else returns False.
-
mandatory
¶
-
optional
¶
-
Clinical classes¶
Clinical Container¶
-
class
tmtk.clinical.
Clinical
(clinical_params=None)[source]¶ Bases:
tmtk.utils.validate.ValidateMixin
Container class for all clinical data related objects, i.e. the column mapping, word mapping, and clinical data files.
This object has methods that add data files, and for lookups of clinical files and variables.
-
ColumnMapping
¶
-
WordMapping
¶
-
add_datafile
(filename, dataframe=None)[source]¶ Add a clinical data file to study.
Parameters: - filename – path to file or filename of file in clinical directory.
- dataframe – if given, add pd.DataFrame to study.
-
all_variables
¶ Dictionary where {tmtk.VarID: tmtk.Variable} for all variables in the column mapping file.
-
apply_column_mapping_template
(template)[source]¶ Update the column mapping by applying a template.
Parameters: template – expected input is a dictionary where keys are column names as found in clinical datafiles. Each column header name has a dictionary describing the path and data label. For example:
- {‘GENDER’: {‘path’: ‘CharacteristicsDemographics’,
- ’label’: ‘Gender’},
- ’BPBASE’: {‘path’: ‘Lab resultsBlood’,
- ’label’: ‘Blood pressure (baseline)’}
}
-
call_boris
(height=650)[source]¶ Use The Arborist to modify only information in the column and word mapping files. :param height: set the height of the output cell
-
clinical_files
¶
-
get_datafile
(name: str)[source]¶ Find datafile object by filename.
Parameters: name – name of file. Returns: tmtk.DataFile object.
-
get_variable
(var_id: tuple)[source]¶ Return a Variable object based on variable id.
Parameters: var_id – tuple of filename and column number. Returns: tmtk.Variable.
-
load_to
¶
-
params
¶
-
ColumnMapping¶
-
class
tmtk.clinical.
ColumnMapping
(params=None)[source]¶ Bases:
tmtk.utils.filebase.FileBase
,tmtk.utils.validate.ValidateMixin
Class with utilities for the column mapping file for clinical data. Can be initiated with by giving a clinical params file object.
-
append_from_datafile
(datafile)[source]¶ Appends the column mapping file with rows based on datafile column names.
Parameters: datafile – tmtk.DataFile object.
-
build_index
(df=None)[source]¶ Build index for the column mapping dataframe. If pd.DataFrame (optional) is given, modify and return that.
Parameters: df – pd.DataFrame. Returns: pd.DataFrame.
-
get_concept_path
(var_id: tuple)[source]¶ Return concept path for given variable identifier tuple.
Parameters: var_id – tuple of filename and column number. Return str: concept path for this variable.
-
ids
¶ A list of variable identifier tuples.
-
included_datafiles
¶ List of datafiles included in column mapping file.
-
path_changes
(silent=False)[source]¶ Determine changes made to column mapping file.
Parameters: silent – if True, only print output. Returns: if silent=False return dictionary with changes since load.
-
path_id_dict
¶ Dictionary with all variable ids as keys and paths as value.
-
select_row
(var_id: tuple)[source]¶ Select row based on variable identifier tuple. Raises exception if variable is not in this column mapping.
Parameters: var_id – tuple of filename and column number. Returns: list of items in selected row.
-
set_concept_path
(var_id: tuple, path, label)[source]¶ Return concept path for given variable identifier tuple.
Parameters: - var_id – tuple of filename and column number.
- path – new value for path.
- label – new value for data label.
-
subj_id_columns
¶ A list of tuples with datafile and column index for SUBJ_ID, e.g. (‘cell-line.txt’, 1).
-
DataFile¶
-
class
tmtk.clinical.
DataFile
(path=None)[source]¶ Bases:
tmtk.utils.filebase.FileBase
Class for clinical data files, does not do much more than tmkt.FileBase.
Variable¶
-
class
tmtk.clinical.
Variable
(datafile, column: int = None, clinical_parent=None)[source]¶ Bases:
object
Base class for clinical variables
-
column_map_data
¶ Column mapping row as dictionary where keys are short descriptors.
Returns: dict.
-
concept_path
¶ Concept path after conversions by transmart-batch.
Returns: str.
-
data_label
¶ Variable data label.
Returns: str.
-
forced_categorical
¶ Check if forced categorical by entering ‘CATEGORICAL’ in 7th column.
Returns: bool.
-
is_empty
¶ Check if variable is fully empty.
Returns: bool.
-
is_in_wordmap
¶ Check if variable is represented in word mapping file.
Returns: bool.
