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comparison lifelines_tool/lifelineskmcph.xml @ 1:232b874046a7 draft

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author fubar
date Thu, 10 Aug 2023 07:15:22 +0000
parents dd49a7040643
children dd5e65893cb8
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0:dd49a7040643 1:232b874046a7
1 <tool name="lifelineskmcph" id="lifelineskmcph" version="0.01"> 1 <tool name="lifelineskmcph" id="lifelineskmcph" version="0.01">
2 <!--Source in git at: https://github.com/fubar2/galaxy_tf_overlay--> 2 <!--Source in git at: https://github.com/fubar2/galaxy_tf_overlay-->
3 <!--Created by toolfactory@galaxy.org at 09/08/2023 17:43:16 using the Galaxy Tool Factory.--> 3 <!--Created by toolfactory@galaxy.org at 10/08/2023 15:48:43 using the Galaxy Tool Factory.-->
4 <description>Lifelines KM and optional Cox PH models</description> 4 <description>Lifelines KM and optional Cox PH models</description>
5 <requirements> 5 <requirements>
6 <requirement version="1.5.3" type="package">pandas</requirement> 6 <requirement version="1.5.3" type="package">pandas</requirement>
7 <requirement version="3.7.2" type="package">matplotlib</requirement> 7 <requirement version="3.7.2" type="package">matplotlib</requirement>
8 <requirement version="0.27.7" type="package">lifelines</requirement> 8 <requirement version="0.27.7" type="package">lifelines</requirement>
38 38
39 # script for a lifelines ToolFactory KM/CPH tool for Galaxy 39 # script for a lifelines ToolFactory KM/CPH tool for Galaxy
40 # km models for https://github.com/galaxyproject/tools-iuc/issues/5393 40 # km models for https://github.com/galaxyproject/tools-iuc/issues/5393
41 # test as 41 # test as
42 # python plotlykm.py --input_tab rossi.tab --htmlout "testfoo" --time "week" --status "arrest" --title "test" --image_dir images --cphcol="prio,age,race,paro,mar,fin" 42 # python plotlykm.py --input_tab rossi.tab --htmlout "testfoo" --time "week" --status "arrest" --title "test" --image_dir images --cphcol="prio,age,race,paro,mar,fin"
43 43 # Ross Lazarus July 2023
44 import argparse 44 import argparse
45
45 import os 46 import os
46 import sys 47 import sys
47 48
48 import lifelines 49 import lifelines
49 50
50 from matplotlib import pyplot as plt 51 from matplotlib import pyplot as plt
51 52
52 import pandas as pd 53 import pandas as pd
53 54
54 # Ross Lazarus July 2023 55
55 56 def trimlegend(v):
57 """
58 for int64 quintiles - must be ints - otherwise get silly legends with long float values
59 """
60 for i, av in enumerate(v):
61 x = int(av)
62 v[i] = str(x)
63 return v
56 64
57 kmf = lifelines.KaplanMeierFitter() 65 kmf = lifelines.KaplanMeierFitter()
58 cph = lifelines.CoxPHFitter() 66 cph = lifelines.CoxPHFitter()
59 67
60 parser = argparse.ArgumentParser() 68 parser = argparse.ArgumentParser()
61 a = parser.add_argument 69 a = parser.add_argument
62 a('--input_tab', default='', required=True) 70 a('--input_tab', default='rossi.tab', required=True)
63 a('--header', default='') 71 a('--header', default='')
64 a('--htmlout', default="test_run.html") 72 a('--htmlout', default="test_run.html")
65 a('--group', default='') 73 a('--group', default='')
66 a('--time', default='', required=True) 74 a('--time', default='', required=True)
67 a('--status',default='', required=True) 75 a('--status',default='', required=True)
73 args = parser.parse_args() 81 args = parser.parse_args()
74 sys.stdout = open(args.readme, 'w') 82 sys.stdout = open(args.readme, 'w')
75 df = pd.read_csv(args.input_tab, sep='\t') 83 df = pd.read_csv(args.input_tab, sep='\t')
76 NCOLS = df.columns.size 84 NCOLS = df.columns.size
77 NROWS = len(df.index) 85 NROWS = len(df.index)
86 QVALS = [.2, .4, .6, .8] # for partial cox ph plots
78 defaultcols = ['col%d' % (x+1) for x in range(NCOLS)] 87 defaultcols = ['col%d' % (x+1) for x in range(NCOLS)]
79 testcols = df.columns 88 testcols = df.columns
80 if len(args.header.strip()) > 0: 89 if len(args.header.strip()) > 0:
81 newcols = args.header.split(',') 90 newcols = args.header.split(',')
82 if len(newcols) == NCOLS: 91 if len(newcols) == NCOLS:
104 fig, ax = plt.subplots() 113 fig, ax = plt.subplots()
105 if args.group > '': 114 if args.group > '':
106 names = [] 115 names = []
107 times = [] 116 times = []
108 events = [] 117 events = []
109 rmst = []
110 for name, grouped_df in df.groupby(args.group): 118 for name, grouped_df in df.groupby(args.group):
111 T = grouped_df[args.time] 119 T = grouped_df[args.time]
112 E = grouped_df[args.status] 120 E = grouped_df[args.status]
113 gfit = kmf.fit(T, E, label=name) 121 gfit = kmf.fit(T, E, label=name)
114 kmf.plot_survival_function(ax=ax) 122 kmf.plot_survival_function(ax=ax)
