seqlbled = pd.read_csv("%s/chip2probe/output/homotypic/training/seqlbled.csv" % basepath)
wtdf = get_wtdf("%s/chip2probe/output/array_design_files/Coop2Ets_validation/custom_probes_selected.csv" % basepath, seqlbled)
origdf, neg_orig = arr.read_chamber_file("%s/probedata/191030_coop-PBM_Ets1_v1_2nd/2.processed_gpr/20191004_258614510001_ETS1_550_5_1-4_alldata.txt"%basepath, seqcols=["Name","type","rep","ori"], negcols=["Name","rep","ori"], key="Coop1Ets")
origdf[["Sequence","type","ori"]].drop_duplicates().to_csv("seqsorig.csv",index=False)
import sys
sys.exit()
cust10df, neg10_cust = arr.read_chamber_file("%s/probedata/201128_validation_array_ets1_v2_1/10nMEts1_alexa488_550_20_alldata.txt"%basepath, key="Coop2Ets")
cust20df, neg20_cust = arr.read_chamber_file("%s/probedata/210102_validation_array_ets1_v2_2/20nMEts1_alexa488_550_10_alldata.txt"%basepath, key="Coop2Ets")
cust30df, neg30_cust = arr.read_chamber_file("%s/probedata/210102_validation_array_ets1_v2_2/30nMEts1_alexa488_550_10_alldata.txt"%basepath, key="Coop2Ets")
imads_paths = ["%s/chip2probe/input/site_models/imads_models/Ets1_w12_GGAA.model" % basepath,
"%s/chip2probe/input/site_models/imads_models/Ets1_w12_GGAT.model" % basepath]
imads_cores = ["GGAA", "GGAT"]
imads_models = [iMADSModel(path, core, 12, [1, 2, 3]) for path, core in zip(imads_paths, imads_cores)]
imads = iMADS(imads_models, 0.19) # 0.2128
orig_pred = imads.predict_sequences(get_wtmt(origdf, wtdf), key_colname="id_numeric", sequence_colname="Sequence")
orig_plot = imads.make_plot_data(orig_pred)
cust_pred = imads.predict_sequences(get_wtmt(cust10df, wtdf), key_colname="id_numeric", sequence_colname="Sequence")
cust_plot = imads.make_plot_data(cust_pred)
sp = SitesPlotter()
sp.plot_seq_combine([orig_plot], filepath="origplot.pdf")
sp.plot_seq_combine([cust_plot], filepath="custplot.pdf")
import sys
sys.exit()
df = pd.read_csv(trainingpath, sep="\t")
# select only genomic (i.e. non-custom) sequences
# df = df[~df['name'].str.contains("dist|weak")]
ct = CoopTrain(df,
corelen=4,
flip_th=True,
positive_cores=["GGAA", "GGAT"])
# using custom imads model
imads_paths = [
"input/site_models/imads_models/Ets1_w12_GGAA.model",
"input/site_models/imads_models/Ets1_w12_GGAT.model"
]
imads_cores = ["GGAA", "GGAT"]
imads_models = [
iMADSModel(path, core, 12, [1, 2, 3])
for path, core in zip(imads_paths, imads_cores)
]
imads = iMADS(imads_models, 0.19) # 0.2128
# get the features from the CoopTrain class
feature_dict = {
"distance": {
"type": "numerical"
},
"orientation": {
"positive_cores": ["GGAA", "GGAT"],
"one_hot": True
},
"affinity": {
"imads": imads
df = pd.read_csv(trainingpath) #, sep="\t")
# select only genomic (i.e. non-custom) sequences
df = df[~df['name'].str.contains("dist|weak")]
cooptr = CoopTrain(df, corelen=4)
rf_param_grid = {
'n_estimators': [500], #, 1000, 1500],
'max_depth':[5], # 10, 15],
"min_samples_leaf" : [10], # 15, 20],
"min_samples_split" :[10], # 15 ,20]
}
# using custom imads model
imads8_paths = ["input/site_models/imads_models/Ets1_w8_GGAA.model", "input/site_models/imads_models/Ets1_w8_GGAT.model"]
imads8_cores = ["GGAA", "GGAT"]
imads8_models = [iMADSModel(path, core, 8, [1, 2, 3]) for path, core in zip(imads8_paths, imads8_cores)]
imads8 = iMADS(imads8_models, 0.19) # 0.2128
imads12_paths = ["input/site_models/imads_models/Ets1_w12_GGAA.model", "input/site_models/imads_models/Ets1_w12_GGAT.model"]
imads12_cores = ["GGAA", "GGAT"]
imads12_models = [iMADSModel(path, core, 12, [1, 2, 3]) for path, core in zip(imads12_paths, imads12_cores)]
imads12 = iMADS(imads12_models, 0.19) # 0.2128
best_models = {
"distance":
BestModel(clf="sklearn.ensemble.RandomForestClassifier",
param_grid=rf_param_grid,
train_data=cooptr.get_training_df({
"distance":{"type":"numerical"}
})
).run_all(),
