| Type: | Package |
| Title: | Signature Survival Analysis |
| Version: | 1.0.0 |
| Date: | 2023-07-17 |
| Maintainer: | Yuan-De Tan <tanyuande@gmail.com> |
| Description: | When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. Weights are calculated using the survival p-values of genes and are utilized to calculate expression values of the signature across the selected genes in all patients in a cohort. A Single or multiple univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort can be performed by using the gene-expression signature and visualized using our survival plots. |
| License: | GPL (≥ 3) |
| Depends: | R(≥ 3.5.0) |
| Imports: | stats, utils,graphics,grDevices,dplyr,forestplot, gplots, gtools, survival,survminer,ggplot2 |
| Suggests: | Rmisc |
| LazyLoad: | yes |
| NeedsCompilation: | no |
| Encoding: | UTF-8 |
| LazyData: | true |
| Packaged: | 2023-07-18 13:18:21 UTC; yxt477 |
| Author: | Yuan-De Tan  [aut,
cre],
Yuguang Ban [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2023-07-19 11:10:02 UTC |
Signature Survival Analysis
Description
When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. Weights are calculated using the survival p-values of genes and are utilized to calculate expression values of the signature across the selected genes in all patients in a cohort. A Single or multiple univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort can be performed by using the gene-expression signature and visualized using our survival plots.
Details
The DESCRIPTION file:
| Package: | signatureSurvival |
| Type: | Package |
| Title: | Signature Survival Analysis |
| Version: | 1.0.0 |
| Date: | 2023-07-17 |
| Authors@R: | c(person(given = "Yuan-De", family = "Tan", role = c("aut", "cre"), email = "tanyuande@gmail.com", comment = c(ORCID = "0000-0002-0364-2223")), person(given = "Yuguang", family = "Ban", role = "ctb")) |
| Maintainer: | Yuan-De Tan <tanyuande@gmail.com> |
| Description: | When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. Weights are calculated using the survival p-values of genes and are utilized to calculate expression values of the signature across the selected genes in all patients in a cohort. A Single or multiple univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort can be performed by using the gene-expression signature and visualized using our survival plots. |
| License: | GPL(>=3) |
| Depends: | R(>= 3.5.0) |
| Imports: | stats, utils,graphics,grDevices,dplyr,forestplot, gplots, gtools, survival,survminer,ggplot2 |
| Suggests: | Rmisc |
| LazyLoad: | yes |
| NeedsCompilation: | no |
| Encoding: | UTF-8 |
| LazyData: | true |
| Packaged: | 2023-07-17 13:44:39 UTC; yxt477 |
| Author: | Yuan-De Tan [aut, cre] (<https://orcid.org/0000-0002-0364-2223>), Yuguang Ban [ctb] |
Index of help topics:
GSE50081 Survival data from cohort GSE50081
MKMplot Multivariate Kaplan-Meier survival curve plot
MMKMplot Multiple multivariate Kaplan-Meier survival
curve plots
MUKMplot Function multiple univariate Kaplan-Meier
survival curve plots
MVKMresult Multivariate survival analysis with multiple
specified independent variables
SKMCresult Univariate Cox proportional hazard survival
analysis with a specified independent variable
TCGA_forestplt Data for forestplot
TCGA_survivalData TCGA data for survival analysis
TS_signature A signature constructed with a set of tumor
suppressor genes
UKMplot Univariate Kaplan-Meier survival curve plot
musvtest Multiple univariate suvival tests with a set of
genes
mvstest Multivariate Cox proportional hazard survival
analyses with multiple genes
results results of univariate Cox proportional hazard
analysis of patients with ADC in three cohorts.
signatureExp Signature expression or signature score
signatureSurvival-package
Signature Survival Analysis
signature_weight Weights of genes in a signature
survivalForest Forestplot for result of multivariate Cox
proportional hazard survival analysis
weight Caculation of Weights for signature genes
This package is used to create up and down signatures,do univariate or multivariate survival analysis and make forest plot for the results of multivariate Cox proportional hazard survival analysis. The steps for screening signature are as following: At step1, users should perform differential expression analysis of genes in one or multiple microarray datasets or RNA-seq count datasets or the other expression datasets and then mark these differentally expressed (DE) genes selected with "up" and "down" using negative or positive t-values. At step2, retrieve survival (or clinical data) using these DE genes and construct a new survival data(age, sex, stages/smoking, month, status, and DE genes in column and patients in row). Note that expression data ofthe DE genes are listed in the right side in the survival data. At step 3, perform musvtest.R (multiple univariate survival tests) or mvstest (multiple multivariate survival tests) with covariates age, sex and/smoking ect. Use p-value to select genes in big difference between low and high-survival probalities and use HR and up and down-regulation to classify genes selected into up and down groups in multiple cohorts. At step 4,use weight.R to calculate weight values of each gene in signature and use signatureExp.R to caculate expression values of signature in all patients and move the expression values to the last column in survival data. At step 5, perform MUKMplot.R or MMKMplot.R on signature in the survival data to plot Kaplan-Meier survival curves.
