Package: csr2transmart
This package contains a script that transforms Central Subject Registry data to a format
that can be loaded into theTranSMARTplatform,
an open source data sharing and analytics platform for translational biomedical research.The output of the transformation is a collection of tab-separated files that can be loaded into
a TranSMART database using thetransmart-copytool.⚠️ Note: this is a very preliminary version, still under development.
Issues can be reported athttps://github.com/thehyve/python_csr2transmart/issues.Installation and usageTo install csr2transmart, do:pip install csr2transmartor from sources:git clone https://github.com/thehyve/python_csr2transmart.git
cd python_csr2transmart
pip install .Data modelThe Central Subject Registry (CSR) data model contains individual,
diagnosis, biosource and biomaterial entities. The data model is defined
as a data class incsr/csr.pyTo learn how to add changes to the database model, seechanging-data-model.rstdocument.UsageThis repository contains a number of command line tools:sources2csr: Reads from source files and produces tab delimited CSR files.csr2transmart: Reads CSR files and transforms the data to the TranSMART data model,
creating files that can be imported to TranSMART usingtransmart-copy.csr2cbioportal: Reads CSR files and transforms the data to patient and sample files
to imported into cBioPortal.sources2csrsources2csr <input_dir> <output_dir> <config_dir>The tool reads input files from<input_dir>and
writes CSR files in tab-delimited format (one file per entity type) to<output_dir>.
The output directory<output_dir>needs to be either empty or not yet existing.The sources configuration will be read from<config_dir>/sources_config.json,
a JSON file that contains the following attributes:entities: a map from entity type name to a description of the sources for that entity type. E.g.,{"Individual":{"attributes":[{"name":"individual_id","sources":[{"file":"individual.tsv","column":"individual_id"}]},{"name":"birth_date","sources":[{"file":"individual.tsv","date_format":"%d-%m-%Y"}]}]}}The entity type names have to match the entity type names in the CSR data model and
the attribute names should match the attribute names in the data model as well.
Thecolumnfield is optional, by default the column name is assumed to be
the same as the attribute name.
For date fields, adate_formatcan be specified. If not specified, it is
assumed to be%Y-%m-%dor any otherdate formats supported by Pydantic.
If multiple input files are specified for an attribute, data for that attribute
is read in that order, i.e., only if the first file has no data for an attribute
for a specific entity, data for that attribute for that entity is read from the next file, etc.codebooks: a map from input file name to codebook file name, e.g.,{"individual.tsv": "codebook.txt"}.
Naming convention: <fileName>_codebook.txtfile_format: a map from input file name to file format configuration,
which allows you to configure the delimiter character (default:\t).
E.g.,{"individual.tsv": {"delimiter":","}}.Seetest_data/input_data/config/sources_config.jsonfor an example.Content of the codebook files has to match the following format:First a header line with a number and column names the codes apply to.
The first field has a number, the second field a space separated list of column names, e.g.,1\tSEX GENDER.The lines following the header start with an empty field.
Then the lines follow the format ofcode\tvalueuntil the end of the line,
e.g.,\t1\tMale\t2\tFemale.The start of a new header, which is detected by the first field not being empty
starts the process over again.Seetest_data/input_data/codebooks/valid_codebook.txtfor a codebook file example.csr2transmartcsr2transmart <input_dir> <output_dir> <config_dir>The tool reads CSR files from<input_dir>(one file per entity type),
transforms the CSR data to the TranSMART data model.
In addition, if there is--ngs-dirspecified,
the tool will read the NGS files inside to determine values of additional CSR biomaterial variables.
The tool writes the output intransmart-copyformat to<output_dir>.
The output directory<output_dir>needs to be either empty or not yet existing.The ontology configuration will be read from<config_dir>/ontology_config.json.
Seetest_data/input_data/config/ontology_config.jsonfor an example.csr2cbioportalcsr2cbioportal <input_dir> [--ngs-dir <ngs_dir>] <output_dir>The tool reads CSR files from<input_dir>(one file per entity type),
and optionally NGS data (genomics data) from<ngs_dir>,
transforms the CSR data to the clinical data format for cBioPortal and
writes the following data types to<output_dir>:Clinical dataMutation dataCNA Segment dataCNA Continuous dataCNA Discrete dataFile structure, case lists and meta files will also be added to the output folder.
