dataOvarian1: Data on time-to-recurrence and 158 gene expressions for 912...
In joint.Cox: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis
Description
Usage
Format
Details
Source
References
Examples
Description
Meta-analytic data containing 158 gene expressions and time-to-relapse information for ovarian cancer patients.
The data include time-to-recurrence, residual tumour size (>=1cm> vs. <1cm), and associated 158 gene expressions.
The dataset is a subset of the curated ovarian data of Ganzfried et al (2013).
We prepared the dataset by using "patientselection.config" in "Curated ovarian data" around October 2016.
Usage
1
data("dataOvarian1")
Format
A data frame with 912 observations on the following 162 variables.
t.event: time-to-recurrence in days
event: event indicator (1=recurrence, 0=no recurrence)
group: study ID; group=4, 9, 12, or 16
debulk: residual tumour size (>=1cm> vs. <1cm)
ABI3BPa numeric vector
ADAM12a numeric vector
ADORA3a numeric vector
ANKRD27a numeric vector
AP2M1a numeric vector
AP3S1a numeric vector
ARHGAP28a numeric vector
ARHGAP29a numeric vector
ARTNa numeric vector
ASAP3a numeric vector
B4GALT5a numeric vector
BCAP31a numeric vector
BRD4a numeric vector
C1QTNF3a numeric vector
CALD1a numeric vector
CCNE1a numeric vector
CCNL1a numeric vector
CDC42a numeric vector
CDV3a numeric vector
CEBPBa numeric vector
CLIC4a numeric vector
COL10A1a numeric vector
COL11A1a numeric vector
COL16A1a numeric vector
COL3A1a numeric vector
COL5A1a numeric vector
COL5A2a numeric vector
COMPa numeric vector
CRISPLD2a numeric vector
CRYABa numeric vector
CSE1La numeric vector
CTSKa numeric vector
CXCL12a numeric vector of gene expressions.
The CXCL12 gene expression is a predictive biomarker of survival in ovarian cancer (Popple et al. 2012).
It has been known that CXCL12 promotes tumour growth, participates in tumour metastasis,
and suppresses tumour immunity (Kryczek et al. 2007).
The statistical significance of the CXCL12 expression on survival is first examined by Popple et al. (2012), and
is further confirmed by Ganzfried et al. (2013) based on the meta-analysis of 14 independent studies.
A meta-analysis using a joint model further confirmed that the expression of CXCL12 gene is predictive of
both cancer relapse and death (Emura et al. 2017; 2018).
CYR61a numeric vector
DCUN1D1a numeric vector
DDX27a numeric vector
DIAPH3a numeric vector
DNAJB4a numeric vector
DNAJC13a numeric vector
DNAJC8a numeric vector
DPYSL3a numeric vector
DVL3a numeric vector
EFNB2a numeric vector
EIF3Ka numeric vector
ELK1a numeric vector
ENPP1a numeric vector
EPYCa numeric vector
FABP4a numeric vector
FAM69Aa numeric vector
FAPa numeric vector
FERMT2a numeric vector
FGF1a numeric vector
FN1a numeric vector
FOSL2a numeric vector
FSTL1a numeric vector
GABRG3a numeric vector
GAS1a numeric vector
GFRA1a numeric vector
GFRA3a numeric vector
GJC1a numeric vector
GLIPR1a numeric vector
GPATCH1a numeric vector
HLTFa numeric vector
HP1BP3a numeric vector
HSD17B6a numeric vector
INHBAa numeric vector
ITGB1a numeric vector
JUNa numeric vector
KIAA0226a numeric vector
KIAA0355a numeric vector
KIAA1598a numeric vector
KINa numeric vector
KLHL25a numeric vector
KPNA6a numeric vector
KRT7a numeric vector
KRTAP5.8a numeric vector
L2HGDHa numeric vector
LGALS1a numeric vector
LOXa numeric vector
LPPa numeric vector
LUMa numeric vector
LUZP1a numeric vector
MAP7D1a numeric vector
MAPRE1a numeric vector
MCL1a numeric vector
MEOX2a numeric vector
METTL9a numeric vector
MFN1a numeric vector
MICAL2a numeric vector
MMP12a numeric vector
MRPS22a numeric vector
MXD1a numeric vector
MXRA8a numeric vector
N4BP2L2a numeric vector
NCOA3a numeric vector of gene expressions.
