def summarize_report(filenames, matrices, num_factors, start_time,
file_layout):
import time
import subprocess
from genomicode import htmllib
from genomicode import parselib
#def highlight(s):
# return htmllib.SPAN(s, style="background-color:yellow")
assert len(filenames) == len(matrices)
lines = []
w = lines.append
w("<HTML>")
w(htmllib.HEAD(htmllib.TITLE("BFRMNormalize Report")))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(htmllib.EM("BFRMNormalize") + " Report")))
w(htmllib.H3("I. Overview"))
files = [os.path.split(x)[1] for x in filenames]
x1 = "one data set"
if len(files) > 1:
x1 = "the following data sets"
x2 = "factor"
if num_factors > 1:
x2 = "factors"
x = "I normalized %s using %d %s." % (x1, num_factors, x2)
l = [x]
for i in range(len(files)):
name = files[i]
num_samples = matrices[i].ncol()
x = "%s (%d samples)" % (name, num_samples)
l.append(htmllib.LI() + x)
l = "\n".join(l)
w(htmllib.UL(l))
w(htmllib.P())
x = os.path.split(file_layout.DS_PROC)[1]
w("The merged gene expression data set is available at " +
htmllib.A(x, href=x) + ".")
w(htmllib.BR())
x = os.path.split(file_layout.DS_FINAL)[1]
w("The normalized data set is available at " + htmllib.A(x, href=x) + ".")
w(htmllib.P())
w(htmllib.H3("II. Results"))
# Make the table of the heatmaps.
x = os.path.split(file_layout.DS_PROC_HEATMAP)[1]
x1 = htmllib.CENTER(
htmllib.B("Before Normalization") + htmllib.BR() +
htmllib.A(htmllib.IMG(height=480, src=x), href=x))
x = os.path.split(file_layout.DS_FINAL_HEATMAP)[1]
x2 = htmllib.CENTER(
htmllib.B("After Normalization") + htmllib.BR() +
htmllib.A(htmllib.IMG(height=480, src=x), href=x))
row1 = htmllib.TR(htmllib.TD(x1) + htmllib.TD(x2))
x = htmllib.TD(
htmllib.B("Figure 1: Heatmaps. ") +
"These heatmaps show the expression patterns in the data before "
"and after normalization. "
"The rows contain the %d genes that exhibit the highest variance "
"in gene expression across the original data set. "
"The columns contain the samples in the data sets provided. "
"The genes and samples are in the same order in both heatmaps. "
"Warm colors indicate high expression of the gene, and cool colors "
"indicate low expression." % NUM_FILTERED_GENES,
colspan=2)
row2 = htmllib.TR(x)
w(htmllib.TABLE(row1 + row2, border=0, cellspacing=10, width="50%%"))
w(htmllib.P())
# Make the table of the scatter plots.
x = os.path.split(file_layout.DS_PROC_SCATTER)[1]
x1 = htmllib.CENTER(
htmllib.B("Before Normalization") + htmllib.BR() +
htmllib.A(htmllib.IMG(height=400, src=x), href=x))
x = os.path.split(file_layout.DS_FINAL_SCATTER)[1]
x2 = htmllib.CENTER(
htmllib.B("After Normalization") + htmllib.BR() +
htmllib.A(htmllib.IMG(height=400, src=x), href=x))
row1 = htmllib.TR(htmllib.TD(x1) + htmllib.TD(x2))
x1 = (
"These plots show the samples projected onto the first two principal "
"components of the expression profiles of the %d genes that "
"exhibit the highest variance across the original data set. " %
NUM_FILTERED_GENES)
x2 = ("Each point represents a sample, and samples from the same data "
"set have the same color. "
"If there are batch effects, the samples from the same data set "
"(the same color) will cluster together. "
"If there are no batch effects, the colors should be mixed.")
if len(filenames) == 1:
x2 = ""
x = htmllib.TD(htmllib.B("Figure 2: PCA Plots. ") + x1 + x2, colspan=2)
row2 = htmllib.TR(x)
w(htmllib.TABLE(row1 + row2, border=0, cellspacing=10, width="50%%"))
# Format the current time.
end_time = time.time()
time_str = parselib.pretty_date(start_time)
x = int(end_time - start_time)
num_min = x / 60
num_secs = x % 60
if num_min == 0:
run_time = "%ss" % parselib.pretty_int(num_secs)
else:
run_time = "%sm %ss" % (parselib.pretty_int(num_min), num_secs)
# Get the hostname.
cmd = "hostname"
p = subprocess.Popen(cmd,
shell=True,
bufsize=0,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
close_fds=True)
wh, r = p.stdin, p.stdout
wh.close()
hostname = r.read().strip()
assert hostname, "I could not get the hostname."
