def generate_TCGA_LUAD_MessengerRNA():
return MessengerRNA(data=os.path.join(cohort_folder_path,
"LUAD__geneExp.txt"),
transpose=True,
usecols="GeneSymbol|TCGA",
gene_index="GeneSymbol",
gene_level="gene_name")
def get_expression_data(list_of_contents,
list_of_names,
data_type,
cohort_name=None,
genes_col_name=None,
columns_selected=None,
transposed=None):
"""
Args:
list_of_contents:
list_of_names:
data_type:
cohort_name:
genes_col_name:
columns_selected:
transposed:
"""
if columns_selected:
columns = "|".join(columns_selected)
else:
columns = None
if transposed == "True":
transposed = True
elif transposed == "False":
transposed = False
file = handle_filestreams(list_of_contents, list_of_names)
if data_type == MicroRNA.name():
expression_data = MicroRNA(file, transpose=transposed, usecols=columns)
elif data_type == MessengerRNA.name():
expression_data = MessengerRNA(file,
transpose=transposed,
usecols=columns)
elif data_type == LncRNA.name():
expression_data = LncRNA(file, transpose=transposed, usecols=columns)
elif data_type == Protein.name():
expression_data = Protein(file, transpose=transposed, usecols=columns)
else:
print(data_type)
return expression_data
def test_TCGA_LUAD_multiomics_transcriptomics(generate_TCGA_LUAD):
"""
Args:
generate_TCGA_LUAD:
"""
assert all(
elem in generate_TCGA_LUAD.get_omics_list() for elem in
[MessengerRNA.name(),
MicroRNA.name(),
LncRNA.name(),
Protein.name()])
def test_import_expression_table_size(generate_TCGA_LUAD_MessengerRNA,
generate_TCGA_clinical):
"""
Args:
generate_TCGA_LUAD_MessengerRNA:
"""
cohort_name = "LUAD"
luad_data = MultiOmics(cohort_name)
luad_data.add_clinical_data(generate_TCGA_clinical)
luad_data.add_omic(generate_TCGA_LUAD_MessengerRNA)
luad_data.build_samples()
print(luad_data.data.keys())
assert (luad_data.data[MessengerRNA.name()].shape ==
generate_TCGA_LUAD_MessengerRNA.expressions.shape)
def test_import_expression_table_size(generate_TCGA_LUAD_MessengerRNA):
"""
Args:
generate_TCGA_LUAD_MessengerRNA:
"""
cohort_name = "LUAD"
luad_data = MultiOmics(cohort_name)
luad_data.add_clinical_data(clinical_data=os.path.join(
cohort_folder_path,
"nationwidechildrens.org_clinical_patient_luad.txt"))
luad_data.add_omic(generate_TCGA_LUAD_MessengerRNA)
luad_data.build_samples()
print(luad_data.data.keys())
assert luad_data.data[MessengerRNA.name(
)].shape == generate_TCGA_LUAD_MessengerRNA.expressions.shape
def generate_TCGA_LUAD_MessengerRNA():
data = MessengerRNA(
data=os.path.join(cohort_folder_path, "LUAD__geneExp.txt"),
transpose=True,
usecols="GeneSymbol|TCGA",
gene_index="GeneSymbol",
gene_level="gene_name",
)
data.drop_genes(data.expressions.columns[50:])
data.drop_samples(data.expressions.index[:100])
return data
def generate_TCGA_LUAD_MessengerRNA_dask():
data = MessengerRNA(
data=os.path.join(cohort_folder_path, "LUAD__geneExp.txt"),
transpose=True,
usecols="GeneSymbol|TCGA",
gene_index="GeneSymbol",
gene_level="gene_name",
npartitions=4,
)
data.drop_genes(data.expressions.columns[50:])
data.initialize_annotations(index="gene_name", gene_list=None)
return data
def control_tabs():
return html.Div(
id='circos-control-tabs',
className='control-tabs',
children=[
dcc.Tabs(
id='circos-tabs',
value='what-is',
children=[
dcc.Tab(
label='Welcome',
value='what-is',
children=html.Div(
className='control-tab',
children=[
html.H5(className='what-is',
children=
"OpenOmics: The Multi-Omics Explorer"),
html.
