{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Here I will list some bio ideas I used in this competition. Although some of them did not work, I think it is still meaningful to discuss them. Many of them are inspired by this discussion:\n\nhttps://www.kaggle.com/competitions/open-problems-multimodal/discussion/361025\n\nMany thanks to @Alexander Chervov for his work!","metadata":{}},{"cell_type":"markdown","source":"# Data Imputation","metadata":{}},{"cell_type":"markdown","source":"Data imputation means using some tools to fill the fake zeros existing in the scRNA-seq data or scATAC-seq data. Fake zeros mean that we believe that these zero values occur not because the gene is not expressed, but because the technology does not capture. I only tried the imputation method for scRNA-seq data. Moreover, I only focus on the imputation of important genes. This approach is inspired by the above discussion and Prof. Nancy Zhang's paper (https://www.nature.com/articles/s41467-020-14391-0) (Many thanks to @jinyang18 for pointing out this paper)!\n\nMoreover, here are two benchmark papers related to this topic, feel free to explore them:\n\n1. https://academic.oup.com/nar/article/50/9/4877/6582166\n\n2. https://link.springer.com/article/10.1186/s13059-020-02132-x","metadata":{}},{"cell_type":"markdown","source":"1. MAGIC. MAGIC is a diffusion map based model which uses cell-cell similarity to fill the zero values. https://www.sciencedirect.com/science/article/pii/S0092867418307244?via%3Dihub","metadata":{}},{"cell_type":"code","source":"import os, gc, pickle, datetime, scipy.sparse,random\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom colorama import Fore, Back, Style\n\nfrom sklearn.model_selection import GroupKFold,train_test_split,KFold\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import TruncatedSVD,PCA\nfrom sklearn.metrics import mean_squared_error\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nimport seaborn as sns\nfrom cycler import cycler\nfrom IPython.display import display\n\nimport scipy.sparse\n\nimport xgboost as xgb\n\nDATA_DIR = \"./\"\nFP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n\nFP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\nFP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\nFP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n\nFP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\nFP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")\n\nVERBOSE = 0","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import magic","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Standardize\ndef std(x):\n    empty_list = []\n    for item in x:\n        empty_list.append((item - np.mean(item)) / np.std(item))\n    return np.array(empty_list)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import anndata as ad","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# constant_cols = list(X.columns[(X == 0).all(axis=0).values]) + list(X_test.columns[(X_test == 0).all(axis=0).values])\nconstant_cols = ['ENSG00000003137_CYP26B1', 'ENSG00000004848_ARX', 'ENSG00000006606_CCL26', 'ENSG00000010379_SLC6A13', 'ENSG00000010932_FMO1', 'ENSG00000017427_IGF1', 'ENSG00000022355_GABRA1', 'ENSG00000041982_TNC', 'ENSG00000060709_RIMBP2', 'ENSG00000064886_CHI3L2', 'ENSG00000065717_TLE2', 'ENSG00000067798_NAV3', 'ENSG00000069535_MAOB', 'ENSG00000073598_FNDC8', 'ENSG00000074219_TEAD2', 'ENSG00000074964_ARHGEF10L', 'ENSG00000077264_PAK3', 'ENSG00000078053_AMPH', 'ENSG00000082684_SEMA5B', 'ENSG00000083857_FAT1', 'ENSG00000084628_NKAIN1', 'ENSG00000084734_GCKR', 'ENSG00000086967_MYBPC2', 'ENSG00000087258_GNAO1', 'ENSG00000089505_CMTM1', 'ENSG00000091129_NRCAM', 'ENSG00000091986_CCDC80', 'ENSG00000092377_TBL1Y', 'ENSG00000092969_TGFB2', 'ENSG00000095397_WHRN', 'ENSG00000095970_TREM2', 'ENSG00000099715_PCDH11Y', 'ENSG00000100197_CYP2D6', 'ENSG00000100218_RSPH14', 'ENSG00000100311_PDGFB', 'ENSG00000100362_PVALB', 'ENSG00000100373_UPK3A', 'ENSG00000100625_SIX4', 'ENSG00000100867_DHRS2', 'ENSG00000100985_MMP9', 'ENSG00000101197_BIRC7', 'ENSG00000101298_SNPH', 'ENSG00000102387_TAF7L', 'ENSG00000103034_NDRG4', 'ENSG00000104059_FAM189A1', 'ENSG00000104112_SCG3', 'ENSG00000104313_EYA1', 'ENSG00000104892_KLC3', 'ENSG00000105088_OLFM2', 'ENSG00000105261_OVOL3', 'ENSG00000105290_APLP1', 'ENSG00000105507_CABP5', 'ENSG00000105642_KCNN1', 'ENSG00000105694_ELOCP28', 'ENSG00000105707_HPN', 'ENSG00000105894_PTN', 'ENSG00000106018_VIPR2', 'ENSG00000106541_AGR2', 'ENSG00000107317_PTGDS', 'ENSG00000108688_CCL7', 'ENSG00000108702_CCL1', 'ENSG00000108947_EFNB3', 'ENSG00000109193_SULT1E1', 'ENSG00000109794_FAM149A', 'ENSG00000109832_DDX25', 'ENSG00000110195_FOLR1', 'ENSG00000110375_UPK2', 'ENSG00000110436_SLC1A2', 'ENSG00000111339_ART4', 'ENSG00000111863_ADTRP', 'ENSG00000112761_WISP3', 'ENSG00000112852_PCDHB2', 'ENSG00000114251_WNT5A', 'ENSG00000114279_FGF12', 'ENSG00000114455_HHLA2', 