From f2f67a475439a76bc06a05601ca0c05f6b0ff86d Mon Sep 17 00:00:00 2001 From: Guangyuan Li Date: Sun, 27 Dec 2020 22:32:11 -0500 Subject: [PATCH] fix bugs --- .DS_Store | Bin 10244 -> 10244 bytes README.md | 20 +++++- data/hla2paratopeTable_aligned.txt | 63 ++++++++++++++++++ deepimmuno-cnn.py | 15 +++-- deepimmuno-gan.py | 14 +++- models/.DS_Store | Bin 8196 -> 8196 bytes models/cnn_model_331_3_7/.data-00000-of-00001 | Bin 517379 -> 173503 bytes models/cnn_model_331_3_7/.index | Bin 4870 -> 1819 bytes 8 files changed, 102 insertions(+), 10 deletions(-) create mode 100644 data/hla2paratopeTable_aligned.txt diff --git a/.DS_Store b/.DS_Store index 7a00a5bd141d8e474bb8b5f0ff9836eb09c7ce48..676e822724e245c0317b58788c570c2bdcfd20cd 100644 GIT binary patch delta 877 zcmbV~&u$V?6voe~D4&Ee!?G`+4`FL+5!Tm;`cf%xv_JP?-#UgfoL>G#J&r(dL>k_jxTVu3v*Luj-Gd8JaXoc}B>l#_l z8qSfmLQIR-p{KKF$`@qQl&MP6jeELWB6^mlnQgPGGwR?JMkSLtsJzCt^EJ`b4*z|8 zMHd!_;>$2WH?J+=9*utU(i=!)tg4TksJ+!!~?FH-_;P z&R_!PF@-lVgNrEP9b6jN$8Z;OVtB`(;wrCuuUXHTRA*G>J!f9zuT1zhK!YmZHK4%C za1InMD4gd9jf~EeK>$GDuCLto@<)Gh^#=qes60NuC5*`qp?h1L=w20*BLR&kc|a2v k{~l1>>|OTzaK!r0+2&Ru_}1HVN4~qd@BJ_SoACyJ0)ndW`~Uy| delta 66 zcmZn(XbG6$C7U^hRb%48k^`^lB!XC^-ul9*gC>;PhK?h)Z(1T#{_7IAKD2x8jI RuJDUxvY@#1<~;EiOaLPB7wZ53 diff --git a/README.md b/README.md index 5f3f6b9..2ace92c 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ We recommend to try out our web application for that: https://deepimmuno.herokua > Plase refer to **DeepImmuno-GAN** if you want to generate immunogenic peptide -Enjoy and don't hesitate to ask me questions (contact at the bottom), I will be responsive! +Enjoy and don't hesitate to ask me questions (contact at the bottom), I will be responsive! Feel free to raise an issue on github page! ## Citation If you find that tool useful in your research, please consider citing our preprint: @@ -25,12 +25,16 @@ numpy = 1.18.5 pandas = 1.1.1 +``` +Note: This is the enviroment that I used for development and I also tested it. But as long as you use python > 3, tensorflow = 2.3, It should also work. +``` + #### How to use? If you want to query a single epitope (peptide + HLA), for example you want to query peptide _**HPPLMNVER**_ along with _**HLA-A*0201**_. You need to ``` -python3 deepimmuno-cnn.py --mode "single" --epitope "HPPLMNVER" --HLA "HLA-A*0201" +python3 deepimmuno-cnn.py --mode "single" --epitope "HPPLMNVER" --hla "HLA-A*0201" ``` If you want to query multiple epitopes, you just need to prepare a csv file like this: @@ -47,6 +51,10 @@ Then you run: python3 deepimmuno-cnn.py --mode "multiple" --intdir "/path/to/above/file" --outdir "/path/to/output/folder" ``` +**1. Please note, when you specify the output dir, don't include the forward slash at the end, for example, use "~/Desktop" instead "~/Desktop/"** + +**2. PLease note, if python3 doesn't work, you can replace python3 to python, it depends your installed python interpreter** + A full help prompt is as below: ``` @@ -76,6 +84,10 @@ numpy = 1.18.4 pandas = 1.0.5 +``` +Note: This is the enviroment that I used for development and I also tested it. But as long as you use python > 3, pytorch = 1.4, It should also work. +``` + #### How to use Pretty simple, just run like this @@ -97,6 +109,10 @@ optional arguments: --outdir OUTDIR specifying your output folder ``` +**1. Please note, when you specify the output dir, don't include the forward slash at the end, for example, use "~/Desktop" instead "~/Desktop/"** + +**2. PLease