In [ ]:
from compass.utils import plot_embed_with_label
from compass import PreTrainer, FineTuner, loadcompass #, get_minmal_epoch
from compass.utils import plot_embed_with_label, plot_performance, score2
from compass.tokenizer import CANCER_CODE
In [31]:
import os
from tqdm import tqdm
from itertools import chain
import pandas as pd
import numpy as np
import random, torch
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style = 'white', font_scale=1.3)
import warnings
warnings.filterwarnings("ignore")

Download finetuned model¶

dowanload the finetuner models from here¶

In [32]:
## load finetuner, your can load any finetuners
## finetuner_all_50.pt, finetuner_all_40.pt, finetuner_without_gide.pt
## Here we load finetuner_without_gide.pt to test the Gide cohort performance:

finetuner = loadcompass('./tmpignore/finetuner_without_gide.pt')

## read data
df_label = pd.read_pickle('./tmpignore/ITRP.PATIENT.TABLE')
df_tpm = pd.read_pickle('./tmpignore/ITRP.TPM.TABLE')

df_label = df_label[df_label.cohort == 'Gide']
df_tpm = df_tpm.loc[df_label.index]

df_tpm.shape, df_label.shape
Out[32]:
((73, 15672), (73, 110))

Prepare model inputs¶

In [33]:
dfcx = df_label.cancer_type.map(CANCER_CODE).to_frame('cancer_code').join(df_tpm)
df_task = onehot(df_label.response_label)
dfcx.head()
Out[33]:
cancer_code A1BG A1CF A2M A2ML1 A4GALT A4GNT AAAS AACS AADAC ... ZWILCH ZWINT ZXDA ZXDB ZXDC ZYG11A ZYG11B ZYX ZZEF1 ZZZ3
Index
1_ipiPD1_PRE 25 5.23 0.02 82.96 0.10 0.75 0.03 27.57 3.23 0.04 ... 10.48 3.47 0.70 1.63 2.43 0.05 2.98 10.81 6.30 4.01
2_ipiPD1_PRE 25 7.39 0.00 1154.40 0.00 0.95 0.03 48.91 2.10 0.01 ... 17.20 7.46 0.44 0.79 5.81 0.00 5.02 37.27 13.47 8.14
6_ipiPD1_PRE 25 3.91 0.00 168.14 0.11 0.52 0.01 18.20 2.08 0.00 ... 4.73 1.54 0.57 1.06 1.81 0.01 2.79 4.11 6.77 3.74
7_ipiPD1_PRE 25 1.85 0.01 80.62 0.00 0.21 0.03 4.82 0.84 0.06 ... 4.07 1.58 0.44 0.39 0.87 0.00 2.00 8.44 2.20 2.92
8_ipiPD1_PRE 25 5.39 0.00 76.01 0.02 0.81 0.09 49.43 3.93 0.00 ... 14.25 10.21 0.89 1.91 3.05 0.03 11.61 17.74 7.96 18.08

5 rows × 15673 columns

Make predictions¶

In [34]:
dfe, df_pred = finetuner.predict(dfcx, batch_size = 16)

100%|##########| 5/5 [00:00<00:00,  6.13it/s]

The prediction of the model contains 2 outputs. The first output is the score of the cell-type level concepts. The second is the predicted probability. 0 corresponds to non-response and 1 corresponds to response.

In [35]:
## conceptor score
dfe.head()
Out[35]:
Adipocyte Apoptosis_pathway Bcell CANCER CD4_Tcell CD8_Tcell CPI_Response CPI_effect Cytokine_chemokine Cytotoxic_Tcell ... Stem Stroma TGFb_pathway TLS TMB Tcell_exclusion Tcell_general Treg cDC pDC
Index
1_ipiPD1_PRE 1.946114 2.308815 1.268282 -0.543625 1.952781 2.179384 1.341070 1.850306 2.191977 1.561325 ... 1.865696 2.445551 1.399902 1.656181 1.957647 2.202881 2.109776 1.358847 1.394624 1.409620
2_ipiPD1_PRE 1.797584 2.147074 1.385810 -0.543746 1.896353 2.264272 0.690322 1.905378 2.098906 1.571620 ... 2.329527 2.331889 1.670644 1.729452 1.884305 2.308213 2.294847 1.057095 1.322861 1.641564
6_ipiPD1_PRE 1.906188 2.301974 1.043642 -0.543417 1.945815 2.360919 1.212628 1.899567 2.061909 1.662967 ... 2.018662 2.398139 1.444633 1.215814 1.696890 2.053318 2.169455 1.283464 1.484758 1.247126
7_ipiPD1_PRE 2.020110 2.352570 1.356010 -0.543264 1.973025 2.315243 1.371591 1.970148 2.251877 1.642058 ... 2.094480 2.469353 1.384351 1.641669 1.575712 2.238491 2.118487 1.298826 1.560758 1.381392
8_ipiPD1_PRE 1.821828 2.048581 1.024990 -0.543961 1.918190 2.438015 0.937945 1.834857 2.089520 1.717699 ... 2.145675 2.337186 1.359336 1.514439 1.842595 1.866308 2.208838 1.179389 1.387992 1.456456

5 rows × 45 columns

In [36]:
## conceptor prediction probabilities
df_pred.head()
Out[36]:
0 1
Index
1_ipiPD1_PRE 0.399619 0.600381
2_ipiPD1_PRE 0.997979 0.002022
6_ipiPD1_PRE 0.939994 0.060006
7_ipiPD1_PRE 0.047343 0.952657
8_ipiPD1_PRE 0.999004 0.000996

Analysis performance¶

In [37]:
## masure the performance
dfp = df_task.join(df_pred)
y_true, y_prob, y_pred = dfp['R'], dfp[1], dfp[[0, 1]].idxmax(axis=1)
fig = plot_performance(y_true, y_prob, y_pred)
No description has been provided for this image
In [38]:
roc, prc, f1, acc, mcc = score2(y_true, y_prob, y_pred)
print("Test performance:roc={:.3f},prc={:.3f}, f1={:.3f}, acc={:.3f}, mcc={:.3f}.".format(roc, prc, f1, acc, mcc))

Test performance:roc=0.895,prc=0.903, f1=0.843, acc=0.822, mcc=0.640.
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