The NCOA3 gene encodes a nuclear receptor coactivator, and amplification of the gene
occurs in breast and ovarian cancers (Anzick et al. 1997).
The overexpression of NCOA3 is associated with tumor size (Spears et al. 2012) and
tamoxifen resistance (Osborne et al. 2003), which are involved in the progression.
Yoshida et al. (2005) reported that NCOA3 could contribute to ovarian cancer progression by promoting cell migration.
In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse
(Coefficient=0.194, P-value<0.00001) and time-to-death (Coefficient=0.237, P-value<0.00001).
This result is consistent with the function of these reports.
NDRG3a numeric vector
NINJ1a numeric vector
NNMTa numeric vector
NOTCH2a numeric vector
NPYa numeric vector
NTMa numeric vector
NUAK1a numeric vector
OATa numeric vector
OLFML2Ba numeric vector
PARD3a numeric vector
PCYT1Aa numeric vector
PDE1Aa numeric vector
PDGFDa numeric vector
PDPNa numeric vector of gene expressions. The PDPN gene encodes the podoplanin protein.
It is reported that cancer cells with higher PDPN expression have higher malignant potential due to enhanced platelet aggregation,
which promotes alteration of metastasis, cell motility, and epithelial-mesenchymal transition (Shindo et al. 2013).
Zhang et al. (2011) reported that overexpression of PDPN in fibroblasts is significantly associated with a poor prognosis in ovarian carcinoma.
In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse
(Coefficient=0.222, P-value<0.00001) and time-to-death (Coefficient=0.161, P-value<0.0001).
PGRMC1a numeric vector
PLAUa numeric vector
PLOD2a numeric vector
PLSCR4a numeric vector
POSTNa numeric vector
PPICa numeric vector
PRDM2a numeric vector
PSMC4a numeric vector
RAB22Aa numeric vector
RAB31a numeric vector
RAB32a numeric vector
RARRES1a numeric vector
RPS16a numeric vector
SERPINE1a numeric vector
SGK1a numeric vector
SH3PXD2Aa numeric vector
SKILa numeric vector
SLC12A8a numeric vector
SPARCa numeric vector
SPHK1a numeric vector
STAU1a numeric vector
SULF1a numeric vector
SUPT5Ha numeric vector
TAGLNa numeric vector
TBCBa numeric vector
TEAD1a numeric vector of gene expressions.
TEAD1 encodes a ubiquitous transcriptional enhancer factor that is a member of the TEA/ATTS domain family.
It is reported that the protein level of TEAD1 was associated with poor prognosis in prostate cancer patients (Knight et al. 2008).
In Emura et al. (2018), the overexpression of the gene was highly associated with
time-to-relapse (Coefficient=0.195, P-value<0.00001) and time-to-death (Coefficient=0.223, P-value<0.00001).
TESK1a numeric vector
TGM5a numeric vector
THEMIS2a numeric vector
TIMP2a numeric vector of gene expressions. TIMP2 is a member of the TIMP gene family.
The proteins encoded by this gene family are natural inhibitors of the matrix metalloproteinases (MMPs).
MMPs and their inhibitors (TIMP gene family) play an important regulatory role in the homeostasis of
the extracellular matrix (Halon et al. 2012). In addition to inhibitors of MMPs,
TIMP2 has additional functions that are associated with cell proliferation and survival (Bourboulia et al., 2011).
In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.235, P-value<0.00001).
TIMP3a numeric vector
TJP1a numeric vector
TP73.AS1a numeric vector
TPM2a numeric vector
TPM4a numeric vector
TSC22D2a numeric vector
TUBB2Aa numeric vector
TUBB6a numeric vector
TUFT1a numeric vector
URI1a numeric vector
USP48a numeric vector
VCANa numeric vector
VSIG4a numeric vector
YWHABa numeric vector of gene expressions.