'ENSG00000114757_PEX5L', 'ENSG00000115155_OTOF', 'ENSG00000115266_APC2', 'ENSG00000115297_TLX2', 'ENSG00000115590_IL1R2', 'ENSG00000115844_DLX2', 'ENSG00000116194_ANGPTL1', 'ENSG00000116661_FBXO2', 'ENSG00000116774_OLFML3', 'ENSG00000117322_CR2', 'ENSG00000117971_CHRNB4', 'ENSG00000118322_ATP10B', 'ENSG00000118402_ELOVL4', 'ENSG00000118520_ARG1', 'ENSG00000118946_PCDH17', 'ENSG00000118972_FGF23', 'ENSG00000119771_KLHL29', 'ENSG00000120549_KIAA1217', 'ENSG00000121316_PLBD1', 'ENSG00000121905_HPCA', 'ENSG00000122224_LY9', 'ENSG00000124194_GDAP1L1', 'ENSG00000124440_HIF3A', 'ENSG00000124657_OR2B6', 'ENSG00000125462_C1orf61', 'ENSG00000125895_TMEM74B', 'ENSG00000126838_PZP', 'ENSG00000128422_KRT17', 'ENSG00000128918_ALDH1A2', 'ENSG00000129170_CSRP3', 'ENSG00000129214_SHBG', 'ENSG00000129673_AANAT', 'ENSG00000129910_CDH15', 'ENSG00000130294_KIF1A', 'ENSG00000130307_USHBP1', 'ENSG00000130545_CRB3', 'ENSG00000131019_ULBP3', 'ENSG00000131044_TTLL9', 'ENSG00000131183_SLC34A1', 'ENSG00000131386_GALNT15', 'ENSG00000131400_NAPSA', 'ENSG00000131914_LIN28A', 'ENSG00000131941_RHPN2', 'ENSG00000131951_LRRC9', 'ENSG00000132170_PPARG', 'ENSG00000132681_ATP1A4', 'ENSG00000132958_TPTE2', 'ENSG00000133454_MYO18B', 'ENSG00000134545_KLRC1', 'ENSG00000134853_PDGFRA', 'ENSG00000135083_CCNJL', 'ENSG00000135100_HNF1A', 'ENSG00000135116_HRK', 'ENSG00000135312_HTR1B', 'ENSG00000135324_MRAP2', 'ENSG00000135436_FAM186B', 'ENSG00000135472_FAIM2', 'ENSG00000135898_GPR55', 'ENSG00000135929_CYP27A1', 'ENSG00000136002_ARHGEF4', 'ENSG00000136099_PCDH8', 'ENSG00000136274_NACAD', 'ENSG00000137078_SIT1', 'ENSG00000137142_IGFBPL1', 'ENSG00000137473_TTC29', 'ENSG00000137474_MYO7A', 'ENSG00000137491_SLCO2B1', 'ENSG00000137691_CFAP300', 'ENSG00000137731_FXYD2', 'ENSG00000137747_TMPRSS13', 'ENSG00000137878_GCOM1', 'ENSG00000138411_HECW2', 'ENSG00000138741_TRPC3', 'ENSG00000138769_CDKL2', 'ENSG00000138823_MTTP', 'ENSG00000139908_TSSK4', 'ENSG00000140832_MARVELD3', 'ENSG00000142178_SIK1', 'ENSG00000142538_PTH2', 'ENSG00000142910_TINAGL1', 'ENSG00000143217_NECTIN4', 'ENSG00000143858_SYT2', 'ENSG00000144130_NT5DC4', 'ENSG00000144214_LYG1', 'ENSG00000144290_SLC4A10', 'ENSG00000144366_GULP1', 'ENSG00000144583_MARCH4', 'ENSG00000144771_LRTM1', 'ENSG00000144891_AGTR1', 'ENSG00000145087_STXBP5L', 'ENSG00000145107_TM4SF19', 'ENSG00000146197_SCUBE3', 'ENSG00000146966_DENND2A', 'ENSG00000147082_CCNB3', 'ENSG00000147614_ATP6V0D2', 'ENSG00000147642_SYBU', 'ENSG00000147869_CER1', 'ENSG00000149403_GRIK4', 'ENSG00000149596_JPH2', 'ENSG00000150630_VEGFC', 'ENSG00000150722_PPP1R1C', 'ENSG00000151631_AKR1C6P', 'ENSG00000151704_KCNJ1', 'ENSG00000152154_TMEM178A', 'ENSG00000152292_SH2D6', 'ENSG00000152315_KCNK13', 'ENSG00000152503_TRIM36', 'ENSG00000153253_SCN3A', 'ENSG00000153902_LGI4', 'ENSG00000153930_ANKFN1', 'ENSG00000154040_CABYR', 'ENSG00000154118_JPH3', 'ENSG00000154175_ABI3BP', 'ENSG00000154645_CHODL', 'ENSG00000157060_SHCBP1L', 'ENSG00000157087_ATP2B2', 'ENSG00000157152_SYN2', 'ENSG00000157168_NRG1', 'ENSG00000157680_DGKI', 'ENSG00000158246_TENT5B', 'ENSG00000158477_CD1A', 'ENSG00000158481_CD1C', 'ENSG00000158488_CD1E', 'ENSG00000159189_C1QC', 'ENSG00000159217_IGF2BP1', 'ENSG00000160683_CXCR5', 'ENSG00000160801_PTH1R', 'ENSG00000160973_FOXH1', 'ENSG00000161594_KLHL10', 'ENSG00000162409_PRKAA2', 'ENSG00000162840_MT2P1', 'ENSG00000162873_KLHDC8A', 'ENSG00000162944_RFTN2', 'ENSG00000162949_CAPN13', 'ENSG00000163116_STPG2', 'ENSG00000163288_GABRB1', 'ENSG00000163531_NFASC', 'ENSG00000163618_CADPS', 'ENSG00000163637_PRICKLE2', 'ENSG00000163735_CXCL5', 'ENSG00000163873_GRIK3', 'ENSG00000163898_LIPH', 'ENSG00000164061_BSN', 'ENSG00000164078_MST1R', 'ENSG00000164123_C4orf45', 'ENSG00000164690_SHH', 'ENSG00000164761_TNFRSF11B', 'ENSG00000164821_DEFA4', 'ENSG00000164845_FAM86FP', 'ENSG00000164867_NOS3', 'ENSG00000166073_GPR176', 'ENSG00000166148_AVPR1A', 'ENSG00000166250_CLMP', 'ENSG00000166257_SCN3B', 'ENSG00000166268_MYRFL', 'ENSG00000166523_CLEC4E', 'ENSG00000166535_A2ML1', 'ENSG00000166819_PLIN1', 'ENSG00000166928_MS4A14', 'ENSG00000167210_LOXHD1', 'ENSG00000167306_MYO5B', 'ENSG00000167634_NLRP7', 'ENSG00000167748_KLK1', 'ENSG00000167889_MGAT5B', 'ENSG00000168140_VASN', 'ENSG00000168546_GFRA2', 'ENSG00000168646_AXIN2', 'ENSG00000168955_TM4SF20', 'ENSG00000168993_CPLX1', 'ENSG00000169075_Z99496.1', 'ENSG00000169194_IL13', 'ENSG00000169246_NPIPB3', 'ENSG00000169884_WNT10B', 'ENSG00000169900_PYDC1', 'ENSG00000170074_FAM153A', 'ENSG00000170075_GPR37L1', 'ENSG00000170289_CNGB3', 'ENSG00000170356_OR2A20P', 'ENSG00000170537_TMC7', 'ENSG00000170689_HOXB9', 'ENSG00000170827_CELP', 'ENSG00000171346_KRT15', 'ENSG00000171368_TPPP', 'ENSG00000171501_OR1N2', 'ENSG00000171532_NEUROD2', 'ENSG00000171611_PTCRA', 'ENSG00000171873_ADRA1D', 'ENSG00000171916_LGALS9C', 'ENSG00000172005_MAL', 'ENSG00000172987_HPSE2', 'ENSG00000173068_BNC2', 'ENSG00000173077_DEC1', 'ENSG00000173210_ABLIM3', 