• 沒有找到結果。

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

26

第五章、 結論與建議

本研究以切片平均變異數估計法,估計預測變數 X 對反應變數 Y 的中央子 空間的基底向量,得以將資料進行維度縮減。並以邊際維度檢定法估計原始資料 被縮減的程度,即中央子空間的維度,將此方法獲得整體相關性檢定,並運用在 基因組分析問題上,以檢定基因組表現量與連續型外顯變數的相關顯著性,此方 法能應用來探討疾病與基因組的關係。在模型分配假設為常態-常態模型時,為

了更有效利用以Y 為分組依據所增加的訊息,我們將分組後組內平均數的訊息加

入原統計量中,稱為改良型邊際維度檢定法。我們並考慮以排列重抽法獲得檢定 之顯著值。在論文中,我們以電腦模擬來探究邊際維度檢定法之表現。另一方面,

為了瞭解邊際維度檢定法之實用性,我們也以一組攝護腺癌病患資料進行實證分 析。

在線性迴歸架構下,本研究以常見的常態-常態、線性迴歸模型進行電腦模

擬。在虛無假設中,我們發展的兩種邊際維度檢定法有相似表現且均能適當的控 制型一誤差率。而在對立假設中,雖然改良型邊際維度檢定法比原方法有較高的 檢定力,但兩種邊際維度檢定法明顯劣於 Dinu 等學者(2013)所提出的線性組合 法。

在實證分析中,兩種邊際維度檢定法分析結果相似,且此兩種方法判斷為顯 著的基因組遠多於線性組合法。此結果與電腦模擬的發現恰巧相反,在模擬中的 邊際維度檢定法則有較低檢定力。由於實際資料中,常態-常態、線性迴歸模型 假設不一定成立,故會有不一致的結論發生。我們也發現部分邊際維度檢定法所 偵測出的顯著基因組中的基因,已經被生物學界證實與外顯特徵變數 LEP 有關,

但利用線性組合法時並未獲得顯著結果。

以下為未來可能的三點研究方向與建議。首先,我們可考慮更多不同模型,

除了以理論探討各條件分佈之二階動差是否包含Y 的資訊,並以電腦模擬探討邊

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

27

際維度法的表現,另外也可與更多已發展之基因組分析方法做比較。第二則是考 慮將邊際維度檢定法發展成適用於其他類型外顯特徵變數的基因組分析方法。另 外,由於需計算各切片之變異數矩陣,邊際維度檢定法之最大限制為其計算效率,

當基因個數多時將牽涉到大量計算工作與時間,未來可考慮簡化原有邊際維度檢 定統計量,以增加其計算效率。

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

28

參考文獻

Becker, C. and Gather, U. (2007) A note on the choice of the number of slices in sliced inverse regression. Technical Report, 475.

Biernacka, J.M., Geske, J., Jenkins, G.D., Colby, C., Rider, D.N., Karpyak, V.M., Choi, D. and Fridley, B.L. (2012) Genome-wide gene-set analysis for identification of pathways associated with alcohol dependence. International

Journal of Neuropsychopharmacology, 16, 271-278.

Chang, S., Hursting, S.D., Contois, J.H., Strom, S.S., Yamamura, Y., Babaian, R.J., Troncoso, P., Scardino, P.T., Wheeler, T.M., Amos, C.I. and Spitz, M.R. (2001) Leptin and prostate cancer. The Prostate, 46, 62-67.

Chen, J., Pamuklar, Z., Spagnoli, A. and Torquati, A. (2012) Serum leptin levels are inversely correlated with omental gene expression of adiponectin and markedly decreased after gastric bypass surgery. Surg Endosc, 26, 1476-1480.

Cook, R.D. (1996) Graphics for regression with a binary response. Journal of the

American Statistical Association, 91, 983-992.

Cook, R.D. and Lee, H. (1999) Dimension reduction in binary response regression.

Journal of the American Statistical Association, 94, 1187-1200.

Cook, R.D. and Weisberg, S. (1991) Discussion of ”Sliced Inverse Regression for Dimension Reduction.” Journal of the American Statistical Association, 86, 328-332.

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

29

Dinu, I., Potter, J.D., Mueller, T., Liu, Q., Adewale, A.J., Jhangri, G.S., Einecke, G., Famulski, K.S., Halloran, P. and Yasui, Y. (2007) Improving gene set analysis of microarray data by SAM-GS. Bioinformatics, 8, 242.

Dinu, I., Wang, X., Kelemen, L.E., Vatanpour, S. and Pyne, S. (2013) Linear combination test for gene set analysis of a continuous phenotype. BMC

Bioinformatics, 14, 212.

Eid, M.A., Kumar, M.V., Iczkowski, K.A., Bostwick, D.G. and Tindall, D.J.