YWHAB encodes a protein belonging to the 14-3-3 family of proteins,
members of which mediate signal transduction by binding to phosphoserine-containing proteins.
It is reported that the protein of YWHAB can regulate cell survival, proliferation, and motility (Tzivion 2006).
Actually, it is reported that overexpression of this gene promotes tumor progression and was associated with
extrahepatic metastasis and worse survival in hepatocellular carcinoma (Liu et al. 2011).
In Emura et al. (2018), the overexpression of the gene was highly associated with
time-to-relapse (Coefficient=0.169, P-value<0.0001) and time-to-death (Coefficient=0.263, P-value<0.00001)
ZFP36a numeric vector
ZFP36L2a numeric vector
ZMYM1a numeric vector
ZNF148a numeric vector
ZNF79a numeric vector
Details
4 studies are combined (group=4, 9, 12, and 16). The numbers 4, 9, 12 and 16 corresponds to the IDs
from the original data of Ganzfried et al. (2013).
Source
Ganzfried BF et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013.
References
Bourboulia D, et al. (2011), Endogenous angiogenesis inhibitor blocks tumor growth via direct and indirect effects on tumor microenvironment. Am J Pathol 179:2589-600
Emura T, Nakatochi M, Murotani K, Rondeau V (2017), A joint frailty-copula model between tumour progression and death for meta-analysis,
Stat Methods Med Res 26(6):2649-66
Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V (2018), Personalized dynamic prediction of death according to tumour progression and high-dimensional
genetic factors: meta-analysis with a joint model, Stat Methods Med Res 27(9):2842-58
Ganzfried BF et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013.
Halon A, et al. (2012), Enhanced immunoreactivity of TIMP-2 in the stromal compartment of tumor as a marker of favorable prognosis in ovarian cancer patients.
J Histochem Cytochem 60:491-501
Knight JF, et al. (2008), TEAD1 and c-Cbl are novel prostate basal cell markers that correlate with poor clinical outcome in prostate cancer. Br J Cancer 99:1849-58
Kryczek I, et al. (2007), Stroma-derived factor (SDF-1/CXCL12) and human tumor pathogenesis. Am J Physiol 292:987-95
Liu TA, et al. (2011), Increased expression of 14-3-3beta promotes tumor progression and predicts extrahepatic metastasis and worse survival
in hepatocellular carcinoma. Am J Pathol 179:2698-708
Osborne CK, et al. (2003), Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer. J Natl Cancer Inst 95:353-61
Popple A, et al. (2012), The chemokine, CXCL12, is an independent predictor of poor survival in ovarian cancer. Br J Cancer 106:1306-13
Shindo K, et al. (2013), Podoplanin expression in cancer-associated fibroblasts enhances tumor progression of invasive ductal carcinoma of the pancreas. Mol Cancer 12:168
Tzivion G, et al. (2006), 14-3-3 proteins as potential oncogenes. Semin Cancer Biol 16:203-13
Yoshida H, et al. (2005), Steroid receptor coactivator-3, a homolog of Taiman that controls cell migration in the Drosophila ovary,
regulates migration of human ovarian cancer cells. Mol Cell Endocrinol 245:77-85
Zhang Y, et al. (2011), Ovarian cancer-associated fibroblasts contribute to epithelial ovarian carcinoma metastasis by promoting angiogenesis,
lymphangiogenesis and tumor cell invasion. Cancer Lett 303:47-55
Examples
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data(dataOvarian1)
######## univariate Cox ##########
t.event=dataOvarian1$t.event
event=dataOvarian1$event
X.mat=dataOvarian1[,-c(1,2,3,4)] ## gene expression
Symbol=colnames(dataOvarian1)[-c(1,2,3,4)] ## gene symbol
p=ncol(X.mat)
P_value=coef=NULL
for(j in 1:p){
res=summary(coxph(Surv(t.event,event)~X.mat[,j]))$coefficients
P_value=c(P_value,res[5])
coef=c(coef,res[1])
}
data.frame( gene=Symbol[order(P_value)], P=P_value[order(P_value)],
coef=round(coef[order(P_value)],3) )
joint.Cox documentation built on Feb. 4, 2022, 5:08 p.m.