115 rst = lifelines.utils.restricted_mean_survival_time(gfit)
116 rmst.append(rst)
117 names.append(str(name)) 123 names.append(str(name))
118 times.append(T) 124 times.append(T)
119 events.append(E) 125 events.append(E)
120 ax.set_title(args.title) 126 ax.set_title(args.title)
121 fig.savefig(os.path.join(args.image_dir,'KM_%s.png' % args.title)) 127 fig.savefig(os.path.join(args.image_dir,'KM_%s.png' % args.title))
122 ngroup = len(names) 128 ngroup = len(names)
123 if ngroup == 2: # run logrank test if 2 groups 129 if ngroup == 2: # run logrank test if 2 groups
124 results = lifelines.statistics.logrank_test(times[0], times[1], events[0], events[1], alpha=.99) 130 results = lifelines.statistics.logrank_test(times[0], times[1], events[0], events[1], alpha=.99)
125 print('Logrank test for %s - %s vs %s\n' % (args.group, names[0], names[1])) 131 print('Logrank test for %s - %s vs %s\n' % (args.group, names[0], names[1]))
126 results.print_summary() 132 results.print_summary()
127 elif ngroup > 1:
128 fig, ax = plt.subplots(nrows=ngroup, ncols=1, sharex=True)
129 for i, rst in rmst:
130 lifelines.plotting.rmst_plot(rst, ax=ax)
131 fig.savefig(os.path.join(args.image_dir,'RMST_%s.png' % args.title))
132 else: 133 else:
133 kmf.fit(df[args.time], df[args.status]) 134 kmf.fit(df[args.time], df[args.status])
134 kmf.plot_survival_function(ax=ax) 135 kmf.plot_survival_function(ax=ax)
135 ax.set_title(args.title) 136 ax.set_title(args.title)
136 fig.savefig(os.path.join(args.image_dir,'KM_%s.png' % args.title)) 137 fig.savefig(os.path.join(args.image_dir,'KM_%s.png' % args.title))
138 print('#### No grouping variable, so no log rank or other Kaplan-Meier statistical output is available')
137 if len(args.cphcols) > 0: 139 if len(args.cphcols) > 0:
138 fig, ax = plt.subplots() 140 fig, ax = plt.subplots()
139 ax.set_title('Cox PH model: %s' % args.title) 141 ax.set_title('Cox-PH model: %s' % args.title)
140 cphcols = args.cphcols.strip().split(',') 142 cphcols = args.cphcols.strip().split(',')
141 cphcols = [x.strip() for x in cphcols] 143 cphcols = [x.strip() for x in cphcols]
142 notfound = sum([(x not in df.columns) for x in cphcols]) 144 notfound = sum([(x not in df.columns) for x in cphcols])
143 if notfound > 0: 145 if notfound > 0:
144 sys.stderr.write('## CRITICAL USAGE ERROR (not a bug!): One or more requested Cox PH columns %s not found in supplied column header %s' % (args.cphcols, df.columns)) 146 sys.stderr.write('## CRITICAL USAGE ERROR (not a bug!): One or more requested Cox PH columns %s not found in supplied column header %s' % (args.cphcols, df.columns))
145 sys.exit(6) 147 sys.exit(6)
148 colsdf = df[cphcols]
146 print('### Lifelines test of Proportional Hazards results with %s as covariates on %s' % (', '.join(cphcols), args.title)) 149 print('### Lifelines test of Proportional Hazards results with %s as covariates on %s' % (', '.join(cphcols), args.title))
147 cphcols += [args.time, args.status] 150 cutcphcols = [args.time, args.status] + cphcols
148 cphdf = df[cphcols] 151 cphdf = df[cutcphcols]
152 ucolcounts = colsdf.nunique(axis=0)
149 cph.fit(cphdf, duration_col=args.time, event_col=args.status) 153 cph.fit(cphdf, duration_col=args.time, event_col=args.status)
150 cph.print_summary() 154 cph.print_summary()
155 for i, cov in enumerate(colsdf.columns):
156 if ucolcounts[i] > 10:
157 v = pd.Series.tolist(cphdf[cov].quantile(QVALS))
158 vdt = df.dtypes[cov]
159 if vdt == 'int64':
160 v = trimlegend(v)
161 axp = cph.plot_partial_effects_on_outcome(cov, cmap='coolwarm', values=v)
162 axp.set_title('Cox-PH %s quintile partials: %s' % (cov,args.title))
163 figr = axp.get_figure()
164 oname = os.path.join(args.image_dir,'%s_CoxPH_%s.%s' % (args.title, cov, args.image_type))
165 figr.savefig(oname)
166 else:
167 v = pd.unique(cphdf[cov])
168 v = [str(x) for x in v]
169 try:
170 axp = cph.plot_partial_effects_on_outcome(cov, cmap='coolwarm', values=v)
171 axp.set_title('Cox-PH %s partials: %s' % (cov,args.title))
172 figr = axp.get_figure()
173 oname = os.path.join(args.image_dir,'%s_CoxPH_%s.%s' % (args.title, cov, args.image_type))
174 figr.savefig(oname)
175 except:
176 pass
151 cphaxes = cph.check_assumptions(cphdf, p_value_threshold=0.01, show_plots=True) 177 cphaxes = cph.check_assumptions(cphdf, p_value_threshold=0.01, show_plots=True)
152 for i, ax in enumerate(cphaxes): 178 for i, ax in enumerate(cphaxes):
153 figr = ax[0].get_figure() 179 figr = ax[0].get_figure()
154 titl = figr._suptitle.get_text().replace(' ','_').replace("'","") 180 titl = figr._suptitle.get_text().replace(' ','_').replace("'","")
155 oname = os.path.join(args.image_dir,'CPH%s.%s' % (titl, args.image_type)) 181 oname = os.path.join(args.image_dir,'CPH%s.%s' % (titl, args.image_type))