-
is_numeric
¶ True if transmart-batch will load this concept as numerical. This includes information from word mapping and column mapping.
Returns: bool.
-
is_numeric_in_datafile
¶ True if the datafile contains only numerical items.
Returns: bool.
-
mapped_values
¶ Data items after word mapping.
Returns: list.
-
unique_values
¶ Returns: Unique set of values in the datafile.
-
values
¶ Returns: All values as found in the datafile.
-
var_id
¶ Returns: Variable identifier tuple (datafile.name, column).
-
word_map_dict
¶ A dictionary with word mapped categoricals. Keys are items in the datafile, values are what they will be mapped to through the word mapping file. Unmapped items are also added as key, value pair.
Returns: dict.
-
WordMapping¶
-
class
tmtk.clinical.
WordMapping
(params=None)[source]¶ Bases:
tmtk.utils.filebase.FileBase
,tmtk.utils.validate.ValidateMixin
Class representing the word mapping file.
-
build_index
(df=None)[source]¶ Build and sort multi-index for dataframe based on filename and column number columns. If no df parameter is not set, build index for self.df.
Parameters: df – pd.DataFrame. Returns: pd.DataFrame.
-
get_word_map
(var_id)[source]¶ Return dict with value in data file, and the mapped value as keyword-value pairs.
Parameters: var_id – tuple of filename and column number. Returns: dict.
-
word_map_changes
(silent=False)[source]¶ Determine changes made to word mapping file.
Parameters: silent – if True, only print output. Returns: if silent=False return dictionary with changes since load.
-
word_map_dicts
¶ Dictionary with all variable ids as keys and word map dicts as value.
-
Annotations¶
Annotations Container¶
Base class: AnnotationBase¶
-
class
tmtk.annotation.AnnotationBase.
AnnotationBase
(params=None, path=None)[source]¶ Bases:
tmtk.utils.filebase.FileBase
,tmtk.utils.validate.ValidateMixin
Base class for annotation files.
-
load_to
¶
-
marker_type
¶
-
ChromosomalRegions¶
-
class
tmtk.annotation.ChromosomalRegions.
ChromosomalRegions
(params=None, path=None)[source]¶ Bases:
tmtk.annotation.AnnotationBase.AnnotationBase
Subclass for CNV (aCGh, qDNAseq) annotation
-
biomarkers
¶
-
MicroarrayAnnotation¶
-
class
tmtk.annotation.MicroarrayAnnotation.
MicroarrayAnnotation
(params=None, path=None)[source]¶ Bases:
tmtk.annotation.AnnotationBase.AnnotationBase
Subclass for microarray (mRNA) expression annotation files.
-
biomarkers
¶
-
MirnaAnnotation¶
-
class
tmtk.annotation.MirnaAnnotation.
MirnaAnnotation
(params=None, path=None)[source]¶ Bases:
tmtk.annotation.AnnotationBase.AnnotationBase
Subclass for micro RNA (miRNA) expression annotation files.
-
biomarkers
¶
-
ProteomicsAnnotation¶
-
class
tmtk.annotation.ProteomicsAnnotation.
ProteomicsAnnotation
(params=None, path=None)[source]¶ Bases:
tmtk.annotation.AnnotationBase.AnnotationBase
Subclass for proteomics annotation
-
biomarkers
¶
-
High Dimensional data¶
HighDim¶
-
class
tmtk.highdim.HighDim.
HighDim
(params_list=None, parent=None)[source]¶ Bases:
tmtk.utils.validate.ValidateMixin
Container class for all High Dimensional data types.
Parameters: params_list – contains a list with Params objects. -
high_dim_files
¶
-
sample_mapping_files
¶
-
HighDimBase¶
-
class
tmtk.highdim.HighDimBase.
HighDimBase
(params=None, path=None, parent=None)[source]¶ Bases:
tmtk.utils.filebase.FileBase
,tmtk.utils.validate.ValidateMixin
Base class for high dimensional data structures.
-
load_to
¶
-
CopyNumberVariation¶
-
class
tmtk.highdim.CopyNumberVariation.
CopyNumberVariation
(params=None, path=None, parent=None)[source]¶ Bases:
tmtk.highdim.HighDimBase.HighDimBase
Base class for copy number variation datatypes (aCGH, qDNAseq)
-
allowed_header
¶
-
samples
¶
-
Expression¶
-
class
tmtk.highdim.Expression.
Expression
(params=None, path=None, parent=None)[source]¶ Bases:
tmtk.highdim.HighDimBase.HighDimBase
Base class for microarray mRNA expression data.