w(htmllib.P())
w(htmllib.HR())
w(
htmllib.EM(
"This analysis was run on %s on %s. It took %s to complete." %
(time_str, hostname, run_time)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
outfile = file_layout.REPORT
open(outfile, 'w').write(x)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import shutil
from Betsy import bie3
outfile_folder = outfile
outfile = os.path.join(outfile_folder, 'report.html')
if not os.path.exists(outfile_folder):
os.mkdir(outfile_folder)
result_files = []
for index, data_node in enumerate(antecedents):
filename = data_node.identifier
new_name = os.path.join(outfile_folder,
os.path.split(filename)[-1])
#rename one of the pcaplot filename
if index == 2:
new_name = os.path.join(outfile_folder,
'after_' + os.path.split(filename)[-1])
if os.path.isdir(filename):
shutil.copytree(filename, new_name)
else:
shutil.copyfile(filename, new_name)
result_files.append(os.path.split(new_name)[-1])
data_node1, data_node2, data_node3, data_node4, data_node5, data_node6 = antecedents
#write the report.html
from genomicode import parselib
from genomicode import htmllib
#def highlight(s):
# from genomicode import htmllib
# return htmllib.SPAN(s, style="background-color:yellow")
#def smaller(s):
# from genomicode import htmllib
# return htmllib.FONT(s, size=-1)
try:
lines = []
w = lines.append
w("<HTML>")
title = "Normalization Results"
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
w('I generated a file that contains the normalized gene expression values'
)
w(htmllib.P())
w(htmllib.A(result_files[0], result_files[0]))
w(htmllib.P())
w(htmllib.A("Methods", href="#methods_normalization"))
w(htmllib.P())
## if pipelines[1] == pipelines[2]:
## w(htmllib.A(htmllib.IMG(height=500,
## src=result_files[1]), href=result_files[1]))
## else:
rows = []
x = htmllib.TR(
htmllib.TD(htmllib.A(htmllib.IMG(height=500,
src=result_files[1]),
href=result_files[1]),
align="CENTER") + htmllib.TD(htmllib.A(
htmllib.IMG(height=500, src=result_files[2]),
href=result_files[2]),
align="CENTER"))
rows.append(x)
x = htmllib.TR(
htmllib.TH("Before", align="CENTER") +
htmllib.TH("After", align="CENTER"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=None,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
w(htmllib.P())
name = 'Figure 1: This pca plot shows the similarities among your samples'
w(htmllib.B(name))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=500, src=result_files[3]),
href=result_files[3]))
w(htmllib.P())
name = 'Figure 2: This boxplot shows the distribution of signal values'
w(htmllib.B(name))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=500, src=result_files[4]),
href=result_files[4]))
w(htmllib.P())
name = 'Figure 3: This plot shows the values of ACTB and TUBB genes'
w(htmllib.B(name))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=500, src=result_files[5]),
href=result_files[5]))
name = 'Figure 4: This plot shows the average values control genes'
w(htmllib.P())
w(htmllib.B(name))
w(htmllib.HR())
w(
htmllib.A("<methods_normalization>",
name="methods_normalization"))
w(htmllib.CENTER(htmllib.H2("Methods")))
w(htmllib.H3("1.Normalization File"))
w('To generate this file, I ran the following analysis:')
w(htmllib.P())
bie3.plot_network_gv(os.path.join(outfile_folder, "network.png"),
network)
w(
htmllib.A(htmllib.IMG(height=500, src="network.png"),
href="network.png"))
w('I used the following parameters:')
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in data_node1.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(data_node1.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
w(htmllib.H3("2. PCA analysis"))
w('I made a principal component plot that shows the similarities among your samples.'
)
w(htmllib.P())
w(htmllib.H3("3. Signal distribution"))
w('I made a box plot that shows the distribution of signal values.'
