Welcome to AltamISA’s documentation!

AltamISA is a Python 3 library for representing the ISA-tools data model and reading and writing ISA-Tab file format. The documentation is split into three parts (accessible through the navigation on the left):

Installation & Getting Started

Instructions for the installation of the module and some examples to get you started.

API Documentation

This section contains the API documentation for the module

Project Info

More information on the project, including the changelog, list of contributing authors, and contribution instructions.

Quick Example

Start parsing an ISA-Tab dataset by reading and validating an investigation file (download it to your working directory first):

from altamisa.isatab import *

with open("i_investigation.txt", "rt") as investigation_file:
    investigation = InvestigationReader.from_stream(investigation_file).read()

InvestigationValidator(investigation).validate()

For more inspiration on how to use AltamISA, see Examples.

Features

The main features are

• immutable data structures (named tuples) for representing records,
• simple implementation as directed acyclic graph (DAG) between ISA material and process nodes,
• strictly validating parser with various sanity checks,
• well-tested code, and well-documented API,
• example applications, e.g., conversion of ISA-tab to Graphviz dot.

Special Extensions

In addition to the original ISA-Tab format specifications, AltamISA supports

the following special modifications to improve specific use cases:

• List of values in Characterics or Parameter Value fields by using semicolon-separators (“;”). Note, for ontology terms the same number of splits is expected in the associated field Term Source REF and Term Accession Number.
• Material name Library Name for improved library annotation in nucleotide sequencing assays.

Note

Although these modifications have been discussed by the ISA community (list of values; library name), they are not supported by official ISA software, yet.

Frequently Asked Questions

Why another Python library for ISA-tab?

Attempting to use the official Python isa-api package led to quite some frustration on our side. Even the official ISA-tab examples parsed into non-expected graph structures. Further, the official Python API has several features that were irrelevant for us, e.g., conversion from and and to various other formats.

Is validation a big deal?

Yes it is. A lot of ISA-tab files that we found out in the wild while exploring the model and format were not validating. (We’re looking at you, MetaboLights). The aim of the projects that we are using ISA-tab are not just describing experiments so humans can read the experiment descriptions. Rather, we want to have machine readable (and interpretable) data formats. Here, strict syntax and ideally semantic validation is key.

What’s the state?

The ISA standard and ISA-Tab file I/O is fully implemented, tested, and we’re actively using it in other applications. The validation is also working stably yet we are planning several extensions and additional checks.

What’s the aim?

The aim is to have a stable and correct library for parsing, representing, and writing ISA-tab.

What’s on the roadmap?

Mostly fine-tuning, stabilization, and additional validations. At some point we might want to add support for ISA-JSON but that is a secondary aim at the moment. The advantage of ISA-Tab is that you can edit it with spreadsheet application.

Installation

Using pip

To install AltamISA, run this command in your terminal:

$ pip install altamisa

This is the preferred method to install AltamISA, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

Using conda

If you like conda as much as we do, you can install AltamISA from the Bioconda channel. This assumes that you have already setup conda and the Bioconda channel as described in the Bioconda manual.

$ conda install altamisa

From sources

The sources for AltamISA can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/bihealth/altamisa

Or download the tarball:

$ curl  -OL https://github.com/bihealth/altamisa/tarball/master

Once you have a copy of the source, you can install it with:

$ python setup.py install

Getting Started

After installation, you can use AltamISA in your project simply by importing the module.

from altamisa.isatab import *

That’s all, continue and look at the list of examples.

Examples

To run the examples, download a full test dataset (all files) from the AltamISA repository to your working directory.

Import AltamISA (if not already done) and other required modules.

from altamisa.isatab import *
import os
import warnings

Parsing and validation

AltamISA provides separate functions to parse, validate and export ISA-tab investigation, study or assay files, respectivly. Investigation, study and assay files may be parsed independently from each other. Reading and validating a single investigation file is performed as follows:

# Parse and validate an investigation file
with open("i_investigation.txt", "rt") as investigation_file:
    investigation = InvestigationReader.from_stream(investigation_file).read()

InvestigationValidator(investigation).validate()

For study and assay parsing, unique ids need to be set to enable unambiguous identification of study , assay and, in particular, their nodes in later applications (such as a complete graph creation):

# Parse ad study and an assay file
with open("s_BII-S-1.txt", "rt") as inputf:
    study = StudyReader.from_stream("S1", inputf).read()

with open("a_transcriptome.txt", "rt") as inputf:
    assay = AssayReader.from_stream("S1", "A1", inputf).read()

However, in real use cases an ISA-Tab dataset contains related investigation, study and assay files and thus should be handled as unit. In particular, validation of studies and assays requires information from parent elements such as the investigation or study, respectively. Thus, joint parsing and validation of a complete ISA-Tab dataset may look like this:

# Read investigation
with open("i_investigation.txt", "rt") as investigation_file:
    investigation = InvestigationReader.from_stream(investigation_file).read()

# Validate investigation
InvestigationValidator(investigation).validate()

# Read studies and assays
path_in = os.path.normpath(os.path.dirname("i_investigation.txt"))
studies = {}
assays = {}
for s, study_info in enumerate(investigation.studies):
    if study_info.info.path:
        with open(os.path.join(path_in, study_info.info.path), "rt") as inputf:
            studies[s] = StudyReader.from_stream("S{}".format(s + 1), inputf).read()
    if study_info.assays:
        assays[s] = {}
    for a, assay_info in enumerate(study_info.assays):
        if assay_info.path:
            with open(os.path.join(path_in, assay_info.path), "rt") as inputf:
                assays[s][a] = AssayReader.from_stream(
                    "S{}".format(s + 1), "A{}".format(a + 1), inputf
                ).read()

# Validate studies and assays
for s, study_info in enumerate(investigation.studies):
    if study_info.info.path:
        StudyValidator(investigation, study_info, studies[s]).validate()
    for a, assay_info in enumerate(study_info.assays):
        if assay_info.path:
            AssayValidator(investigation, study_info, assay_info, assays[s][a]).validate()

Writing

Having a set of AltamISA investigation, studies and assays available as parsed above, the models can be given out as ISA-Tab files again as follows (make sure to not use the same path for the investigation, as files might be overwritten otherwise):

# Write investigation
path_out = "/tmp/altamisa_example/"
os.makedirs(path_out, exist_ok=True)
with open(
    os.path.join(path_out, "i_investigation.txt"), "wt", newline=""
) as output_investigation_file:
    InvestigationWriter.from_stream(investigation, output_investigation_file).write()

# Write studies and assays
for s, study_info in enumerate(investigation.studies):
    if study_info.info.path:
        with open(os.path.join(path_out, study_info.info.path), "wt", newline="") as outputf:
            StudyWriter.from_stream(studies[s], outputf).write()
    for a, assay_info in enumerate(study_info.assays):
        if assay_info.path:
            with open(os.path.join(path_out, assay_info.path), "wt", newline="") as outputf:
                AssayWriter.from_stream(assays[s][a], outputf).write()

Working with AltamISA warnings

Parsing, validating and writing of ISA-Tab files may results in AltamISA warnings, if format or data is not conform to the specifications (except for AltamISA’s Special Extensions). Warnings will not stop AltamISA from parsing an ISA-Tab dataset into a technically valid model. However, any AltamISA warning should be reported to the user to allow him to improve or correct his ISA-Tab files. Furthermore, we discourage from accepting and working with ISA-Tab datasets which result in warnings of the category CriticalIsaValidationWarning.

Warnings may be collected as follows, to enable joint notification or evaluation:

# Show all warnings of same type and content
warnings.simplefilter("always")

# Collect warnings
with warnings.catch_warnings(record=True) as records:
    # Work with ISA-Tab files here, e.g.:
    InvestigationValidator(investigation).validate()

# Print warnings
for record in records:
    warnings.showwarning(
        record.message, record.category, record.filename, record.lineno, record.line
    )

Manual ISA model creation

A minimal AltamISA ISA model may be created as follows. To create more comprehensive models with, for instance, more filled investigation sections, material or process information, please refer to the description of the Models.

def create_and_write(out_path):
    """Create an investigation with a study and assay and write to ``output_path``."""

    # Prepare one or more study sections
    # Prepare basic study information
    study_info = models.BasicInfo(
        path="s_minimal.txt",
        identifier="s_minimal",
        title="Germline Study",
        description=None,
        submission_date=None,
        public_release_date=None,
        comments=(
            models.Comment(name="Study Grant Number", value=None),
            models.Comment(name="Study Funding Agency", value=None),
        ),
        headers=[],
    )

    # Create one or more assays
    assay_01 = models.AssayInfo(
        measurement_type=models.OntologyTermRef(
            name="exome sequencing assay",
            accession="http://purl.obolibrary.org/obo/OBI_0002118",
            ontology_name="OBI",
        ),
        technology_type=models.OntologyTermRef(
            name="nucleotide sequencing",
            accession="http://purl.obolibrary.org/obo/OBI_0000626",
            ontology_name="OBI",
        ),
        platform=None,
        path="a_minimal.txt",
        comments=(),
        headers=[],
    )

    # Prepare one or more protocols
    protocol_01 = models.ProtocolInfo(
        name="sample collection",
        type=models.OntologyTermRef(name="sample collection"),
        description=None,
        uri=None,
        version=None,
        parameters={},
        components={},
        comments=(),
        headers=[],
    )
    protocol_02 = models.ProtocolInfo(
        name="nucleic acid sequencing",
        type=models.OntologyTermRef(name="nucleic acid sequencing"),
        description=None,
        uri=None,
        version=None,
        parameters={},
        components={},
        comments=(),
        headers=[],
    )

    # Create study object
    study_01 = models.StudyInfo(
        info=study_info,
        designs=(),
        publications=(),
        factors={},
        assays=(assay_01,),
        protocols={protocol_01.name: protocol_01, protocol_02.name: protocol_02},
        contacts=(),
    )

