.. image:: https://travis-ci.org/ucscXena/xena-GDC-ETL.svg?branch=master :target: https://travis-ci.org/ucscXena/xena-GDC-ETL
Extract, transform and load GDC data <https://portal.gdc.cancer.gov/>
onto UCSC Xena <https://xenabrowser.net/>
.
Table of Contents
Dependencies
_Installation
_Basic usage with command line tools
_Advanced usage with XenaDataset and its subclasses
_GDC ETL settings
_Documentation
_Specific versions mentioned below have been tested. Eariler versions may still work but not guaranteed.
Python 2.7, 3.5+
This pipeline has been tested with python 2.7, 3.5, 3.6 and 3.7. It may also
work with other python 3 versions since it was originally designed to be
single-source Python 2/3 compatible <https://docs.python.org/3/howto/pyporting.html#the-short-explanation>
_.
Requests <http://python-requests.org>
_ v1.2.3
Numpy <https://www.numpy.org/>
_ v1.15.0
Pandas <https://pandas.pydata.org/>
_ v0.23.2
Jinja2 <http://jinja.pocoo.org/>
_ v2.10.1: used for generating metadata JSON.
lxml <https://lxml.de/>
_ v4.2.0: used for parsing TCGA phenotype data
xlrd <https://xlrd.readthedocs.io/en/latest/>
_ v1.1.0: used for reading TARGET phenotype data
First clone the repository from GitHub by running
git clone https://github.com/ucscXena/xena-GDC-ETL.git
. Now, cd
into
xena-GDC-ETL
directory and install the package using pip: pip install .
If you are developing the package, you can use pip
's edit mode for
installation: pip install -e .
.
pip
to install the package. To get the latest
code from GitHub master branch, run:
pip install git+https://github.com/ucscXena/xena-GDC-ETL
.
To get the latest stable version, run:
pip install xena-GDC-ETL
.pip install -r requirements.txt
.In general,
gdc.py
contains functionalities related to GDC API, which requires no other modules in this package;xena_dataset.py
contains core functionalities for importing data from GDC to Xena and needs the gdc.py
module in this package;gdc2xena.py
defines a command line tool
__ which requires both gdc.py
module and xena_dataset.py
module in this package;_ gdc2xena
gdc_check_new.py
defines a command line tool
__ which requiresthe gdc.py
module in this package.__ gdc_checknew
.. _gdc2xena:
Import selected project(s) and selected type(s) of data from GDC to Xena
.. code:: bash
xge etl [-h] [-r ROOT] [-p PROJECTS [PROJECTS ...] | -P NOT_PROJECTS [NOT_PROJECTS ...]] [-t DATATYPE [DATATYPE ...] | -T NOT_DATATYPE [NOT_DATATYPE ...]] [-D DELETE]
This tool will perform a full import of dataset(s) into the root directory (specified by the -r
option) with a default directory tree. In general, a full import has 3 steps: downloading raw data, making Xena matrix from raw data and generating matrix associated metadata. Data from each step will be saved to corresponding directories, whose structure is like this:
::
root └── projects ├── "Raw_Data" │ └── xena_dtype │ ├── data1 │ ├── data2 │ ├── ... │ └── dataN └── "Xena_Matrices" ├── projects.xena_dtype(1).tsv ├── projects.xena_dtype(1).tsv.json ├── projects.xena_dtype(2).tsv ├── projects.xena_dtype(2).tsv.json ├── ... ├── projects.xena_dtype(N).tsv └── projects.xena_dtype(N).tsv.json
A dataset is defined by its project and data type. Projects of interest are provided through -p
or -P
option, and data types of interest are provided through the -t
or -T
option. Multiple inputs separated by whitespace are allowed and will be treated separately with all possible combinations. Valid projects should be valid project_id on GDC. Valid data types includes (without quotation marks): 'htseq_counts', 'htseq_fpkm', 'htseq_fpkm-uq', 'mirna', 'masked_cnv', 'muse_snv', 'mutect2_snv', 'somaticsniper_snv', 'varscan2_snv', 'phenotype', and 'survival'. Upper case options (-P
or -T
) are mutually exclusive with corresponding lower case options, and they are used to define datasets of interest by excluding selections from either all projects on GDC or all supported data types. For example, the following command line imports 3 types of RNA-seq data for all but FM-AD projects from GDC to /home/user/xena_root:
.. code:: bash
mkdir -p /home/user/xena_root xge etl -P FM-AD -t htseq_counts htseq_fpkm htseq_fpkm-uq
Notes:
advanced usage with XenaDataset and its subclasses
_, if you want to customize the importing process with selected (rather than all possible) combinations of your input projects and data types or selected (rather than all 3) importing step(s).Generate metadata of a xena matrix
.. code:: bash
xge metadata --project TCGA-BRCA --datatype htseq_counts --matrix path/to/matrix.tsv --release 10
This tool generates metadata for a xena matrix. For the shown example, metadata
is generated for the matrix matrix.tsv
for release 10
, project
TCGA-BRCA
and datatype htsep_counts
. Note that, metadata JSON file is
saved at the same directory as the matrix.tsv
file.