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Description Usage Format Details Source References Examples
Description
Meta-analytic data containing 158 gene expressions and time-to-relapse information for ovarian cancer patients. The data include time-to-recurrence, residual tumour size (>=1cm> vs. <1cm), and associated 158 gene expressions. The dataset is a subset of the curated ovarian data of Ganzfried et al (2013). We prepared the dataset by using "patientselection.config" in "Curated ovarian data" around October 2016.
Usage
1 | data("dataOvarian1")
|
Format
A data frame with 912 observations on the following 162 variables.
t.event: time-to-recurrence in days
event: event indicator (1=recurrence, 0=no recurrence)
group: study ID; group=4, 9, 12, or 16
debulk: residual tumour size (>=1cm> vs. <1cm)
ABI3BPa numeric vector
ADAM12a numeric vector
ADORA3a numeric vector
ANKRD27a numeric vector
AP2M1a numeric vector
AP3S1a numeric vector
ARHGAP28a numeric vector
ARHGAP29a numeric vector
ARTNa numeric vector
ASAP3a numeric vector
B4GALT5a numeric vector
BCAP31a numeric vector
BRD4a numeric vector
C1QTNF3a numeric vector
CALD1a numeric vector
CCNE1a numeric vector
CCNL1a numeric vector
CDC42a numeric vector
CDV3a numeric vector
CEBPBa numeric vector
CLIC4a numeric vector
COL10A1a numeric vector
COL11A1a numeric vector
COL16A1a numeric vector
COL3A1a numeric vector
COL5A1a numeric vector
COL5A2a numeric vector
COMPa numeric vector
CRISPLD2a numeric vector
CRYABa numeric vector
CSE1La numeric vector
CTSKa numeric vector
CXCL12a numeric vector of gene expressions. The CXCL12 gene expression is a predictive biomarker of survival in ovarian cancer (Popple et al. 2012). It has been known that CXCL12 promotes tumour growth, participates in tumour metastasis, and suppresses tumour immunity (Kryczek et al. 2007). The statistical significance of the CXCL12 expression on survival is first examined by Popple et al. (2012), and is further confirmed by Ganzfried et al. (2013) based on the meta-analysis of 14 independent studies. A meta-analysis using a joint model further confirmed that the expression of CXCL12 gene is predictive of both cancer relapse and death (Emura et al. 2017; 2018).
CYR61a numeric vector
DCUN1D1a numeric vector
DDX27a numeric vector
DIAPH3a numeric vector
DNAJB4a numeric vector
DNAJC13a numeric vector
DNAJC8a numeric vector
DPYSL3a numeric vector
DVL3a numeric vector
EFNB2a numeric vector
EIF3Ka numeric vector
ELK1a numeric vector
ENPP1a numeric vector
EPYCa numeric vector
FABP4a numeric vector
FAM69Aa numeric vector
FAPa numeric vector
FERMT2a numeric vector
FGF1a numeric vector
FN1a numeric vector
FOSL2a numeric vector
FSTL1a numeric vector
GABRG3a numeric vector
GAS1a numeric vector
GFRA1a numeric vector
GFRA3a numeric vector
GJC1a numeric vector
GLIPR1a numeric vector
GPATCH1a numeric vector
HLTFa numeric vector
HP1BP3a numeric vector
HSD17B6a numeric vector
INHBAa numeric vector
ITGB1a numeric vector
JUNa numeric vector
KIAA0226a numeric vector
KIAA0355a numeric vector
KIAA1598a numeric vector
KINa numeric vector
KLHL25a numeric vector
KPNA6a numeric vector
KRT7a numeric vector
KRTAP5.8a numeric vector
L2HGDHa numeric vector
LGALS1a numeric vector
LOXa numeric vector
LPPa numeric vector
LUMa numeric vector
LUZP1a numeric vector
MAP7D1a numeric vector
MAPRE1a numeric vector
MCL1a numeric vector
MEOX2a numeric vector
METTL9a numeric vector
MFN1a numeric vector
MICAL2a numeric vector
MMP12a numeric vector
MRPS22a numeric vector
MXD1a numeric vector
MXRA8a numeric vector
N4BP2L2a numeric vector
NCOA3a numeric vector of gene expressions. The NCOA3 gene encodes a nuclear receptor coactivator, and amplification of the gene occurs in breast and ovarian cancers (Anzick et al. 1997). The overexpression of NCOA3 is associated with tumor size (Spears et al. 2012) and tamoxifen resistance (Osborne et al. 2003), which are involved in the progression. Yoshida et al. (2005) reported that NCOA3 could contribute to ovarian cancer progression by promoting cell migration. In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.194, P-value<0.00001) and time-to-death (Coefficient=0.237, P-value<0.00001). This result is consistent with the function of these reports.