)
w(htmllib.P())
w(htmllib.H3("4. Control signal"))
w('I made two plots that show the values of control signal.')
w(htmllib.P())
# Write out the footer.
#time_str = parselib.pretty_date(time.time())
#hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
#w(htmllib.EM(
# "This analysis was run on %s on %s. \n" %
# (time_str, hostname)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
open(outfile, 'w').write(x)
except:
raise
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import shutil
import math
from Betsy import bie3
import arrayio
from genomicode import htmllib
from genomicode import parselib
outfile_folder = outfile
outfile = os.path.join(outfile_folder, 'report.html')
if not os.path.exists(outfile_folder):
os.mkdir(outfile_folder)
result_files = []
for data_node in antecedents:
filename = data_node.identifier
new_name = os.path.join(outfile_folder,
os.path.split(filename)[-1])
if os.path.isdir(filename):
shutil.copytree(filename, new_name)
else:
shutil.copyfile(filename, new_name)
result_files.append(os.path.split(new_name)[-1])
(data_node1, data_node2, data_node3, data_node4, data_node5,
data_node6, data_node7, data_node8, data_node9, data_node10,
data_node11) = antecedents
#write the report.html
#def highlight(s):
# from genomicode import htmllib
# return htmllib.SPAN(s, style="background-color:yellow")
#def smaller(s):
# from genomicode import htmllib
# return htmllib.FONT(s, size=-1)
try:
lines = []
w = lines.append
w("<HTML>")
title = "Classification Results"
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
#------------------------------------
w(htmllib.H3("SVM"))
w(htmllib.P())
w(htmllib.A("Methods", href="#methods_svm"))
w(htmllib.P())
#------------------------------------
whole_row = []
name = 'Table 1: Table of genes used in classification'
w(htmllib.B(name))
w(htmllib.P())
M = arrayio.read(os.path.join(outfile_folder, result_files[0]))
ids = M._row_order
genes = M.row_names(ids[0])
ncolumn = 3
nrow = 8
rows = []
for i in range(min(nrow, len(genes) / ncolumn)):
a = []
for j in range(0, ncolumn):
a.append('<td>' + genes[ncolumn * i + j] + '</td>')
x = htmllib.TR("\n".join(a))
rows.append(x)
more_genes = 0
if len(genes) > ncolumn * nrow:
more_genes = len(genes) - ncolumn * nrow
y = htmllib.TR(
htmllib.TD(htmllib.TABLE(
"\n".join(rows), border=1, cellpadding=3, cellspacing=0),
align='CENTER') + htmllib.TD(htmllib.A(
htmllib.IMG(height=400, src=result_files[5]),
href=result_files[5]),
align='CENTER'))
#---------------------------------
whole_row.append(y)
y = htmllib.TR(
htmllib.TD(htmllib.A(
str(more_genes) + ' more genes', result_files[0]),
align='LEFT') +
htmllib.TD(htmllib.B(
'Figure 1: This figure shows the PCA plot of samples colored by prediction'
),
align='CENTER'))
whole_row.append(y)
x = htmllib.TR(
htmllib.TD(htmllib.A(htmllib.IMG(height=400,
src=result_files[4]),
href=result_files[4]),
align="CENTER") + htmllib.TD(htmllib.A(
htmllib.IMG(height=400, src=result_files[2]),
href=result_files[2]),
align="CENTER"))
whole_row.append(x)
x = htmllib.TR(
htmllib.TH(htmllib.A("Figure 2. Loocv result on training data",
result_files[3]),
align="CENTER") + htmllib.TH(htmllib.A(
"Figure 3. Prediction result on test data",
result_files[1]),
align="CENTER"))
whole_row.append(x)
w(
htmllib.TABLE("\n".join(whole_row),
border=None,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
#------------------------------------
w(htmllib.H3("Weighted Voting"))
w(htmllib.P())
w(htmllib.A("Methods", href="#methods_wv"))
w(htmllib.P())
#------------------------------------
whole_row = []
name = 'Table 1: Table of genes used in classification'
w(htmllib.B(name))
w(htmllib.P())
nfeature = 10
if 'num_features_value' in user_options:
nfeature = user_options['num_features_value']
M = arrayio.read(os.path.join(outfile_folder, result_files[0]))
ids = M._row_order
genes = M.row_names(ids[0])[0:nfeature]
nrow = min(8, int(math.ceil(float(len(genes)) / ncolumn)))
ncolumn = 3
if len(genes) < nrow * ncolumn:
genes.extend([''] * (nrow * ncolumn - len(genes)))
rows = []
for i in range(nrow):
a = []
for j in range(ncolumn):
a.append('<td>' + genes[ncolumn * i + j] + '</td>')
x = htmllib.TR("\n".join(a))
rows.append(x)
more_genes = 0
if len(genes) > ncolumn * nrow:
more_genes = len(genes) - ncolumn * nrow
y = htmllib.TR(
htmllib.TD(htmllib.TABLE(
"\n".join(rows), border=1, cellpadding=3, cellspacing=0),
align='CENTER') + htmllib.TD(htmllib.A(
htmllib.IMG(height=400, src=result_files[10]),
href=result_files[10]),
align='CENTER'))
#---------------------------------
whole_row.append(y)
y = htmllib.TR(
htmllib.TD(htmllib.A(
str(more_genes) + ' more genes', result_files[0]),
align='LEFT') +
htmllib.TD(htmllib.B(
