Perform data normalization using data for named metabolites from the metabolomics workbench or uploaded data files.
Import Python modules...
from __future__ import print_function
import os
import sys
import time
import re
import requests
import pandas as pd
import numpy as np
import ipywidgets as widgets
from IPython.display import display, HTML
from IPython import __version__ as ipyVersion
# Import MW modules from the current directory or default Python directory...
import MWUtil
%matplotlib inline
print("Python: %s.%s.%s" % sys.version_info[:3])
print("IPython: %s" % ipyVersion)
print()
print(time.asctime())
The URL PATH
The MW REST URL consists of three main parts, separated by forward slashes, after the common prefix specifying the invariant base URL (https://www.metabolomicsworkbench.org/rest/):
https://www.metabolomicsworkbench.org/rest/context/input_specification/output_specification
Part 1: The context determines the type of data to be accessed from the Metabolomics Workbench, such as metadata or results related to the submitted studies, data from metabolites, genes/proteins and analytical chemistry databases as well as other services related to mass spectrometry and metabolite identification:
context = study | compound | refmet | gene | protein | moverz | exactmass
Part 2: The input specification consists of two required parameters describing the REST request:
input_specification = input_item/input_value
Part 3: The output specification consists of two parameters describing the output generated by the REST request:
output_specification = output_item/(output_format)
The first parameter is required in most cases. The second parameter is optional. The input and output specifications are context sensitive. The context determines the values allowed for the remaining parameters in the input and output specifications as detailed in the sections below.
Setup MW REST base URL...
MWBaseURL = "https://www.metabolomicsworkbench.org/rest"
Retrieve or upload data for named metabolites...
# Initialize data...
StudiesResultsData = None
RetrievedMWData = None
# Setup UIF info text...
TopInfoTextHTML = widgets.HTML(value = "<strong>Retrieve or upload data and process any missing values</strong>",
placeholder='', description='')
# Setup UIF to process any missing values...
MissingValuesMethods = ["NoAction", "DeleteRows", "DeleteColumns", "ReplaceByColumnMean", "ReplaceColumnMedian", "ReplaceByZero" , "LinearInterpolation"]
MissingValuesMethodsDropdown = widgets.Dropdown(options = MissingValuesMethods,
value = "NoAction",
description = " ")
ProcessMissingValueTopTextHTML = widgets.HTML(value = "Method for processing missing values:",
placeholder='', description='')
# Setup UIF to retrieve...
StudyIDText = widgets.Text(value = "ST000001 ST000002", description = "Study ID (s)",
placeholder = "Type study ID", disabled = False,
layout = widgets.Layout(margin='0 10px 0 0'))
RetrieveDataBtn = widgets.Button(description = 'Retrieve Data', disabled = False, button_stype = '',
tooltip = "Retrieve data for study ID")
RetrieveDataOutput = widgets.Output()
def RetrieveDataBtnEventHandler(Object):
global StudiesResultsData, RetrievedMWData
RetrievedMWData = True
StudiesResultsData = None
StudyIDs = StudyIDText.value
MissingValuesMethod = MissingValuesMethodsDropdown.value
RetrieveDataOutput.clear_output()
UploadDataOutput.clear_output()
with RetrieveDataOutput:
if len(StudyIDs):
print("\nProcessing study ID(s): %s" % StudyIDs)
StudiesResultsData = MWUtil.RetrieveStudiesAnalysisAndResultsData(StudyIDs, MWBaseURL, MissingValuesMethod)
DisplayData = False if len(StudiesResultsData.keys()) > 5 else True
MWUtil.ListStudiesAnalysisAndResultsData(StudiesResultsData, DisplayDataFrame = DisplayData,
IPythonDisplayFuncRef = display, IPythonHTMLFuncRef = HTML)
else:
print("\nNo study ID(s) specified...")