Author(s)
Yuan-De Tan [aut, cre] (<https://orcid.org/0000-0002-0364-2223>), Yuguang Ban [ctb]
Maintainer: Yuan-De Tan <tanyuande@gmail.com>
Examples
data(GSE50081)
res<-musvtest(sdata=GSE50081,stn=3500,gn=3506,time="month",status="status",
quant=c("no",-0.2,0.2))
Survival data from cohort GSE50081
Description
This dataset was derived from cohort GSE50081. It contains observation of 129 patients and 3577 probeids (without gene names),two signaturs "up" and "down" and 17 clinical variables suchas sex, age, smoking, stages, status(death, relapse, moth). 3577 probeids listgene expression values in 129 patients.
Usage
data("GSE50081")Format
A data frame with 129 observations on the following 3596 variables.
sex1a character vector
sexa numeric vector
cell_typea character vector
t.stagea numeric vector
n.stagea numeric vector
m.stagea numeric vector
stagea character vector
status1a numeric vector
agea numeric vector
smokinga character vector
month1a numeric vector
status1.1a character vector
statusa numeric vector
montha numeric vector
recurrencea character vector
X.Sample_characteristics_ch1a character vector
ID_REFa character vector
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upa numeric vector
downa numeric vector
Source
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50081
References
Der SD, Sykes J, Pintilie M, Zhu CQ et al. Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol 2014 Jan;9(1):59-64. PMID: 24305008.
Examples
data(GSE50081)
Multivariate Kaplan-Meier survival curve plot
Description
Function MKMplot plots the outcome of multivariate survival analysis of patients in a cohort. Independent variable is specified as a gene or a signature. Covariates are sex, age, stage, genotype etc. If user needs one or multiple covariates, for example,smoking, tumor stage, etc., user can use X to specify the covariates.
Usage
MKMplot(data, mol, X, HR="hazard risk",time = "month", status = "status",
sml="hv",quant=c("No",-0.2,0.2),plotmethod="plot",adjx)
Arguments
data |
survival data including clinical data such as status, month, age, sex and/or smoking and expression data of genes or signatures. |
mol |
char value specified a gene symbol or a gene id or a signature existed in data or int value for a column number of a specified gene or a signature in data. |
X |
a string or string vector for one or multiple specified covariates. For example, X="smoking" or X=c("age","sex",smoking","genotype"). 10 covariates are limited in the current version. All covariates should be listed in the data. |
HR |
a string to specify hazard risk or hazard rate. If HR="Hazard risk" or
"Hazard_risk", then plot would show coefficient, which has negative, zero, or
positive value. The domain is (- |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
status |
string to specify status name in the input survival data. For example, status may be "death", "relapse", or "status", depending on user's data. The default value is "status". |
sml |
surv.median.line: character vector for drawing a horizontal/vertical line at median survival. Allowed values include one of c("none", "hv", "h", "v"). v: vertical, h:horizontal. The value of sml is used in only ggsurvplot. The default is "hv". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose qunatile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
plotmethod |
string value: choose a method to plot Kaplan-Meier survival curves. In current version, we have two methods for chose: plot and ggsurvplot. The defauld is plot method. In plot method, we show p-value for result of Ward t-test but in ggsurvplot, we show p-value for the result of log-rank test. |
adjx |
numeric value used to adjust x-axis position of p-value and HR in only plot. |
Details
survival data contain clinical data such as status, month, age, sex and/or smoking and expression of gene or protein. The status may be death, recurrence or relapse and must be a binary variable: 1 for an event (such as death) or 0 for no event (such as alive). The month, age, and gene are numeric continuous variables but sex is a binary variable. The other covariates may be numeric continuous or binary variables. They are listed in column and patients or observations are listed in row.
Value
output a survival curve plot of multivariate survival analysis.
Note
To plot outcome of univariate survival, user can use UKMplot. All inputting parametes are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
require(survival)
require(ggplot2)
data(GSE50081)
MKMplot(data=GSE50081,mol=56,X=c("t.stage","n.stage", "m.stage"),time="month",
status="status1",sml="none",quant=c("No",-0.2,0.2),plotmethod="ggsurvplot",
adjx = 5)
Multiple multivariate Kaplan-Meier survival curve plots
Description
Function MMKMplot plots outcomes of multivariate survival analyses of patients with covariates age and sex in a cohort with a specified set of genes. Each gene has a plot and each plot takes a page.