'ENSG00000173267_SNCG', 'ENSG00000173369_C1QB', 'ENSG00000173372_C1QA', 'ENSG00000173391_OLR1', 'ENSG00000173626_TRAPPC3L', 'ENSG00000173698_ADGRG2', 'ENSG00000173868_PHOSPHO1', 'ENSG00000174407_MIR1-1HG', 'ENSG00000174807_CD248', 'ENSG00000175206_NPPA', 'ENSG00000175746_C15orf54', 'ENSG00000175985_PLEKHD1', 'ENSG00000176043_AC007160.1', 'ENSG00000176399_DMRTA1', 'ENSG00000176510_OR10AC1', 'ENSG00000176697_BDNF', 'ENSG00000176826_FKBP9P1', 'ENSG00000176988_FMR1NB', 'ENSG00000177324_BEND2', 'ENSG00000177335_C8orf31', 'ENSG00000177535_OR2B11', 'ENSG00000177614_PGBD5', 'ENSG00000177707_NECTIN3', 'ENSG00000178033_CALHM5', 'ENSG00000178175_ZNF366', 'ENSG00000178462_TUBAL3', 'ENSG00000178732_GP5', 'ENSG00000178750_STX19', 'ENSG00000179058_C9orf50', 'ENSG00000179101_AL590139.1', 'ENSG00000179388_EGR3', 'ENSG00000179611_DGKZP1', 'ENSG00000179899_PHC1P1', 'ENSG00000179934_CCR8', 'ENSG00000180537_RNF182', 'ENSG00000180712_LINC02363', 'ENSG00000180988_OR52N2', 'ENSG00000181001_OR52N1', 'ENSG00000181616_OR52H1', 'ENSG00000181634_TNFSF15', 'ENSG00000182021_AL591379.1', 'ENSG00000182230_FAM153B', 'ENSG00000182853_VMO1', 'ENSG00000183090_FREM3', 'ENSG00000183562_AC131971.1', 'ENSG00000183615_FAM167B', 'ENSG00000183625_CCR3', 'ENSG00000183770_FOXL2', 'ENSG00000183779_ZNF703', 'ENSG00000183831_ANKRD45', 'ENSG00000183844_FAM3B', 'ENSG00000183960_KCNH8', 'ENSG00000184106_TREML3P', 'ENSG00000184227_ACOT1', 'ENSG00000184363_PKP3', 'ENSG00000184434_LRRC19', 'ENSG00000184454_NCMAP', 'ENSG00000184571_PIWIL3', 'ENSG00000184702_SEPT5', 'ENSG00000184908_CLCNKB', 'ENSG00000184923_NUTM2A', 'ENSG00000185070_FLRT2', 'ENSG00000185156_MFSD6L', 'ENSG00000185567_AHNAK2', 'ENSG00000185686_PRAME', 'ENSG00000186190_BPIFB3', 'ENSG00000186191_BPIFB4', 'ENSG00000186231_KLHL32', 'ENSG00000186431_FCAR', 'ENSG00000186715_MST1L', 'ENSG00000187116_LILRA5', 'ENSG00000187185_AC092118.1', 'ENSG00000187268_FAM9C', 'ENSG00000187554_TLR5', 'ENSG00000187867_PALM3', 'ENSG00000188153_COL4A5', 'ENSG00000188158_NHS', 'ENSG00000188163_FAM166A', 'ENSG00000188316_ENO4', 'ENSG00000188959_C9orf152', 'ENSG00000189013_KIR2DL4', 'ENSG00000189409_MMP23B', 'ENSG00000196092_PAX5', 'ENSG00000196260_SFTA2', 'ENSG00000197358_BNIP3P1', 'ENSG00000197446_CYP2F1', 'ENSG00000197540_GZMM', 'ENSG00000198049_AVPR1B', 'ENSG00000198134_AC007537.1', 'ENSG00000198156_NPIPB6', 'ENSG00000198221_AFDN-DT', 'ENSG00000198626_RYR2', 'ENSG00000198759_EGFL6', 'ENSG00000198822_GRM3', 'ENSG00000198963_RORB', 'ENSG00000199090_MIR326', 'ENSG00000199753_SNORD104', 'ENSG00000199787_RF00406', 'ENSG00000199872_RNU6-942P', 'ENSG00000200075_RF00402', 'ENSG00000200296_RNU1-83P', 'ENSG00000200683_RNU6-379P', 'ENSG00000201044_RNU6-268P', 'ENSG00000201343_RF00019', 'ENSG00000201564_RN7SKP50', 'ENSG00000201616_RNU1-91P', 'ENSG00000201737_RNU1-133P', 'ENSG00000202048_SNORD114-20', 'ENSG00000202415_RN7SKP269', 'ENSG00000203395_AC015969.1', 'ENSG00000203721_LINC00862', 'ENSG00000203727_SAMD5', 'ENSG00000203737_GPR52', 'ENSG00000203783_PRR9', 'ENSG00000203867_RBM20', 'ENSG00000203907_OOEP', 'ENSG00000203999_LINC01270', 'ENSG00000204010_IFIT1B', 'ENSG00000204044_SLC12A5-AS1', 'ENSG00000204091_TDRG1', 'ENSG00000204121_ECEL1P1', 'ENSG00000204165_CXorf65', 'ENSG00000204173_LRRC37A5P', 'ENSG00000204248_COL11A2', 'ENSG00000204424_LY6G6F', 'ENSG00000204539_CDSN', 'ENSG00000204583_LRCOL1', 'ENSG00000204677_FAM153C', 'ENSG00000204709_LINC01556', 'ENSG00000204711_C9orf135', 'ENSG00000204792_LINC01291', 'ENSG00000204850_AC011484.1', 'ENSG00000204851_PNMA8B', 'ENSG00000204909_SPINK9', 'ENSG00000205037_AC134312.1', 'ENSG00000205038_PKHD1L1', 'ENSG00000205089_CCNI2', 'ENSG00000205106_DKFZp779M0652', 'ENSG00000205364_MT1M', 'ENSG00000205502_C2CD4B', 'ENSG00000205746_AC126755.1', 'ENSG00000205856_C22orf42', 'ENSG00000206052_DOK6', 'ENSG00000206579_XKR4', 'ENSG00000206645_RF00019', 'ENSG00000206786_RNU6-701P', 'ENSG00000206846_RF00019', 'ENSG00000206848_RNU6-890P', 'ENSG00000207088_SNORA7B', 'ENSG00000207181_SNORA14B', 'ENSG00000207234_RNU6-125P', 'ENSG00000207326_RF00019', 'ENSG00000207359_RNU6-925P', 'ENSG00000211677_IGLC2', 'ENSG00000211699_TRGV3', 'ENSG00000211895_IGHA1', 'ENSG00000212385_RNU6-817P', 'ENSG00000212391_RF00554', 'ENSG00000212607_SNORA3B', 'ENSG00000212829_RPS26P3', 'ENSG00000213083_AC010731.1', 'ENSG00000213216_AC007066.1', 'ENSG00000213222_AC093724.1', 'ENSG00000213228_RPL12P38', 'ENSG00000213250_RBMS2P1', 'ENSG00000213272_RPL7AP9', 'ENSG00000213303_AC008481.1', 'ENSG00000213402_PTPRCAP', 'ENSG00000213471_TTLL13P', 'ENSG00000213588_ZBTB9', 'ENSG00000213609_RPL7AP50', 'ENSG00000213757_AC020898.1', 'ENSG00000213931_HBE1', 'ENSG00000213950_RPS10P2', 'ENSG00000213994_AL157395.1', 'ENSG00000214787_MS4A4E', 'ENSG00000214866_DCDC2C', 'ENSG00000214908_AL353678.1', 'ENSG00000214975_PPIAP29', 'ENSG00000215198_AL353795.1', 'ENSG00000215208_KRT18P60', 'ENSG00000215218_UBE2QL1', 'ENSG00000215297_AL354941.1', 'ENSG00000215464_AP000354.1', 