    # Prepare other investigation section
    # Prepare one or more ontology term source references
    onto_ref_01 = models.OntologyRef(
        name="OBI",
        file="http://data.bioontology.org/ontologies/OBI",
        version="31",
        description="Ontology for Biomedical Investigations",
        comments=(),
        headers=[],
    )

    # Prepare basic investigation information
    invest_info = models.BasicInfo(
        path="i_minimal.txt",
        identifier="i_minimal",
        title="Minimal Investigation",
        description=None,
        submission_date=None,
        public_release_date=None,
        comments=(),
        headers=[],
    )

    # Create investigation object
    investigation = models.InvestigationInfo(
        ontology_source_refs={onto_ref_01.name: onto_ref_01},
        info=invest_info,
        publications=(),
        contacts=(),
        studies=(study_01,),
    )

    # Validate investigation
    InvestigationValidator(investigation).validate()

    # Write the investigation as ISA-Tab txt file
    with open(os.path.join(out_path, investigation.info.path), "wt", newline="") as outputf:
        InvestigationWriter.from_stream(investigation=investigation, output_file=outputf).write()

    # Create a corresponding Study graph

    # Create at least on source, one sample and one collection process
    # Unique names are required for unambiguous node identification
    source_01 = models.Material(
        type="Source Name",
        unique_name="S1-source-0815",
        name="0815",
        extract_label=None,
        characteristics=(),
        comments=(),
        factor_values=(),
        material_type=None,
        headers=[table_headers.SOURCE_NAME],
    )

    sample_01 = models.Material(
        type="Sample Name",
        unique_name="S1-sample-0815-N1",
        name="0815-N1",
        extract_label=None,
        characteristics=(),
        comments=(),
        factor_values=(),
        material_type=None,
        headers=[table_headers.SAMPLE_NAME],
    )

    process_01 = models.Process(
        protocol_ref="sample collection",
        unique_name="S1-sample collection-2-1",
        name=None,
        name_type=None,
        date=None,
        performer=None,
        parameter_values=(),
        comments=(),
        array_design_ref=None,
        first_dimension=None,
        second_dimension=None,
        headers=[table_headers.PROTOCOL_REF],
    )

    # Create the arcs to connect the material and process nodes, referenced by the unique name
    arc_01 = models.Arc(tail="S1-source-0815", head="S1-sample collection-2-1")
    arc_02 = models.Arc(tail="S1-sample collection-2-1", head="S1-sample-0815-N1")

    # Create the study graph object
    study_graph_01 = models.Study(
        file=investigation.studies[0].info.path,
        header=None,
        materials={source_01.unique_name: source_01, sample_01.unique_name: sample_01},
        processes={process_01.unique_name: process_01},
        arcs=(arc_01, arc_02),
    )

    # Validate study graph
    StudyValidator(
        investigation=investigation, study_info=investigation.studies[0], study=study_graph_01
    ).validate()

    # Write the study as ISA-Tab txt file
    with open(
        os.path.join(out_path, investigation.studies[0].info.path), "wt", newline=""
    ) as outputf:
        StudyWriter.from_stream(study_or_assay=study_graph_01, output_file=outputf).write()

    # Create a corresponding Assay graph

    # Create at least on samples, one output material and one collection process
    # Unique names are required for unambiguous node identification
    # Explicit header definition per node is currently required to enable export to ISA-Tab
    sample_01 = models.Material(
        type="Sample Name",
        unique_name="S1-sample-0815-N1",
        name="0815-N1",
        extract_label=None,
        characteristics=(),
        comments=(),
        factor_values=(),
        material_type=None,
        headers=[table_headers.SAMPLE_NAME],
    )

    data_file_01 = models.Material(
        type="Raw Data File",
        unique_name="S1-A1-0815-N1-DNA1-WES1_L???_???_R1.fastq.gz-COL4",
        name="0815-N1-DNA1-WES1_L???_???_R1.fastq.gz",
        extract_label=None,
        characteristics=(),
        comments=(),
        factor_values=(),
        material_type=None,
        headers=[table_headers.RAW_DATA_FILE],
    )

    data_file_02 = models.Material(
        type="Raw Data File",
        unique_name="S1-A1-0815-N1-DNA1-WES1_L???_???_R2.fastq.gz-COL5",
        name="0815-N1-DNA1-WES1_L???_???_R2.fastq.gz",
        extract_label=None,
        characteristics=(),
        comments=(),
        factor_values=(),
        material_type=None,
        headers=[table_headers.RAW_DATA_FILE],
    )

    process_01 = models.Process(
        protocol_ref="nucleic acid sequencing",
        unique_name="S1-A1-0815-N1-DNA1-WES1-3",
        name="0815-N1-DNA1-WES1",
        name_type="Assay Name",
        date=None,
        performer=None,
        parameter_values=(),
        comments=(),
        array_design_ref=None,
        first_dimension=None,
        second_dimension=None,
        headers=[table_headers.PROTOCOL_REF, table_headers.ASSAY_NAME],
    )

    # Create the arcs to connect the material and process nodes, referenced by the unique name
    arcs = (
        models.Arc(tail="S1-sample-0815-N1", head="S1-A1-0815-N1-DNA1-WES1-3"),
        models.Arc(
            tail="S1-A1-0815-N1-DNA1-WES1-3",
            head="S1-A1-0815-N1-DNA1-WES1_L???_???_R1.fastq.gz-COL4",
        ),
        models.Arc(
            tail="S1-A1-0815-N1-DNA1-WES1_L???_???_R1.fastq.gz-COL4",
            head="S1-A1-0815-N1-DNA1-WES1_L???_???_R2.fastq.gz-COL5",
        ),
    )

    # Create the assay graph object
    assay_graph_01 = models.Assay(
        file=investigation.studies[0].assays[0].path,
        header=None,
        materials={
            sample_01.unique_name: sample_01,
            data_file_01.unique_name: data_file_01,
            data_file_02.unique_name: data_file_02,
        },
        processes={process_01.unique_name: process_01},
        arcs=arcs,
    )

    # Validate assay graph
    AssayValidator(
        investigation=investigation,
        study_info=investigation.studies[0],
        assay_info=investigation.studies[0].assays[0],
        assay=assay_graph_01,
    ).validate()

    # Create output path, if not existing
    if not os.path.exists(out_path):
        os.makedirs(out_path, exist_ok=True)

    # Write the assay as ISA-Tab txt file
    with open(
        os.path.join(out_path, investigation.studies[0].assays[0].path), "wt", newline=""
    ) as outputf:
        AssayWriter.from_stream(study_or_assay=assay_graph_01, output_file=outputf).write()

Parsers

Classes to read data from ISA-Tab investigation, study and assay files and create the corresponding AltamISA models. Note that parsers can issue warnings, see Validators on how to capture them properly.

Contents

• Parsers
  • altamisa.isatab.InvestigationReader
  • altamisa.isatab.AssayReader
  • altamisa.isatab.AssayRowReader
  • altamisa.isatab.StudyReader
  • altamisa.isatab.StudyRowReader

altamisa.isatab.InvestigationReader

class altamisa.isatab.InvestigationReader(input_file: TextIO, filename=None)

Main class to read an investigation file into an InvestigationInfo object.

Parameters:input_file (TextIO) – ISA-Tab investigation file

classmethod from_stream(input_file: TextIO, filename=None)

Construct from file-like object

read() → altamisa.isatab.models.InvestigationInfo

Read the investigation file

Return type:models.InvestigationInfo Returns:Investigation model including all information from the investigation file

altamisa.isatab.AssayReader

class altamisa.isatab.AssayReader(study_id: str, assay_id: str, input_file: TextIO, filename=None)

Read an ISA-TAB assay file (a_*.txt) into a Assay object.

This is the main facade class for reading assay objects. Prefer it over using the more low-level code.

Parameters:

• study_id (str) – Unique identifier for the study, needed to disambiguate nodes between files.
• assay_id (str) – Unique identifier for the assay, needed to disambiguate nodes between files.
• input_file (TextIO) – ISA-Tab assay file

classmethod from_stream(study_id: str, assay_id: str, input_file: TextIO, filename=None)

Construct from file-like object

read()

Parse the assay file

Return type:models.Assay Returns:Assay model including graph of material and process nodes

altamisa.isatab.AssayRowReader

class altamisa.isatab.AssayRowReader(study_id: str, assay_id: str, input_file: TextIO, filename: str)

Read an ISA-TAB assay file (a_*.txt) into a tabular/object representation.

This is a more low-level part of the interface. Please prefer using :py:AssayReader: over using this class.

Parameters:

• study_id (str) – Unique identifier for the study, needed to disambiguate nodes between files.
• assay_id (str) – Unique identifier for the assay, needed to disambiguate nodes between files.
• input_file (TextIO) – ISA-Tab assay file

classmethod from_stream(study_id: str, assay_id: str, input_file: TextIO, filename: str = None)

Construct from file-like object

read()

Read assays rows

Returns:Nodes per row of the assay file

altamisa.isatab.StudyReader

class altamisa.isatab.StudyReader(study_id: str, input_file: TextIO, filename=None)

Read an ISA-TAB study file (s_*.txt) into a Study object.

This is the main facade class for reading study objects. Prefer it over using the more low-level code.

Parameters:

• study_id (str) – Unique identifier for the study, needed to disambiguate nodes between files.
• input_file (TextIO) – ISA-Tab study file

classmethod from_stream(study_id: str, input_file: TextIO, filename=None)

Construct from file-like object

read()

Parse the study file

Return type:models.Study Returns:Study model including graph of material and process nodes

altamisa.isatab.StudyRowReader

class altamisa.isatab.StudyRowReader(study_id: str, input_file: TextIO, filename: str)

Read an ISA-TAB study file (s_*.txt) into a tabular/object representation.

This is a more low-level part of the interface. Please prefer using :py:StudyReader: over using this class.