Usage
MMKMplot(sdata, stn, gn, X, HR="hazard risk", status = "status",
time = "month", sml="hv",quant=c("No",-0.2,0.2),plotmethod="plot",adjx,
outdir, file)
Arguments
sdata |
survival data including clinical data such as status, month, age, sex, and expressions of genes or expressions of proteins. Status such as death, recurrence or relapse must be 1 for event (such as death) or 0 for no event (such as alive), month is numeric data, age is numeric data, sex is binary data and genes are numeric data. They are listed in columns and patients are listed in rows. |
stn |
character value specifying gene symbol or gene id existed in sdata or int value specifying column number for the first gene in survival data. |
gn |
character value specifying gene symbol or gene id existed in sdata or int value specifying column number for the last gene in survival data. |
X |
string vector for covariate(s), an option with inputting names of one, two, three or more covariates. For example, X="smoking", X=c("age","sex","smoking","stage_n" ). The number of covariates is limited to 10. All covariates should be listed in the data. |
HR |
a string for specifying hazard risk or hazard rate. If HR="Hazard risk" or
"Hazard_risk", then plot would show coefficient, which has negative, zero, or
positive value. The domain is (- |
status |
string for a specifying status name. For example, status may be death, relapse or recurrence. User can set status="death", "relapse" or "recurrence" or "status", depending on the data. |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
sml |
surv.median.line: character vector for drawing a horizontal/vertical line at median survival. Allowed values include one of c("none", "hv", "h", "v"). v: vertical, h:horizontal.The value of sml is used in only ggsurvplot. The default is "hv". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
plotmethod |
string value: choose a method to plot Kaplan-Meier survival curves. In current version, we have two methods for choice: plot and ggsurvplot. The defauld is plot method. In plot method, we show p-value for result of Ward t-test but in ggsurvplot, we show p-value for the result of log-rank-sum test. |
adjx |
numeric value used to adjust x-axis position of p-value and HR in only plot. |
outdir |
string, a path to save a file. If user uses setwd to set a dir for saving file, the outdir can be set "NULL", MMKMplot can automatically save pdf file in this dir folder in user local computer. |
file |
string for file name. If user sets a path with setwd or a path to outdir, then file just sets file name. The file is a pdf file containing all plot pages. |
Value
output multiple pdf pages for multiple survival curve plots and saved in a outputting pdf file. If file ="NULL", MUKMplot would output the last survival plot page.
Note
User may not perform this function if unnecessary.
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
data(GSE50081)
XX=c("age","sex","t.stage","n.stage","m.stage")
hr="hazard rate"
MMKMplot(sdata=GSE50081,stn="X232578_at", gn="down",X=XX,HR=hr,status="status1",
time="month", quant=c("yes",0.25,0.75),plotmethod="plot",adjx = 3.5,file =
"NULL")
Function multiple univariate Kaplan-Meier survival curve plots
Description
MUKMplot plots the outcomes of univariate survival analyses of patients in a cohort using all selected genes.
Usage
MUKMplot(svdata, stn, gn,HR="hazard risk",time = "month", status = "status",
sml="hv",quant=c("No",-0.2,0.2),plotmethod="plot",adjx, outdir =
"NULL", file)
Arguments
svdata |
survival data including clinical data such as status, month, and expressions of genes or proteins. Status such as death, recurrence or relapse must be 1 for event (such as death) or 0 for no event (such as alive). Genes or signatures are numeric continuous variables. They are listed in columns and patients or observations are listed in rows. |
stn |
character value specifying gene symbol or gene id existed in sdata or int value specifying column number for the first gene in survival data. |
gn |
character value specifying gene symbol or gene id existed in sdata or int value specifying column number for the first gene in survival data. |
HR |
a string for specifying hazard risk or hazard rate. If HR="Hazard risk" or
"Hazard_risk", then plot would show coefficient, which has negative, zero, or
positive value. The domain is (- |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
status |
string for a specifying status name. For example, status may be death, relapse or recurrence. User can set status="death", "relapse" or "recurrence" or "status", depending on the data. |
sml |
surv.median.line: character vector for drawing a horizontal/vertical line at median survival. Allowed values include one of c("none", "hv", "h", "v"). v: vertical, h:horizontal.The value of sml is used in only ggsurvplot. The default is "hv". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
plotmethod |
string value: choose a method to plot Kaplan-Meier survival curves. In current version, we have two methods for choice: "plot" and "ggsurvplot". The defauld is plot method. In plot method, we show p-value for result of Ward-test but in ggsurvplot, we show p-value for the result of log-rank test. |
adjx |
numeric value used to adjust x-axis position of p-value and HR in only plot. |
outdir |
string for a folder path to save the image file. If user uses setwd to set a path to save the image file, then outdir is not necessary to be given. The default outdir is "NULL". |
file |
string for a file name or path and file name. If user sets a path with setwd or set path to outdir, then file just contain file name. The file is pdf file containing all plot pages. |
Value
output multiple pdf pages for multiple survival curve plots and saved in a outputting pdf file. If file ="NULL", MUKMplot would output the last survival plot page.
Note
User can use MKMplot to plot all signatures and gives a plot file name to save all signature suvival curve plots.