NDRG3a numeric vector
NINJ1a numeric vector
NNMTa numeric vector
NOTCH2a numeric vector
NPYa numeric vector
NTMa numeric vector
NUAK1a numeric vector
OATa numeric vector
OLFML2Ba numeric vector
PARD3a numeric vector
PCYT1Aa numeric vector
PDE1Aa numeric vector
PDGFDa numeric vector
PDPNa numeric vector of gene expressions. The PDPN gene encodes the podoplanin protein. It is reported that cancer cells with higher PDPN expression have higher malignant potential due to enhanced platelet aggregation, which promotes alteration of metastasis, cell motility, and epithelial-mesenchymal transition (Shindo et al. 2013). Zhang et al. (2011) reported that overexpression of PDPN in fibroblasts is significantly associated with a poor prognosis in ovarian carcinoma. In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.222, P-value<0.00001) and time-to-death (Coefficient=0.161, P-value<0.0001).
PGRMC1a numeric vector
PLAUa numeric vector
PLOD2a numeric vector
PLSCR4a numeric vector
POSTNa numeric vector
PPICa numeric vector
PRDM2a numeric vector
PSMC4a numeric vector
RAB22Aa numeric vector
RAB31a numeric vector
RAB32a numeric vector
RARRES1a numeric vector
RPS16a numeric vector
SERPINE1a numeric vector
SGK1a numeric vector
SH3PXD2Aa numeric vector
SKILa numeric vector
SLC12A8a numeric vector
SPARCa numeric vector
SPHK1a numeric vector
STAU1a numeric vector
SULF1a numeric vector
SUPT5Ha numeric vector
TAGLNa numeric vector
TBCBa numeric vector
TEAD1a numeric vector of gene expressions. TEAD1 encodes a ubiquitous transcriptional enhancer factor that is a member of the TEA/ATTS domain family. It is reported that the protein level of TEAD1 was associated with poor prognosis in prostate cancer patients (Knight et al. 2008). In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.195, P-value<0.00001) and time-to-death (Coefficient=0.223, P-value<0.00001).
TESK1a numeric vector
TGM5a numeric vector
THEMIS2a numeric vector
TIMP2a numeric vector of gene expressions. TIMP2 is a member of the TIMP gene family. The proteins encoded by this gene family are natural inhibitors of the matrix metalloproteinases (MMPs). MMPs and their inhibitors (TIMP gene family) play an important regulatory role in the homeostasis of the extracellular matrix (Halon et al. 2012). In addition to inhibitors of MMPs, TIMP2 has additional functions that are associated with cell proliferation and survival (Bourboulia et al., 2011). In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.235, P-value<0.00001).