'ENSG00000215483_LINC00598', 'ENSG00000215817_ZC3H11B', 'ENSG00000215861_AC245297.1', 'ENSG00000215910_C1orf167', 'ENSG00000216475_AL024474.1', 'ENSG00000217195_AL513475.1', 'ENSG00000217414_DDX18P3', 'ENSG00000217512_AL356776.1', 'ENSG00000218351_RPS3AP23', 'ENSG00000218418_AL591135.1', 'ENSG00000218749_AL033519.1', 'ENSG00000218766_AL450338.1', 'ENSG00000218792_HSPD1P16', 'ENSG00000219249_AMZ2P2', 'ENSG00000219395_HSPA8P15', 'ENSG00000219410_AC125494.1', 'ENSG00000219932_RPL12P8', 'ENSG00000220091_LAP3P1', 'ENSG00000220237_RPS24P12', 'ENSG00000220494_YAP1P1', 'ENSG00000221102_SNORA11B', 'ENSG00000221887_HMSD', 'ENSG00000222276_RNU2-33P', 'ENSG00000222370_SNORA36B', 'ENSG00000222421_RF00019', 'ENSG00000222431_RNU6-141P', 'ENSG00000223342_AL158817.1', 'ENSG00000223379_AL391987.3', 'ENSG00000223403_MEG9', 'ENSG00000223519_KIF28P', 'ENSG00000223576_AL355001.1', 'ENSG00000223668_EEF1A1P24', 'ENSG00000223741_PSMD4P1', 'ENSG00000223779_AC239800.1', 'ENSG00000223783_LINC01983', 'ENSG00000223784_LINP1', 'ENSG00000223855_HRAT92', 'ENSG00000223884_AC068481.1', 'ENSG00000223899_SEC13P1', 'ENSG00000224067_AL354877.1', 'ENSG00000224072_AL139811.1', 'ENSG00000224081_SLC44A3-AS1', 'ENSG00000224099_AC104823.1', 'ENSG00000224116_INHBA-AS1', 'ENSG00000224137_LINC01857', 'ENSG00000224155_AC073136.2', 'ENSG00000224321_RPL12P14', 'ENSG00000224402_OR6D1P', 'ENSG00000224479_AC104162.1', 'ENSG00000224599_BMS1P12', 'ENSG00000224689_ZNF812P', 'ENSG00000224848_AL589843.1', 'ENSG00000224908_TIMM8BP2', 'ENSG00000224957_LINC01266', 'ENSG00000224959_AC017002.1', 'ENSG00000224988_AL158207.1', 'ENSG00000224993_RPL29P12', 'ENSG00000225096_AL445250.1', 'ENSG00000225101_OR52K3P', 'ENSG00000225107_AC092484.1', 'ENSG00000225187_AC073283.1', 'ENSG00000225313_AL513327.1', 'ENSG00000225345_SNX18P3', 'ENSG00000225393_BX571846.1', 'ENSG00000225422_RBMS1P1', 'ENSG00000225423_TNPO1P1', 'ENSG00000225531_AL807761.2', 'ENSG00000225554_AL359764.1', 'ENSG00000225650_EIF2S2P5', 'ENSG00000225674_IPO7P2', 'ENSG00000225807_AC069281.1', 'ENSG00000226010_AL355852.1', 'ENSG00000226084_AC113935.1', 'ENSG00000226251_AL451060.1', 'ENSG00000226383_LINC01876', 'ENSG00000226491_FTOP1', 'ENSG00000226501_USF1P1', 'ENSG00000226545_AL357552.1', 'ENSG00000226564_FTH1P20', 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'ENSG00000266903_AC243964.2', 'ENSG00000266944_AC005262.1', 'ENSG00000266946_MRPL37P1', 'ENSG00000266947_AC022916.1', 'ENSG00000267034_AC010980.2', 'ENSG00000267044_AC005757.1', 'ENSG00000267147_LINC01842', 'ENSG00000267175_AC105094.2', 'ENSG00000267191_AC006213.3', 'ENSG00000267275_AC020911.2', 'ENSG00000267288_AC138150.2', 'ENSG00000267313_KC6', 'ENSG00000267316_AC090409.2', 'ENSG00000267323_SLC25A1P5', 'ENSG00000267345_AC010632.1', 'ENSG00000267387_AC020931.1', 'ENSG00000267395_DM1-AS', 'ENSG00000267429_AC006116.6', 'ENSG00000267452_LINC02073', 'ENSG00000267491_AC100788.1', 'ENSG00000267529_AP005131.4', 'ENSG00000267554_AC015911.8', 'ENSG00000267601_AC022966.1', 'ENSG00000267638_AC023855.1', 'ENSG00000267665_AC021683.3', 'ENSG00000267681_AC135721.1', 'ENSG00000267703_AC020917.2', 'ENSG00000267731_AC005332.2', 'ENSG00000267733_AP005264.5', 'ENSG00000267750_RUNDC3A-AS1', 'ENSG00000267890_AC010624.2', 'ENSG00000267898_AC026803.2', 'ENSG00000267927_AC010320.1', 'ENSG00000268070_AC006539.2', 'ENSG00000268355_AC243960.3', 'ENSG00000268416_AC010329.1', 'ENSG00000268520_AC008750.5', 'ENSG00000268636_AC011495.2', 'ENSG00000268696_ZNF723', 'ENSG00000268777_AC020914.1', 'ENSG00000268849_SIGLEC22P', 'ENSG00000268903_AL627309.6', 'ENSG00000268983_AC005253.2', 'ENSG00000269019_HOMER3-AS1', 'ENSG00000269067_ZNF728', 'ENSG00000269103_RF00017', 'ENSG00000269274_AC078899.4', 'ENSG00000269288_AC092070.3', 'ENSG00000269352_PTOV1-AS2', 'ENSG00000269400_AC008734.2', 'ENSG00000269506_AC110792.2', 'ENSG00000269653_AC011479.3', 'ENSG00000269881_AC004754.1', 'ENSG00000269926_DDIT4-AS1', 'ENSG00000270048_AC068790.4', 'ENSG00000270050_AL035427.1', 'ENSG00000270503_YTHDF2P1', 'ENSG00000270706_PRMT1P1', 'ENSG00000270765_GAS2L2', 'ENSG00000270882_HIST2H4A', 'ENSG00000270906_MTND4P35', 'ENSG00000271013_LRRC37A9P', 'ENSG00000271129_AC009027.1', 'ENSG00000271259_AC010201.1', 'ENSG00000271524_BNIP3P17', 'ENSG00000271543_AC021443.1', 'ENSG00000271743_AF287957.1', 'ENSG00000271792_AC008667.4', 'ENSG00000271868_AC114810.1', 'ENSG00000271973_AC141002.1', 'ENSG00000271984_AL008726.1', 'ENSG00000271996_AC019080.4', 'ENSG00000272070_AC005618.1', 'ENSG00000272138_LINC01607', 'ENSG00000272150_NBPF25P', 'ENSG00000272265_AC034236.3', 'ENSG00000272279_AL512329.2', 'ENSG00000272473_AC006273.1', 'ENSG00000272510_AL121992.3', 'ENSG00000272582_AL031587.3', 'ENSG00000272695_GAS6-DT', 'ENSG00000272732_AC004982.1', 'ENSG00000272770_AC005696.2', 'ENSG00000272788_AP000864.1', 'ENSG00000272824_AC245100.7', 'ENSG00000272825_AL844908.1', 'ENSG00000272848_AL135910.1', 'ENSG00000272916_AC022400.6', 