.. _gdc_check_new:
Check against a list of updated files for affected dataset(s)
.. code:: bash
xge gdc_check_new [-h] URL
This tool takes in a file (either a URL or a local file readable by pandas.read_csv
) of table and read one of its columns named as "New File UUID". It then checks all file UUIDs in this table on GDC and summarize all their associated project(s), data type(s) and analysis workflow type(s). Such tables are usually provided in GDC's data release note. With the summarized info, you can design specific imports to just update datasets which are updated on GDC. For example, the following command:
.. code:: bash
xge gdc_check_new https://docs.gdc.cancer.gov/Data/Release_Notes/DR9.0_files_swap.txt.gz
should give you:
.. code:: bash
analysis.workflow_type cases.project.project_id data_type HTSeq - FPKM TARGET-NBL Gene Expression Quantification HTSeq - FPKM-UQ TARGET-NBL Gene Expression Quantification HTSeq - Counts TARGET-NBL Gene Expression Quantification
.. _version:
Shows the current version of xena_gdc_etl
.. code:: bash
xge --version
.. _xena-eql:
Check equality of two xena matrices
.. code:: bash
xge xena-eql path/to/matrix1.tsv path/to/matrix2.tsv
This tool takes path to two xena matrices and output if they are equal or not.
.. _merge-xena:
Merge xena matrices
.. code:: bash
xge merge-xena -f path/to/matrix1.tsv path/to/matrix2.tsv -t htseq_counts -o path/to/output -n new_name.tsv -c TCGA-BRCA
This tool merges xena matrices and outputs the merged matrix. For the given
example the tool will merge matrix1.tsv
and matrix2.tsv
matrices and
store the merged matrix in path/to/output
directory with the name
new_name.tsv
. Note that, had the argument -n
not been
specified, the merged matrix would have been saved as
TCGA-BRCA.htseq_counts.tsv
.
The XenaDataset
class
Though this is not an abstract class, it is designed as a generalized class representing one Xena dataset and its importing process. For doing an import of GDC data, use its subclasses_, which have preloaded with some default settings, might be simpler.
A Xena dataset is defined by its study project (cohort) and the type of data in this dataset. A typical importing process has the following 3 steps:
The download_map
property defines a dict of raw data to be downloaded, with the key being the URL and the value being the path, including the filename, for saving corresponding downloaded file. The download
method will read the download_map
and perform the downloading, creating non-existing directories as needed. After downloading all files, a list of paths for downloaded files will be recorded in the raw_data_list
property. The download
method needs only a valid download_map
. It will return the object itself, therefore can be chained with transform
.
One assumption for data transformation is that there might be multiple raw data (in the raw_data_list
) supporting the single Xena matrix in a dataset. Therefore, the transform
method will first merge data and then process merged matrix as needed. It will open the file one by one accordingly (by extension), and read the file object and transform its data with a function defined by read_raw
. The list of transformed single data will be merged and processed by a function defined by raws2matrix
, which gives the finalized Xena matrix. The transform
method requires a valid list of raw data, besides read_raw
and raws2matrix
. A valid list of raw data can be either explicitly defined by raw_data_list
or can be derived from raw_data_dir
with all files under raw_data_dir
being treated as raw data. It will return the object itself, therefore can be chained with metadata
.