(1998) Expression of early growth response genes in human prostate cancer. Cancer

Res, 58, 2461-2468.

Gao, T., Han, Y., Yu, L., Ao, S., Li, Z. and Ji, J. (2014) CCNA2 is a prognostic biomarker for ER+ breast cancer and tamoxifen resistance. PLOS ONE, 9, 3.

Hsueh, H.M., Zhou, D.W., Tsai, C.A. (2013) Random forests-based differential analysis of gene sets for gene expression data. Gene, 518, 179-186.

Li, K.C. (1991) Sliced Inverse Regression for Dimension Reduction. Journal of the

American Statistical Association, 86, 316-327.

Murphy, K.P. (2007) Conjugate bayesian analysis of the gaussian distribution.

Technical report, University of British Columbia.

Riedel, K.S. (1991) A Sherman Morrison Woodbury identity for rank augmenting

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

30

matrices with application to centering. SIAM J. MAT. ANAL., 12(1), 80-85.

Sch ̈fer, J. and Strimmer, K. (2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in

Genetics and Molecular Biology, 4, 1.

Shao, Y. and Cook, R.D. and Weisberg, S. (2007) Marginal tests with sliced average variance estimation. Biometrika, 94, 285-296.

Singh, S.K., Grifson, J.J., Mavuduru, R.S., Agarwal, M.M., Mandal, A.K. and Jha, V.

(2010) Serum leptin: a marker of prostate cancer irrespective of obesity. Cancer

Biomarkers, 7, 11-15.

Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S. and Mesirov, J.P. (2005) Gene set enrichment analysis : a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences

of the United States of America, 102, 15545-15550.

Pang, H., Lin, A., Holford, M., Enerson, B.E., Lu, B., Lawton, M.P., Floyd, E. and Zhao, H. (2006) Pathway analysis using random forests classification and regression. Bioinformatics, 22, 2028-2036.

Terrasi, M., Riolfi, M., Ferla, R., Scolaro, L., Micciolo, R., Guidi, M. and Surmacz, E.

(2009) Leptin and its receptor are overexpressed in brain tumors and correlate with the degree of malignancy. International Society of Neuropathology, 20(2), 481-489.

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

31

Tusher, V.G., Tibshirani, R. and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of

Sciences of the United States of America, 98, 5116-5121.

Wang, X., Pyne, S. and Dinu, I. (2014) Gene set enrichment analysis for multiple continuous phenotypes. AIMSCS Research Report, No.: RR2014-05.

Wrann, C.D., Eguchi, J., Bozec, A., Xu, Z., Mikkelsen, T., Gimble, J., Nave, H., Wagner, E.F., Ong, S.E., Rosen, E.D. (2012) FOSL2 promotes leptin gene expression in human and mouse adipocytes. J Clin Invest, 122(3), 1010-1021.

Yan, X. and Sun, F. (2008) Testing gene set enrichment for subset of genes: Sub-GSE.

BMC Bioinformatics, 9, 362.

Ye, C. and Eskin, E. (2007) Discovering tightly regulated and differentially expressed gene sets in whole genome expression data. Bioinformatics, 23 (2), 84-90.

徐碩亨、薛慧敏 (2013) Application of sufficient dimension reduction to global test.

國立政治大學統計學系碩士論文,台北市。

相關網站:

1. 攝護腺癌資料來源 NCBI, GEO 資料庫

(http://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE6956&format=file1) 2. 基因組定義: MSigDB , C2 catalog (2008 年 v2.5)

(http://www.broadinstitute.org/gsea/msigdb/download_file.jsp?filePath=/resources/ms igdb/2.5/c2.all.v2.5.symbols.gmt)

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

33

1 ‖ ‖

1 ‖ ‖ ( 1 ‖ ‖ ) 。

𝐸( | ) 𝐸( ) 𝐸 ( 1

1 ‖ ‖ ) ‖ ‖

1 ‖ ‖ , 且

𝐸 ([ | ] ) 𝐸[𝐸 | ]

𝐸 { | 2[ | ] } [𝐸 | ]

2𝐸 {[ | ] } 2 [ ] 2 [ (

‖ ‖ ) ]。

所以得下列結果:

i. 𝑡𝑟 {𝐸( | ) } ‖ ‖

‖ ‖ 𝑡𝑟 ( ‖ ‖‖ ‖ ) , ii. 𝑡𝑟 {𝐸 ([ | ] ) 𝐸[𝐸 | ] } 𝑡𝑟 {2 [ (

‖ ‖ ) ]} 。

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

34

附錄 B

附錄 B-1: 邊際維度檢定法 MD1 分析結果

基因組 size p-value 基因組 size p-value 基因組 size p-value

SHIPP_DLBCL_CURED_DN 32 0 SMITH_HTERT_UP 95 0.001 LIZUKA_L1_GR_G1 20 0.002

FMLPPATHWAY 36 0 LEE_MYC_TGFA_UP 58 0.001 NFATPATHWAY 51 0.002

BYSTROM_IL5_UP 40 0 GLYCEROLIPID_METABOLISM 43 0.001 IFN_BETA_GLIOMA_UP 58 0.002

PTDINSPATHWAY 20 0 LEE_ACOX1_UP 58 0.001 HDACI_COLON_SUL48HRS_DN 61 0.002

OXIDATIVE_PHOSPHORYLATION 57 0 NO1PATHWAY 27 0.001 HDACI_COLON_CUR_DN 35 0.002

GRANDVAUX_IRF3_DN 20 0 SIG_BCR_SIGNALING_PATHWAY 45 0.001 CORDERO_KRAS_KD_VS_CONTROL_DN 52 0.003

IGF1MTORPATHWAY 19 0 ZHANG_EFT_EWSFLI1_UP 78 0.001 41BBPATHWAY 18 0.003

FERRARI_4HPR_UP 21 0 STOSSI_ER_UP 47 0.001 LEE_CIP_UP 57 0.003

DORSEY_DOXYCYCLINE_UP 30 0 ZHAN_PCS_MULTIPLE_MYELOMA_SPKD 22 0.001 DORSAM_HOXA9_DN 29 0.003

KRETZSCHMAR_IL6_DIFF 120 0 LEI_MYB_REGULATED_GENES 225 0.001 LEE_E2F1_UP 59 0.003

4NQO_UNIQUE_FIBRO_UP 21 0 UVB_NHEK3_C8 62 0.001 HESS_HOXAANMEIS1_DN 52 0.003

ADIP_VS_PREADIP_DN 35 0 OXSTRESS_RPETHREE_DN 26 0.001 BRG1_ALAB_UP 36 0.003

EGF_HDMEC_UP 43 0 ET743_SARCOMA_UP 60 0.001 TPA_SENS_LATE_DN 220 0.003

CMV_8HRS_DN 42 0 OXSTRESS_BREASTCA_UP 24 0.001 HDACI_COLON_TSA12HRS_UP 20 0.003

UVC_HIGH_D6_DN 26 0 HDACI_COLON_BUT16HRS_UP 31 0.001 GAMMA-UV_FIBRO_UP 32 0.003

MMS_MOUSE_LYMPH_HIGH_4HRS_UP 31 0 CMV-UV_HCMV_6HRS_UP 113 0.001 UVB_NHEK1_C2 21 0.003

WERNERONLY_FIBRO_DN 44 0 UVB_NHEK3_C7 52 0.001 HSA01030_GLYCAN_STRUCTURES_BIOSYNTHESIS_1 82 0.003

INOS_ALL_DN 70 0 ESR_FIBROBLAST_UP 49 0.001 CCR5PATHWAY 17 0.004

HDACI_COLON_TSABUT_UP 53 0 CCR3PATHWAY 21 0.002 LEE_MYC_UP 51 0.004

HSA00510_N_GLYCAN_BIOSYNTHESIS 32 0 HIVNEFPATHWAY 52 0.002 HINATA_NFKB_DN 19 0.004

HSA04210_APOPTOSIS 78 0 HDACPATHWAY 29 0.002 NADLER_OBESITY_HYPERGLYCEMIA 40 0.004

HSA04720_LONG_TERM_POTENTIATION 64 0 APOPTOSIS_GENMAPP 41 0.002 ZHAN_MM_CD138_CD2_VS_REST 26 0.004

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

35

基因組 size p-value 基因組 size p-value 基因組 size p-value

RAY_P210_DIFF 53 0.004 HSA04310_WNT_SIGNALING_PATHWAY 129 0.006 BASSO_HCL_DIFF 78 0.009

HSC_MATURE_SHARED 178 0.004 BYSTROM_IL5_DN 56 0.007 ELONGINA_KO_UP 138 0.009

DAC_PANC50_UP 43 0.004 PROSTAGLANDIN_AND_LEUKOTRI

ENE_METABOLISM

30 0.007

UVC_HIGH_D7_DN 29 0.009

E2F3_ONCOGENIC_SIGNATURE 160 0.004 AGED_MOUSE_HYPOTH_UP 41 0.009