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
data(TCGA_survivalData)
MUKMplot(svdata =TCGA_survivalData,stn=157,gn=160,time ="month", status="status",
plotmethod="plot",adjx = 170,file="NULL")
Multivariate survival analysis with multiple specified independent variables
Description
Function MVKMresult performs multivariate Cox proportional hazard survival analysis with a set of patients and an independent variable (a specified gene or a feature) and a set of covariates (age, sex and/or smoking or stage) and outputs hazard risks (HR), z-scores, and p-values of the gene and these covariates.
Usage
MVKMresult(data,X,mol, status="status",time="month",quant=c("No",-0.2,0.2))
Arguments
data |
survival data containing p patients in row, s survival variables such as survival status for death, recurrence or relapse, survival time such as day, month,or year, covariates age, sex and/or smoking, etc. and a set of genes in column. |
X |
string for covariate(s), number of covariates is limited to 10. If X=NULL, then MVKMresult would be stopped. |
mol |
int value for column number of the first gene specified. |
status |
string for survival status of patients, may be "relapse" or "recurrence" or "death". User can set status="death", "relapse" or "recurrence","status" depending on user's survival data. The default value is "status". |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
Details
Function MVKMresult performs multivariate Cox proportional hazard survival analyses with p patients and a specified gene and a set of covariates specified and outputs hazard risk (HR),z-score and p-value for the specified gene or signature and covariates.
Value
output a matrix with one row and multiple columns for gene name, Hazard risk, hazard rate, standard error, z-value, p-value of gene and covariates.
Note
User can also use SKMCresult to screen a prognostic signature and a weight vector without covariates.
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
data(GSE50081)
res<-MVKMresult(data=GSE50081,X=c("t.stage","n.stage","m.stage"),mol=3500,
status="status",time="month",quant=c("no",-0.2,0.2))
Univariate Cox proportional hazard survival analysis with a specified independent variable
Description
Function SKMCresult performs univariate Cox proportional hazard survival analysis of patients with a specified independent variable or a gene without covariates and outputs hazard risk (HR), z-score, and p-value of this specified gene.
Usage
SKMCresult(data,mol, time="month", status="status",quant=c("No",-0.2,0.2))
Arguments
data |
survival and gene-expression data containing patients in row, survival status for death, recurrence or relapse, survival time and a set of genes in column. |
mol |
char value specified a gene symbol or a gene id or a signature existed in data or int value for a column number of a specified gene or a signature in data. |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
status |
string for survival status of patients, may be "relapse" or "recurrence" or "death". User can set status="death", "relapse" or "recurrence","status" depending on user's survival data. The default value is "status". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
Details
Patient survival status is binary variable with 1 for event (such as death) and 0 for no event(such as alive). Genes have expression values(numeric values), which are used to calculate z-scores for classifying patients into two groups: high-expression patients and low-expression patients. SKMCresult performs univariate Cox proportional hazard survival analyses of patients with expression values of a specified gene and outputs hazard risk (HR), z-score and p-value of this specified gene.
Value
output a matrix with one row and 6 columns for gene name, Hazard risk, hazard rate, standard error, z-value, p-value of the specified gene.
Note
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
data(GSE50081)
res<-SKMCresult(data=GSE50081,mol=350, time="month", status="status")
Data for forestplot
Description
The data are derived from results of performing multivariate Cox proportional hazard survival analysis on patients with ADC in TCGA lung cancer.
Usage
data("TCGA_forestplt")Format
A data frame with 28 observations on the following 5 variables.
stagea character vector
variatea character vector
betaa numeric vector
sea numeric vector
p_valuea numeric vector
Details
The dataset was constructed with 28 observations for up and down signatures, sex and age in stages 1A, 1B, stage2 and stage3 and 5 variables stage, variate, beta, se and p_value.
Examples
data(TCGA_forestplt)
TCGA data for survival analysis
Description
TCGA data is a clinical and microarray dataset consisting of 572 samples (observations) in row and 162 variables (20 clinical variables such as age, sex month, status,race, genotype and 142 DE genes)
Usage
data("TCGA_survivalData")Format
A data frame with 572 observations on the following 162 variables.