Metadata for Xena matrix is a JSON file rendered by the metadata
method with metadata_vars
(dict) through Jinja2 from metadata_template
. This JSON file will be saved under the same directory as the matrix, with a filename being the matrix name plus the '.json' postfix. The metadata
method requires an existing file of Xena matrix.
.. _directory related settings:
root_dir
is both an optional instantiation arguments and a property. By default, it points to the current working directory. It is worth mentioning that the default directory structure mentioned above is implemented in the class. However, you are free to changed the setting with the following properties:
root_dir
during instantiation or set the root_dir
property explicitly after instantiation.raw_data_dir
.matrix
under matrix_dir
. The directory path in matrix
has the priority over matrix_dir
. By default, Xena matrix will be saved under the "matrix_dir" as "matrix
(i.e. no way to change this behavior)... _subclasses:
Build GDC importing pipelines with GDCOmicset
, GDCPhenoset
or GDCSurvivalset
classes
GDCOmicset
, GDCPhenoset
and GDCSurvivalset
are subclasses of XenaDataset
and are preloaded with settings for importing GDC genomic data, TCGA phenotype data on GDC, TARGET phenotype data on GDC and GDC's survival data respecitively. These settings can be customized by setting corresponding properties described below. For more details, please check the next section <#gdc-etl-settings>
_ and the documentation <https://github.com/ucscXena/xena-GDC-ETL/blob/master/docs/API.rst>
.
The script for gdc2xena.py
command line is a good example for basic usage of these classes. Similar to XenaDataset
, a GDC dataset is defined by projects
, which is one or a list of valid GDC "project_id". For GDCOmicset
, a dataset should also be defined with one of the supported xena_dtype
(find out with the class method GDCOmicset.get_supported_dtype()
). The xena_dtype
is critical for a GDCOmicset
object selecting correct default settings. For GDCPhenoset
and GDCSurvivalset
, data type are self-explanatory and cannot be changed. Therefore, you can instantiate these classes like this:
.. code:: python
from xena_dataset import GDCOmicset, GDCPhenoset, GDCSurvivalset, GDCAPIPhenoset
gdc_omic_cohort = GDCOmicset('TCGA-BRCA', 'htsep_counts')
tcga_pheno_cohort = GDCPhenoset('TCGA-BRCA')
target_pheno_cohort = GDCPhenoset('TARGET-NBL')
gdc_survival_cohort = GDCSurvivalset('TCGA-BRCA')
gdc_api_pheno_cohort = GDCAPIPhenoset('CPTAC-3')
With such a dataset object, it is fine to call download
, transform
and/or metadata
method(s). These methods will use preloaded settings and save files under root_dir
accordingly. You are free to call/chain some but not all 3 methods; just keep in mind the pre-requisites for each method and set related properties properly. Aside from directory related settings
_ described above, you can change some default importing settings through the following properties.
.. _Customize GDCOmicset:
GDCOmicset
|
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Attributes | Usage | Type and Format\ :sup:1 |
Default settings | +===================+========================================================================================================================================================+==========================================================================================================================================================================================================================================+================================================================================================================================================================================================================================================+ | gdc_filter | Used for deriving default download_map as the GDC search filters. |
dict : the key is 1 GDC available file field and the value is either a string or a list, meaning the value of the file field matches a string or number in (a list) |
Check GDC download settings _ for details. |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | gdc_prefix | Used for deriving default download_map as the GDC search fields. |
str : 1 GDC available file field whose value will be the prefix of the filename of corresponding downloaded file. |
Check GDC download settings _ for details. |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | download_map | Used by the download method for downloading GDC raw data supporting this dataset. |
dict : the key is download URL and the value is the desired path for saving the downloaded file. |
Download URLs are in the pattern of "https://api.gdc.cancer.gov/data/raw_data_dir >/ |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | read_raw | Used by the transform method when reading a single GDC raw data. |
callable : takes only 1 file object as its argument and returns an arbitrary result which will be put in a list and passed on to raws2matrix . |
Check GDC genomic transform settings _ for details |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | raws2matrix | Used by the transform method and responsible for both merging multiple GDC raw data into one Xena matrix and processing new Xena matrix as needed. |