'Figure 4: This figure shows the PCA plot of samples colored by prediction'
),
align='CENTER'))
whole_row.append(y)
x = htmllib.TR(
htmllib.TD(htmllib.A(htmllib.IMG(height=400,
src=result_files[9]),
href=result_files[9]),
align="CENTER") + htmllib.TD(htmllib.A(
htmllib.IMG(height=400, src=result_files[7]),
href=result_files[7]),
align="CENTER"))
whole_row.append(x)
x = htmllib.TR(
htmllib.TH(htmllib.A("Figure 2. Loocv result on training data",
result_files[8]),
align="CENTER") + htmllib.TH(htmllib.A(
"Figure 3. Prediction result on test data",
result_files[6]),
align="CENTER"))
whole_row.append(x)
w(
htmllib.TABLE("\n".join(whole_row),
border=None,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
#--------------------------------
w(htmllib.HR())
w(htmllib.A("<methods_svm>", name="methods_svm"))
w('To generate these files, I ran the following analysis:')
bie3.plot_network_gv(os.path.join(outfile_folder, "network.png"),
network)
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=500, src="network.png"),
href="network.png"))
w(htmllib.CENTER(htmllib.H2("SVM Methods")))
w(htmllib.H3("Prediction Result"))
w('I used the following parameters:')
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in data_node2.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(data_node2.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
w(htmllib.A("<methods_wv>", name="methods_wv"))
w(htmllib.CENTER(htmllib.H2("Weighted Voting Methods")))
w(htmllib.H3("Prediction Result"))
w('I used the following parameters:')
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in data_node7.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(data_node7.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
# Write out the footer.
#time_str = parselib.pretty_date(time.time())
#hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
#w(htmllib.EM(
# "This analysis was run on %s on %s. \n" %
# (time_str, hostname)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
open(outfile, 'w').write(x)
except:
raise
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
from genomicode import parselib
from genomicode import htmllib
from Betsy import reportlib
from Betsy import bie3
out_path = outfile
outfile = os.path.join(out_path, 'report.html')
if not os.path.exists(out_path):
os.mkdir(out_path)
#(data_node1, data_node2, data_node3, data_node4, data_node5,
# data_node6, data_node7, data_node8, data_node9) = antecedents
# Make a list of the (relative) files for each input nodes:
# 0 SignalFile Preprocessed gene expression data.
# 1 IntensityPlot Box plot of signal intensity values.
# 2 BiotinPlot
# 3 PcaPlot Has normalization.
# 4 ActbPlot From _SignalFile_Impute.
# 5 PcaPlot No normalization or anything.
# 6 HousekeepingPlot
# 7 Hyb_barPlot No normalization or anything.
# 8 ControlFile File with Illumina control probes.
def rename_pca_file(i, in_file):
out_file = in_file
if i == 3:
out_file = 'after_%s' % in_file
return out_file
filenames = reportlib.extract_filenames(antecedents, out_path,
rename_pca_file)
for x in filenames:
in_file, out_file, in_filename, out_filename = x
reportlib.copy_file_or_path(in_filename, out_filename)
signal_file = filenames[0][1]
intensity_file = filenames[1][1]
biotin_file = filenames[2][1]
after_pca_file = filenames[3][1]
actb_file = filenames[4][1]
before_pca_file = filenames[5][1]
housekeeping_file = filenames[6][1]
hyb_file = filenames[7][1]
control_file = filenames[8][1]
signal_node = antecedents[0]
#write the report.html
IMG_HEIGHT = 400
lines = []
w = lines.append
w("<HTML>")
title = "Normalization Results"
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
# Provide a link to the signal file.
w('Preprocessed signal values: ')
# TODO: Show the attributes for this data.
w(htmllib.A(signal_file, signal_file))
w(htmllib.P())
##w(htmllib.A("Methods", href="#methods_normalization"))
##w(htmllib.P())
## if pipelines[1] == pipelines[2]:
## w(htmllib.A(htmllib.IMG(height=500,
## src=result_files[1]), href=result_files[1]))
## else:
# Show the PCA plot before and after normalization.
rows = []
col1 = htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=before_pca_file),
href=before_pca_file)
col2 = htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=after_pca_file),
href=after_pca_file)
x = htmllib.TR(
htmllib.TD(col1, align="CENTER") +
htmllib.TD(col2, align="CENTER"))
rows.append(x)
x = htmllib.TR(
htmllib.TH("Before", align="CENTER") +
htmllib.TH("After", align="CENTER"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=None,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
w(htmllib.P())
name = 'Figure 1: This pca plot shows the similarities among your samples'
w(htmllib.B(name))
w(htmllib.P())