samplea character vector
sampleIDa numeric vector
statusa numeric vector
vital_status.demographica character vector
daya numeric vector
montha numeric vector
agea numeric vector
genedera character vector
sexa numeric vector
race.demographica character vector
racea numeric vector
year_of_birth.demographica numeric vector
year_of_death.demographica numeric vector
age_at_diagnosis.diagnosesa numeric vector
days_to_death.demographica numeric vector
egfr_mutation_performeda character vector
eml4_alk_translocation_performeda character vector
followup_treatment_successa character vector
additional_pharmaceutical_therapya character vector
additional_radiation_therapya character vector
SLC25A13a numeric vector
PSMC4a numeric vector
MRE11Aa numeric vector
PTCD2a numeric vector
STYK1a numeric vector
GOLGA5a numeric vector
IARS2a numeric vector
KIF2Aa numeric vector
HLTFa numeric vector
C14orf166a numeric vector
GEMIN2a numeric vector
CDC6a numeric vector
MSH2a numeric vector
FKBP3a numeric vector
PSMC6a numeric vector
PSMB5a numeric vector
CSNK2A1a numeric vector
KNOP1a numeric vector
AVL9a numeric vector
DNAJC2a numeric vector
RPA3a numeric vector
TMEM106Ba numeric vector
COA1a numeric vector
GAPDHa numeric vector
FAF2a numeric vector
UCHL5a numeric vector
CBX3a numeric vector
HNRNPA2B1a numeric vector
CALUa numeric vector
GAD1a numeric vector
PDLIM4a numeric vector
TIMM17Aa numeric vector
NAA35a numeric vector
GOLM1a numeric vector
SULF1a numeric vector
FANCIa numeric vector
CLTCa numeric vector
SDHCa numeric vector
MRPL9a numeric vector
ACP1a numeric vector
FBXO28a numeric vector
PDIA6a numeric vector
SIGMAR1a numeric vector
ADAM12a numeric vector
XRCC4a numeric vector
PLOD2a numeric vector
TMEM87Ba numeric vector
BUB3a numeric vector
DPY19L4a numeric vector
HLCSa numeric vector
PSMC2a numeric vector
SLBPa numeric vector
ORC5a numeric vector
SGPL1a numeric vector
GREM1a numeric vector
VKORC1a numeric vector
ZNF146a numeric vector
CHAF1Aa numeric vector
TK1a numeric vector
FEN1a numeric vector
FRMD5a numeric vector
DPY19L1a numeric vector
CKAP5a numeric vector
ZNF93a numeric vector
SEMA4Ba numeric vector
PRMT3a numeric vector
ERO1La numeric vector
DNAJC9a numeric vector
FMO3a numeric vector
FHL1a numeric vector
DAPK2a numeric vector
PREX2a numeric vector
VPS13Da numeric vector
TGFBR3a numeric vector
NDST1a numeric vector
LIMS2a numeric vector
GLI2a numeric vector
ACACBa numeric vector
PIK3C3a numeric vector
PCM1a numeric vector
RUNX1T1a numeric vector
NCOA1a numeric vector
TTC28a numeric vector
CBX7a numeric vector
CPED1a numeric vector
PTGDSa numeric vector
SPOCK2a numeric vector
GAB1a numeric vector
FOXP1a numeric vector
RAP1Aa numeric vector
TCF21a numeric vector
WDR35a numeric vector
WBP4a numeric vector
SORBS3a numeric vector
TBX2a numeric vector
ITIH5a numeric vector
PZPa numeric vector
ATP1B2a numeric vector
CBFA2T3a numeric vector
GSTM5a numeric vector
GSTM3a numeric vector
ADAMTS8a numeric vector
GYPCa numeric vector
ARHGAP24a numeric vector
PDE5Aa numeric vector
FBLN5a numeric vector
IQSEC1a numeric vector
SNCAa numeric vector
HMBOX1a numeric vector
NFIBa numeric vector
PAMR1a numeric vector
SAP18a numeric vector
IGSF10a numeric vector
ANGPT1a numeric vector
JAM2a numeric vector
PTH1Ra numeric vector
NFASCa numeric vector
DLC1a numeric vector
FIGFa numeric vector
PKNOX2a numeric vector
NDRG2a numeric vector
FRAT1a numeric vector
ANAPC16a numeric vector
TNXBa numeric vector
SFTPCa numeric vector
KCNK3a numeric vector
SYNPOa numeric vector
ID4a numeric vector
PER1a numeric vector
SLIT3a numeric vector
FOXO4a numeric vector
GJA4a numeric vector
PCBP2a numeric vector
ADARB1a numeric vector
PARVAa numeric vector
CFDa numeric vector
ARHGEF15a numeric vector
GPR20a numeric vector
HSBP1a numeric vector
RPS28a numeric vector
TNXAa numeric vector
FXYD1a numeric vector
Source
https://www.cancer.gov/ccg/access-data
Examples
data(TCGA_survivalData)
A signature constructed with a set of tumor suppressor genes
Description
TS_signature was constructed with a set of 26 tumor suppressor genes screened from microarray data GSE19804 and cohorts GSE18842, GSE40419, and GSE21933 using differential analysis and bioinformatics methods.
Usage
data("TS_signature")Format
A data frame with 28 observations on the following 2 variables.
probeida character vector
genea character vector
Details
TS signature has 26 tumor suppressor genes (26 probeids). These genes are normally expressed but repressed in cancer or tumor cells. Therefore, a TS signature also has low expression in normal tissues but high expression in cancer cells of patients.