callable : takes only 1 list of read_raw returns as its argument and returns an object (usually a pandas DataFrame) which has a to_csv method for saving as a file. |
Check GDC genomic transform settings _ for details |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | metadata_template | Used by the metadata method for rendering metadata by Jinja2. |
jinja2.environment.Template or str : a jinja2.environment.Template used directly by Jinja2; if it's a string, it is a path to the template file which will be silently read and converted to jinja2.environment.Template . |
Resources <https://github.com/ucscXena/xena-GDC-ETL/blob/master/Resources> _ |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | metadata_vars | Used by the metadata method for rendering metadata by Jinja2. |
dict : used directly by Jinja2 which should match variables in metadata_template . |
:: |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{ | |||||||||||||||||||||||||||||||||||||||
'project_id': <projects >, |
|||||||||||||||||||||||||||||||||||||||
'date': <the time of last modification of matrix >, |
|||||||||||||||||||||||||||||||||||||||
'gdc_release': <gdc_release >, |
|||||||||||||||||||||||||||||||||||||||
'xena_cohort': <Xena specific cohort name for TCGA data or GDC project_id for TARGET data, with (for both) "GDC " prefix> | |||||||||||||||||||||||||||||||||||||||
} | |||||||||||||||||||||||||||||||||||||||
* The first element of the "url" field in metadata will be "gdc_release" URL, and the second will be specific URL for raw data file if there is only 1 raw data file for this dataset; or it will be just "https://api.gdc.cancer.gov/data/". |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| gdc_release | Used by the metadata
method for rendering metadata, showing the GDC data release of this dataset. | str
: an URL pointing to corresponding GDC Data Release Note. | Current data release version when the gdc_release
is being used/called, queried through "https://api.gdc.cancer.gov/status". |
+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
\1. GDC API Available File Fields: https://docs.gdc.cancer.gov/API/Users_Guide/Appendix_A_Available_Fields/#file-fields
GDCPhenoset
for TCGA projectsTCGA phenotype data for Xena includes both clinical data and biospecimen data, as detailed below <#transform-phenotype>
_. Downloading and transformation of clinical data and biospecimen data are in fact delegated by two independent GDCOmicset
object respecitively. Corresponding subdatasets can be accessed through clin_dataset
and bio_dataset
attributes and hence can be customized as mentioned above. Because of such complexity of TCGA phenotype data, the download
and transform
methods are coded specifically and overrode corresponding methods of the base class, XenaDataset
. Customization for downloading and matrix transformation is very limited and should be done in the following steps:
GDCPhenoset
;GDCOmicset
objects for clinical data and/or biospecimen data as needed;GDCOmicset
objects to corresponding attributes, clin_dataset
and bio_dataset
;download
and/or transform
).Customize download
step
This step can be customized only through customized clin_dataset
and bio_dataset
, since the whole downloading process is delegated by these two GDCOmicset objects.
Customize transform
step
The first part of transform
is delegated by transform
methods of clin_dataset
and bio_dataset
. Therefore, the only way to customized this process is to customize clin_dataset
and bio_dataset
. How the two matrices are then merged into one Xena phenotype matrix is hard coded and cannot be customized. It is worth noting that if you want to call transfrom
but skip the downloading step, you will need to define clin_dataset
and bio_dataset
before calling transform
.
Customize metadata
step
Different from download
and transform
, there is no special settings for the metadata
method of GDCPhenoset
. Therefore, similar to that of GDCOmicset
, this step can be customized through metadata_template
, metadata_vars
and gdc_release
properties. And to call just the metadata
method, an existing matrix
is enough.
Customize GDCPhenoset
for TARGET projects
TARGET phenotype data for Xena contains only the clinical data (no biospecimen data), as detailed below <#transform-phenotype>
_. The importing process is quite similar to that of a GDCOmicset
. You can customize TARGETPhenoset
with download_map
, read_raw
, raws2matrix
, metadata_template
, metadata_vars
and gdc_release
in the same way as that of GDCOmicset <#customize-gdcomicset>
_.
GDCSurvivalset
GDC data supporting Xena survival matrix does not come any GDC files. It comes from the "analysis/survival" endpoint of GDC API. Therefore, the download
and transform
methods are re-designed, overriding those of the base class, XenaDataset
. Aside from redefining download
and transform
methods, there is no simple way to customize download
and transform
steps. You can still call transform
without download
by just defining a valid list of raw data with raw_data_list
or raw_data_dir
. However, only this first file in the list will be read and used.