Parameters:

• study_id (str) – Unique identifier for the study, needed to disambiguate nodes between files.
• input_file (TextIO) – ISA-Tab study file

classmethod from_stream(study_id: str, input_file: TextIO, filename: str = None)

Construct from file-like object

read()

Read the study rows

Returns:Nodes per row of the study file

Writers

Classes to write AltamISA model files to ISA-Tab investigation, study and assay files. Note that writers can issue warnings, see Writers on how to capture them properly.

Contents

• Writers
  • altamisa.isatab.InvestigationWriter
  • altamisa.isatab.AssayWriter
  • altamisa.isatab.StudyWriter

altamisa.isatab.InvestigationWriter

class altamisa.isatab.InvestigationWriter(investigation: altamisa.isatab.models.InvestigationInfo, output_file: TextIO, quote=None, lineterminator=None)

Main class to write an investigation file from an InvestigationInfo object.

Parameters:

• investigation (models.InvestigationInfo) – The investigation model to write
• output_file (TextIO) – Output ISA-Tab investigation file
• quote (str) – Optional quoting character (none by default)
• lineterminator (str) – Optional line terminator (OS specific by default)

classmethod from_stream(investigation: altamisa.isatab.models.InvestigationInfo, output_file: TextIO, quote=None, lineterminator=None)

Construct from file-like object

write()

Write investigation file

altamisa.isatab.AssayWriter

class altamisa.isatab.AssayWriter(study_or_assay: NamedTuple, output_file: TextIO, quote=None, lineterminator=None)

Class that writes a file from an Assay object.

Parameters:

• study_or_assay (models.Assay) – The assay model to write
• output_file (TextIO) – Output ISA-Tab assay file
• quote (str) – Optional quoting character (none by default)
• lineterminator (str) – Optional line terminator (OS specific by default)

altamisa.isatab.StudyWriter

class altamisa.isatab.StudyWriter(study_or_assay: NamedTuple, output_file: TextIO, quote=None, lineterminator=None)

Class that writes a file from an Study object.

Parameters:

• study_or_assay (models.Study) – The study model to write
• output_file (TextIO) – Output ISA-Tab study file
• quote (str) – Optional quoting character (none by default)
• lineterminator (str) – Optional line terminator (OS specific by default)

Validators

Classes to validate the integrity of the investigation model as well as study and assay graphs with respect to ISA-Tab format specifications.

AltamISA uses the Python warnings module for reporting validation warnings.

# Parse investigation and validate, capture warnings in `ws`.
with open("investigation.tsv", "rt") as inputf:
    with warnings.catch_warnings(record=True) as warnings:
        isa_inv = InvestigationReader.from_stream(input_file=inputf).read()
        InvestigationValidator(isa_inv).validate()

# Iterate over all captured warnings and handle them.
for warning in warnings:
    somehow_handle(warning)

Note

You can use the IsaWarning class hierarchy for getting severity information.

Contents

• Validators
  • altamisa.isatab.InvestigationValidator
  • altamisa.isatab.AssayValidator
  • altamisa.isatab.StudyValidator

altamisa.isatab.InvestigationValidator

class altamisa.isatab.InvestigationValidator(investigation: altamisa.isatab.models.InvestigationInfo)

Validator for Investigation

Parameters:investigation (models.InvestigationInfo) – The investigation model to validate

validate()

Validate the investigation

altamisa.isatab.AssayValidator

class altamisa.isatab.AssayValidator(investigation: altamisa.isatab.models.InvestigationInfo, study_info: altamisa.isatab.models.StudyInfo, assay_info: altamisa.isatab.models.AssayInfo, assay: altamisa.isatab.models.Assay, parent_study: altamisa.isatab.models.Study = None)

Validator for Assay

Parameters:

• investigation (models.InvestigationInfo) – The corresponding investigation model
• study_info (models.StudyInfo) – The corresponding study information
• assay_info (models.AssayInfo) – The corresponding assay information
• assay (models.Assay) – The Assay model to validate
• parent_study (models.Study) – Optional: The parent Study of the current Assay (for extended validation)

validate()

Validate the assay

altamisa.isatab.StudyValidator

class altamisa.isatab.StudyValidator(investigation: altamisa.isatab.models.InvestigationInfo, study_info: altamisa.isatab.models.StudyInfo, study: altamisa.isatab.models.Study)

Validator for Study

Parameters:

• investigation (models.InvestigationInfo) – The corresponding investigation model
• study_info (models.StudyInfo) – The corresponding study information
• study (models.Study) – The Study model to validate

Models

Class models for storing and representing ISA data, with particular focus on ISA-Tab compatibility. The modeling follows the structure of the specifications with different classes for each file type (investigation, study, assay), investigation sections, the different study and assay column types etc. In particular, study and assay data (i.e. corresponding materials and processes) are represented by use of directed acyclic graphs.

Note that all models are immutable after construction. Here is a common pattern for getting a copy with modifying select members.

>>> import attr
>>> from altamisa.isatab import Comment
>>> c1 = Comment(name="Name", value="the value")
>>> c1
Comment(name='Name', value='value')
>>> c2 = Comment(**{**attr.asdict(c1), "name": "Another Name"})
>>> c2
Comment(name='Another Name', value='value')

Contents

• Models
  • altamisa.isatab.AnnotatedStr
  • altamisa.isatab.OntologyTermRef
  • altamisa.isatab.Comment
  • altamisa.isatab.OntologyRef
  • altamisa.isatab.BasicInfo
  • altamisa.isatab.PublicationInfo
  • altamisa.isatab.ContactInfo
  • altamisa.isatab.DesignDescriptorsInfo
  • altamisa.isatab.FactorInfo
  • altamisa.isatab.AssayInfo
  • altamisa.isatab.ProtocolComponentInfo
  • altamisa.isatab.ProtocolInfo
  • altamisa.isatab.StudyInfo
  • altamisa.isatab.InvestigationInfo
  • altamisa.isatab.Characteristics
  • altamisa.isatab.FactorValue
  • altamisa.isatab.ParameterValue
  • altamisa.isatab.Material
  • altamisa.isatab.Process
  • altamisa.isatab.Arc
  • altamisa.isatab.Study
  • altamisa.isatab.Assay

altamisa.isatab.AnnotatedStr

class altamisa.isatab.AnnotatedStr(value, **kwargs)

A str that can be flagged with values.

Example Usage

>>> x = AnnotateStr("text", key1="value1", key2=2)
>>> x
"text"
>>> x.key1
"value1"
>>> x.key2
2

altamisa.isatab.OntologyTermRef

class altamisa.isatab.OntologyTermRef(name: str = None, accession: str = None, ontology_name: str = None)

Reference to a term into an ontology.

Can be either initialized with

• all three of a name, an accession, and an ontology_name
• only a name
• nothing (empty)

name = None

Human-readable name of the term

accession = None

The accession of the referenced term

ontology_name = None

Name of the ontology (links to OntologyRef.name)

altamisa.isatab.Comment

class altamisa.isatab.Comment(name: str, value: str)

Representation of a Comment[*] cell.

name = None

Comment name

value = None

Comment value

altamisa.isatab.OntologyRef

class altamisa.isatab.OntologyRef(name: str, file: str, version: str, description: str, comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Description of an ontology term source, as used for investigation file.

name = None

The name of the ontology (e.g., CEBI)

file = None

Path to file or URI to ontology

version = None

Version of the ontology

description = None

Description of the ontology

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.BasicInfo

class altamisa.isatab.BasicInfo(path: pathlib.Path, identifier: str, title: str, description: str, submission_date: datetime.date, public_release_date: datetime.date, comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Basic metadata for an investigation or study (INVESTIGATION or STUDY).

path = None

Path to the investigation or study file

identifier = None

Investigation/Study identifier

title = None

Investigation/Study title

description = None

Investigation/Study description

submission_date = None

Investigation/Study submission date

public_release_date = None

Investigation/Study public release date

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.PublicationInfo

class altamisa.isatab.PublicationInfo(pubmed_id: str, doi: str, authors: str, title: str, status: Union[altamisa.isatab.models.OntologyTermRef, str], comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Information regarding an investigation publication (INVESTIGATION PUBLICATIONS).

pubmed_id = None

Publication PubMed ID

doi = None

Publication DOI

authors = None

Publication author list string

title = None

Publication title

status = None

Publication status

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.ContactInfo

class altamisa.isatab.ContactInfo(last_name: str, first_name: str, mid_initial: str, email: str, phone: str, fax: str, address: str, affiliation: str, role: Union[altamisa.isatab.models.OntologyTermRef, str], comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Investigation contact information

last_name = None

Last name of contact

first_name = None

First name of contact

mid_initial = None

Middle initial of contact

email = None

Email of contact

phone = None

Phone of contact

fax = None

Fax no. of contact

address = None

Address of contact

affiliation = None

Affiliation of contact

role = None

Role of contact

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.DesignDescriptorsInfo

class altamisa.isatab.DesignDescriptorsInfo(type: Union[altamisa.isatab.models.OntologyTermRef, str], comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Study design descriptors information

type = None

Design descriptors type

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.FactorInfo

class altamisa.isatab.FactorInfo(name: str, type: Union[altamisa.isatab.models.OntologyTermRef, str], comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Study factor information

name = None

Factor name

type = None

Factor type

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.AssayInfo

class altamisa.isatab.AssayInfo(measurement_type: Union[altamisa.isatab.models.OntologyTermRef, str], technology_type: Union[altamisa.isatab.models.OntologyTermRef, str], platform: str, path: pathlib.Path, comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Study assay information

measurement_type = None

Assay measurement type

technology_type = None

Assay technology type

platform = None

Assay platform

path = None

Path to assay file

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.ProtocolComponentInfo

class altamisa.isatab.ProtocolComponentInfo(name: str, type: Union[altamisa.isatab.models.OntologyTermRef, str])