References
Zhang, C., Jiang, M., Zhou, N. et al. Use tumor suppressor genes as biomarkers for diagnosis of non-small cell lung cancer. Sci Rep 11, 3596 (2021). https://doi.org/10.1038/s41598-020-80735-x.
Examples
data(ST_signature)
Univariate Kaplan-Meier survival curve plot
Description
Function UKMplot is used to plot outcome of survival analysis of patients in a cohort using a specified gene or a signature.
Usage
UKMplot(data,mol,HR="hazard risk", time="month",status="status", sml="hv",
quant=c("No",-0.2,0.2), plotmethod="plot",adjx)
Arguments
data |
survival data in which columns contain patient survival data such as survival status, survival month, age, sex,cell type and genes and rows list all patients. Status and sex are binary data. In status, 1 is defined as an event occurrece (such as death) and 0 as no event occurrence (such as alive). Month, age, and genes, signature are numeric and continuous data. |
mol |
char value specified a gene symbol or a gene id or a signature existed in data or int value for a column number of a specified gene or signature in data. |
HR |
a string for specifying hazard risk or hazard rate. If HR="Hazard risk" or
"Hazard_risk", then plot would show coefficient, which has negative, zero, or
positive value. The domain is (- |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
status |
string for survival status of patients, may be "relapse" or "recurrence" or "death". User can set status="death", "relapse" or "recurrence","status" depending on user's survival data. The default value is "status". |
sml |
surv.median.line: character vector for drawing a horizontal/vertical line at median survival. Allowed values include one of c("none", "hv", "h", "v"). v: vertical, h:horizontal.The value of sml is used in ggsurvplot, not in plot. The default is "hv". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
plotmethod |
string value: choose a method to plot Kaplan-Meier survival curves. In current version, we have two methods for chose: plot and ggsurvplot. The defauld is plot method |
adjx |
a numeric value used to adjust x-axis position of p-value and HR in plot. |
Details
UKMplot is a function invoked by MKMplot. By invoking UMKplot, MKMplot can make a set of survival plots for a set of genes or a set of signatures.
Value
outputs a survival curve plot of a specified gene.
Note
For a multivariate survival analysis, user can use MVKMplot to plot survival outcome.
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
require(survival)
require(ggplot2)
data(TCGA_survivalData)
UKMplot(data=TCGA_survivalData,mol=78,time="month", status="status",
quant=c("No",-0.2,0.2),adjx=100)
UKMplot(data=TCGA_survivalData,mol="RPS28",time="month", status="status",adjx=100)
Multiple univariate suvival tests with a set of genes
Description
Function musvtest performs SKMCresult by an iteration from the specified first gene to the specified last gene. The output of musvtest is used to build a weight vector for signature survival analysis.
Usage
musvtest(sdata,stn,gn,time="month",status="status",quant=c("No",-0.2,0.2))
Arguments
sdata |
survival and gene-expression data containing patients in row, survival status for death or recurrence or relapse, survival time such as month, day or year and a set of genes in columns. |
stn |
character value specifying the first gene symbol or gene id existed in sdata or int value specifying column number for the first gene in survival data. |
gn |
character value specifying the last gene symbol or gene id existed in sdata or int value specifying column number for the last gene in survival data. |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
status |
string for survival status which is binary variable: 1 for event occurrence and 0 for no event occurrence. status may be "death","relapse" or "recurrence", depending on clinical data. |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
Details
Patient survival status is a binary variable with 1 for an event (such as death) and 0 for no event(such as alive). Genes have expression values(numeric values), which are used to calculate z-scores for classifying patients into two groups: high-expression patients and low-expression patients. SKMCresult performs univariate Cox proportional hazard survival analyses of patients with expression values of a specified gene and outputs hazard risk (HR), z-score and p-value of this specified gene. At first, user can run this function by performing musvtest to screen genes for prognostic signature by using HRs, z-scores, and p-values. Once getting a set of genes for signature, user can perform this function to build a weight vector using
w_i=\frac{log_{10}(p_i)}{\sum_{i=1}^g{log_{10}(p_i)}}
where p_i is p-value for Ward-test of gene i. For a patient,
the signature score or expression value is given by weighting expression values
of genes in the signature:
y_j=\sum_{i}^g{w_ix_{ij}}
where x_{ij} is expression of gene i in patient j.
Value
output a matrix with n rows for gene name and hazard risk, hazard rate, standard error, z-value and p-value of each gene.