See thecBioPortal file formatsdocumentation for further details.The output directory<output_dir>needs to be either empty or not yet existing.Source data assumptions and validationGeneral file characteristicsDelimiterThe source data should be provided as delimited text files. The delimiter can beconfiguredper
data file. If not configured, a tab-delimited file is assumed.CommentsComment lines may be present, indicated by a#as the first character. These lines will be ignored.HeaderThe first non-comment line is assumed to be the header. It should be exactly one line.Field numberThe number of fields (columns) is determined by the header. Every other line in the file
should have this same number of fields (no blank lines).WhitespaceLeading or trailing whitespace is not trimmed. If present, it will persist in the final observation.EncodingAll files are assumed to be utf-8 encoded.CSR entitiesAll characteristics and relationships of the CSR data model are defined incsr/csr.py. Any field present in the
source data that you would like to load to tranSMART, must be linked to a CSR field via the sources_config. Additional
fields not present in the sources_config will be ignored.Regarding the source data, we can distinguish four types of validation:Value validationIndependent validation of a single field value. This comprises type validation (e.g. string, integer or date), nullability (whether a field may be empty), and unique constraints.Record validationValidation across different fields from the same record within the same entity. This validation is relevant when the validity of a field value is dependent on the other fields of the same record (e.g. a biosource record with src_biosource_id = BS1, is invalid when biosource_id = BS1).Entity validationConcerns the integrity check of all records within a single entity (e.g. do all src_biosource_id values also have corresponding biosource_id records within the biosource entity).Across-entityvalidationChecks the validity of relationships between records of different entities.The data validation of the current pipeline is implemented for type 1 and to a limited extent for type 2 and 4.
Hence, the source data is assumed to be coherent regarding its relationships within the same entity and across
different entities. While most erroneous relationships across entities, in respect of missing entity records, will be
detected (e.g. a biomaterial linked to a non-existing biosource), logically impossible relationships are not (e.g.
biomaterial BM2 is derived from BM1, but from a different biosource).All entities must have a link to an individual, either through their individual-referencing field
or through a reference to an entity of other type that has an individual-referencing field.
Otherwise an error will be thrown.Additionally, any individual needs to have at least one observation to be included.
This means that merely a collection of related ID values, without observations
linked to any of those IDs, will not become available in tranSMART.NGS dataAll NGS data should follow reference genome HG38.
The sample ID is structured as <BiosourceID>_<BiomaterialID>,
based on the IDs of biosources and biomaterials from clinical data, e.g. PMCBS000AAA_PMCBM000AAA.The naming conventions for NGS input data are as follows:mutation data (Small nucleotide variants): <fileName>.maf.gzSegment data: <fileName>.segContinuous CNA per gene: <fileName>_all_data_by_genes.txtDiscrete CNA per gene: <fileName>_all_thresholded.by_genes.txtMultiple files per type are supported. Note that mutation files must be always compressed asmaf.gz. See theNGS test datafor an example.Each of the NGS file names can contain the optional string ‘_WGS’ or ‘_WSX’ to indicate the NGS analysis type, e.g. <fileName>_WXS_all_data_by_genes.txt. If present, each sample in the NGS file will be associated with the corresponding value for the derived variableanalysis_strategy. String mapping rules are encoded inngs_reader.py.Additionally, the derived variablelibrary_strategyis generated for each sample based on the NGS file extension. See specific NGS reader scripts insources2csrfor mapping rules between file type and and library strategy.Both derived variables are associated to the entity Biomaterial in TranSMART (seengs2csr.py); to have them appear in the TranSMART ontology tree after data loading, you need to include them inontology_config.json, e.g.:{"name":"Biomaterial information","children":[{"name":"Library strategy","concept_code":"Biomaterial.library_strategy"},{"name":"Analysis type","concept_code":"Biomaterial.analysis_type"}]}Python versionsThis package supports Python versions 3.7 - 3.10.Package management and dependenciesThis project usespipfor installing dependencies and package management.Dependencies should be added torequirements.txt.Testing and code coverageTests are in thetestsfolder.Thetestsfolder contains tests for each of the tools and
a test that checks whether your code conforms to the Python style guide (PEP 8) (file:test_lint.py)The testing framework used isPyTestTests can be run withpython setup.py testCoding style conventions and code qualityCheck your code style withprospectorYou may need runpip install .[dev]first, to install the required dependenciesLicenseCopyright (c) 2019 The Hyve B.V.The CSR to TranSMART loader is licensed under the MIT License. See the fileLICENSE.