Protocol component information

name = None

Protocol component name

type = None

Protocol component type

altamisa.isatab.ProtocolInfo

class altamisa.isatab.ProtocolInfo(name: str, type: Union[altamisa.isatab.models.OntologyTermRef, str], description: str, uri: str, version: str, parameters: Dict[str, Union[altamisa.isatab.models.OntologyTermRef, str]], components: Dict[str, altamisa.isatab.models.ProtocolComponentInfo], comments: Tuple[altamisa.isatab.models.Comment], headers: List[str])

Protocol information

name = None

Protocol name

type = None

Protocol type

description = None

Protocol

uri = None

Protocol URI

version = None

Protocol version

parameters = None

Protocol parameters

components = None

Protocol components

comments = None

Comments

headers = None

Headers from/for ISA-tab parsing/writing

altamisa.isatab.StudyInfo

class altamisa.isatab.StudyInfo(info: altamisa.isatab.models.BasicInfo, designs: Tuple[altamisa.isatab.models.DesignDescriptorsInfo], publications: Tuple[altamisa.isatab.models.PublicationInfo], factors: Dict[str, altamisa.isatab.models.FactorInfo], assays: Tuple[altamisa.isatab.models.AssayInfo], protocols: Dict[str, altamisa.isatab.models.ProtocolInfo], contacts: Tuple[altamisa.isatab.models.ContactInfo])

The full metadata regarding one study

info = None

Basic study information

designs = None

Study designs by name

publications = None

Publication list for study

factors = None

Study factors by name

assays = None

Study assays

protocols = None

Study protocols by name

contacts = None

Study contact list

altamisa.isatab.InvestigationInfo

class altamisa.isatab.InvestigationInfo(ontology_source_refs: Dict[str, altamisa.isatab.models.OntologyRef], info: altamisa.isatab.models.BasicInfo, publications: Tuple[altamisa.isatab.models.PublicationInfo], contacts: Tuple[altamisa.isatab.models.ContactInfo], studies: Tuple[altamisa.isatab.models.StudyInfo])

Representation of an ISA investigation

ontology_source_refs = None

Ontologies defined for investigation

info = None

Basic information on investigation

publications = None

List of investigation publications

contacts = None

Contact list for investigation

studies = None

List of studies in this investigation

altamisa.isatab.Characteristics

class altamisa.isatab.Characteristics(name: str, value: List[Union[altamisa.isatab.models.OntologyTermRef, str]], unit: Union[altamisa.isatab.models.OntologyTermRef, str])

Representation of a Characteristics[*] cell.

name = None

Characteristics name

value = None

Characteristics value

unit = None

Characteristics unit

altamisa.isatab.FactorValue

class altamisa.isatab.FactorValue(name: str, value: Union[altamisa.isatab.models.OntologyTermRef, str], unit: Union[altamisa.isatab.models.OntologyTermRef, str])

Representation of a Factor Value[*] cell.

name = None

Factor name

value = None

Factor value

unit = None

Factor value unit

altamisa.isatab.ParameterValue

class altamisa.isatab.ParameterValue(name: str, value: List[Union[altamisa.isatab.models.OntologyTermRef, str]], unit: Union[altamisa.isatab.models.OntologyTermRef, str])

Representation of a Parameter Value[*] cell.

name = None

Parameter name

value = None

Parameter value

unit = None

Parameter value unit

altamisa.isatab.Material

class altamisa.isatab.Material(type: str, unique_name: str, name: str, extract_label: Union[altamisa.isatab.models.OntologyTermRef, str], characteristics: Tuple[altamisa.isatab.models.Characteristics], comments: Tuple[altamisa.isatab.models.Comment], factor_values: Tuple[altamisa.isatab.models.FactorValue], material_type: Union[altamisa.isatab.models.OntologyTermRef, str], headers: List[str])

Representation of a Material or Data node.

type = None

The type of node (i.e. column name)

unique_name = None

The unique name of the material node. This is usually created with respect to study/assay and column. The unique name is necessary to distinguish materials of different type with potential overlaps in names. Otherwise graph representation might be incorrect (ambiguous arcs, loops) and the original relation of material and process not conclusively reproducible.

name = None

Original name of a material or data file

extract_label = None

The label of a Labeled Extract

characteristics = None

Material characteristics

comments = None

Material comments

factor_values = None

Material factor values

material_type = None

Material type

headers = None

Columns headers from/for ISA-tab parsing/writing

altamisa.isatab.Process

class altamisa.isatab.Process(protocol_ref: str, unique_name: str, name: str, name_type: str, date: datetime.date, performer: str, parameter_values: Tuple[altamisa.isatab.models.ParameterValue], comments: Tuple[altamisa.isatab.models.Comment], array_design_ref: str, first_dimension: Union[altamisa.isatab.models.OntologyTermRef, str], second_dimension: Union[altamisa.isatab.models.OntologyTermRef, str], headers: List[str])

Representation of a Process or Assay node.

protocol_ref = None

Referenced to protocol name from investigation

unique_name = None

The unique name of the process node. This is usually created with respect to study/assay and column. The unique name is necessary to distinguish processes of different protocols with potential overlaps in names. Otherwise graph representation might be incorrect (ambiguous arcs, loops) and the original relation of material and process not conclusively reproducible. When “Protocol REF” is given without a further qualifying name, this is generated from the protocol reference with an auto-incrementing number.

name = None

Original name of a process (e.g. from Assay Name etc.)

name_type = None

Type of original name (e.g. Assay Name)

date = None

Process date

performer = None

Performer of process

parameter_values = None

Tuple of parameters values

comments = None

Tuple of process comments

array_design_ref = None

Array design reference (special case annotation)

Technology types: “DNA microarray”, “protein microarray”

Protocol types: “nucleic acid hybridization”, “hybridization”

first_dimension = None

First dimension (special case annotation, INSTEAD of Gel Electrophoresis Assay Name)

Technology types: “gel electrophoresis”

Protocol types: “electrophoresis”

second_dimension = None

Second dimension (special case annotation, INSTEAD of Gel Electrophoresis Assay Name)

Technology types: “gel electrophoresis”

Protocol types: “electrophoresis”

headers = None

Columns headers from/for ISA-tab parsing/writing

altamisa.isatab.Arc

class altamisa.isatab.Arc(tail: str, head: str)

Representation of an arc between two Material and/or Process nodes.

tail = None

The arc’s tail name

head = None

The arc’s head name

altamisa.isatab.Study

class altamisa.isatab.Study(file: pathlib.Path, header: Tuple, materials: Dict[str, altamisa.isatab.models.Material], processes: Dict[str, altamisa.isatab.models.Process], arcs: Tuple[altamisa.isatab.models.Arc])

Representation of an ISA study.

file = None

Path to ISA study file

header = None

The study’s header

materials = None

A mapping from material name to Material object (Data is a kind of material)

processes = None

A mapping from process name to Process object

arcs = None

The processing arcs

altamisa.isatab.Assay

class altamisa.isatab.Assay(file: pathlib.Path, header: Tuple, materials: Dict[str, altamisa.isatab.models.Material], processes: Dict[str, altamisa.isatab.models.Process], arcs: Tuple[altamisa.isatab.models.Arc])

Representation of an ISA assay.

file = None

Path to ISA assay file

header = None

The assay’s header

materials = None

A mapping from material name to Material object (Data is a kind of material)

processes = None

A mapping from process name to Process object

arcs = None

The processing arcs

Constants

Constants are used internally to ensure consistent use of keywords when parsing and writing investigation, study and assay files (in particular in the headers). They can also be used when creating a model from scratch, i.e. to provide a desired header order (used for writing) when creating study/assay nodes or investigation sections (or section entries).

Contents

• Constants
  • Investigation Headers
  • Table Headers
  • Table Restrictions
  • Table Tokens

Investigation Headers

Constants of valid vertical row headers for investigation parsing and writing, as specified in the ISA-Tab investigation specifications.

altamisa.constants.investigation_headers.ONTOLOGY_SOURCE_REFERENCE = 'ONTOLOGY SOURCE REFERENCE'

altamisa.constants.investigation_headers.INVESTIGATION = 'INVESTIGATION'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATIONS = 'INVESTIGATION PUBLICATIONS'

altamisa.constants.investigation_headers.INVESTIGATION_CONTACTS = 'INVESTIGATION CONTACTS'

altamisa.constants.investigation_headers.STUDY = 'STUDY'

altamisa.constants.investigation_headers.STUDY_DESIGN_DESCRIPTORS = 'STUDY DESIGN DESCRIPTORS'

altamisa.constants.investigation_headers.STUDY_PUBLICATIONS = 'STUDY PUBLICATIONS'

altamisa.constants.investigation_headers.STUDY_FACTORS = 'STUDY FACTORS'

altamisa.constants.investigation_headers.STUDY_ASSAYS = 'STUDY ASSAYS'

altamisa.constants.investigation_headers.STUDY_PROTOCOLS = 'STUDY PROTOCOLS'

altamisa.constants.investigation_headers.STUDY_CONTACTS = 'STUDY CONTACTS'

altamisa.constants.investigation_headers.TERM_SOURCE_NAME = 'Term Source Name'

altamisa.constants.investigation_headers.TERM_SOURCE_FILE = 'Term Source File'

altamisa.constants.investigation_headers.TERM_SOURCE_VERSION = 'Term Source Version'

altamisa.constants.investigation_headers.TERM_SOURCE_DESCRIPTION = 'Term Source Description'

altamisa.constants.investigation_headers.ONTOLOGY_SOURCE_REF_KEYS = ('Term Source Name', 'Term Source File', 'Term Source Version', 'Term Source Description')