'ENSG00000273133_AC116651.1', 'ENSG00000273177_AC092954.2', 'ENSG00000273212_AC000068.2', 'ENSG00000273218_AC005776.2', 'ENSG00000273245_AC092653.1', 'ENSG00000273274_ZBTB8B', 'ENSG00000273312_AL121749.1', 'ENSG00000273325_AL008723.3', 'ENSG00000273369_AC096586.2', 'ENSG00000273474_AL157392.4', 'ENSG00000273599_AL731571.1', 'ENSG00000273724_AC106782.5', 'ENSG00000273870_AL138721.1', 'ENSG00000273920_AC103858.2', 'ENSG00000274023_AL360169.2', 'ENSG00000274029_AC069209.1', 'ENSG00000274114_ALOX15P1', 'ENSG00000274124_AC074029.3', 'ENSG00000274139_AC090164.2', 'ENSG00000274281_AC022929.2', 'ENSG00000274308_AC244093.1', 'ENSG00000274373_AC148476.1', 'ENSG00000274386_TMEM269', 'ENSG00000274403_AC090510.2', 'ENSG00000274570_SPDYE10P', 'ENSG00000274670_AC137590.2', 'ENSG00000274723_AC079906.1', 'ENSG00000274742_RF00017', 'ENSG00000274798_AC025166.1', 'ENSG00000274911_AL627230.2', 'ENSG00000275106_AC025594.2', 'ENSG00000275197_AC092794.2', 'ENSG00000275302_CCL4', 'ENSG00000275348_AC096861.1', 'ENSG00000275367_AC092111.1', 'ENSG00000275489_C17orf98', 'ENSG00000275527_AC100835.2', 'ENSG00000275995_AC109809.1', 'ENSG00000276070_CCL4L2', 'ENSG00000276255_AL136379.1', 'ENSG00000276282_AC022960.2', 'ENSG00000276547_PCDHGB5', 'ENSG00000276704_AL442067.2', 'ENSG00000276952_AL121772.3', 'ENSG00000276984_AL023881.1', 'ENSG00000276997_AL513314.2', 'ENSG00000277117_FP565260.3', 'ENSG00000277152_AC110048.2', 'ENSG00000277186_AC131212.1', 'ENSG00000277229_AC084781.1', 'ENSG00000277496_AL357033.4', 'ENSG00000277504_AC010536.3', 'ENSG00000277531_PNMA8C', 'ENSG00000278041_AL133325.3', 'ENSG00000278344_AC063943.1', 'ENSG00000278467_AC138393.3', 'ENSG00000278513_AC091046.2', 'ENSG00000278621_AC037198.2', 'ENSG00000278713_AC120114.2', 'ENSG00000278716_AC133540.1', 'ENSG00000278746_RN7SL660P', 'ENSG00000278774_RF00004', 'ENSG00000279091_AC026523.2', 'ENSG00000279130_AC091925.1', 'ENSG00000279141_LINC01451', 'ENSG00000279161_AC093503.3', 'ENSG00000279187_AC027601.5', 'ENSG00000279263_OR2L8', 'ENSG00000279315_AL158212.4', 'ENSG00000279319_AC105074.1', 'ENSG00000279332_AC090772.4', 'ENSG00000279339_AC100788.2', 'ENSG00000279365_AP000695.3', 'ENSG00000279378_AC009159.4', 'ENSG00000279384_AC080188.2', 'ENSG00000279404_AC008739.5', 'ENSG00000279417_AC019322.4', 'ENSG00000279444_AC135584.1', 'ENSG00000279486_OR2AG1', 'ENSG00000279530_AC092881.1', 'ENSG00000279590_AC005786.4', 'ENSG00000279619_AC020907.5', 'ENSG00000279633_AL137918.1', 'ENSG00000279636_LINC00216', 'ENSG00000279672_AP006621.5', 'ENSG00000279690_AP000280.1', 'ENSG00000279727_LINC02033', 'ENSG00000279861_AC073548.1', 'ENSG00000279913_AP001962.1', 'ENSG00000279970_AC023024.2', 'ENSG00000280055_TMEM75', 'ENSG00000280057_AL022069.2', 'ENSG00000280135_AL096816.1', 'ENSG00000280310_AC092437.1', 'ENSG00000280422_AC115284.2', 'ENSG00000280432_AP000962.2', 'ENSG00000280693_SH3PXD2A-AS1', 'ENSG00000281490_CICP14', 'ENSG00000281530_AC004461.2', 'ENSG00000281571_AC241585.2', 'ENSG00000282772_AL358790.1', 'ENSG00000282989_AP001206.1', 'ENSG00000282996_AC022021.1', 'ENSG00000283023_FRG1GP', 'ENSG00000283031_AC009242.1', 'ENSG00000283097_AL159152.1', 'ENSG00000283141_AL157832.3', 'ENSG00000283209_AC106858.1', 'ENSG00000283538_AC005972.3', 'ENSG00000284240_AC099062.1', 'ENSG00000284512_AC092718.8', 'ENSG00000284657_AL031432.5', 'ENSG00000284664_AL161756.3', 'ENSG00000284931_AC104389.5', 'ENSG00000285016_AC017002.6', 'ENSG00000285117_AC068724.4', 'ENSG00000285162_AC004593.3', 'ENSG00000285210_AL136382.1', 'ENSG00000285215_AC241377.4', 'ENSG00000285292_AC021097.2', 'ENSG00000285498_AC104389.6', 'ENSG00000285534_AL163541.1', 'ENSG00000285577_AC019127.1', 'ENSG00000285611_AC007132.1', 'ENSG00000285629_AL031847.2', 'ENSG00000285641_AL358472.6', 'ENSG00000285649_AL357079.2', 'ENSG00000285650_AL157827.2', 'ENSG00000285662_AL731733.1', 'ENSG00000285672_AL160396.2', 'ENSG00000285763_AL358777.1', 'ENSG00000285865_AC010285.3', 'ENSG00000285879_AC018628.2']\nprint('Constant cols:', len(constant_cols))\n\nimportant_cols = ['ENSG00000135218_CD36',\n 'ENSG00000010278_CD9',\n 'ENSG00000204287_HLA-DRA',\n 'ENSG00000117091_CD48',\n 'ENSG00000004468_CD38',\n 'ENSG00000173762_CD7',\n 'ENSG00000137101_CD72',\n 'ENSG00000019582_CD74',\n 'ENSG00000169442_CD52',\n 'ENSG00000170458_CD14',\n 'ENSG00000272398_CD24',\n 'ENSG00000026508_CD44',\n 'ENSG00000114013_CD86',\n 'ENSG00000174059_CD34',\n 'ENSG00000139193_CD27',\n 'ENSG00000105383_CD33',\n 'ENSG00000085117_CD82',\n 'ENSG00000177455_CD19',\n 'ENSG00000002586_CD99',\n 'ENSG00000196126_HLA-DRB1',\n 'ENSG00000135404_CD63',\n 'ENSG00000012124_CD22',\n 'ENSG00000134061_CD180',\n 'ENSG00000105369_CD79A',\n 'ENSG00000116824_CD2',\n 'ENSG00000010610_CD4',\n 'ENSG00000139187_KLRG1',\n 'ENSG00000204592_HLA-E',\n 'ENSG00000090470_PDCD7',\n 'ENSG00000206531_CD200R1L',\n'ENSG00000166710_B2M',\n 'ENSG00000198034_RPS4X',\n 'ENSG00000188404_SELL',\n 'ENSG00000130303_BST2',\n 'ENSG00000128040_SPINK2',\n 