Note
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
data(GSE50081)
res<-musvtest(sdata=GSE50081,stn=3500,gn=3506,time="month",status="status")
#res
# Gene Hazad risk hazard rate standard error z-value p-value
#3500 X209170_s_at -0.6510414 0.5215024 0.3133664 -2.0775721 0.037748792
#3501 X1556325_at -0.6041918 0.5465159 0.3507459 -1.7225913 0.084962455
#3502 X228915_at -0.4992865 0.6069636 0.3394520 -1.4708606 0.141328818
#3503 X1555216_a_at -0.5465844 0.5789238 0.3143044 -1.7390290 0.082029656
#3504 X203548_s_at -0.2004345 0.8183751 0.3018504 -0.6640193 0.506677970
#3505 X205433_at -1.3528063 0.2585138 0.4134465 -3.2720229 0.001067809
#3506 X209614_at -0.8389441 0.4321666 0.3905470 -2.1481262 0.031703733
Multivariate Cox proportional hazard survival analyses with multiple genes
Description
Function mvstest performs MVKMresult by iteration from the first gene to the last gene. The output result of musvtest is used to build a vector of weights for screening genes as prognostic signature or doing signature survival analysis.
Usage
mvstest(sdata,X,stn,gn, status,time,quant=c("No",-0.2,0.2))
Arguments
sdata |
survival data containing p patients in row, survival status for death or recurrence or relapse, survival time such as day, month, or year, and covariates age, sex and/or smoking, tumor stage, or chemotherapy, and a set of genes in column. |
X |
string specifying one or multiple covariates such as age, sex, smoking, or tumor stage or genotype. The number of covariates in X is limited to 10. |
stn |
character value specifying gene symbol or gene id existed in sdata or int value specifying column number for the first gene in survival data. |
gn |
character value specifying gene symbol or gene id existed in sdata or int value specifying column number for the last gene in survival data. |
status |
string for survival status which must be binary variable: 1 for event occurrence and 0 for no event occurrence. status may be "death", "relapse" or "recurrence", depending on clinical data. |
time |
string for survival time and may be one of c("day", "month", "year"), depending on the clinical data. The default value is "month". |
quant |
vector for quantile, low and high values. the low and high values are used to define or classify low and high expression groups. If quantile is "yes" or "YES", then the low and high are non-negative precent values, for example, quant=c("yes",0.25,0.75). If quantile is "no", then the low and high values are z-scores, the low value may be negative. For example, quant=c("no",-0.2, 0.2). The quantile = "yes" or = "No" may produce different results of survival analysis. User should carefully choose quantile or no quantile according to the data. The default values are c("no",-0.2,0.2). |
Details
Patient survival status is binary variable with 1 for event (such as death) and 0 for no event (such as alive). Genes have expression values(numeric values), which are used to calculate z-scores for classifying patients into two groups: high-expression patients and low-expression patients. SKMCresult performs univariate Cox proportional hazard survival analyses of patients with expression values of a specified gene and outputs hazard risk (HR), z-score and p-value of this specified gene. At first, user can run this function by performing msvtest to screen genes for prognostic signature by using HRs, z-scores, and p-values. Once getting a set of genes for signature, user can perform this function to build a weight vector using
w_i=\frac{log_{10}(p_i)}{\sum_{i=1}^g{log_{10}(p_i)}}
where p_i is p-value for Ward-test of gene i. For a patient, the signature
score or expression value is given by weighting expression values of g genes in
the signature:
y_j=\sum_{i}^g{w_ix_{ij}}
where x_{ij} is expression of gene i in patient j.
Value
output a matrix with n rows for n genes, m+1 columns for Hazard risk, hazard rate, standard error, z-value, p-value of gene and m covariates.
Note
All inputting parameters are not sensitive to upper or lower. That is, user can input upper or lower string or letter. For example, both time ="MONTH" or time = "month" work.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
Examples
data(GSE50081)
res<-mvstest(sdata=GSE50081,X=c("t.stage","n.stage"),stn=3550,gn=3555,
status="status",time="month",quant=c("No",-0.2,0.2))
results of univariate Cox proportional hazard analysis of patients with ADC in three cohorts.
Description
The "results" is a list consisting of three results generated by performing univariate Cox proportional hazard survival analysis in three different cohorts. Each result has four columns gene(gene symbol or geneid), zahard.ratio, zscore, and p.value for each gene. The results list is used to construct weight vector in the signature.
Usage
data("results")Format
The format is: List of 3 $ :'data.frame': 28 obs. of 4 variables: ..$ Gene : chr [1:28] "CDKN1C" "CDKN2B" "DAB2IP" "DCC" ... ..$ Hazard.ratio: num [1:28] 1.027 0.685 0.663 0.494 0.634 ... ..$ zscore : num [1:28] 0.0734 -1.162 -1.4084 -1.9798 -1.4282 ... ..$ p.value : num [1:28] 0.9415 0.2452 0.159 0.0477 0.1532 ... $ :'data.frame': 26 obs. of 4 variables: ..$ Gene : chr [1:26] "201335_s_at" "201621_at" "203185_at" "203525_s_at" ... ..$ Hazard.ratio: num [1:26] 2.712 0.849 0.742 0.99 0.402 ... ..$ zscore : num [1:26] 1.7257 -0.3047 -0.6031 -0.0198 -1.6817 ... ..$ p.value : num [1:26] 0.0844 0.7606 0.5464 0.9842 0.0926 ... $ :'data.frame': 34 obs. of 4 variables: ..$ Gene : chr [1:34] "age" "gender" "sex" "cell_type" ... ..$ Hazard.ratio: num [1:34] NA NA NA NA NA ... ..$ zscore : num [1:34] NA NA NA NA NA ... ..$ p.value : num [1:34] NA NA NA NA NA ...