Collected header keys of ONTOLOGY SOURCE REFERENCE section

altamisa.constants.investigation_headers.INVESTIGATION_IDENTIFIER = 'Investigation Identifier'

altamisa.constants.investigation_headers.INVESTIGATION_TITLE = 'Investigation Title'

altamisa.constants.investigation_headers.INVESTIGATION_DESCRIPTION = 'Investigation Description'

altamisa.constants.investigation_headers.INVESTIGATION_SUBMISSION_DATE = 'Investigation Submission Date'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLIC_RELEASE_DATE = 'Investigation Public Release Date'

altamisa.constants.investigation_headers.INVESTIGATION_INFO_KEYS = ('Investigation Identifier', 'Investigation Title', 'Investigation Description', 'Investigation Submission Date', 'Investigation Public Release Date')

Collected header keys of INVESTIGATION section

altamisa.constants.investigation_headers.INVESTIGATION_PUBMED_ID = 'Investigation PubMed ID'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATION_DOI = 'Investigation Publication DOI'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATION_AUTHOR_LIST = 'Investigation Publication Author List'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATION_TITLE = 'Investigation Publication Title'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATION_STATUS = 'Investigation Publication Status'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATION_STATUS_TERM_ACCESSION_NUMBER = 'Investigation Publication Status Term Accession Number'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATION_STATUS_TERM_SOURCE_REF = 'Investigation Publication Status Term Source REF'

altamisa.constants.investigation_headers.INVESTIGATION_PUBLICATIONS_KEYS = ('Investigation PubMed ID', 'Investigation Publication DOI', 'Investigation Publication Author List', 'Investigation Publication Title', 'Investigation Publication Status', 'Investigation Publication Status Term Accession Number', 'Investigation Publication Status Term Source REF')

Collected header keys of INVESTIGATION PUBLICATIONS

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_LAST_NAME = 'Investigation Person Last Name'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_FIRST_NAME = 'Investigation Person First Name'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_MID_INITIALS = 'Investigation Person Mid Initials'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_EMAIL = 'Investigation Person Email'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_PHONE = 'Investigation Person Phone'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_FAX = 'Investigation Person Fax'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_ADDRESS = 'Investigation Person Address'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_AFFILIATION = 'Investigation Person Affiliation'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_ROLES = 'Investigation Person Roles'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_ROLES_TERM_ACCESSION_NUMBER = 'Investigation Person Roles Term Accession Number'

altamisa.constants.investigation_headers.INVESTIGATION_PERSON_ROLES_TERM_SOURCE_REF = 'Investigation Person Roles Term Source REF'

altamisa.constants.investigation_headers.INVESTIGATION_CONTACTS_KEYS = ('Investigation Person Last Name', 'Investigation Person First Name', 'Investigation Person Mid Initials', 'Investigation Person Email', 'Investigation Person Phone', 'Investigation Person Fax', 'Investigation Person Address', 'Investigation Person Affiliation', 'Investigation Person Roles', 'Investigation Person Roles Term Accession Number', 'Investigation Person Roles Term Source REF')

Collected header keys of INVESTIGATION CONTACTS section

altamisa.constants.investigation_headers.STUDY_IDENTIFIER = 'Study Identifier'

altamisa.constants.investigation_headers.STUDY_TITLE = 'Study Title'

altamisa.constants.investigation_headers.STUDY_DESCRIPTION = 'Study Description'

altamisa.constants.investigation_headers.STUDY_SUBMISSION_DATE = 'Study Submission Date'

altamisa.constants.investigation_headers.STUDY_PUBLIC_RELEASE_DATE = 'Study Public Release Date'

altamisa.constants.investigation_headers.STUDY_FILE_NAME = 'Study File Name'

altamisa.constants.investigation_headers.STUDY_INFO_KEYS = ('Study Identifier', 'Study Title', 'Study Description', 'Study Submission Date', 'Study Public Release Date', 'Study File Name')

Collected header keys of STUDY section

altamisa.constants.investigation_headers.STUDY_DESIGN_TYPE = 'Study Design Type'

altamisa.constants.investigation_headers.STUDY_DESIGN_TYPE_TERM_ACCESSION_NUMBER = 'Study Design Type Term Accession Number'

altamisa.constants.investigation_headers.STUDY_DESIGN_TYPE_TERM_SOURCE_REF = 'Study Design Type Term Source REF'

altamisa.constants.investigation_headers.STUDY_DESIGN_DESCR_KEYS = ('Study Design Type', 'Study Design Type Term Accession Number', 'Study Design Type Term Source REF')

Collected header keys of STUDY DESIGN DESCRIPTORS section

altamisa.constants.investigation_headers.STUDY_PUBMED_ID = 'Study PubMed ID'

altamisa.constants.investigation_headers.STUDY_PUBLICATION_DOI = 'Study Publication DOI'

altamisa.constants.investigation_headers.STUDY_PUBLICATION_AUTHOR_LIST = 'Study Publication Author List'

altamisa.constants.investigation_headers.STUDY_PUBLICATION_TITLE = 'Study Publication Title'

altamisa.constants.investigation_headers.STUDY_PUBLICATION_STATUS = 'Study Publication Status'

altamisa.constants.investigation_headers.STUDY_PUBLICATION_STATUS_TERM_ACCESSION_NUMBER = 'Study Publication Status Term Accession Number'

altamisa.constants.investigation_headers.STUDY_PUBLICATION_STATUS_TERM_SOURCE_REF = 'Study Publication Status Term Source REF'

altamisa.constants.investigation_headers.STUDY_PUBLICATIONS_KEYS = ('Study PubMed ID', 'Study Publication DOI', 'Study Publication Author List', 'Study Publication Title', 'Study Publication Status', 'Study Publication Status Term Accession Number', 'Study Publication Status Term Source REF')

Collected header keys of STUDY PUBLICATIONS section

altamisa.constants.investigation_headers.STUDY_FACTOR_NAME = 'Study Factor Name'

altamisa.constants.investigation_headers.STUDY_FACTOR_TYPE = 'Study Factor Type'

altamisa.constants.investigation_headers.STUDY_FACTOR_TYPE_TERM_ACCESSION_NUMBER = 'Study Factor Type Term Accession Number'

altamisa.constants.investigation_headers.STUDY_FACTOR_TYPE_TERM_SOURCE_REF = 'Study Factor Type Term Source REF'

altamisa.constants.investigation_headers.STUDY_FACTORS_KEYS = ('Study Factor Name', 'Study Factor Type', 'Study Factor Type Term Accession Number', 'Study Factor Type Term Source REF')

Collected header keys of STUDY FACTORS section

altamisa.constants.investigation_headers.STUDY_ASSAY_FILE_NAME = 'Study Assay File Name'

altamisa.constants.investigation_headers.STUDY_ASSAY_MEASUREMENT_TYPE = 'Study Assay Measurement Type'

altamisa.constants.investigation_headers.STUDY_ASSAY_MEASUREMENT_TYPE_TERM_ACCESSION_NUMBER = 'Study Assay Measurement Type Term Accession Number'

altamisa.constants.investigation_headers.STUDY_ASSAY_MEASUREMENT_TYPE_TERM_SOURCE_REF = 'Study Assay Measurement Type Term Source REF'

altamisa.constants.investigation_headers.STUDY_ASSAY_TECHNOLOGY_TYPE = 'Study Assay Technology Type'

altamisa.constants.investigation_headers.STUDY_ASSAY_TECHNOLOGY_TYPE_TERM_ACCESSION_NUMBER = 'Study Assay Technology Type Term Accession Number'

altamisa.constants.investigation_headers.STUDY_ASSAY_TECHNOLOGY_TYPE_TERM_SOURCE_REF = 'Study Assay Technology Type Term Source REF'

altamisa.constants.investigation_headers.STUDY_ASSAY_TECHNOLOGY_PLATFORM = 'Study Assay Technology Platform'

altamisa.constants.investigation_headers.STUDY_ASSAYS_KEYS = ('Study Assay File Name', 'Study Assay Measurement Type', 'Study Assay Measurement Type Term Accession Number', 'Study Assay Measurement Type Term Source REF', 'Study Assay Technology Type', 'Study Assay Technology Type Term Accession Number', 'Study Assay Technology Type Term Source REF', 'Study Assay Technology Platform')

Collected header keys of STUDY ASSAYS section

altamisa.constants.investigation_headers.STUDY_PROTOCOL_NAME = 'Study Protocol Name'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_TYPE = 'Study Protocol Type'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_TYPE_TERM_ACCESSION_NUMBER = 'Study Protocol Type Term Accession Number'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_TYPE_TERM_SOURCE_REF = 'Study Protocol Type Term Source REF'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_DESCRIPTION = 'Study Protocol Description'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_URI = 'Study Protocol URI'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_VERSION = 'Study Protocol Version'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_PARAMETERS_NAME = 'Study Protocol Parameters Name'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_PARAMETERS_NAME_TERM_ACCESSION_NUMBER = 'Study Protocol Parameters Name Term Accession Number'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_PARAMETERS_NAME_TERM_SOURCE_REF = 'Study Protocol Parameters Name Term Source REF'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_COMPONENTS_NAME = 'Study Protocol Components Name'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_COMPONENTS_TYPE = 'Study Protocol Components Type'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_COMPONENTS_TYPE_TERM_ACCESSION_NUMBER = 'Study Protocol Components Type Term Accession Number'

altamisa.constants.investigation_headers.STUDY_PROTOCOL_COMPONENTS_TYPE_TERM_SOURCE_REF = 'Study Protocol Components Type Term Source REF'

altamisa.constants.investigation_headers.STUDY_PROTOCOLS_KEYS = ('Study Protocol Name', 'Study Protocol Type', 'Study Protocol Type Term Accession Number', 'Study Protocol Type Term Source REF', 'Study Protocol Description', 'Study Protocol URI', 'Study Protocol Version', 'Study Protocol Parameters Name', 'Study Protocol Parameters Name Term Accession Number', 'Study Protocol Parameters Name Term Source REF', 'Study Protocol Components Name', 'Study Protocol Components Type', 'Study Protocol Components Type Term Accession Number', 'Study Protocol Components Type Term Source REF')