P('OpenOmics provides a bioinformatics API and web-app '
'platform integrate, analyze, and visualize the '
'multi-omics and clinical data. You can interact with the '
'data input tabs to integrate the various different data types '
'including bio-databases, multi-omics expression, genomics, '
'and clinical data.'),
html.Div([
'Cite us at: ',
html.
A('OpenOmics paper',
href=
'http://www.doi.org/10.1101/gr.092759.109'
), '.'
]),
html.Div([
'For a look into the OpenOmics API and usage documentation, please visit the '
'original repository ',
html.
A('OpenOmics GitHub',
href=
'https://github.com/JonnyTran/OpenOmics.git'
), '.'
]),
html.Br(),
html.Button(id='start-button',
style={"align": "center"},
n_clicks=0,
children='Get Started'),
])),
dcc.Tab(
label='Import Clinical Data',
value='clinical-upload',
className='control-tab',
children=[
html.Div([
'Cohort name: ',
]),
dcc.Input(
id='clinical-cohort',
style={'width': '100%'},
placeholder="Leave empty to use filename",
type='text'),
html.Br(),
html.Div(['Select clinical data type']),
dcc.RadioItems(
id='clinical-data-type',
options=[
{
'label': 'Patients data',
'value': 'Patients data'
},
{
'label': 'Samples data',
'value': 'Samples data'
},
{
'label': 'Drug responses',
'value': 'Drug responses'
},
],
value='Patients data',
),
html.Br(),
html.Div([
'Import Clinical Data',
]),
html.Div(children=[
dcc.Upload(id='upload-clinical',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
},
multiple=False)
]),
html.Br(),
html.Div(
id='clinical-column-select',
children=[
html.Div([
'Select the patient id column to index by:'
]),
dcc.Dropdown(
id='clinical-patient-col-name',
disabled=True),
html.Div([
'Select the column prefixes to import:'
]),
dcc.Dropdown(
id='clinical-data-columns-select',
disabled=True,
)
]),
html.Br(),
html.Button(id='clinical-submit-button',
n_clicks=0,
children='Submit'),
]),
dcc.Tab(
label='Import Omics Data',
value='data-upload',
className='control-tab',
children=[
html.Div([
'Cohort name: ',
]),
dcc.Input(
id='data-table-cohort',
style={'width': '100%'},
placeholder="Leave empty to use filename",
type='text'),
html.Br(),
html.Br(),
html.Div([
'Select data type:',
]),
dcc.RadioItems(
id='data-table-type',
options=[
{
'label': 'Protein Expression',
'value': Protein.name()
},
{
'label': 'miRNA Expression',
'value': MicroRNA.name()
},
{
'label': 'lncRNA Expression',
'value': LncRNA.name()
},
{
'label': 'mRNA Expression',
'value': MessengerRNA.name()
},
],
value=MicroRNA.name(),
),
html.Br(),
html.Div([
'Import a data file',
]),
html.Div(children=[
dcc.Upload(id='upload-data-table',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
},
multiple=True)
]),
html.Br(),
html.Div(
id='data-table-column-select',
children=[
html.Div([
'Select the gene id/name column to index by:'
]),
dcc.Dropdown(
id='data-table-genes-col-name',
disabled=True),
html.Div([
'Select the column prefixes to import:'
]),
dcc.Dropdown(
id='data-table-columns-select',
disabled=True,
)
]),
html.Br(),
html.Div(['Each gene is indexed by:']),
dcc.RadioItems(
id='data-table-genes_index',
options=[
{
'label': 'Gene symbol',
'value': 'gene_name'
},
{
'label': 'Gene index',
'value': 'gene_index'
},
{
'label': 'Transcript name',
'value': 'transcript_name'
},
{
'label': 'Transcript index',
'value': 'transcript_index'
},
],
value='gene_name',
),
html.Br(),
html.Div(['Is the table transposed?']),
dcc.RadioItems(
id='data-table-transpose',
options=[
{
'label': 'Sample name columns',
'value': "True"
},
{
'label': 'Gene name in columns',
'value': "False"
},
],
value="True",
),
html.Br(),
html.Button(id='upload-data-table-submit',
n_clicks=0,
children='Submit'),
]),
dcc.Tab(label='Annotate database',
value='annotatte-db',
className='control-tab',
children=[
html.Div('To be implemented'),
]),
])
])