'ENSG00000206503_HLA-A',\n 'ENSG00000108107_RPL28',\n 'ENSG00000143226_FCGR2A',\n 'ENSG00000133112_TPT1',\n 'ENSG00000166091_CMTM5',\n 'ENSG00000026025_VIM',\n 'ENSG00000205542_TMSB4X',\n 'ENSG00000109099_PMP22',\n 'ENSG00000145425_RPS3A',\n 'ENSG00000172247_C1QTNF4',\n 'ENSG00000072274_TFRC',\n 'ENSG00000234745_HLA-B',\n 'ENSG00000075340_ADD2',\n 'ENSG00000119865_CNRIP1',\n 'ENSG00000198938_MT-CO3',\n 'ENSG00000135046_ANXA1',\n 'ENSG00000235169_SMIM1',\n 'ENSG00000101200_AVP',\n 'ENSG00000167996_FTH1',\n 'ENSG00000163565_IFI16',\n 'ENSG00000117450_PRDX1',\n 'ENSG00000124570_SERPINB6',\n 'ENSG00000112077_RHAG',\n 'ENSG00000051523_CYBA',\n 'ENSG00000107130_NCS1',\n 'ENSG00000055118_KCNH2',\n 'ENSG00000029534_ANK1',\n 'ENSG00000169567_HINT1',\n 'ENSG00000142089_IFITM3',\n 'ENSG00000139278_GLIPR1',\n 'ENSG00000142227_EMP3',\n 'ENSG00000076662_ICAM3',\n 'ENSG00000143627_PKLR',\n 'ENSG00000130755_GMFG',\n 'ENSG00000160593_JAML',\n 'ENSG00000095932_SMIM24',\n 'ENSG00000197956_S100A6',\n 'ENSG00000171476_HOPX',\n 'ENSG00000116675_DNAJC6',\n 'ENSG00000100448_CTSG',\n 'ENSG00000100368_CSF2RB',\n 'ENSG00000047648_ARHGAP6',\n 'ENSG00000198918_RPL39',\n 'ENSG00000196154_S100A4',\n 'ENSG00000233968_AL157895.1',\n 'ENSG00000137642_SORL1',\n 'ENSG00000133816_MICAL2',\n 'ENSG00000130208_APOC1',\n 'ENSG00000105610_KLF1']\nprint('important columns ',len(important_cols))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"metadata_df = pd.read_csv(FP_CELL_METADATA, index_col = 'cell_id')\nmetadata_df = metadata_df[metadata_df.technology == \"citeseq\"]\n\n# Read train and convert to sparse matrix\n# X = pd.read_hdf(FP_CITE_TRAIN_INPUTS)\nX = pd.read_hdf(FP_CITE_TRAIN_INPUTS).drop(columns = constant_cols)\n# X = pd.read_csv(\"/gpfs/ysm/home/tl688/scrnahpc/tl688/openproblems/scGNN/outputdir/train_protein_recon.csv\",index_col=0).T.drop(columns = constant_cols)\ncell_index = X.index\nmeta = metadata_df.reindex(cell_index)\n\nXt = pd.read_hdf(FP_CITE_TEST_INPUTS).drop(columns = constant_cols)\ncell_index_test = Xt.index\nmeta_test = metadata_df.reindex(cell_index_test)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_total = pd.concat([X, Xt])\ndel X,Xt","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_total","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"magic_operator = magic.MAGIC()\nX_magic = magic_operator.fit_transform(data_total, genes=important_cols)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_magic","metadata":{"scrolled":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"2. DeepImpute. DeepImpute is a tool based on divide-and-conquer sub neural networks. The author used a model similar to denoising auto-encoder for highly correlated genes. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1837-6","metadata":{}},{"cell_type":"code","source":"import os, gc, pickle, datetime, scipy.sparse,random\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from deepimpute.multinet import MultiNet","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_hdf(FP_CITE_TRAIN_INPUTS).drop(columns = constant_cols)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data[important_cols]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = MultiNet()\nmodel.fit(data)\nimputed = model.predict(data)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"imputed.to_csv(\"train_cite_full_impute.csv\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# GNN based on cell-cell similarity","metadata":{}},{"cell_type":"markdown","source":"Since we can assume the cells with high similarity based on gene expression are co-expressed or have common functions, we can construct a graph based on such relation, and utilize Graph auto-encoder to perform prediction. The easiest approach to develop a GNN is to utilize PYG: https://pytorch-geometric.readthedocs.io/en/latest/","metadata":{}},{"cell_type":"code","source":"import torch\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch_geometric.data import Data\nfrom torch_geometric.nn import GATConv\nfrom torch_geometric.nn import GAE\nfrom torch_geometric.nn import GCNConv","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"graph = data.Data(x=torch.FloatTensor(adata.X), edge_index=edges, \n                  edge_attr = torch.FloatTensor(adata.obsp['connectivities'][adata.obsp['connectivities'].nonzero()]),\n                  y=torch.FloatTensor(multi_train_y)) #Also, we consider the weights in the connectivity matrix.\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class GAT(torch.nn.Module):\n    def __init__(self):\n        super(GAT, self).