HSA00512_O_GLYCAN_BIOSYNTHESIS 20 0.004 BROCKE_IL6 120 0.007 HSA00190_OXIDATIVE_PHOSPHORYLATION 101 0.009

CK1PATHWAY 16 0.005 ZHAN_MM_CD1_VS_CD2_DN 35 0.007 GLUTAMATE_METABOLISM 23 0.01

REN_E2F1_TARGETS 33 0.005 PARK_RARALPHA_UP 38 0.007 GLYCOLYSIS 50 0.01

INTEGRINPATHWAY 33 0.005 FERRANDO_MLL_T_ALL_DN 81 0.007 HALMOS_CEBP_UP 48 0.01

ADDYA_K562_HEMIN_TREATMENT 69 0.005 ZHAN_MM_CD138_MF_VS_REST 34 0.007 IRITANI_ADPROX_DN 57 0.01

NICK_RHAPC_UP 26 0.005 ADIP_DIFF_CLUSTER2 37 0.007 PARP_KO_UP 30 0.01

AGEING_BRAIN_DN 112 0.005 OXSTRESS_RPE_HNETBH_DN 44 0.007 ST_GA12_PATHWAY 22 0.011

UVC_HIGH_D3_DN 43 0.005 IFNALPHA_RESIST_DN 15 0.007 NKTPATHWAY 28 0.011

BRENTANI_DEATH 67 0.006 AGUIRRE_PANCREAS_CHR19 67 0.008 TH1TH2PATHWAY 17 0.011

PROPANOATE_METABOLISM 30 0.006 RNA_TRANSCRIPTION_REACTOME 35 0.008 POMEROY_MD_TREATMENT_GOOD_VS_POOR_DN 24 0.011

GILDEA_BLADDER_UP 26 0.006 WANG_MLL_CBP_VS_GMP_DN 37 0.008 ASTIER_FN_DIFF 54 0.011

HOFFMANN_BIVSBII_BI 83 0.006 GERY_CEBP_TARGETS 106 0.008 AGEING_KIDNEY_SPECIFIC_DN 105 0.011

CMV_HCMV_TIMECOURSE_14HRS_DN 38 0.006 UVC_HIGH_D4_DN 43 0.008 HSC_HSC_SHARED 162 0.011

HDACI_COLON_TSA2HRS_UP 42 0.006 DFOSB_BRAIN_2WKS_UP 30 0.008 UVB_SCC_UP 77 0.011

HPV31_UP 37 0.006 HDACI_COLON_BUT48HRS_DN 89 0.008 UVB_NHEK1_C1 50 0.011

BLEO_MOUSE_LYMPH_LOW_24HRS_DN 22 0.006 HSC_STHSC_ADULT 26 0.008 TARTE_BCELL 36 0.012

STEMCELL_HEMATOPOIETIC_UP 186 0.006 CELL_MOTILITY 97 0.009 STEFFEN_AML_PML_PLZF_TRGT 45 0.012

STRESS_TPA_SPECIFIC_UP 40 0.006 GCRPATHWAY 17 0.009 TAKEDA_NUP8_HOXA9_10D_UP 134 0.012

HSA00563_GLYCOSYLPHOSPHATIDYLINOS ITOL_ANCHOR_BIOSYNTHESIS

18 0.006

NAKAJIMA_MCSMBP_EOS 27 0.009 TAKEDA_NUP8_HOXA9_8D_DN 163 0.012

GUO_HEX_DN 52 0.009 HDACI_COLON_BUT16HRS_DN 88 0.012

HOHENKIRK_MONOCYTE_DEND_UP 106 0.018 IFNALPHA_NL_HCC_UP 17 0.018

HSA05110_CHOLERA_INFECTION 38 0.012 ECMPATHWAY 22 0.016 UVB_NHEK3_C2 35 0.018

JISON_SICKLE_CELL 30 0.013 N_GLYCAN_BIOSYNTHESIS 21 0.016 HSC_MATURE_ADULT 181 0.018

ZELLER_MYC_UP 23 0.013 ZHAN_MM_CD138_LB_VS_REST 21 0.016 CASPASEPATHWAY 21 0.019

SMITH_HTERT_DN 57 0.013 BOQUEST_CD31PLUS_VS_

CD31MINUS_UP

225 0.016

IGF1PATHWAY 20 0.019

GENOTOXINS_ALL_24HRS_REG 24 0.013 PASSERINI_PROLIFERATION 62 0.019

ELONGINA_KO_DN 145 0.013 ABBUD_LIF_UP 39 0.016 DER_IFNG_UP 60 0.019

UVB_NHEK3_C0 78 0.013 TAVOR_CEBP_UP 47 0.016 ZHAN_MM_CD138_HP_VS_REST 34 0.019

RIBAVIRIN_RSV_DN 40 0.013 HSA04520_ADHERENS_JUNCTION 73 0.016 HSA00530_AMINOSUGARS_METABOLISM 26 0.019 TPA_SENS_LATE_UP 50 0.013 SHEPARD_CRASH_AND_BURN_MUT_