RetrieveDataBtn.on_click(RetrieveDataBtnEventHandler)
# Setup UIF to upload data file(s)...
FileUploadBtn = widgets.FileUpload(description = 'Upload File(s)', accept='.csv,.txt,.tsv', multiple = True,
disabled = False)
FileUploadTextHTML = widgets.HTML(value = "<strong>File format:</strong> Col 1: Sample names; \
Col 2: Class identifiers; Remaining cols: Named metabolites; \
<strong>Exts: </strong>.csv, .txt, or .tsv", placeholder='', description='')
UploadDataOutput = widgets.Output()
def FileUploadBtnEventHandler(Change):
global StudiesResultsData, RetrievedMWData
RetrievedMWData = False
StudiesResultsData = None
MissingValuesMethod = MissingValuesMethodsDropdown.value
UploadedDataInfo = FileUploadBtn.value
RetrieveDataOutput.clear_output()
UploadDataOutput.clear_output()
with UploadDataOutput:
StudiesResultsData = MWUtil.RetrieveUploadedData(UploadedDataInfo, MissingValuesMethod)
DisplayData = False if len(StudiesResultsData.keys()) > 5 else True
MWUtil.ListStudiesAnalysisAndResultsData(StudiesResultsData, DisplayDataFrame = DisplayData,
IPythonDisplayFuncRef = display, IPythonHTMLFuncRef = HTML)
FileUploadBtn.observe(FileUploadBtnEventHandler, names = 'value')
# Setup UIF to retrieve or upload data file...
DataWarningTextHTML = widgets.HTML(value = "<div class='alert alert-warning'><strong>Warning:</strong> Don't re-run the current cell after specifying study ID(s) or selecting file(s) and retrieving the data. Click on the next cell to advance.</div>", placeholder='', description='')
OrTextHTML = widgets.HTML(value = "<strong>Or</strong>", placeholder='', description='')
UIFDataBoxes = []
UIFDataBoxes.append(widgets.HBox([TopInfoTextHTML]))
UIFDataBoxes.append(widgets.HBox([ProcessMissingValueTopTextHTML, MissingValuesMethodsDropdown]))
UIFDataBoxes.append(widgets.HBox([StudyIDText, RetrieveDataBtn],
layout = widgets.Layout(margin='10px 0 0 0')))
UIFDataBoxes.append(widgets.HBox([OrTextHTML]))
UIFDataBoxes.append(widgets.HBox([FileUploadBtn]))
UIFDataBoxes.append(widgets.HBox([FileUploadTextHTML]))
UIFDataBoxes.append(widgets.HBox([DataWarningTextHTML]))
for UIFDataBox in UIFDataBoxes:
display(UIFDataBox)
display(RetrieveDataOutput)
display(UploadDataOutput)
MWUtil.CheckAndWarnEmptyStudiesData(StudiesResultsData, RetrievedMWData, StudyIDText.value)
Setup UIF for selecting and plotting available data...
# Setup UIF data...
StudiesUIFData = MWUtil.SetupUIFDataForStudiesAnalysisAndResults(StudiesResultsData)
MWUtil.CheckAndWarnEmptyStudiesUIFData(StudiesUIFData, RetrievedMWData, StudyIDText.value)
# Setup a function to normalize data...
def NormalizeData(InputDataFrame, Method = "Median", ClassColID = "Class", ClassNumColID = "ClassNum"):
# Track and drop columns before performing data normalization...
DataFrame = InputDataFrame
# Track and drop columns before performing data normalization...
TrackColIDs = []
if ClassColID is not None:
DataFrame = DataFrame.drop(ClassColID, axis = 1)
TrackColIDs.append(ClassColID)
if ClassNumColID is not None:
DataFrame = DataFrame.drop(ClassNumColID, axis = 1)
TrackColIDs.append(ClassNumColID)
# Center data by mean...
DataFrame = DataFrame - DataFrame.mean()
if re.match("^Median$", Method, re.I):
DataFrame = DataFrame / DataFrame.median()
elif re.match("^auto$", Method, re.I):
DataFrame = DataFrame / DataFrame.std()
elif re.match("^Pareto$", Method, re.I):
DataFrame = DataFrame / np.sqrt(DataFrame.std())
elif re.match("^Range$", Method, re.I):
DataFrame = DataFrame / (DataFrame.max() - DataFrame.min())
else:
print("***Warning: Failed to normalize data: Unknown method %s..." % Method)