Different from download
and transform
, there is no special settings for the metadata
method of GDCSurvivalset
. Therefore, similar to that of GDCOmicset
, this step can be customized through metadata_template
, metadata_vars
and gdc_release
properties. To call just the metadata
method, an existing matrix
is enough.
GDCAPIPhenoset
The data for this class comes from GDC API only. Therefore, the download
and transform
methods are re-designed, overriding those of the base class,
XenaDataset
. Aside from redefining download
and transform
methods,
there is no simple way to customize download
and transform
steps.
Different from download
and transform
, there is no special settings
for the metadata
method of GDCAPIPhenoset
. Therefore, similar to that
of GDCOmicset
, this step can be customized through metadata_template
,
metadata_vars
and gdc_release
properties. To call just the metadata
method, an existing matrix
is enough.
.. _GDC download settings:
Settings for downloading/getting raw data (files) from GDC
+-------------------+-------------------+-----------------------------------+-----------------------------------------------+--------------------------+------------------------------------------------------+ | | | GDC data filter | | |
.. _GDC genomic transform settings:
Settings for transform "Omic" data into Xena matrix
+-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | xena_dtype | Raw data has header? | Select columns (in order) | Row index | Skip rows start with? | Merge into matrix as | Process matrix | +===================+======================+============================================================================================================================================================================+=================+=======================+===============================+=======================================================================================================================+ | htseq_counts | No | 1, 2 | Ensembl_ID | _ | 1 new column based on index | 1. Average if there are multiple data from the same sample vial; | [Ensembl_ID, Counts] | 2. log2(counts + 1) | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | htseq_fpkm | No | 1, 2 | Ensembl_ID | _ | 1 new column based on index | 1. Average if there are multiple data from the same sample vial; | [Ensembl_ID, Counts] | 2. log2(counts + 1) | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | htseq_fpkm-uq | No | 1, 2 | Ensembl_ID | _ | 1 new column based on index | 1. Average if there are multiple data from the same sample vial; | [Ensembl_ID, Counts] | 2. log2(counts + 1) | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | mirna | Yes | 1, 3 | miRNA_ID | N/A | 1 new column based on index | 1. Average if there are multiple data from the same sample vial; | [miRNA_ID, RPM] | 2. log2(counts + 1) | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | mirna_isoform | Yes | 2, 4 | isoform_coords | N/A | 1 new column based on index | 1. Average if there are multiple data from the same sample vial; | [isoform_coords, RPM] | 2. log2(counts + 1) | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | cnv | Yes | 2, 3, 4, 6 | sample | N/A | New rows based on column name | 1. Rename columns as:: | [Chromosome, Start, End, Segment_Mean] | { | 'Chromosome': 'Chrom', | 'Segment_Mean': 'value' | } | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | masked_cnv | Yes | 1, 2, 3, 5 | sample | N/A | New rows based on column name | 1. Rename columns as:: | [Chromosome, Start, End, Segment_Mean] | { | 'Chromosome': 'Chrom', | 'Segment_Mean': 'value' | } | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | gistic | Yes | 1 | Ensembl_ID | _ | N/A | 1. Drop "Gene ID" and "Cytoband" column; | [Ensembl_ID] | 2. Map "samples.portions.analytes.aliquots.aliquot_id" into "samples.submitter_id" using GDC API and use it as index. | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | muse_snv | Yes | 13, 37, 5, 6, 7, 40, 42, 52, 1, 11, 16, 111 | N/A | # | N/A | 1. Calculate variant allele frequency (dna_vaf) by "t_alt_count"/"t_depth"; | mutect2_snv | [Tumor_Seq_Allele2, HGVSp_Short, Chromosome, Start_Position, End_Position, t_depth, t_alt_count, Consequence, Hugo_Symbol, Reference_Allele, Tumor_Sample_Barcode, FILTER] | 2. Delete "t_alt_count" and "t_depth" columns; | somaticsniper_snv | 3. Trim "Tumor_Sample_Barcode" to sample vial level; | varscan2_snv | 4. Rename columns as:: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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{ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Hugo_Symbol': 'gene', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Chromosome': 'chrom', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Start_Position': 'start', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'End_Position': 'end', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Reference_Allele': 'ref', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Tumor_Seq_Allele2': 'alt', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Tumor_Sample_Barcode': 'sampleid', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'HGVSp_Short': 'Amino_Acid_Change', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'Consequence': 'effect', | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
'FILTER': 'filter' | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
} |
