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What are you trying to do?
I’m trying to run the script
with the same data but receiving that warning
import numpy as np
import pandas as pd
from beakerx.object import beakerx
from sklearn.model_selection import train_test_split
import cimcb_lite as cb
beakerx.pandas_display_table() # by default display pandas tables as BeakerX interactive tables
print('All packages successfully loaded')
# The path to the input file (Excel spreadsheet)
filename = 'D:\USER\Downloads\GastricCancer_NMR.xlsx'
# Load Peak and Data tables into two variables
dataTable, peakTable = cb.utils.load_dataXL(filename, DataSheet='Data', PeakSheet='Peak')
# Create a clean peak table
rsd = peakTable['QC_RSD']
percMiss = peakTable['Perc_missing']
peakTableClean = peakTable[(rsd < 20) & (percMiss < 10)]
print("Number of peaks remaining: {}".format(len(peakTableClean)))
# Extract and scale the metabolite data from the dataTable
peaklist = peakTableClean['Name'] # Set peaklist to the metabolite names in the peakTableClean
X = dataTable[peaklist].values # Extract X matrix from dataTable using peaklist
Xlog = np.log10(X) # Log scale (base-10)
Xscale = cb.utils.scale(Xlog, method='auto') # methods include auto, pareto, vast, and level
Xknn = cb.utils.knnimpute(Xscale, k=3) # missing value imputation (knn - 3 nearest neighbors)
print("Xknn: {} rows & {} columns".format(*Xknn.shape))
cb.plot.pca(Xknn,
pcx=1, # pc for x-axis
pcy=2, # pc for y-axis
group_label=dataTable['SampleType']) # labels for Hover in PCA loadings plot
And this are the warning
BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead
BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead
What have you tried that did NOT work as expected? If you have also posted this question elsewhere (e.g. StackOverflow), please include a link to that post.
I tried to google the warning but there was no solution
any hints will be appreciated
Many
In some cases, a screenshot of your plot will also be helpful.