return InputDataFrame
# Format normalized values...
DataFrame = DataFrame.applymap("{0:.4f}".format)
# Add any tracked col IDs...
if len(TrackColIDs):
TrackedColsDataFrame = InputDataFrame[TrackColIDs]
DataFrame = pd.concat([TrackedColsDataFrame, DataFrame], axis = 1)
return DataFrame
# Setup UIF...
FirstStudyID = StudiesUIFData["StudyIDs"][0]
StudiesDropdown = widgets.Dropdown(options = StudiesUIFData["StudyIDs"], value = FirstStudyID,
description="Study:", disabled = False)
FirstAnalysisID = StudiesUIFData["AnalysisIDs"][FirstStudyID][0]
AnalysisDropdown = widgets.Dropdown(options = StudiesUIFData["AnalysisIDs"][FirstStudyID], value = FirstAnalysisID,
description = "Analysis:", disabled = False)
Methods = ["Median", "Auto", "Pareto", "Range"]
MethodsDropdown = widgets.Dropdown(options = Methods, value = Methods[0], description = "Methods:")
NormalizeDataBtn = widgets.Button(description = 'Normalize Data', disabled = False, button_stype = '',
tooltip = "Normaliza data for a study ID")
DataLayout = widgets.Layout(margin='0 0 4px 0')
StudiesDataHBox = widgets.HBox([StudiesDropdown, AnalysisDropdown], layout = DataLayout)
MethodsDataHBox = widgets.HBox([MethodsDropdown], layout = DataLayout)
NormalizeDataHBox = widgets.HBox([NormalizeDataBtn], layout = DataLayout)
Output = widgets.Output()
def PerformDataNormalization(Object):
Output.clear_output()
StudyID = StudiesDropdown.value
AnalysisID = AnalysisDropdown.value
NormalizationMethod = MethodsDropdown.value
ResultsDataFrame = StudiesResultsData[StudyID][AnalysisID]["data_frame"]
with Output:
NormalizedResultsDataFrame = NormalizeData(ResultsDataFrame, NormalizationMethod)
MWUtil.ListClassInformation(StudiesResultsData, StudyID, AnalysisID, RetrievedMWData)
# Setup links to download data as CSV files...
if RetrievedMWData:
FileName = "%s_%s_Data.csv" % (StudyID, AnalysisID)
HTMLText = MWUtil.SetupCSVDownloadLink(ResultsDataFrame, Title = "Download data", CSVFilename = FileName)
display(HTML(HTMLText))
if RetrievedMWData:
FileName = "%s_%s_Normalized_Data_%s.csv" % (StudyID, AnalysisID, NormalizationMethod)
else:
FileRoot, FileExt = os.path.splitext(StudyID)
FileName = "%s_Normalized_Data.csv" % (FileRoot)
HTMLText = MWUtil.SetupCSVDownloadLink(NormalizedResultsDataFrame, Title = "Download normalized data", CSVFilename = FileName)
display(HTML(HTMLText))
print("Normalized data:\n")
display(HTML(NormalizedResultsDataFrame.to_html(max_rows = 10, max_cols = 10)))
# Setup function to update dropdown options...
def UpdateAnalysisDropdown(StudyID):
AnalysisDropdown.options = StudiesUIFData["AnalysisIDs"][StudyID]
AnalysisDropdown.value = StudiesUIFData["AnalysisIDs"][StudyID][0]
# Setup dropdown event handlers...
def StudiesDropdownEventHandler(Change):
StudyID = Change["new"]
UpdateAnalysisDropdown(StudyID)
# Bind required event handlers...
StudiesDropdown.observe(StudiesDropdownEventHandler, names = 'value')
NormalizeDataBtn.on_click(PerformDataNormalization)
display(StudiesDataHBox)
display(MethodsDataHBox)
display(NormalizeDataHBox)
display(Output)