VS_WT_DN

129 0.017

BYSTRYKH_HSC_CIS_GLOCUS 83 0.02

UVC_HIGH_D8_DN 27 0.013 TOLLPATHWAY 29 0.02

NTHIPATHWAY 22 0.014 WELCSH_BRCA_UP 36 0.017 HEMATOP_STEM_ALL_UP 34 0.02

SA_G1_AND_S_PHASES 15 0.014 LEE_MYC_E2F1_UP 52 0.017 HIPPOCAMPUS_DEVELOPMENT_PRENATAL 29 0.02

MA_ATRA_EMP_DN 28 0.014 ST_INTERLEUKIN_4_PATHWAY 26 0.017 GH_EXOGENOUS_ANY_DN 73 0.02

IGF1_NIH3T3_UP 31 0.014 VIPPATHWAY 26 0.017 SRC_ONCOGENIC_SIGNATURE 50 0.02

HYPOPHYSECTOMY_RAT_UP 34 0.014 TAKEDA_NUP8_HOXA9_16D_DN 156 0.017 UEDA_MOUSE_LIVER 116 0.021

ADIP_DIFF_CLUSTER3 31 0.014 ZHAN_MMPC_SIM 39 0.017 NI2_MOUSE_UP 38 0.021

STANELLE_E2F1_UP 28 0.015 CELL_SURFACE_RECEPTOR_LINKED_

SIGNAL_TRANSDUCTION

120 0.018

GENOTOXINS_24HRS_DISCR 30 0.021

ST_P38_MAPK_PATHWAY 34 0.015 STRESS_ARSENIC_SPECIFIC_UP 127 0.021

GLYCINE_SERINE_AND_THREONINE_

METABOLISM

32 0.015

HSA03010_RIBOSOME 65 0.017 IDX_TSA_UP_CLUSTER2 58 0.021

P53PATHWAY 16 0.018 ET743_SARCOMA_72HRS_UP 57 0.021

TAKEDA_NUP8_HOXA9_16D_UP 119 0.015 CIRCADIAN_EXERCISE 40 0.018 JECHLINGER_EMT_UP 53 0.022

CALRES_MOUSE_NEOCORTEX_DN 61 0.015 PENG_RAPAMYCIN_UP 148 0.018 INSULIN_SIGNALING 92 0.022

ERKPATHWAY 29 0.022 BRENTANI_REPAIR 34 0.026 GNATENKO_PLATELET_UP 42 0.029

SMOOTH_MUSCLE_CONTRACTION 126 0.022 PASSERINI_ADHESION 35 0.026 ZUCCHI_EPITHELIAL_UP 39 0.03

ALCALAY_AML_NPMC_DN 168 0.022 APOPTOSIS 66 0.026 STRESSPATHWAY 25 0.03

HSA05218_MELANOMA 68 0.022 SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES 32 0.026 LEE_TCELLS3_UP 79 0.03