__init__()\n        self.hid = 8\n        self.in_head = 8\n        self.out_head = 1\n        \n        \n        self.conv1 = GATConv(graph.x.shape[1], self.hid, heads=self.in_head, dropout=0.6)\n        self.conv2 = GATConv(self.hid*self.in_head, graph.y.shape[1], concat=False,\n                             heads=self.out_head, dropout=0.6)\n\n    def forward(self, data):\n        x, edge_index = data.x, data.edge_index\n                \n        x = F.dropout(x, p=0.6, training=self.training)\n        x = self.conv1(x, edge_index)\n        x = F.elu(x)\n        x = F.dropout(x, p=0.6, training=self.training)\n        x = self.conv2(x, edge_index)\n        \n        return x\n    \n# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ndevice = \"cpu\"\n\nmodel = GAT().to(device)\ndata1 = graph.to(device)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch_geometric.datasets import Planetoid\nfrom torch_geometric.loader import NeighborLoader\n\nloader = NeighborLoader(\n    data1,\n    # Sample 30 neighbors for each node for 2 iterations\n    num_neighbors=[30] * 2,\n    # Use a batch size of 128 for sampling training nodes\n    batch_size=128\n)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4)\n\nmodel.train()\nfor epoch in range(2000):\n    for batch in loader:\n        optimizer.zero_grad()\n        out = model(batch)\n        loss = F.mse_loss(out, batch.y)\n\n        if epoch%200 == 0:\n            print(loss)\n\n        loss.backward()\n        optimizer.step()\n","metadata":{"scrolled":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model each protein independently","metadata":{}},{"cell_type":"markdown","source":"It means we use correlation to select high correlated gene-protein relation, and then predict the protein one by one. The ideas come from this notebook(https://www.kaggle.com/code/fabiencrom/msci-correlations-eda-citeseq) and Nancy Zhang's paper.","metadata":{}},{"cell_type":"code","source":"# Use neural networks as one example","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LR_START = 5e-4#5e-3\nBATCH_SIZE = 512\n\ndef create_model():\n    \n    reg1 = 9.613e-06\n    reg2 = 1e-07\n    REG1 = tf.keras.regularizers.l2(reg1)\n    REG2 = tf.keras.regularizers.l2(reg2)\n    DROP = 0.1\n\n    activation = 'selu'\n    inputs = Input(shape =(100,))\n\n    x0 = Dense(64, \n              kernel_regularizer = REG1,\n              activation = activation,\n             )(inputs)\n    \n\n    x0 = Dropout(DROP)(x0)\n    \n    \n    \n    x2 = Dense(32, \n               kernel_regularizer = REG1,\n               activation = activation,\n             )(x0) \n    \n    x2 = Dropout(DROP)(x2)\n    \n    x = Dense(1, \n                kernel_regularizer = REG2,\n                activation=\"linear\",\n                )(x2)\n    \n    \n    model = Model(inputs, x)\n    \n\n    return model","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"correlation_table = pd.read_csv(\"./top100features_cite.csv\", index_col=0)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein_names = correlation_table.columns","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X[correlation_table.loc[:,protein_names[0]]].values","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X[correlation_table.loc[:,protein_names[0]]].values","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nEPOCHS = 5\nN_SPLITS = 3\n\npred_train = np.zeros((Y.shape[0],1))\n\nseed_tensorflow(1)\nscore_list = []\nkf = GroupKFold(n_splits=N_SPLITS)\n# kf = sklearn.model_selection.KFold(5)\nscore_list = []\n\nfor fold, (idx_tr, idx_va) in enumerate(kf.split(X, groups=meta.day)):\n    start_time = datetime.datetime.now()\n    model = None\n    gc.collect()\n\n    for protein_index in range(140):\n        X_tr = X[correlation_table.loc[:,protein_names[protein_index]]].values[idx_tr]\n        y_tr = Y[idx_tr,protein_index].reshape(-1,1)\n        \n        X_va = X[correlation_table.loc[:,protein_names[protein_index]]].values[idx_va]\n        y_va = Y[idx_va,protein_index].reshape(-1,1)\n\n        lr = ReduceLROnPlateau(\n                        monitor = \"val_loss\",\n                        factor = 0.9, \n                        patience = 4, \n                        verbose = VERBOSE)\n\n\n        es = EarlyStopping(\n                        monitor = \"val_loss\",\n                        patience = 100, \n                        verbose = VERBOSE,\n                        mode = \"min\", \n                        restore_best_weights = True)\n        \n        \n        checkpoint_filepath = \"./citeseq_new/checkpointfile.hdf5\"\n\n        model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n                        filepath = checkpoint_filepath,\n                        save_weights_only = True,\n                        monitor = 'val_loss',\n                        mode = 'min',\n                        verbose=1,\n                        save_best_only = True)\n\n        callbacks = [   lr, \n                        es, \n                        model_checkpoint_callback\n                        ]\n\n        model = create_model()\n\n\n        model.compile(\n                    optimizer = tfa.optimizers.AdaBelief(learning_rate = LR_START),\n                    metrics = [\"mse\"],\n                    loss = \"mse\"\n                     )\n        # Training\n        \n        print(\"start training\")\n        \n        model.fit( X_tr,\n                    y_tr, \n                    validation_data=(\n                                    X_va,\n                                    y_va), \n                    epochs = EPOCHS,\n                    verbose = VERBOSE,\n                    batch_size = BATCH_SIZE,\n                    shuffle = True,\n                    callbacks = callbacks)\n\n        del X_tr, y_tr \n        gc.collect()\n\n        print(\"end training\")\n        \n        model.load_weights(checkpoint_filepath)\n        model.save(f\"./submissions/model_{fold}_{protein_index}\")\n        print('model saved')\n\n        #  Model validation\n        y_va_pred = model.predict(X_va)\n        corrscore = correlation_score(y_va, y_va_pred)\n        pred_train[idx_va] = y_va_pred\n\n        print(f\"Fold {fold}, correlation =  {corrscore:.5f}\")\n        del X_va, y_va, y_va_pred\n        gc.collect()\n        score_list.append(corrscore)\n\n# Show overall score\n\nprint(f\"{Fore.GREEN}{Style.BRIGHT}Mean corr = {np.array(score_list).mean():.5f}{Style.RESET_ALL}\")\nscore_total = correlation_score(Y, pred_train)\nprint(f\"{Fore.BLUE}{Style.BRIGHT}Oof corr   = {score_total:.5f}{Style.RESET_ALL}\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Encode meta information into our input","metadata":{}},{"cell_type":"markdown","source":"We have some meta information in this competition, including three explicit meta information (days, donors, and cell type information), and we also have one implicit meta information (gender). We can encode them into our input based on one hot encoder or embedding. A similar approach was used by JAE in the 1st open problems in single cell competition, and they won the track: joint embedding analysis (https://github.com/kimmo1019/JAE).","metadata":{}},{"cell_type":"code","source":"# use cell type as one example\nct_list = metadata_df.cell_type","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dict_celltype = {}\ncount = 0\nfor i in sorted(list(set(ct_list.values))):\n    dict_celltype[i] = count \n    count += 1","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"celltype_new = [dict_celltype[i] for i in metadata_df.cell_type.values]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Embedding(3, 6, input_length = len(metadata_df.cell_type)))\nmodel.compile('rmsprop', 'mse')\noutput_array_celltype = model.predict(celltype_new)\noutput_array_celltype","metadata":{"scrolled":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"metadata_df[\"celltype_index\"] = [i for i in range(len(output_array_celltype))]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Then we can use np.hstack in the following work.","metadata":{}},{"cell_type":"markdown","source":"# Calculating gene activity score","metadata":{}},{"cell_type":"markdown","source":"For multiome data, since the scATAc-seq is always very sparse, it may contain a lot of noise. To denoise the data, we can utilize some tools to transfer peaks information into information simialr to gene expression, which is known as gene activity score. ","metadata":{}},{"cell_type":"markdown","source":"Here are two notebooks related to this topic:\n\nhttps://www.kaggle.com/code/llttyy/calculate-the-gene-activity-score-of-multiome-data/notebook\n\nhttps://www.kaggle.com/code/masato114/msci-multiome-using-geneactivity","metadata":{}},{"cell_type":"markdown","source":"As far as I know, the best tool to perform such transformation is Seurat. https://satijalab.org/seurat/.\n\nIf the organizers can provide some data based on RDS form or Seuratdata form, we can try to use this tool to calculate the gene activity score rather than relying on episcanpy. I do not recommend using seuratdisk or other packages to transfer h5ad files into seuratdata files, because this tool is very old.","metadata":{}},{"cell_type":"markdown","source":"# Other ideas I have tried","metadata":{}},{"cell_type":"markdown","source":"Here are some other ideas I have thought and tried:\n\n1. I check the correlation between our model's output and true values, and find that some proteins have very high correlation, while others have very low correlation. Therefore, I firstly trained the protein with high correlation, and then use anther models to fit the proteins with low correlation. However, it does not work out.\n\n2. Since this is a pseudo-time (The time series is not very precise from my end, because our sequence results only represent the day time shot of our data, and we must kill the cells to finsh sequencing), so we can only use prior data to predict posterior data. For example, only use data in day 2\\&3 to predict data in day 4.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}