-
samples
¶
-
Mirna¶
-
class
tmtk.highdim.Mirna.
Mirna
(params=None, path=None, parent=None)[source]¶ Bases:
tmtk.highdim.HighDimBase.HighDimBase
Base class for proteomics data.
-
samples
¶
-
Proteomics¶
-
class
tmtk.highdim.Proteomics.
Proteomics
(params=None, path=None, parent=None)[source]¶ Bases:
tmtk.highdim.HighDimBase.HighDimBase
Base class for proteomics data.
-
samples
¶
-
ReadCounts¶
SampleMapping¶
-
class
tmtk.highdim.SampleMapping.
SampleMapping
(path=None)[source]¶ Bases:
tmtk.utils.filebase.FileBase
,tmtk.utils.validate.ValidateMixin
Base class for subject sample mapping
-
get_concept_paths
¶ Get all concept paths from file, replaces ATTR1 and ATTR2.
Returns: dictionary with md5 hash values as key and paths as value
-
platform
¶ Returns: the platform id in this sample mapping file.
-
samples
¶
-
slice_path
(path)[source]¶ Give slice of the dataframe where the paths are equal to given path. :param path: path (will be converted using global logic). :return: slice of dataframe.
-
study_id
¶ Returns: study_id in sample mapping file
-
Metadata Tags¶
Tags¶
Bases:
tmtk.utils.filebase.FileBase
,tmtk.utils.validate.ValidateMixin
generator that gets tags from tags file.
Returns: tuples (<path>, <title>, <description>)
Return tag paths delimited by the path_converter.
Utilities¶
FileBase¶
Generic module¶
-
tmtk.utils.Generic.
clean_for_namespace
(path) → str[source]¶ Converts a path and returns a namespace safe variant. Converts characters that give errors to underscore.
Parameters: path – usually a descriptive subdirectory Returns: string
-
tmtk.utils.Generic.
df2file
(df=None, path=None, overwrite=False)[source]¶ Write a dataframe to file safely. Does not overwrite existing files automatically. This function converts concept path delimiters.
Parameters: - df – pd.DataFrame
- path – path to write to
- overwrite – False (default) or True
-
tmtk.utils.Generic.
file2df
(path=None)[source]¶ Load a file specified by path into a Pandas dataframe. If hashed is True, return a a (dataframe, hash) value tuple.
Parameters: path – to file to load Returns: pd.DataFrame
-
tmtk.utils.Generic.
find_fully_unique_columns
(df)[source]¶ Check if a dataframe contains a fully unique column (SUBJ_ID candidate).
Parameters: df – pd.DataFrame Returns: list of names of unique columns
-
tmtk.utils.Generic.
fix_everything
()[source]¶ Scans over all the data and indicates which errors have been fixed. This function is great for stress relieve.
Returns: All your problems fixed by Rick
-
tmtk.utils.Generic.
is_numeric
(values)[source]¶ Check if list of values are numeric.
Parameters: values – iterable
-
tmtk.utils.Generic.
md5
(s: str)[source]¶ utf-8 encoded md5 hash string of input s.
Parameters: s – string Returns: md5 hash string
-
tmtk.utils.Generic.
merge_two_dicts
(x, y)[source]¶ Given two dicts, merge them into a new dict as a shallow copy.
-
tmtk.utils.Generic.
path_converter
(path, internal=False)[source]¶ Convert paths by creating delimiters of backslash “” and “+” sign, additionally converting underscores “_” to a single space.
Parameters: - path – concept path
- internal – if path is for internal use delimit with Mappings.PATH_DELIM
Returns: delimited path
-
tmtk.utils.Generic.
path_join
(*args)[source]¶ Join items with the used path delimiter.
Parameters: args – path items Returns: path as string
-
tmtk.utils.Generic.
summarise
(list_or_dict=None, max_items: int = 7) → str[source]¶ Takes an iterable and returns a summarized string statement. Picks a random sample if number of items > max_items.
Parameters: - list_or_dict – list or dict to summarise
- max_items – maximum number of items to keep.
Returns: the items joined as string with end statement.
utils.CPrint module¶
utils.Exceptions module¶
-
exception
tmtk.utils.Exceptions.
ClassError
(found=None, expected=None)[source]¶ Bases:
BaseException
Error raised when unexpected class is found.
Parameters: - found – is the Object class of found
- expected – is the required Object class
-
exception
tmtk.utils.Exceptions.
DatatypeError
(found=None, expected=None)[source]¶ Bases:
BaseException
Error raised when incorrect datatype is found.