TIMP3a numeric vector
TJP1a numeric vector
TP73.AS1a numeric vector
TPM2a numeric vector
TPM4a numeric vector
TSC22D2a numeric vector
TUBB2Aa numeric vector
TUBB6a numeric vector
TUFT1a numeric vector
URI1a numeric vector
USP48a numeric vector
VCANa numeric vector
VSIG4a numeric vector
YWHABa numeric vector of gene expressions. YWHAB encodes a protein belonging to the 14-3-3 family of proteins, members of which mediate signal transduction by binding to phosphoserine-containing proteins. It is reported that the protein of YWHAB can regulate cell survival, proliferation, and motility (Tzivion 2006). Actually, it is reported that overexpression of this gene promotes tumor progression and was associated with extrahepatic metastasis and worse survival in hepatocellular carcinoma (Liu et al. 2011). In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.169, P-value<0.0001) and time-to-death (Coefficient=0.263, P-value<0.00001)
ZFP36a numeric vector
ZFP36L2a numeric vector
ZMYM1a numeric vector
ZNF148a numeric vector
ZNF79a numeric vector
Details
4 studies are combined (group=4, 9, 12, and 16). The numbers 4, 9, 12 and 16 corresponds to the IDs from the original data of Ganzfried et al. (2013).
Source
Ganzfried BF et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013.
References
Bourboulia D, et al. (2011), Endogenous angiogenesis inhibitor blocks tumor growth via direct and indirect effects on tumor microenvironment. Am J Pathol 179:2589-600
Emura T, Nakatochi M, Murotani K, Rondeau V (2017), A joint frailty-copula model between tumour progression and death for meta-analysis, Stat Methods Med Res 26(6):2649-66
Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V (2018), Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: meta-analysis with a joint model, Stat Methods Med Res 27(9):2842-58
Ganzfried BF et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013.
Halon A, et al. (2012), Enhanced immunoreactivity of TIMP-2 in the stromal compartment of tumor as a marker of favorable prognosis in ovarian cancer patients. J Histochem Cytochem 60:491-501
Knight JF, et al. (2008), TEAD1 and c-Cbl are novel prostate basal cell markers that correlate with poor clinical outcome in prostate cancer. Br J Cancer 99:1849-58
Kryczek I, et al. (2007), Stroma-derived factor (SDF-1/CXCL12) and human tumor pathogenesis. Am J Physiol 292:987-95
Liu TA, et al. (2011), Increased expression of 14-3-3beta promotes tumor progression and predicts extrahepatic metastasis and worse survival in hepatocellular carcinoma. Am J Pathol 179:2698-708
Osborne CK, et al. (2003), Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer. J Natl Cancer Inst 95:353-61
Popple A, et al. (2012), The chemokine, CXCL12, is an independent predictor of poor survival in ovarian cancer. Br J Cancer 106:1306-13
Shindo K, et al. (2013), Podoplanin expression in cancer-associated fibroblasts enhances tumor progression of invasive ductal carcinoma of the pancreas. Mol Cancer 12:168
Tzivion G, et al. (2006), 14-3-3 proteins as potential oncogenes. Semin Cancer Biol 16:203-13
Yoshida H, et al. (2005), Steroid receptor coactivator-3, a homolog of Taiman that controls cell migration in the Drosophila ovary, regulates migration of human ovarian cancer cells. Mol Cell Endocrinol 245:77-85
Zhang Y, et al. (2011), Ovarian cancer-associated fibroblasts contribute to epithelial ovarian carcinoma metastasis by promoting angiogenesis, lymphangiogenesis and tumor cell invasion. Cancer Lett 303:47-55
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data(dataOvarian1)
######## univariate Cox ##########
t.event=dataOvarian1$t.event
event=dataOvarian1$event
X.mat=dataOvarian1[,-c(1,2,3,4)] ## gene expression
Symbol=colnames(dataOvarian1)[-c(1,2,3,4)] ## gene symbol
p=ncol(X.mat)
P_value=coef=NULL
for(j in 1:p){
res=summary(coxph(Surv(t.event,event)~X.mat[,j]))$coefficients
P_value=c(P_value,res[5])
coef=c(coef,res[1])
}
data.frame( gene=Symbol[order(P_value)], P=P_value[order(P_value)],
coef=round(coef[order(P_value)],3) )
|
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