note, if python3 doesn't work, you can replace python3 to python, it depends your installed python interpreter** + ## Contact Guangyuan(Frank) Li diff --git a/data/hla2paratopeTable_aligned.txt b/data/hla2paratopeTable_aligned.txt new file mode 100644 index 0000000..378af8e --- /dev/null +++ b/data/hla2paratopeTable_aligned.txt @@ -0,0 +1,63 @@ +HLA pseudo +HLA-A*0203 ------------YYEKVAH---TVDTLYRY-YYTKWEQWYTWY--- +HLA-A*0205 ------Y-----YYEKVV---------YDYYRDTKWQWYWY----- +HLA-A*0206 -----MY-----YYEKVAH---TVDTLYRYHYYTKWVQLYTWY--- +HLA-A*0207 ------M-----YYEKVAH---TVDTLYR-HYYTKWVQLYTWY--- +HLA-A*3001 ------M-----YYENV-QT-----DTLYIYHYTKWWQLYWY---- +HLA-A*6801 -----MY-----YYRNNAQT---V--DTLYYDYTKWAVQWYTWY-- +HLA-A*9235 ------M-----YYEKV-HT-----DTYRYHYYTKWVQLYWY---- +HLA-A*9253 ------M-----YYEKHT---------VDTRYYTKWYTWY------ +HLA-B*0602 ----YDS------YEKYRQ-------ANKYWYYTKWEQWYWY---- +HLA-B*1557 ---------------YQWN-----TLYRYEFYWTKWAAYW------ +HLA-B*1801 ------Y----HSYRNITNTYES-NLYLR-YSYTKWVQYTW----- +HLA-B*3505 -------------YSGFYNDMNH-NSYPG----------------- +HLA-B*4001 ---MYHT-----KYREITNT-ESNY-----RYYSKVQYWY------ +HLA-B*4002 ---MYHT----FKYREISNTYES-NLYLSYYYITKWVQLYEWY--- +HLA-B*4044 ---MYHT----FKYREISNTYES-NLYLSYYYITKWVQLYEWY--- +HLA-B*4102 ----YHT-----KYREIT---ESNL---YLYYYTKWQDYWY----- +HLA-B*4104 ---MYHT-----KYREISNT-ESNL---YLYYYTKWAVQDYTWY-- +HLA-B*4202 ------------YYRNIYAQTES-NLYL--YYYTKWAVQDYWY--- +HLA-B*4601 -----------MYYREKY-QTVS-NLY---RYYTKWEQWYLWY--- +HLA-B*5706 ------M-----YYENSTNI---------YIYYIKWQLYWY----- +HLA-B*5712 ------M-----YYENMTYN------NYIYDYWTKWQLYWY----- +HLA-B*8103 -----------YYYRNIYAQTES-NYY---NYYSKVQDYEWY---- +HLA-B*9234 ------------MYEKH--T-----DTLYRYYYTKWVQYTWY---- +HLA-C*0102 -----------YHYRES--ATIF-NT-YWHFYWSKSEHQYWY---- +HLA-C*0304 ------M-----YREKRQT-----SNLY--RYYYTWEQYWY----- +HLA-C*0401 ------Y-----YREKYRQD----NKLYLRFFYTKWRYWY------ +HLA-C*0517 ------M-----YYEKRQT-----NKLYRYNFYTKWERYWY----- +HLA-C*0602 MYDSYEK------YRQADV-------NKLYWYYTKWQWYWY----- +HLA-C*0756 -----YD----FSYREKY-QAVS-NLYR--SDYTKLAQLYTWY--- +HLA-C*1604 -----------YYYRNIFNTYES-NLY---RYYTKWQLYLWY---- +HLA-A*0101 ------M-----YYQENTHT---NTLYIIYRDYTKWAQRYYRGRY- +HLA-A*0201 ---MYMM-----YYEKVVHHTHTVDTLYRYYYYTKWVQLYYYYY-- +HLA-A*0224 ----MYM-----YYEKH-HT----DTLYRYYYYTKWVQLYWY---- +HLA-A*0301 -----MY-----YQENVVAQ------DTYYRDYTKWEQLYTWY--- +HLA-A*0362 ------M-----YYENA-QT-----DTLYYRDYTKWEQLYWY---- +HLA-A*1101 ------Y-----YYENAQV---TVDTLYYYRDYTKWAAQQYWY--- +HLA-A*2301 ------------YYEKVAHT------NIYLFYYTKWVQQYTGY--- +HLA-A*2402 ------M-----YYEKKVHT----NIYLMFHYYTKWVQYYTYRY-- +HLA-A*3003 ----YSM-----YYENVHTE----NTLYIYEHYTKWRLYTWY---- +HLA-A*9234 ------------MYEKV-HT----HDT-YRYYYTKWVLLYWY---- +HLA-B*0702 -----------YYYRNIY-QTES-NLYYY-DYYTKWEQRYEWY--- +HLA-B*0801 ---MYDY----YFYRNIFNTDES-NLYLSYNYYTKWVVQDYWYY-- +HLA-B*1402 ---YYSY----YSEYNICTNTES-NLYLW-YFYTKWELYTW----- +HLA-B*1501 MYYAMYY----YYREI--NTYES-NLYLRYSYYTKWAEQWYLWY-- +HLA-B*2703 ----YHT-----EHREICTE-D---TLY-LYDYTKWVQLYEWY--- +HLA-B*2705 ---YYHT----EYREEICTE-DEDTLYL-YYDYTKWVQLAYEWY-- +HLA-B*2709 ---MYHT----EYREQICTE-TDDTY---YYHYTKWVVQLYEWY-- +HLA-B*2713 ---MYHTEVVRFYREEICAK-DTDYYYHYHDAYTKWVRQLYYECWY +HLA-B*3501 -----MM----YYRNIFTNTYES-NLYIRYSSYTKWAVQLYWY--- +HLA-B*3508 ----MMY----YYRQIITNTYES-NLYIR-YSYTKWQRWYYWY--- +HLA-B*3901 -----YY----YSEYNIC-TTES-NLYLR-YFYTKWVQYYWY---- +HLA-B*4201 ------------YYRNIYAQTES-NY-L--YYYTKWAVQDYWY--- +HLA-B*4402 ---MYYT-----KYREISNT-ENNTT--YIYDYTKWVVQDYLSRY- +HLA-B*4403 ---MYYT-----KYREITTE---NTY--IRYDYTKWVVQLYLSRY- +HLA-B*4405 ---MYYT-----KYREITTE---NTY--I-YYYTKWVVQDYLSRY- +HLA-B*5101 ------Y-----YYRNINT------YNIYWYYYTKWEQLYLW---- +HLA-B*5301 ------Y-----YYRNIFT----NTENIYIRYYTKWVQLYWY---- +HLA-B*5701 ------M-----YYENTYNI------YIYDSYWTKWVQLYWY---- +HLA-B*5703 ----MYY-----YEENA-ST----NTYNIYYYYTKWVQLYWYY--- +HLA-B*5801 ------------YYENSTYE---NIYIRYSAYYTKWVQLYWYY--- +HLA-B*8102 ------M----YYYRNIYAQTES-NLYY--NYYSKAVQLYWY---- +HLA-C*1510 -----MY-----YYENMQTD---DNIYIYIYDYTKWVQLYLWY--- diff --git a/deepimmuno-cnn.py b/deepimmuno-cnn.py index 6f754d6..deea5ff 100644 --- a/deepimmuno-cnn.py +++ b/deepimmuno-cnn.py @@ -10,8 +10,6 @@ from tensorflow.keras import layers import numpy as np import pandas as pd -import matplotlib.pyplot as plt -from sklearn.metrics import auc,precision_recall_curve,roc_curve,confusion_matrix import argparse import os @@ -138,7 +136,7 @@ def rescue_unknown_hla(hla, dic_inventory): -def hla_data_aaindex(hla_dic,hla_type,after_pca): # return numpy array [34,12,1] +def hla_data_aaindex(hla_dic,hla_type,after_pca,dic_inventory): # return numpy array [46,12,1] try: seq = hla_dic[hla_type] except KeyError: @@ -148,7 +146,7 @@ def hla_data_aaindex(hla_dic,hla_type,after_pca): # return numpy array [34,12 encode = encode.reshape(encode.shape[0], encode.shape[1], -1) return encode -def construct_aaindex(ori,hla_dic,after_pca): +def construct_aaindex(ori,hla_dic,after_pca,dic_inventory): series = [] for i in range(ori.shape[0]): peptide = ori['peptide'].iloc[i] @@ -157,7 +155,7 @@ def construct_aaindex(ori,hla_dic,after_pca): encode_pep = peptide_data_aaindex(peptide,after_pca) # [10,12] - encode_hla = hla_data_aaindex(hla_dic,hla_type,after_pca) # [46,12] + encode_hla = hla_data_aaindex(hla_dic,hla_type,after_pca,dic_inventory) # [46,12] series.append((encode_pep, encode_hla, immuno)) return series @@ -167,6 +165,7 @@ def hla_df_to_dic(hla): col1 = hla['HLA'].iloc[i] # HLA allele col2 = hla['pseudo'].iloc[i] # pseudo sequence dic[col1] = col2 + return dic @@ -217,13 +216,19 @@ def file_process(upload,download): def main(args): mode = args.mode if mode == 'single': + print("mode is single") epitope = args.epitope + print("queried epitope is {}".format(epitope)) hla= args.hla + print("queried epitope is {}".format(hla)) score = computing_s(epitope,hla) print(score) elif mode == 'multiple': + print("mode is multiple") intFile = args.intdir + print("input file is {}".format(intFile)) outFolder = args.outdir + print("output will be in {}".format(outFolder)) file_process(intFile,outFolder) diff --git a/deepimmuno-gan.py b/deepimmuno-gan.py index 586163b..978aa8b 100644 --- a/deepimmuno-gan.py +++ b/deepimmuno-gan.py @@ -7,9 +7,6 @@ import torch.nn.functional as F import numpy as np import pandas as pd -import matplotlib as mpl -import matplotlib.pyplot as plt -from matplotlib.colors import ListedColormap import argparse import os @@ -126,6 +123,16 @@ def __len__(self): # auxiliary function during training GAN def sample_generator(batch_size): + batch_size = 64 + lr = 0.0001 + num_epochs = 100 + seq_len = 10 + hidden = 128 + n_chars = 21 + d_steps = 10 + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + G = Generator(hidden,seq_len,n_chars,batch_size).to(device) + G.load_state_dict(torch.load('./models/wassGAN_G.pth')) noise = torch.randn(batch_size, 128).to(device) # 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