# Show the distribution of the signal values.
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=intensity_file),
href=intensity_file))
w(htmllib.P())
name = 'Figure 2: This boxplot shows the distribution of signal values'
w(htmllib.B(name))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=actb_file),
href=actb_file))
w(htmllib.P())
name = 'Figure 3: This plot shows the values of ACTB and TUBB genes'
w(htmllib.B(name))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=biotin_file),
href=biotin_file))
w(htmllib.P())
x = 'Figure 4: This plot shows the value of biotin control genes'
w(htmllib.B(x))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=housekeeping_file),
href=housekeeping_file))
w(htmllib.P())
x = 'Figure 5: This plot shows the value of housekeeping control genes'
w(htmllib.B(x))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=hyb_file),
href=hyb_file))
w(htmllib.P())
x = 'Figure 6: This barplot shows the distribution control values'
w(htmllib.B(x))
w(htmllib.P())
# Methods.
w(htmllib.A("<methods_normalization>", name="methods_normalization"))
w(htmllib.CENTER(htmllib.H2("Methods")))
w(htmllib.H3("1.Normalization File"))
w('To generate this file, I ran the following analysis:')
w(htmllib.P())
bie3.plot_network_gv(os.path.join(out_path, "network.png"), network)
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src="network.png"),
href="network.png"))
w(htmllib.P())
w('I used the following parameters:')
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in signal_node.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(signal_node.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
w(htmllib.H3("2. PCA analysis"))
w('I made a principal component plot that shows the similarities among your samples.'
)
w(htmllib.P())
w(htmllib.H3("3. Signal distribution"))
w('I made a box plot that shows the distribution of signal values.')
w(htmllib.P())
w(htmllib.H3("4. Control signal"))
w('I made two plots that show the values of control signal.')
w(htmllib.P())
w(htmllib.H3("5. Control signal"))
w('I made a bar plot that shows the hybridization controls.')
w(htmllib.P())
w('The control file is ')
w(htmllib.A(control_file, control_file))
w(htmllib.P())
# Write out the footer.
#time_str = parselib.pretty_date(time.time())
#hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
#w(htmllib.EM(
# "This analysis was run on %s on %s. \n" %
# (time_str, hostname)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
open(outfile, 'w').write(x)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import shutil
from Betsy import bie3
from genomicode import htmllib
from genomicode import parselib
outfile_folder = outfile
outfile = os.path.join(outfile_folder, 'report.html')
if not os.path.exists(outfile_folder):
os.mkdir(outfile_folder)
result_files = []
for data_node in antecedents:
filename = data_node.identifier
new_name = os.path.join(outfile_folder,
os.path.split(filename)[-1])
if os.path.isdir(filename):
shutil.copytree(filename, new_name)
else:
shutil.copyfile(filename, new_name)
result_files.append(os.path.split(new_name)[-1])
data_node1, data_node2 = antecedents
#write the report.html
#def highlight(s):
# from genomicode import htmllib
# return htmllib.SPAN(s, style="background-color:yellow")
#def smaller(s):
# from genomicode import htmllib
# return htmllib.FONT(s, size=-1)
try:
lines = []
w = lines.append
w("<HTML>")
title = "Geneset Analysis Results"
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
w('I generated a file that contains the analysis result of the geneset'
)
w(htmllib.P())
w(htmllib.A(result_files[0], result_files[0]))
w(htmllib.P())
w(htmllib.A("Methods", href="#methods"))
w(htmllib.P())
filenames = os.listdir(
os.path.join(outfile_folder, result_files[1]))
c = 0
for filename in filenames:
c = c + 1
w(
htmllib.A(htmllib.IMG(height=500,
src=os.path.join(
result_files[1], filename)),
href=os.path.join(result_files[1], filename)))
w(htmllib.P())
name = 'Figure ' + str(c) + ': Geneset Plot.'