Collected header keys of STUDY PROTOCOLS section

altamisa.constants.investigation_headers.STUDY_PERSON_LAST_NAME = 'Study Person Last Name'

altamisa.constants.investigation_headers.STUDY_PERSON_FIRST_NAME = 'Study Person First Name'

altamisa.constants.investigation_headers.STUDY_PERSON_MID_INITIALS = 'Study Person Mid Initials'

altamisa.constants.investigation_headers.STUDY_PERSON_EMAIL = 'Study Person Email'

altamisa.constants.investigation_headers.STUDY_PERSON_PHONE = 'Study Person Phone'

altamisa.constants.investigation_headers.STUDY_PERSON_FAX = 'Study Person Fax'

altamisa.constants.investigation_headers.STUDY_PERSON_ADDRESS = 'Study Person Address'

altamisa.constants.investigation_headers.STUDY_PERSON_AFFILIATION = 'Study Person Affiliation'

altamisa.constants.investigation_headers.STUDY_PERSON_ROLES = 'Study Person Roles'

altamisa.constants.investigation_headers.STUDY_PERSON_ROLES_TERM_ACCESSION_NUMBER = 'Study Person Roles Term Accession Number'

altamisa.constants.investigation_headers.STUDY_PERSON_ROLES_TERM_SOURCE_REF = 'Study Person Roles Term Source REF'

altamisa.constants.investigation_headers.STUDY_CONTACTS_KEYS = ('Study Person Last Name', 'Study Person First Name', 'Study Person Mid Initials', 'Study Person Email', 'Study Person Phone', 'Study Person Fax', 'Study Person Address', 'Study Person Affiliation', 'Study Person Roles', 'Study Person Roles Term Accession Number', 'Study Person Roles Term Source REF')

Collected header keys of STUDY CONTACTS section

Table Headers

Constants of valid column headers for study and assay table parsing and writing, as specified in the ISA-Tab study and assay specifications.

altamisa.constants.table_headers.EXTRACT_NAME = 'Extract Name'

altamisa.constants.table_headers.LABELED_EXTRACT_NAME = 'Labeled Extract Name'

altamisa.constants.table_headers.LIBRARY_NAME = 'Library Name'

Special material (see Special Extensions)

altamisa.constants.table_headers.SAMPLE_NAME = 'Sample Name'

altamisa.constants.table_headers.SOURCE_NAME = 'Source Name'

altamisa.constants.table_headers.ARRAY_DATA_FILE = 'Array Data File'

altamisa.constants.table_headers.ARRAY_DATA_MATRIX_FILE = 'Array Data Matrix File'

altamisa.constants.table_headers.ARRAY_DESIGN_FILE = 'Array Design File'

altamisa.constants.table_headers.DERIVED_ARRAY_DATA_FILE = 'Derived Array Data File'

altamisa.constants.table_headers.DERIVED_ARRAY_DATA_MATRIX_FILE = 'Derived Array Data Matrix File'

altamisa.constants.table_headers.DERIVED_DATA_FILE = 'Derived Data File'

altamisa.constants.table_headers.DERIVED_SPECTRAL_DATA_FILE = 'Derived Spectral Data File'

altamisa.constants.table_headers.IMAGE_FILE = 'Image File'

altamisa.constants.table_headers.METABOLITE_ASSIGNMENT_FILE = 'Metabolite Assignment File'

altamisa.constants.table_headers.PEPTIDE_ASSIGNMENT_FILE = 'Peptide Assignment File'

altamisa.constants.table_headers.POST_TRANSLATIONAL_MODIFICATION_ASSIGNMENT_FILE = 'Post Translational Modification Assignment File'

altamisa.constants.table_headers.PROTEIN_ASSIGNMENT_FILE = 'Protein Assignment File'

altamisa.constants.table_headers.RAW_DATA_FILE = 'Raw Data File'

altamisa.constants.table_headers.RAW_SPECTRAL_DATA_FILE = 'Raw Spectral Data File'

altamisa.constants.table_headers.SPOT_PICKING_FILE = 'Spot Picking File'

altamisa.constants.table_headers.ASSAY_NAME = 'Assay Name'

altamisa.constants.table_headers.DATA_TRANSFORMATION_NAME = 'Data Transformation Name'

altamisa.constants.table_headers.GEL_ELECTROPHORESIS_ASSAY_NAME = 'Gel Electrophoresis Assay Name'

altamisa.constants.table_headers.HYBRIDIZATION_ASSAY_NAME = 'Hybridization Assay Name'

altamisa.constants.table_headers.MS_ASSAY_NAME = 'MS Assay Name'

altamisa.constants.table_headers.NORMALIZATION_NAME = 'Normalization Name'

altamisa.constants.table_headers.PROTOCOL_REF = 'Protocol REF'

altamisa.constants.table_headers.SCAN_NAME = 'Scan Name'

altamisa.constants.table_headers.DATE = 'Date'

altamisa.constants.table_headers.LABEL = 'Label'

altamisa.constants.table_headers.MATERIAL_TYPE = 'Material Type'

altamisa.constants.table_headers.PERFORMER = 'Performer'

altamisa.constants.table_headers.TERM_SOURCE_REF = 'Term Source REF'

altamisa.constants.table_headers.TERM_ACCESSION_NUMBER = 'Term Accession Number'

altamisa.constants.table_headers.UNIT = 'Unit'

altamisa.constants.table_headers.ARRAY_DESIGN_REF = 'Array Design REF'

altamisa.constants.table_headers.FIRST_DIMENSION = 'First Dimension'

altamisa.constants.table_headers.SECOND_DIMENSION = 'Second Dimension'

altamisa.constants.table_headers.CHARACTERISTICS = 'Characteristics'

altamisa.constants.table_headers.COMMENT = 'Comment'

altamisa.constants.table_headers.FACTOR_VALUE = 'Factor Value'

altamisa.constants.table_headers.PARAMETER_VALUE = 'Parameter Value'

altamisa.constants.table_headers.DATA_FILE_HEADERS = ('Array Data File', 'Array Data Matrix File', 'Array Design File', 'Derived Array Data File', 'Derived Array Data Matrix File', 'Derived Data File', 'Derived Spectral Data File', 'Image File', 'Metabolite Assignment File', 'Peptide Assignment File', 'Post Translational Modification Assignment File', 'Protein Assignment File', 'Raw Data File', 'Raw Spectral Data File', 'Spot Picking File')

Collected header keys indicating a data file.

altamisa.constants.table_headers.MATERIAL_NAME_HEADERS = ('Extract Name', 'Labeled Extract Name', 'Library Name', 'Sample Name', 'Source Name', 'Array Data File', 'Array Data Matrix File', 'Array Design File', 'Derived Array Data File', 'Derived Array Data Matrix File', 'Derived Data File', 'Derived Spectral Data File', 'Image File', 'Metabolite Assignment File', 'Peptide Assignment File', 'Post Translational Modification Assignment File', 'Protein Assignment File', 'Raw Data File', 'Raw Spectral Data File', 'Spot Picking File')

Collected header keys indicating a material/data name.

altamisa.constants.table_headers.PROCESS_NAME_HEADERS = ('Assay Name', 'Data Transformation Name', 'Gel Electrophoresis Assay Name', 'Hybridization Assay Name', 'MS Assay Name', 'Normalization Name', 'Scan Name')

Collected header keys indicating a process name.

Table Restrictions

Constants used for validation of data and annotation restrictions in study and assay tables.

altamisa.constants.table_restrictions.PROTEIN_EXPRESSION_PROFILING = 'protein expression profiling'

altamisa.constants.table_restrictions.PROTEIN_IDENTIFICATION = 'protein identification'

altamisa.constants.table_restrictions.METABOLITE_PROFILING = 'metabolite profiling'

altamisa.constants.table_restrictions.DNA_MICROARRAY = 'dna microarray'

altamisa.constants.table_restrictions.GEL_ELECTROPHORESIS = 'gel electrophoresis'

altamisa.constants.table_restrictions.PROTEIN_MICROARRAY = 'protein microarray'

altamisa.constants.table_restrictions.NUCLEOTIDE_SEQUENCING = 'nucleotide sequencing'

altamisa.constants.table_restrictions.MASS_SPECTROMETRY = 'mass spectrometry'

altamisa.constants.table_restrictions.PT_DATA_COLLECTION = 'data collection'

altamisa.constants.table_restrictions.PT_DATA_NORMALIZATION = 'data normalization'

altamisa.constants.table_restrictions.PT_DATA_TRANSFORMATION = 'data transformation'

altamisa.constants.table_restrictions.PT_ELECTROPHORESIS = 'electrophoresis'

altamisa.constants.table_restrictions.PT_HYBRIDIZATION = 'hybridization'

altamisa.constants.table_restrictions.PT_MASS_SPECTROMETRY = 'mass spectrometry'

altamisa.constants.table_restrictions.PT_NUCLEIC_ACID_HYBRIDIZATION = 'nucleic acid hybridization'

altamisa.constants.table_restrictions.RESTRICTED_MATERIALS_ATECH = {'Library Name': {'nucleotide sequencing'}}

Materials restricted to certain assay technologies

altamisa.constants.table_restrictions.RESTRICTED_MATERIALS_AMEAS = {}

Materials restricted to certain assay measurements

altamisa.constants.table_restrictions.RESTRICTED_FILES_ATECH = {'Array Data File': {'dna microarray', 'protein microarray'}, 'Array Data Matrix File': {'dna microarray', 'protein microarray'}, 'Array Design File': {'dna microarray', 'protein microarray'}, 'Derived Array Data File': {'dna microarray', 'protein microarray'}, 'Derived Array Data Matrix File': {'dna microarray', 'protein microarray'}, 'Derived Spectral Data File': {'mass spectrometry'}, 'Metabolite Assignment File': {'mass spectrometry'}, 'Peptide Assignment File': {'mass spectrometry'}, 'Post Translational Modification Assignment File': {'mass spectrometry'}, 'Protein Assignment File': {'mass spectrometry'}, 'Raw Spectral Data File': {'mass spectrometry'}, 'Spot Picking File': {'gel electrophoresis'}}