Parameters: - found – is the datatype of object
- expected – is the required datatype
utils.HighDimUtils module¶
utils.mappings module¶
-
class
tmtk.utils.mappings.
Mappings
[source]¶ Bases:
object
Collection of statics used in various parts of the code.
-
EXT_PATH_DELIM
= '\\'¶
-
PATH_DELIM
= '∕'¶
-
annotation_data_types
= {'rnaseq': 'Messenger RNA data (sequencing)', 'cnv': 'ACGH data', 'expression': 'Messenger RNA data (microarray)', 'mirna': 'micro RNA data (PCR)', 'proteomics': 'Proteomics data (mass spec)', 'vcf': 'Genomic variant data'}¶
-
annotation_marker_types
= {'proteomics_annotation': 'PROTEOMICS', 'cnv_annotation': 'Chromosomal', 'vcf_annotation': 'VCF', 'mirna_annotation': 'MIRNA_QPCR', 'mrna_annotation': 'Gene expression', 'rnaseq_annotation': 'RNASEQ_RCNT'}¶
-
cat_cd
= 'Category Code'¶
-
cat_cd_s
= 'ccd'¶
-
col_num
= 'Column Number'¶
-
col_num_s
= 'col'¶
-
column_mapping_header
= ['Filename', 'Category Code', 'Column Number', 'Data Label', 'Magic 5th', 'Magic 6th', 'Concept Type']¶
-
column_mapping_s
= ['fn', 'ccd', 'col', 'dl', 'm5', 'm6', 'cty']¶
-
concept_type
= 'Concept Type'¶
-
concept_type_s
= 'cty'¶
-
data_label
= 'Data Label'¶
-
data_label_s
= 'dl'¶
-
df_value
= 'Datafile Value'¶
-
df_value_s
= 'dfv'¶
-
filename
= 'Filename'¶
-
filename_s
= 'fn'¶
-
static
get_annotations
(dtype=None)[source]¶ Return mapping for annotations classes. Return only for datatype if dtype is set. Else return full map.
Parameters: dtype – optional datatype (e.g. cnv_annotation) Returns: dict with mapping, or class.
-
static
get_highdim
(dtype=None)[source]¶ Return mapping for high dimensional classes. Return only for datatype if dtype is set. Else return full map.
Parameters: dtype – optional datatype (e.g. cnv) Returns: dict with mapping, or class.
-
static
get_params
(dtype=None)[source]¶ Return mapping for params classes. Return only for datatype if dtype is set. Else return full map.
Parameters: dtype – optional datatype (e.g. cnv) Returns: dict with mapping, or class.
-
magic_5
= 'Magic 5th'¶
-
magic_5_s
= 'm5'¶
-
magic_6
= 'Magic 6th'¶
-
magic_6_s
= 'm6'¶
-
map_value
= 'Mapping Value'¶
-
map_value_s
= 'map'¶
-
word_mapping_header
= ['Filename', 'Column Number', 'Datafile Value', 'Mapping Value']¶
-
Toolbox package¶
Generate chromosomal regions file¶
-
tmtk.toolbox.generate_chromosomal_regions_file.
generate_chromosomal_regions_file
(platform_id=None, reference_build='hg19', **kwargs)[source]¶ This creates a new chromosomal regions annotation file.
Parameters: - platform_id – Give the new platform a name to fill first column
- reference_build – choose either hg18, hg19 or hg38
Returns: a pandas dataframe with the new platform
Remap chromosomal regions data¶
-
tmtk.toolbox.remap_chromosomal_regions.
map_index_to_region_ids
(gene, origin_platform, region_origin)[source]¶
Study Wizard¶
Create study from templates¶
-
tmtk.toolbox.
create_study_from_templates
(ID, source_dir, output_dir=None, sec_req='Y')[source]¶ Create tranSMART files in designated output_dir for all data provided in templates in the source_dir.
Parameters: - ID – study ID.
- source_dir – directory containing all the templates.
- output_dir – directory where the output should be written.
- sec_req – security required? “Y” or “N”, default=”Y”.
Returns: None
The Arborist¶
tmtk.arborist.common module¶
-
tmtk.arborist.common.
call_boris
(to_be_shuffled=None, **kwargs)[source]¶ This function loads the Arborist if it has been properly installed in your environment.
Parameters: to_be_shuffled – has to be either a tmtk.Study object, a Pandas column mapping dataframe, or a path to column mapping file.