Examples
data(results)
Signature expression or signature score
Description
Function signatureExp.R is used to give signature expression or score across all patients in a cohort data by using weight vector of a signature.
Usage
signatureExp(svdata,weight)
Arguments
svdata |
a survival dataset containing clinic data such as death or relapse status, month, covariates age, sex and/smoking, stages, therapy etc. and a set of genes containing subset of genes in the signature in column. |
weight |
a matrix that contains gene and/or gene_id and weight columns. |
Details
a matrix with g \times (2 or 3). If weight contains two columns, then
first column must be gene or gene_id and the second column must be weight. If
weight has three columns, then the first two columns are gene and gene_id but
the third column must be weight. Gene name or gene_id in weigth must be matched
with gene name or gene_id in the survival data.
Value
a survival dataset with signature scores of patients.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
See Also
Examples
data(TCGA_survivalData)
data(signature_weight)
res1<-signatureExp(svdata=TCGA_survivalData,weight=signature_weight)
Weights of genes in a signature
Description
Weights of genes in a signature were estimated by using the following equation
w_i=\frac{log_{10}(p_i)}{\sum_{i=1}^g{log_{10}(p_i)}}
where p_i is p-value for Ward-test of gene i in survival analysis.
Usage
data("signature_weight")Format
A data frame with 84 observations on the following 2 variables.
stage3_downa character vector
weight.7a numeric vector
Details
signature_weight is a matrix with g\times 2 or 3 where g is number of
genes in a signature and there are two or three columns for gene id or gene name
and weights.
Examples
data(signature_weight)
Forestplot for result of multivariate Cox proportional hazard survival analysis
Description
This function provides a publishable forestplot figure that shows results of multivariate Cox proportional hazard regression analysis of patients in a cohort.
Usage
survivalForest(sdata,xtick)
Arguments
sdata |
results of multivariate survival analyses,outputted by performing MKMplot. |
xtick |
x-coordinate size in forestplot. For example, user can set xtick=c(-2,-1,0,1,2) |
Details
The data includes column "model" and/or stage", "variable","HR","SE", and "p_value". In inputing data, stage may be stage IA, stageIB, stageII, stageIIA, stageIIB, stageIII, etc. The Variable may contain signature, sex, age, smoking, genotype,etc.. The variable column may be named with "Variate","variate", "Variable","variable", "factor" or "category" word. "HR" is hazard risk, also called coefficient or beta of Cox proportional hazard regression. So, HR column may be named with "HR","hazard risk", "coefficient","beta" or "Beta" word. "SE" is standard error and may be named with "se","SE" or "standard error". "p_value" is p-value for Ward test/ranksum test, may be named with "pvalue","p_value", "p-value","p value","PV" or "pv" word. The rows include signature (gene or biomarker), age, sex and covariate(s).
Value
output a plot figure.
Note
sdata may contain multiple models such as model1, model2, model3. Model1: signature, age,sex. Model2: signature, age, sex, smoking. Model 3: signature, age, sex, smoking, and genotype. User can use empty row to separate these models.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Yuguang Ban
Yuguang.ban@med.miami.edu
See Also
forestplot
Examples
#library(forestplot)
data(TCGA_forestplt)
xtick=c(-1,-0.5,0,0.5,1)
#pdf(file="TCGA_survival_forestplot.pdf")
survivalForest(sdata=TCGA_forestplt,xtick=xtick)
#dev.off()
Caculation of Weights for signature genes
Description
Weights for signature genes are calculated from the results obtained by performing Cox proportional hazard regression on the training survival datasets.
Usage
weight(results,signature)
Arguments
results |
a list of multiple results obtained by performing survival analysis on the training datasets. |
signature |
a vector or matrix of genes screened by a method from survival data and expression data. |
Details
The results file is a list of multiple results obtained by performing Cox proportional hazard survival analysis based on univariate models in multiple datasets. User can create this list by using results<-list(result1,result2,..., resultn). Signature may be a matrix with two columns and n rows. One column may be gene id(such as probe id or Ensembl id or NCBI Entrez id) and the other is gene symbol or gene name. Signature may be a vector(a set of genes or gene ids).
Value
Output a matrix where gene and/or gene id and weight in column and gene names and/or gene id values and weight values.
Note
Sum of weights may not be equal to 1 if some genes in results are lost.
Author(s)
Yuan-De Tan
yxt477@med.miami.edu
tanyuande@gmail.com
Examples
data(TS_signature)
data(results)
res<-weight(results = results,signature = TS_signature)