DNA_DAMAGE_SIGNALING 87 0.023 BASSO_GERMINAL_CENTER_CD40_DN 63 0.026 HDACI_COLON_TSABUT_DN 16 0.03

AGED_MOUSE_NEOCORTEX_DN 46 0.023 P53_BRCA1_UP 28 0.026 TNF_AND_FAS_NETWORK 16 0.031

BRCA1_SW480_UP 24 0.023 HIF1_TARGETS 32 0.026 UVB_NHEK1_UP 165 0.031

UV-CMV_UNIQUE_HCMV_6HRS_UP 97 0.023 BILE_ACID_BIOSYNTHESIS 25 0.027 SERUM_FIBROBLAST_CORE_UP 152 0.031

HSA04916_MELANOGENESIS 89 0.023 ROME_INSULIN_2F_UP 157 0.027 ELECTRON_TRANSPORT_CHAIN 87 0.032

PHOTOSYNTHESIS 20 0.024 PPARAPATHWAY 49 0.027 GLYCOGEN_METABOLISM 33 0.032

PROTEASOME_DEGRADATION 30 0.024 FSH_OVARY_MCV152_UP 58 0.027 HCC_SURVIVAL_GOOD_VS_POOR_DN 114 0.032

MAGRANGEAS_MULTIPLE_MYELOMA_

ASTIER_BCELL 53 0.032

PENG_GLUCOSE_DN 121 0.032 ST_MYOCYTE_AD_PATHWAY 23 0.024 ST_DICTYOSTELIUM_DISCOIDEUM_

CAMP_CHEMOTAXIS_PATHWAY

30 0.028

AGUIRRE_PANCREAS_CHR22 49 0.032

CMV_HCMV_TIMECOURSE_20HRS_DN 33 0.024 PARK_MSCS_DIFF 27 0.032

IDX_TSA_DN_CLUSTER5 37 0.024 CELL_CYCLE_CHECKPOINT 24 0.028 HYPOXIA_REVIEW 76 0.032

AMINOACYL_TRNA_BIOSYNTHESIS 20 0.025 SANSOM_APC_4_DN 50 0.028 HSA01032_GLYCAN_STRUCTURES_

DEGRADATION

27 0.032

LYSINE_DEGRADATION 27 0.025 TSA_HEPATOMA_CANCER_UP 39 0.028

ETSPATHWAY 16 0.025 INNEREAR_UP 36 0.028 AGUIRRE_PANCREAS_CHR9 20 0.033

TIS7_OVEREXP_DN 15 0.025 HSA00051_FRUCTOSE_AND_MANNOSE_

METABOLISM

34 0.028 0.028

ABBUD_LIF_DN 21 0.033

HDACI_COLON_CUR48HRS_UP 53 0.025 HYPERTROPHY_MODEL 17 0.034

UVC_HIGH_D2_DN 34 0.025 KUROKAWA_5FU_IFN_SENSITIVE_VS_

RESISTANT_DN

31 0.029

PROLIFERATION_GENES 224 0.034

IDX_TSA_UP_CLUSTER1 21 0.025 TAKEDA_NUP8_HOXA9_3D_UP 133 0.034

CMV_HCMV_TIMECOURSE_ALL_DN 231 0.025 GATA3PATHWAY 15 0.029 GALE_FLT3ANDAPL_DN 16 0.034

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

38

基因組 size p-value 基因組 size p-value 基因組 size p-value

TAVOR_CEBP_DN 31 0.034 WNTPATHWAY 23 0.038 FETAL_LIVER_ENRICHED_TRANSCRIPTION_

FACTORS

68 0.042

MUNSHI_MM_VS_PCS_UP 73 0.034 RETT_UP 28 0.038

ZMPSTE24_KO_UP 27 0.034 HSA01031_GLYCAN_STRUCTURES_

BIOSYNTHESIS_2

51 0.038

HDACI_COLON_TSA_UP 86 0.042

HDACI_COLON_BUT2HRS_UP 53 0.034 TSA_HEPATOMA_CANCER_DN 15 0.042

HSA00360_PHENYLALANINE_METABOLISM 23 0.034 CHANG_SERUM_RESPONSE_UP 128 0.039 HDACI_COLON_CUR_UP 89 0.042

EICOSANOID_SYNTHESIS 17 0.035 FRUCTOSE_AND_MANNOSE_METABOLISM 25 0.039 H2O2_CSBRESCUED_UP 51 0.042

PASSERINI_IMMUNE 23 0.035 SANSOM_APC_LOSS5_UP 65 0.039 CTNNB1_ONCOGENIC_SIGNATURE 58 0.042

MA_ATRA_EMP_UP 33 0.035 HDACI_COLON_BUT30MIN_DN 29 0.039 GOLDRATH_HP 117 0.043

GN_CAMP_GRANULOSA_UP 49 0.035 O6BG_RESIST_MEDULLOBLASTOMA_UP 22 0.039 SHEPARD_BMYB_MORPHOLINO_DN 139 0.043

IFN_BETA_UP 63 0.035 ET743_SARCOMA_6HRS_UP 28 0.039 NOS1PATHWAY 21 0.043

HDACI_COLON_BUT12HRS_UP 34 0.035 HSA00071_FATTY_ACID_METABOLISM 42 0.039 DIAB_NEPH_UP 59 0.043

GLUCONEOGENESIS 50 0.036 RACCYCDPATHWAY 22 0.04 TRYPTOPHAN_METABOLISM 51 0.044