w(htmllib.B(name))
w(htmllib.HR())
w(htmllib.A("<methods>", name="methods"))
w(htmllib.CENTER(htmllib.H2("Methods")))
w(htmllib.H3("1.Result File"))
w('To generate this file, I ran the following analysis:')
bie3.plot_network_gv(os.path.join(outfile_folder, "network.png"),
network)
w(
htmllib.A(htmllib.IMG(height=500, src="network.png"),
href="network.png"))
w(htmllib.P())
w('I used the following parameters:')
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in data_node1.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(data_node1.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
# Write out the footer.
#time_str = parselib.pretty_date(time.time())
#hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
#w(htmllib.EM(
# "This analysis was run on %s on %s. \n" %
# (time_str, hostname)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
open(outfile, 'w').write(x)
except:
raise
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import shutil
from Betsy import bie3
in_data = antecedents
outfile_folder = outfile
outfile = os.path.join(outfile_folder, 'report.html')
if not os.path.exists(outfile_folder):
os.mkdir(outfile_folder)
result_files = []
filename = in_data.identifier
new_name = os.path.join(outfile_folder, os.path.split(filename)[-1])
if os.path.isdir(filename):
shutil.copytree(filename, new_name)
else:
shutil.copyfile(filename, new_name)
result_files.append(os.path.split(new_name)[-1])
#write the report.html
from genomicode import parselib
from genomicode import htmllib
#def highlight(s):
# from genomicode import htmllib
# return htmllib.SPAN(s, style="background-color:yellow")
#def smaller(s):
# from genomicode import htmllib
# return htmllib.FONT(s, size=-1)
try:
lines = []
w = lines.append
w("<HTML>")
title = "Heatmap Results"
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
w(htmllib.P())
w(htmllib.A("Methods", href="#methods_clustering"))
w(htmllib.P())
w(
htmllib.A(htmllib.IMG(height=500, src=result_files[0]),
href=result_files[0]))
w(htmllib.P())
name = 'Figure 1: In this heatmap, each row contains a signature and each column \
contains a sample from your data set.'
w(htmllib.B(name))
w(htmllib.HR())
w(htmllib.A("<methods_clustering>", name="methods_clustering"))
w(htmllib.CENTER(htmllib.H2("Methods")))
w(htmllib.H3("1.Heatmap File"))
w('To generate this file, I ran the following analysis:')
bie3.plot_network_gv(os.path.join(outfile_folder, "network.png"),
network)
w(
htmllib.A(htmllib.IMG(height=500, src="network.png"),
href="network.png"))
w(htmllib.P())
w('I used the following parameters:')
w(htmllib.H3("1. Heatmap File"))
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in in_data.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(in_data.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
# Write out the footer.
#time_str = parselib.pretty_date(time.time())
#hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
#w(htmllib.EM(
# "This analysis was run on %s on %s. \n" %
# (time_str, hostname)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
open(outfile, 'w').write(x)
except:
raise
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import shutil
#import time
from genomicode import parselib
from genomicode import htmllib
from Betsy import bie3
out_path = outfile
outfile = os.path.join(out_path, 'report.html')
if not os.path.exists(out_path):
os.mkdir(out_path)
(data_node1, data_node2, data_node3, data_node4, data_node5,
data_node6) = antecedents
# Make a list of the (relative) files for each input nodes:
# 0 SignalFile Preprocessed gene expression data.
# 1 IntensityPlot Box plot of signal intensity values.
# 2 ControlPlot AFFX control probes.
# 3 PcaPlot Has normalization.
# 4 ActbPlot From _SignalFile_Impute.
# 5 PcaPlot No normalization or anything.
result_files = []
for index, data_node in enumerate(antecedents):
in_filename = data_node.identifier # full path
in_path, in_file = os.path.split(in_filename)
out_file = in_file
#rename one of the pcaplot filename
if index == 3:
out_file = "after_%s" % in_file
out_filename = os.path.join(out_path, out_file)
if os.path.isdir(in_filename):
shutil.copytree(in_filename, out_filename)
else:
shutil.copyfile(in_filename, out_filename)
result_files.append(out_file)
#write the report.html
IMG_HEIGHT = 400
lines = []
w = lines.append
w("<HTML>")
title = "Normalization Results"
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
# Provide a link to the signal file.
w('Preprocessed signal values: ')
# TODO: Show the attributes for this data.
w(htmllib.A(result_files[0], result_files[0]))
w(htmllib.P())
##w(htmllib.A("Methods", href="#methods_normalization"))
##w(htmllib.P())
## if pipelines[1] == pipelines[2]:
## w(htmllib.A(htmllib.IMG(height=500,
## src=result_files[1]), href=result_files[1]))
## else:
# Show the PCA plot before and after normalization.
rows = []
col1 = htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=result_files[5]),
href=result_files[5])
col2 = htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=result_files[3]),
href=result_files[3])
x = htmllib.TR(
htmllib.TD(col1, align="CENTER") +
htmllib.TD(col2, align="CENTER"))
rows.append(x)
x = htmllib.TR(
htmllib.TH("Before", align="CENTER") +
htmllib.TH("After", align="CENTER"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=None,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
w(htmllib.P())
name = 'Figure 1: This pca plot shows the similarities among your samples'
w(htmllib.B(name))
w(htmllib.P())