-
tmtk.arborist.common.
launch_arborist_gui
(json_data: str, height=650)[source]¶ Parameters: - json_data –
- height –
Returns:
-
tmtk.arborist.common.
update_clinical_from_json
(clinical, json_data)[source]¶ Parameters: - clinical –
- json_data –
Returns:
tmtk.arborist.connect_to_baas module¶
-
tmtk.arborist.connect_to_baas.
get_json_from_baas
(url, username=None)[source]¶ Get a json file from a Boris as a Service instance.
Parameters: - url – url should study name and version. (e.g. http://transmart-arborist.thehyve.nl/trees/study-name/1/~edit/).
- username – if no username is given, you will be prompted for one.
Returns: the JSON string from BaaS.
-
tmtk.arborist.connect_to_baas.
publish_to_baas
(url, json, study_name, username=None)[source]¶ Publishes a tree on a Boris as a Service instance.
Parameters: - url – url to a BaaS instance.
- json – the stringified json you want to publish.
- study_name – a nice name.
- username – if no username is given, you will be prompted for one.
Returns: the url that points to the study you’ve just uploaded.
tmtk.arborist.jstreecontrol module¶
-
class
tmtk.arborist.jstreecontrol.
ConceptNode
(path, var_id=None, node_type='numeric', data_args=None)[source]¶ Bases:
object
-
class
tmtk.arborist.jstreecontrol.
ConceptTree
(json_data=None)[source]¶ Bases:
object
Build a ConceptTree to be used in the graphical tree editor.
-
add_node
(path, var_id=None, node_type=None, data_args=None)[source]¶ Add ConceptNode object nodes list.
Parameters: - path – Concept path for this node.
- var_id – Unique ID that allows to keep track of a node.
- node_type – Explicitly set node type (highdim, numerical, categorical)
- data_args – Any additional parameters are put a ‘data’ dictionary.
-
column_mapping_file
¶ Returns: Column Mapping file based on ConceptTree object.
-
high_dim_paths
¶ All high dimensional nodes in concept tree as dict
-
jstree
¶
-
word_mapping
¶
-
-
class
tmtk.arborist.jstreecontrol.
JSNode
(path, oid=None, **kwargs)[source]¶ Bases:
object
This class exists as a helper to the JSTree. Its “json_data” method can generate sub-tree JSON without putting the logic directly into the JSTree.
-
class
tmtk.arborist.jstreecontrol.
JSTree
(concept_nodes)[source]¶ Bases:
object
An json like object that converts a list of nodes into something that jQuery jstree can use.
-
json_data
¶ Convert this object to json ready to be consumed by jstree.
-
json_data_string
¶ Returns: Returns the json_data properly formatted as string.
-
-
class
tmtk.arborist.jstreecontrol.
MyEncoder
(skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]¶ Bases:
json.encoder.JSONEncoder
Overwriting the standard JSON Encoder to treat numpy ints as native ints.
-
tmtk.arborist.jstreecontrol.
create_concept_tree
(column_object)[source]¶ Parameters: column_object – tmtk.Study object, tmtk.Clinical object, or ColumnMapping dataframe Returns: json string to be interpreted by the JSTree
Data templates¶
This document describes how you can use tmtk to read your filled in templates and write the data to tranSMART-ready files. The templates can be downloaded here.
Create study templates¶
Using the tmtk.toolbox.create_study_from_templates()
function you
can process any template you have filled in, and output the contents
to a format that can be uploaded to tranSMART. It has the following parameters:
ID
(Mandatory) Unique identifier of the study. This argument does not define the name of the study, that will be derived fromLevel 1
of the clinical data template tree sheet.source_dir
(Mandatory) Path to the folder in which the filled in templates are stored. Template files are not searched recursively, so all should be in the same folder.output_dir
Path to the folder where the tranSMART files should we written to. If the path doesn’t exist the required folder(s) will be created. Default:./<STUDY_ID>_transmart_files
sec_req
Determines whether it should be a public or private study. UseY
for private orN
for public. Default:Y
It is important that your source_dir
contains just one clinical data template, which is detected
by having “clinical” somewhere in the file name (case insensitive). If the template with general
study level metadata is present it should have “general study metadata” in its name (case insensitive).
All high-dimensional templates are detected by content, so file names are not important, as long as the
names don’t conflict with the templates described above.
Note: It is possible to run the function with only high-dimensional templates, but keep in mind that in that case the concept paths will have to be manually added to the subject-sample mapping files.
# Load the toolkit
import tmtk
# Read templates and write to tranSMART files
tmtk.toolbox.create_study_from_templates(ID='MY-TEMPLATE-STUDY',
source_dir='./my_templates_folder/',
sec_req='N')
Contributors¶
- Stefan Payrable
- Ward Weistra