Data files restricted to certain assay technologies

altamisa.constants.table_restrictions.RESTRICTED_FILES_AMEAS = {'Metabolite Assignment File': {'metabolite profiling'}, 'Peptide Assignment File': {'protein expression profiling', 'protein identification'}, 'Post Translational Modification Assignment File': {'protein expression profiling', 'protein identification'}, 'Protein Assignment File': {'protein expression profiling', 'protein identification'}}

Data files restricted to certain assay measurements

altamisa.constants.table_restrictions.RESTRICTED_PROTO_NAMES_ATECH = {'Gel Electrophoresis Assay Name': {'gel electrophoresis'}, 'Hybridization Assay Name': {'dna microarray', 'protein microarray'}, 'MS Assay Name': {'mass spectrometry'}, 'Scan Name': {'dna microarray', 'gel electrophoresis', 'protein microarray'}}

Protocol names restricted to certain assay technologies

altamisa.constants.table_restrictions.RESTRICTED_PROTO_NAMES_PTYPE = {'Data Transformation Name': {'data transformation'}, 'Gel Electrophoresis Assay Name': {'electrophoresis'}, 'Hybridization Assay Name': {'hybridization', 'nucleic acid hybridization'}, 'MS Assay Name': {'mass spectrometry'}, 'Normalization Name': {'data normalization'}, 'Scan Name': {'data collection'}}

Protocol names restricted by certain protocol types (ignored if ref is UNKNOWN)

altamisa.constants.table_restrictions.RESTRICTED_PROTO_ANNOS_ATECH = {'Array Design REF': {'dna microarray', 'protein microarray'}, 'First Dimension': {'gel electrophoresis'}, 'Second Dimension': {'gel electrophoresis'}}

Protocol special case annotations restricted to certain assay technologies

altamisa.constants.table_restrictions.RESTRICTED_PROTO_ANNOS_PTYPE = {'Array Design REF': {'hybridization', 'nucleic acid hybridization'}, 'First Dimension': {'electrophoresis'}, 'Second Dimension': {'electrophoresis'}}

Protocol special case annotations restricted to certain protocol types

Table Tokens

Token constants for study and assay table parsing and writing.

altamisa.constants.table_tokens.TOKEN_ANONYMOUS = 'Anonymous'

Used for marking anonymous/unnamed Processes

altamisa.constants.table_tokens.TOKEN_EMPTY = 'Empty'

Used for marking empty/unnamed Data

altamisa.constants.table_tokens.TOKEN_UNKNOWN = 'Unknown'

Used for named Processes without protocol reference

Headers

This module contains code for the representation of headers from study and assay files and parsing thereof.

class altamisa.isatab.headers.ColumnHeader(column_type, col_no, span)

Column header in a study or assay file

column_type = None

The type of this header

col_no = None

The column number this header refers to

span = None

Number of columns this header spans

term_source_ref_header = None

Link to the TermSourceRefHeader to use

unit_header = None

Link to the UnitHeader to use

get_simple_string() → List[str]

Return a list of simple string representations of the column types

class altamisa.isatab.headers.SimpleColumnHeader(col_no)

Base class for simple column headers.

column_type = None

The value to use for the type argument.

class altamisa.isatab.headers.ExtractHeader(col_no)

Extract header in an assay

column_type = 'Extract Name'

class altamisa.isatab.headers.LabeledExtractHeader(col_no)

Labeled Extract header in an assay

column_type = 'Labeled Extract Name'

class altamisa.isatab.headers.LibraryHeader(col_no)

Library header in an assay

column_type = 'Library Name'

class altamisa.isatab.headers.SampleHeader(col_no)

Sample header in a study or assay

column_type = 'Sample Name'

class altamisa.isatab.headers.SourceHeader(col_no)

Source header in a study

column_type = 'Source Name'

class altamisa.isatab.headers.ArrayDataFileHeader(col_no)

ArrayData header in an assay

column_type = 'Array Data File'

class altamisa.isatab.headers.ArrayDataMatrixFileHeader(col_no)

ArrayData Matrix File header in an assay

column_type = 'Array Data Matrix File'

class altamisa.isatab.headers.ArrayDesignFileHeader(col_no)

ArrayDesignFile header in an assay

column_type = 'Array Design File'

class altamisa.isatab.headers.DerivedArrayDataFileHeader(col_no)

DerivedArrayData header in an assay

column_type = 'Derived Array Data File'

class altamisa.isatab.headers.DerivedArrayDataMatrixFileHeader(col_no)

DerivedArrayData header in an assay

column_type = 'Derived Array Data Matrix File'

class altamisa.isatab.headers.DerivedDataFileHeader(col_no)

Derived Data File header in an assay

column_type = 'Derived Data File'

class altamisa.isatab.headers.DerivedSpectralDataFileHeader(col_no)

DerivedSpectralData header in an assay

column_type = 'Derived Spectral Data File'

class altamisa.isatab.headers.ImageFileHeader(col_no)

Image File header in an assay

column_type = 'Image File'

class altamisa.isatab.headers.MetaboliteAssignmentFileHeader(col_no)

PeptideAssignment header in an assay

column_type = 'Metabolite Assignment File'

class altamisa.isatab.headers.PeptideAssignmentFileHeader(col_no)

PeptideAssignment header in an assay

column_type = 'Peptide Assignment File'

class altamisa.isatab.headers.PostTranslationalModificationAssignmentFileHeader(col_no)

PostTranslationalModificationAssignment header in an assay

column_type = 'Post Translational Modification Assignment File'

class altamisa.isatab.headers.ProteinAssignmentFileHeader(col_no)

ProteinAssignment header in an assay

column_type = 'Protein Assignment File'

class altamisa.isatab.headers.RawDataFileHeader(col_no)

Raw Data header in an assay

column_type = 'Raw Data File'

class altamisa.isatab.headers.RawSpectralDataFileHeader(col_no)

Raw Spectral Data header in an assay

column_type = 'Raw Spectral Data File'

class altamisa.isatab.headers.SpotPickingFileHeader(col_no)

SpotPickingFile header in an assay

column_type = 'Spot Picking File'

class altamisa.isatab.headers.AssayNameHeader(col_no)

Assay Name header in an assay

column_type = 'Assay Name'

class altamisa.isatab.headers.DataTransformationNameHeader(col_no)

DataTransformationName header in an assay

column_type = 'Data Transformation Name'

class altamisa.isatab.headers.GelElectrophoresisAssayNameHeader(col_no)

GelElectrophoresisAssayName header in an assay

column_type = 'Gel Electrophoresis Assay Name'

class altamisa.isatab.headers.HybridizationAssayNameHeader(col_no)

HybridizationAssayName header in an assay

column_type = 'Hybridization Assay Name'

class altamisa.isatab.headers.MsAssayNameHeader(col_no)

MsAssayName header in an assay

column_type = 'MS Assay Name'

class altamisa.isatab.headers.NormalizationNameHeader(col_no)

Normalization Name header in a assay

column_type = 'Normalization Name'

class altamisa.isatab.headers.ProtocolRefHeader(col_no)

Protocol REF header in a study or assay

column_type = 'Protocol REF'

class altamisa.isatab.headers.ScanNameHeader(col_no)

ScanName header in assay

column_type = 'Scan Name'

class altamisa.isatab.headers.ArrayDesignRefHeader(col_no)

ArrayDesignRef header in an assay

column_type = 'Array Design REF'

class altamisa.isatab.headers.DateHeader(col_no)

Date annotation header in a study or assay

column_type = 'Date'

class altamisa.isatab.headers.FirstDimensionHeader(col_no)

First Dimension header in an assay

column_type = 'First Dimension'

class altamisa.isatab.headers.LabelHeader(col_no)

Label header in an assay

column_type = 'Label'

class altamisa.isatab.headers.MaterialTypeHeader(col_no)

Material Type header in an assay

column_type = 'Material Type'

class altamisa.isatab.headers.PerformerHeader(col_no)

Performer header in an assay

column_type = 'Performer'

class altamisa.isatab.headers.SecondDimensionHeader(col_no)

Second Dimension header in an assay

column_type = 'Second Dimension'

class altamisa.isatab.headers.TermRefAnnotationHeader(col_no)

Term reference annotation header

get_simple_string() → List[str]

Return a list of simple string representations of the column types

class altamisa.isatab.headers.UnitHeader(col_no)

Unit annotation header in a study or assay

column_type = 'Unit'

class altamisa.isatab.headers.LabeledColumnHeader(col_no, label)

Base class for labeled column headers.

column_type = None

The value to use for the type argument.

get_simple_string()

Return a list of simple string representations of the column types

class altamisa.isatab.headers.CharacteristicsHeader(col_no, label)

Material Characteristics[*] header in a study or assay

column_type = 'Characteristics'

class altamisa.isatab.headers.CommentHeader(col_no, label)

Comment header in a study or assay

column_type = 'Comment'

class altamisa.isatab.headers.FactorValueHeader(col_no, label)

Factor Value[*] header in a study or assay

column_type = 'Factor Value'

class altamisa.isatab.headers.ParameterValueHeader(col_no, label)

Protocol Parameter Value[*] header in a study or assay

column_type = 'Parameter Value'

class altamisa.isatab.headers.HeaderParserBase(tokens)

Helper base class for parsing a header from a study or assay file.