# Show the distribution of the signal values.
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=result_files[1]),
href=result_files[1]))
w(htmllib.P())
name = 'Figure 2: This boxplot shows the distribution of signal values'
w(htmllib.B(name))
w(htmllib.P())
# Show the actin and tubulin values.
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=result_files[4]),
href=result_files[4]))
w(htmllib.P())
name = 'Figure 3: This plot shows the values of ACTB and TUBB genes'
w(htmllib.B(name))
w(htmllib.P())
# Affymetrix control genes.
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src=result_files[2]),
href=result_files[2]))
name = 'Figure 4: This plot shows the average values Affymetrix control genes'
w(htmllib.P())
w(htmllib.B(name))
w(htmllib.HR())
w(htmllib.A("<methods_normalization>", name="methods_normalization"))
w(htmllib.CENTER(htmllib.H2("Methods")))
w(htmllib.H3("1.Normalization File"))
w('To generate this file, I ran the following analysis:')
w(htmllib.P())
bie3.plot_network_gv(os.path.join(out_path, "network.png"), network)
w(
htmllib.A(htmllib.IMG(height=IMG_HEIGHT, src="network.png"),
href="network.png"))
w(htmllib.P())
w('I used the following parameters:')
rows = []
x = htmllib.TR(
htmllib.TH("Parameter", align="LEFT") +
htmllib.TH("Value", align="LEFT"))
rows.append(x)
for key in data_node1.data.attributes.keys():
x = htmllib.TR(
htmllib.TD(key, align="LEFT") +
htmllib.TD(data_node1.data.attributes[key], align="LEFT"))
rows.append(x)
w(
htmllib.TABLE("\n".join(rows),
border=1,
cellpadding=3,
cellspacing=0))
w(htmllib.P())
# Write out the footer.
#time_str = parselib.pretty_date(time.time())
#hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
#w(htmllib.EM(
# "This analysis was run on %s on %s. \n" %
# (time_str, hostname)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
open(outfile, 'w').write(x)
def summarize_report(
analysis_name, signatures, orig_signatures, report_files, start_time,
why_dropped, file_layout):
import time
from genomicode import parselib
from genomicode import htmllib
def highlight(s):
return htmllib.SPAN(s, style="background-color:yellow")
def smaller(s):
return htmllib.FONT(s, size=-1)
id2orig = {}
for sig in orig_signatures:
id2orig[sig.xID] = sig
id2new = {}
for sig in signatures:
id2new[sig.xID] = sig
assert len(signatures) == len(report_files)
id2reportfile = {}
for sig, file_ in zip(signatures, report_files):
# The report_file in the HTML should be a relative path.
x, file_ = os.path.split(file_)
id2reportfile[sig.xID] = file_
# Figure out which of the signatures were dropped.
missing_ids = []
for sig in orig_signatures:
if sig.xID in id2new:
continue
missing_ids.append(sig.xID)
# Make a list of all the signatures.
all_ids = {}.fromkeys(id2orig.keys() + id2new.keys())
schwartz = [(id2orig[x].Name, x) for x in all_ids]
schwartz.sort()
all_ids = [x[-1] for x in schwartz]
lines = []
w = lines.append
w("<HTML>")
#title = "%s Report" % htmllib.EM("ScoreSignatures")
title = "%s Report" % "ScoreSignatures"
if analysis_name:
title = "%s for %s" % (title, htmllib.EM(analysis_name))
x = parselib.remove_all_tags(title)
w(htmllib.HEAD(htmllib.TITLE(x)))
w("<BODY>")
w(htmllib.CENTER(htmllib.H1(title)))
w(htmllib.H3("I. Signatures"))
# Make a table with each of the signatures.
rows = []
x = htmllib.TR(
htmllib.TH("ID", align="LEFT") +
htmllib.TH("Signature", align="LEFT") +
htmllib.TH("Preprocessing", align="LEFT") +
htmllib.TH("Genes", align="LEFT") +
htmllib.TH("Metagenes", align="LEFT") +
htmllib.TH("Normalization", align="LEFT")
)
rows.append(x)
which_changed = {} # ID -> 1
for id_ in all_ids:
orig = id2orig[id_]
sig = id2new.get(id_)
cols = []
# ID
cols.append(htmllib.TD(orig.xID))
# Name
name = orig.Name
report_file = None
if sig:
report_file = id2reportfile.get(sig.xID)
if report_file:
name = htmllib.A(name, href=report_file)
cols.append(htmllib.TD(name))
# If this signature was not run, then skip the rest of the columns.
if not sig:
x = why_dropped.get(orig.xID, "Skipped for unknown reason.")