SHIPP_FL_VS_DLBCL_DN 32 0.036 TPA_RESIST_LATE_UP 32 0.04 MUNSHI_MM_UP 64 0.044

CROONQUIST_IL6_STROMA_UP 37 0.036 AGED_RHESUS_DN 102 0.04 XU_CBP_DN 33 0.044

TRNA_SYNTHETASES 17 0.036 HSA04010_MAPK_SIGNALING_PATHWAY 230 0.04 HIPPOCAMPUS_DEVELOPMENT_

NEONATAL

24 0.044 TPA_SENS_MIDDLE_DN 234 0.036 HSA04330_NOTCH_SIGNALING_PATHWAY 37 0.04

5FU_RESIST_GASTRIC_UP 20 0.036 UBIQUITIN_MEDIATED_PROTEOLYSIS 21 0.041 JNK_DN 31 0.044

IDX_TSA_UP_CLUSTER5 79 0.036 BHATTACHARYA_ESC_UP 60 0.041 HSA05221_ACUTE_MYELOID_LEUKEMIA 52 0.044

HSA00251_GLUTAMATE_METABOLISM 27 0.036 ALCALAY_AML_NPMC_UP 126 0.041 DAVIES_MGUS_MM 34 0.045

ST_TUMOR_NECROSIS_FACTOR_PATHWAY 27 0.037 IRITANI_ADPROX_VASC 147 0.041 ADIP_DIFF_CLUSTER4 32 0.045

SHIPP_DLBCL_CURED_UP 28 0.037 LI_FETAL_VS_WT_KIDNEY_UP 169 0.041 TRANSLATION_FACTORS 33 0.046

METPATHWAY 35 0.037 TNFA_NFKB_DEP_UP 18 0.041 BLEO_HUMAN_LYMPH_HIGH_24HRS_UP 89 0.046

UVB_NHEK3_C6 26 0.037 OLDONLY_FIBRO_DN 40 0.041 AGED_MOUSE_CORTEX_DN 37 0.046

HSA05222_SMALL_CELL_LUNG_CANCER 85 0.037 OLD_FIBRO_DN 129 0.041 WNT_TARGETS 22 0.047

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

39

基因組 size p-value

MANALO_HYPOXIA_UP 90 0.047

GALINDO_ACT_UP 71 0.047

VERNELL_PRB_CLSTR2 16 0.047

CALRES_MOUSE_DN 40 0.047

LE_MYELIN_DN 85 0.048

GNATENKO_PLATELET 42 0.048

MEF2DPATHWAY 16 0.048

RUIZ_TENASCIN_TARGETS 74 0.048 KAMMINGA_EZH2_TARGETS 32 0.048 LVAD_HEARTFAILURE_UP 81 0.048

CMV_24HRS_UP 63 0.048

BIOPEPTIDESPATHWAY 36 0.049 SASAKI_TCELL_LYMPHOMA_VS_CD4_UP 154 0.049 OXSTRESS_RPE_H2O2HNE_DN 31 0.049

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

40

附錄 B-2: 改良型邊際維度檢定法 MD2 分析結果

基因組 size p-value 基因組 size p-value 基因組 size p-value

CORDERO_KRAS_KD_VS_CONTROL_DN 52 0 UVC_HIGH_D2_DN 34 0 MA_ATRA_EMP_DN 28 0.001

OVARIAN_INFERTILITY_GENES 25 0 UVC_HIGH_D6_DN 26 0 FERRARI_4HPR_UP 21 0.001

SHIPP_DLBCL_CURED_DN 32 0 MMS_MOUSE_LYMPH_HIGH_4HRS_UP 31 0 PARK_RARALPHA_UP 38 0.001

CCR5PATHWAY 17 0 HDACI_COLON_CUR_DN 35 0 LEI_MYB_REGULATED_GENES 225 0.001

LEE_MYC_UP 51 0 INOS_ALL_DN 70 0 WANG_MLL_CBP_VS_GMP_DN 37 0.001

FMLPPATHWAY 36 0 HDACI_COLON_CLUSTER6 27 0 ZHAN_MM_CD138_HP_VS_REST 34 0.001

CELL_CYCLE_REGULATOR 18 0 CALRES_MOUSE_DN 40 0 UVB_NHEK3_C8 62 0.001

HDACPATHWAY 29 0 ESR_FIBROBLAST_UP 49 0 AGEING_BRAIN_DN 112 0.001

NO1PATHWAY 27 0 HSA04210_APOPTOSIS 78 0 DAC_PANC50_UP 43 0.001

SIG_BCR_SIGNALING_PATHWAY 45 0 HSA04662_B_CELL_RECEPTOR_

SIGNALING_PATHWAY

59 0

ET743_SARCOMA_UP 60 0.001

OXIDATIVE_PHOSPHORYLATION 57 0 OXSTRESS_BREASTCA_UP 24 0.001

PROPANOATE_METABOLISM 30 0 HSA04720_LONG_TERM_POTENTIATION 64 0 HDACI_COLON_BUT48HRS_DN 89 0.001

REN_E2F1_TARGETS 33 0 GCRPATHWAY 17 0.001 CMV_8HRS_DN 42 0.001

GRANDVAUX_IRF3_DN 20 0 LEE_MYC_TGFA_UP 58 0.001 IDX_TSA_UP_CLUSTER1 21 0.001