+-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | star_counts | Yes | 1, 2 | EnsemblID | | 1 new column based on index | 1. Average if there are multiple data from the same sample vial; | | | | [Ensembl_ID, Counts] | | | | 2. log2(counts + 1) | +-------------------+----------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-----------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------+
.. _transform phenotype:
Settings for transform phenotype data into Xena matrix
+-------------+------------------------------------------------+-----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | GDC program | GDC raw data | Raw data format | Single data file transformation | Merge and matrix processing | +=============+================================================+=================+=========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+========================================================================================================================================================================================================================+ | TCGA | Clinical Supplement and Biospecimen Supplement | BCR XML | For clincial data, info will be extracted and organized into a per patient based pandas DataFrame. It will have a column named "bcr_patient_barcode" which will be used to join with biospecimen matrix later on. | 1. Multiple clinical data are concatenated directly by row with all empty columns removed. | 2. Multiple biospecimen data are concatenated directly by row with all empty columns removed. | The XML scheme are quite different for different projects. Therefore, to get as much info as possible while still keeping things clear, texts, if any, from all elements that have non-element children are extracted first. After such a "dirty" extraction, two clean ups will be done: | 3. Merged clinical matrix and merged biospecimen matrix are further merged on "bcr_patient_barcode". For conflict/overlapping columns, non-empty value from the clinical data has the priority. | |||||||||
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1. For "race" info, it will be converted into a comma separated list of races, in case there are more than one entry in |
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2. When there is one or more follow ups, the most recent follow up will be find out. All info in the most recent follow up will be used to replace/add to previously extracted matrix. | |||||||||||||||||||||||
For biospecimen data, there is one coherent XML scheme for all TCGA projects. There are two parts to be considered for biospecimen data: per sample/sample specific data and patient data (which is common for all samples). Info from both parts will be extracted and finally organized into a per sample based matrix, having a column named "bcr_patient_barcode", which will be used to join with clinical matrxi later on. In general, info extraction has the following 3 steps: | |||||||||||||||||||||||
1. Common patient data will be extracted first, including texts from direct children of TCGA study name <https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations> . |
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2. Samples from <bio:patient/bio:samples> will be processed and have comman patient data attached one by one. Non-empty texts from direct children of sample will be extracted, i.e. details from nodes like type code 10 <https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes> _ are dropped. |
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3. A column of "bcr_patient_barcode" from <bio:patient/shared:bcr_patient_barcode> will be added to the final biospecimen matrix (same for the whole table). |
+-------------+------------------------------------------------+-----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | TARGET | Clinical Supplement only | XLSX | The excel file is converted to a pandas DataFrame. | 1. Multiple DataFrames will be concatenated directly by row, and arriage return and line feed are replaced by a single space. | | | | | | 2. Clinical data is per case(patient) based, while Xena phenotype matrix is per sample based. All related samples for each case/patient will be identified and phenotype data will be mapped to corresponding samples. | +-------------+------------------------------------------------+-----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Settings for transform survival data into Xena matrix
GDC survival data is returned as JSON from GDC API. During the downloading process, it can and will be converted directly to pandas DataFrame and saved as tab delimited table. During transformation, columns in "primary" Xena survival matrix can be mapped directly (without further processing/calculation) from the raw table like this:
+---------------------+-------------------+ | Primary Xena column | GDC source column | +=====================+===================+ | OS.time | time | +---------------------+-------------------+ | OS | censored | +---------------------+-------------------+ | _PATIENT | submitter_id | +---------------------+-------------------+
GDC survival data is per case(patient) based and so is "primary" Xena survival matrix, while Xena survival matrix is per sample based. All related samples for each case/patient will be identified and survival data will be mapped to corresponding samples.
CPTAC-3 Cohort
CPTAC-3 data consists of RNAseq data (as discussed in GDCOmicset
) and
clinical data from the API. The cases and expand for clinical data are
defined in the constants.py
file.
Check documentation for GDC module and Xena Dataset module here <https://github.com/ucscXena/xena-GDC-ETL/blob/master/docs/API.rst>
_.