x = htmllib.TD(highlight(x), colspan=4)
cols.append(x)
rows.append(
htmllib.TR("\n".join(cols)))
continue
# Preprocessing
x = sig.Normalization
if sig.Normalization != orig.Normalization:
which_changed[sig.xID] = 1
x = "%s<BR>%s" % (
highlight(sig.Normalization),
smaller(htmllib.EM("default: %s" % orig.Normalization)))
cols.append(htmllib.TD(x))
# Genes
x = sig.Genes
if sig.Genes != orig.Genes:
which_changed[sig.xID] = 1
x = "%s<BR>%s" % (
highlight(sig.Genes),
smaller(htmllib.EM("default: %s" % orig.Genes)))
cols.append(htmllib.TD(x))
# Metagenes
x = sig.Metagenes
if sig.Metagenes != orig.Metagenes:
which_changed[sig.xID] = 1
x = "%s<BR>%s" % (
highlight(sig.Metagenes),
smaller(htmllib.EM("default: %s" % orig.Metagenes)))
cols.append(htmllib.TD(x))
# Normalization
norm = []
if sig.Quantile.upper() == "YES":
norm.append("Quantile")
if sig.Shift_Scale.upper() == "YES":
norm.append("Shift-Scale")
norm_str = "None"
if norm:
norm_str = " and ".join(norm)
if sig.Quantile.upper() != orig.Quantile.upper() or \
sig.Shift_Scale.upper() != orig.Shift_Scale.upper():
which_changed[sig.xID] = 1
norm = []
if orig.Quantile.upper() == "YES":
norm.append("Quantile")
if orig.Shift_Scale.upper() == "YES":
norm.append("Shift-Scale")
x = "None"
if norm:
x = " and ".join(norm)
norm_str = "%s<BR>%s" % (
highlight(norm_str), smaller(htmllib.EM("default: %s" % x)))
cols.append(htmllib.TD(norm_str))
#assert sig_changed == getattr(sig, "Changed", False), "%s %s %s" % (
# sig.Name, sig_changed, getattr(sig, "Changed", "missing"))
x = htmllib.TR("\n".join(cols))
rows.append(x)
w(htmllib.TABLE("\n".join(rows), border=1, cellpadding=3, cellspacing=0))
w(htmllib.P())
w(htmllib.B("Table 1: Signatures Analyzed."))
if not which_changed:
w("All signatures were run with the default parameters, "
"as shown above.")
else:
w("The customized parameters are highlighted in yellow.")
w(htmllib.P())
w(htmllib.H3("II. Results"))
prob_file = os.path.split(file_layout.PROBABILITIES_PNG)[1]
w(htmllib.A(htmllib.IMG(height=768, src=prob_file), href=prob_file))
w(htmllib.P())
w(htmllib.B("Figure 1: Predictions."))
w("In this heatmap, each row contains a signature and each column "
"contains a sample from your data set.")
if which_changed:
#names = sorted([id2orig[x].Name for x in which_changed])
w("The asterisks denote the signatures that were run with "
"customized parameters.")
w("The color corresponds to the probability that a pathway is activated "
"in a sample.")
w("Warm colors represent high probabilities, and cool colors low.\n")
w(htmllib.P())
prob_file = os.path.split(file_layout.PROBABILITIES_PCL)[1]
w("The raw values from this plot are available as a "
'PCL-formatted file: %s' % htmllib.A(prob_file, href=prob_file))
# Write out the footer.
end_time = time.time()
time_str = parselib.pretty_date(start_time)
run_time = pretty_runtime(start_time, end_time)
hostname = pretty_hostname()
w(htmllib.P())
w(htmllib.HR())
w(htmllib.EM(
"This analysis was run on %s on %s. It took %s to complete.\n" %
(time_str, hostname, run_time)))
w("</BODY>")
w("</HTML>")
x = "\n".join(lines) + "\n"
outfile = file_layout.REPORT
open(outfile, 'w').write(x)