Parameters:tokens (list) – List of strings, e.g. a split line read from a tsv/cvs file.

allowed_headers = None

Names of the allowed headers

simple_headers = {'Array Data File': <class 'altamisa.isatab.headers.ArrayDataFileHeader'>, 'Array Data Matrix File': <class 'altamisa.isatab.headers.ArrayDataMatrixFileHeader'>, 'Array Design File': <class 'altamisa.isatab.headers.ArrayDesignFileHeader'>, 'Array Design REF': <class 'altamisa.isatab.headers.ArrayDesignRefHeader'>, 'Assay Name': <class 'altamisa.isatab.headers.AssayNameHeader'>, 'Data Transformation Name': <class 'altamisa.isatab.headers.DataTransformationNameHeader'>, 'Date': <class 'altamisa.isatab.headers.DateHeader'>, 'Derived Array Data File': <class 'altamisa.isatab.headers.DerivedArrayDataFileHeader'>, 'Derived Array Data Matrix File': <class 'altamisa.isatab.headers.DerivedArrayDataMatrixFileHeader'>, 'Derived Data File': <class 'altamisa.isatab.headers.DerivedDataFileHeader'>, 'Derived Spectral Data File': <class 'altamisa.isatab.headers.DerivedSpectralDataFileHeader'>, 'Extract Name': <class 'altamisa.isatab.headers.ExtractHeader'>, 'First Dimension': <class 'altamisa.isatab.headers.FirstDimensionHeader'>, 'Gel Electrophoresis Assay Name': <class 'altamisa.isatab.headers.GelElectrophoresisAssayNameHeader'>, 'Hybridization Assay Name': <class 'altamisa.isatab.headers.HybridizationAssayNameHeader'>, 'Image File': <class 'altamisa.isatab.headers.ImageFileHeader'>, 'Label': <class 'altamisa.isatab.headers.LabelHeader'>, 'Labeled Extract Name': <class 'altamisa.isatab.headers.LabeledExtractHeader'>, 'Library Name': <class 'altamisa.isatab.headers.LibraryHeader'>, 'MS Assay Name': <class 'altamisa.isatab.headers.MsAssayNameHeader'>, 'Material Type': <class 'altamisa.isatab.headers.MaterialTypeHeader'>, 'Metabolite Assignment File': <class 'altamisa.isatab.headers.MetaboliteAssignmentFileHeader'>, 'Normalization Name': <class 'altamisa.isatab.headers.NormalizationNameHeader'>, 'Peptide Assignment File': <class 'altamisa.isatab.headers.PeptideAssignmentFileHeader'>, 'Performer': <class 'altamisa.isatab.headers.PerformerHeader'>, 'Post Translational Modification Assignment File': <class 'altamisa.isatab.headers.PostTranslationalModificationAssignmentFileHeader'>, 'Protein Assignment File': <class 'altamisa.isatab.headers.ProteinAssignmentFileHeader'>, 'Protocol REF': <class 'altamisa.isatab.headers.ProtocolRefHeader'>, 'Raw Data File': <class 'altamisa.isatab.headers.RawDataFileHeader'>, 'Raw Spectral Data File': <class 'altamisa.isatab.headers.RawSpectralDataFileHeader'>, 'Sample Name': <class 'altamisa.isatab.headers.SampleHeader'>, 'Scan Name': <class 'altamisa.isatab.headers.ScanNameHeader'>, 'Second Dimension': <class 'altamisa.isatab.headers.SecondDimensionHeader'>, 'Source Name': <class 'altamisa.isatab.headers.SourceHeader'>, 'Spot Picking File': <class 'altamisa.isatab.headers.SpotPickingFileHeader'>, 'Unit': <class 'altamisa.isatab.headers.UnitHeader'>}

Headers that are mapped to SimpleColumnHeader

labeled_headers = {'Characteristics': <class 'altamisa.isatab.headers.CharacteristicsHeader'>, 'Comment': <class 'altamisa.isatab.headers.CommentHeader'>, 'Factor Value': <class 'altamisa.isatab.headers.FactorValueHeader'>, 'Parameter Value': <class 'altamisa.isatab.headers.ParameterValueHeader'>}

Labeled headers

run() → Iterator[altamisa.isatab.headers.ColumnHeader]

Parse the header

class altamisa.isatab.headers.StudyHeaderParser(tokens)

Helper class for parsing header of a study or assay.

file_type = 'study'

allowed_headers = ('Sample Name', 'Source Name', 'Protocol REF', 'Date', 'Performer', 'Characteristics', 'Comment', 'Factor Value', 'Parameter Value', 'Term Source REF', 'Unit')

class altamisa.isatab.headers.AssayHeaderParser(tokens)

Helper class for parsing header of a assay file.

file_type = 'assay'

allowed_headers = ('Extract Name', 'Labeled Extract Name', 'Library Name', 'Sample Name', 'Array Data File', 'Array Data Matrix File', 'Array Design File', 'Derived Array Data File', 'Derived Array Data Matrix File', 'Derived Data File', 'Derived Spectral Data File', 'Image File', 'Metabolite Assignment File', 'Peptide Assignment File', 'Post Translational Modification Assignment File', 'Protein Assignment File', 'Raw Data File', 'Raw Spectral Data File', 'Spot Picking File', 'Assay Name', 'Data Transformation Name', 'Gel Electrophoresis Assay Name', 'Hybridization Assay Name', 'MS Assay Name', 'Normalization Name', 'Protocol REF', 'Scan Name', 'Array Design REF', 'Date', 'First Dimension', 'Label', 'Material Type', 'Performer', 'Second Dimension', 'Characteristics', 'Comment', 'Parameter Value', 'Term Source REF', 'Unit')

Exceptions

Exceptions and Warnings used in the AltamISA library.

exception altamisa.exceptions.IsaException

Base class for exceptions raised by Altamisa.

exception altamisa.exceptions.ParseIsatabException

Exception raised on problems parsing ISA-TAB.

exception altamisa.exceptions.WriteIsatabException

Exception raised on problems writing ISA-TAB.

exception altamisa.exceptions.IsaWarning

Base class for warnings raised by Altamisa.

exception altamisa.exceptions.ParseIsatabWarning

Warning raised on problems parsing ISA-TAB.

exception altamisa.exceptions.WriteIsatabWarning

Warning raised on problems writing ISA-TAB.

exception altamisa.exceptions.IsaValidationWarning

Warning raised on problems validating ISA models or objects.

exception altamisa.exceptions.AdvisoryIsaValidationWarning

Warning raised on uncritical problems when validating ISA models or objects.

exception altamisa.exceptions.ModerateIsaValidationWarning

Warning raised on moderate problems when validating ISA models or objects.

exception altamisa.exceptions.CriticalIsaValidationWarning

Warning raised on critical problems when validating ISA models or objects.

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at https://github.com/bihealth/altamisa/issues.

If you are reporting a bug, please include:

• Your operating system name and version.
• Any details about your local setup that might be helpful in troubleshooting.
• Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation

AltamISA could always use more documentation, whether as part of the official AltamISA docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/bihealth/altamisa/issues.

If you are proposing a feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible, to make it easier to implement.
• Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here’s how to set up altamisa for local development.

1. Fork the altamisa repo on GitHub.

2. Clone your fork locally:

   $ git clone git@github.com:your_name_here/altamisa.git
   

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

   $ mkvirtualenv altamisa
   $ cd altamisa/
   $ python setup.py develop
   

4. Create a branch for local development:

   $ git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

   $ flake8 altamisa tests
   $ python setup.py test or py.test
   $ tox
   

   To get flake8 and tox, just pip install them into your virtualenv.

6. Commit your changes and push your branch to GitHub:

   $ git add .
   $ git commit -m "Your detailed description of your changes."
   $ git push origin name-of-your-bugfix-or-feature
   

7. Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should work for Python 2.7, 3.4, 3.5 and 3.6, and for PyPy. Check https://travis-ci.org/bihealth/altamisa/pull_requests and make sure that the tests pass for all supported Python versions.

Tips

To run a subset of tests:

$ py.test tests.test_altamisa

Deploying

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.md). Then run:

$ git tag vMAJOR.MINOR.PATCH
$ git push
$ git push --tags

This will create a tag on Github. Next, build with sdist:

$ rm -rf dist
$ python setup.py sdist

Ensure that this builds a clean package without hash and -dirty. Now, release to PyPI using twine:

$ twine upload --repository-url https://test.pypi.org/legacy/ dist/altamisa-*.tar.gz
$ twine upload dist/altamisa-*.tar.gz

Credits

• Dieter Beule
• Manuel Holtgrewe
• Jennifer Kirwan
• Mathias Kuhring
• Mikko Nieminen

History

v0.2.8

• Mostly meta adjustments.

v0.2.7

• Adding exception for duplicate node annotations

v0.2.6

• Minor fixes regarding investigation file names and comments.

v0.2.5

• Minor fixes of validation and warnings.
• Fixes optional parameter filename of AssayReader.

v0.2.4

• Ensuring that input order is output order. This is true except for the corner case where materials are not located in “blocks”. Such corner cases would require storing the tabular representation (and keeping it in sync) at all times and does not yield to a robustly usable implementation. NB: the input is also not sorted the test adjusted with this patch shows.
• Adding optional parameter filename to the various readers.
• Exposing RefTableBuilder class with slightly changed interface.

v0.2.3

• Minor fixes and additions with focus on improving the export.

v0.2.2

• Updating documentation for JOSS.

v0.2.1

• Adding JOSS paper draft.
• Fixing problem with writing empty lines on Windows (#52).
• Update documentation with examples for manual model creation.
• Fixing authorship documentation.
• Fixing package (#58).

v0.2.0

• Switching to attrs instead of using Namedtuple. This gets rid of some warts regarding constructor overriding but should offer the same functionality otherwise.
• Various updates to the documentation.

v0.1.0

First public release.

• Started out with ISA-TAB parser and NamedTuple-based data model.

License

You can find the License of AltamISA below.

MIT License

Copyright (c) 2018-2019, Berlin Institute of Health

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.