ZHANG_EFT_EWSFLI1_UP 78 0 GLYCEROLIPID_METABOLISM 43 0.001 UVB_NHEK3_C7 52 0.001

STOSSI_ER_UP 47 0 BYSTROM_IL5_UP 40 0.001 IFNALPHA_RESIST_DN 15 0.001

GUO_HEX_DN 52 0 IGF1RPATHWAY 15 0.001 STRESS_TPA_SPECIFIC_UP 40 0.001

ZHAN_PCS_MULTIPLE_MYELOMA_SPKD 22 0 CK1PATHWAY 16 0.001 HSA00510_N_GLYCAN_BIOSYNTHESIS 32 0.001

HESS_HOXAANMEIS1_DN 52 0 POMEROY_MD_TREATMENT_GOOD_

VS_POOR_DN

24 0.001

ST_GA12_PATHWAY 22 0.002

DORSEY_DOXYCYCLINE_UP 30 0 HCC_SURVIVAL_GOOD_VS_POOR_DN 114 0.002

XU_CBP_DN 33 0 DORSAM_HOXA9_DN 29 0.001 HIVNEFPATHWAY 52 0.002

4NQO_UNIQUE_FIBRO_UP 21 0 VIPPATHWAY 26 0.001 PTDINSPATHWAY 20 0.002

ADIP_VS_PREADIP_DN 35 0 LEE_E2F1_UP 59 0.001 WNT_TARGETS 22 0.002

EGF_HDMEC_UP 43 0 STEFFEN_AML_PML_PLZF_TRGT 45 0.001 TNF_AND_FAS_NETWORK 16 0.002

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

41

基因組 size p-value 基因組 size p-value 基因組 size p-value

IGF1MTORPATHWAY 19 0.002 ELONGINA_KO_UP 138 0.003 AGUIRRE_PANCREAS_CHR19 67 0.005

ZHAN_MM_CD1_VS_CD2_DN 35 0.002 PARP_KO_UP 30 0.003 PROSTAGLANDIN_AND_LEUKOTRIENE_

METABOLISM

30 0.005

RAY_P210_DIFF 53 0.002 HSC_HSC_SHARED 162 0.003

IFN_BETA_GLIOMA_UP 58 0.002 HDACI_COLON_BUT16HRS_UP 31 0.003 SHIPP_FL_VS_DLBCL_DN 32 0.005

HDACI_COLON_SUL48HRS_DN 61 0.002 OXSTRESS_RPE_HNETBH_DN 44 0.003 NING_COPD_UP 135 0.005

TPA_RESIST_LATE_UP 32 0.002 HIPPOCAMPUS_DEVELOPMENT_PRENATAL 29 0.003 PENG_RAPAMYCIN_UP 148 0.005

BRG1_ALAB_UP 36 0.002 GAMMA-UV_FIBRO_UP 32 0.003 ST_PHOSPHOINOSITIDE_3_KINASE_

PATHWAY

31 0.005

ET743_SARCOMA_72HRS_UP 57 0.002 UVB_SCC_UP 77 0.003

WERNERONLY_FIBRO_DN 44 0.002 UVB_NHEK1_C2 21 0.003 NAKAJIMA_MCSMBP_EOS 27 0.005

HSA04916_MELANOGENESIS 89 0.002 HSA00360_PHENYLALANINE_METABOLISM 23 0.003 IRITANI_ADPROX_DN 57 0.005

CCR3PATHWAY 21 0.003 BYSTROM_IL5_DN 56 0.004 GENOTOXINS_24HRS_DISCR 30 0.005

GPCRPATHWAY 33 0.003 WELCSH_BRCA_UP 36 0.004 HPV31_UP 37 0.005

SMITH_HTERT_UP 95 0.003 RADIATION_SENSITIVITY 23 0.004 HDACI_COLON_BUT16HRS_DN 88 0.005

PROTEASOME_DEGRADATION 30 0.003 41BBPATHWAY 18 0.004 GAMMA_ESR_OLD_UNREG 23 0.005

ZUCCHI_EPITHELIAL_UP 39 0.003 PENG_GLUCOSE_DN 121 0.004 HDACI_COLON_TSABUT_UP 53 0.005

INSULINPATHWAY 21 0.003 NFATPATHWAY 51 0.004 AGED_MOUSE_HYPOTH_UP 41 0.005

LEE_CIP_UP 57 0.003 LYSINE_DEGRADATION 27 0.004 NAB_LUNG_UP 25 0.005

LIZUKA_L1_GR_G1 20 0.003 ASTIER_FN_DIFF 54 0.004 E2F3_ONCOGENIC_SIGNATURE 160 0.005

INSULIN_SIGNALING 92 0.003 GERY_CEBP_TARGETS 106 0.004 SRC_ONCOGENIC_SIGNATURE 50 0.005

KAMMINGA_EZH2_TARGETS 32 0.003 OXSTRESS_RPETHREE_DN 26 0.004 HSA00512_O_GLYCAN_BIOSYNTHESIS 20 0.005

UVB_NHEK3_C6 26 0.003 UVB_NHEK3_C2 35 0.004 INTEGRINPATHWAY 33 0.006

GENOTOXINS_ALL_24HRS_REG 24 0.003 TSA_HEPATOMA_CANCER_UP 39 0.004 BOQUEST_CD31PLUS_VS_CD31MINUS_UP 225 0.006

GENOTOXINS_ALL_24HRS_REG 24 0.003 TSA_HEPATOMA_CANCER_UP 39 0.004 BOQUEST_CD31PLUS_VS_CD31MINUS_UP 225 0.006

相關文件