inst/shinyapp/R/workflow.R
In kuppal2/xmsPANDA: R Package for Biomarker Discovery, Network, and Data Exploratory Analysis
source("R/load_packages.R")
workflow<-fluidRow(
tags$div(
id="maindiv2",
column(12,textOutput("txt")),
column(width=12,
id="inputarea",
column(width=6,selectInput(width="350px","analysismode","Select analysis mode:",c("classification","regression"))),
column(width=6,radioButtons("pairedanalysis", "Is this a repeated-measurement design?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "FALSE")),
column(width=12,
conditionalPanel(
condition = "input.analysismode == 'classification' && input.pairedanalysis == 'FALSE'",
column(width=12,
# div(style="display: inline-block;vertical-align:top",tags$p(style="margin:0;padding-top:4px;padding-bottom:4px;font-weight:bold","Choose feature selection methods: ")),
#div(style="display: inline-block;vertical-align:top",actionButton("methodbuttoni", "View")),
#bsModal("method_modali", "Method List", "methodbuttoni", size = "large",
column(width=12,selectInput(width="800px","featselmethodi", "Select feature selection methods for classification (click in the box to edit or select more options)", multiple = TRUE,c('limma (one-way ANOVA using LIMMA)'='limma',
'pls (partial least squares using VIP)'='pls',
'limma2way (two-way ANOVA using LIMMA)'='limma2way',
'limmarobust (one-way ANOVA using LIMMA robust)'='limmarobust',
'limma2wayrobust (two-way ANOVA using LIMMA robust)'='limma2wayrobust',
'lm1wayanova (one-way ANOVA using linear model)'='lm1wayanova',
'lm2wayanova (two-way ANOVA using linear model)'='lm2wayanova',
'lmreg (linear regression)'='lmreg',
'lmregrobust (linear regression using robust sandwich estimator)'='lmregrobust',
'logitreg (logistic regression)'='logitreg',
'logitregrobust (logistic regression using robust sandwich estimator)'='logitregrobust',
'poissonreg (poisson regression)'='poissonreg',
'poissonregrobust (poisson regression using robust sandwich estimator)'='poissonregrobust',
'rfesvm (recursive feature elimination SVM)'='rfesvm',
'ttest (simple t-test)'='ttest',
'wilcox (wilcoxon test)'='wilcox',
'RF (random forest using Boruta algorithm)'='RF',
'MARS (multiple adaptive regression splines)'='MARS',
'spls (sparse partial least squares)'='spls',
'spls2way (two-factor analysis using sparse partial least squares)'='spls2way',
'o1pls (orthogonal partial least squares)'='o1pls',
'pamr (microarrays algorithm based on the nearest shrunked centroid method)'='pamr'),
selected=c('limma','pls')
)
)
#)
)
),
conditionalPanel(
condition = "input.analysismode == 'classification' && input.pairedanalysis == 'TRUE'",
column(width=12,
# div(style="display: inline-block;vertical-align:top",tags$p(style="margin:0;padding-top:4px;padding-bottom:4px;font-weight:bold","Choose feature selection methods: ")),
# div(style="display: inline-block;vertical-align:top",actionButton("methodbuttonii", "View")),
# bsModal("method_modalii", "Method List", "methodbuttonii", size = "large",
column(width=12,selectInput(width="800px","featselmethodii", "Select feature selection methods for classification with repeat measures (click in the box to edit or select more options)", multiple = TRUE,
c('limma1wayrepeat (one-way ANOVA repeated measures using LIMMA)'='limma1wayrepeat',
'limma2wayrepeat (two-way ANOVA repeated measures using LIMMA)'='limma2wayrepeat',
'limma1wayrepeatrobust (one-way ANOVA repeated measures using LIMMA robust)'='limma1wayrepeatrobust',
'limma2wayrepeatrobust (two-way ANOVA repeated measures using LIMMA robust)'='limma2wayrepeatrobust',
'lm1wayanovarepeat (one-way ANOVA repeated measures using linear model)'='lm1wayanovarepeat',
'lm2wayanovarepeat (two-way ANOVA repeated measures using linear model)'='lm2wayanovarepeat',
'spls1wayrepeat (one-way ANOVA repeated measures using sparse partial least squares)'='spls1wayrepeat',
'spls2wayrepeat (two-way ANOVA repeated measures using sparse partial least squares)'='spls2wayrepeat',
'ttestrepeat (simple t-test repeated measures)'='ttestrepeat',
'wilcoxrepeat (wilcoxon test repeated measures)'='wilcoxrepeat'),
selected=c('lm1wayanovarepeat'))
)
# )
)
),
conditionalPanel(
condition = "input.analysismode == 'regression' && input.pairedanalysis == 'FALSE'",
column(width=12,
column(width=12,selectInput(width="800px","featselmethodiii", "Select feature selection methods for regression", multiple = TRUE,
c('lmreg (linear regression)'='lmreg',
'lmregrobust (linear regression using robust sandwich estimator)'='lmregrobust',
'poissonreg (poisson regression)'='poissonreg',
'poissonregrobust (poisson regression using robust sandwich estimator)'='poissonregrobust',
'RF (random forest using boruta algorithm)'='RF',
'MARS (multiple adaptive regression splines)'='MARS',
'pls (partial least squares using VIP)'='pls',
'spls (sparse partial least squares)'='spls',
'o1pls (orthogonal partial least squares)'='o1pls'),
selected=c('lmreg'))
)
)
),
conditionalPanel(
condition = "input.analysismode == 'regression' && input.pairedanalysis == 'TRUE'",
column(width=12,
column(width=12,selectInput(width="800px","featselmethodiv", "Select feature selection methods for regression", multiple = TRUE,
c('lmregrepeat (linear regression with mixed effects model)'='lmregrepeat',
'plsrepeat (multilevel partial least squares using VIP)'='plsrepeat',
'splsrepeat (multilevel sparse partial least squares)'='splsrepeat'),
selected=c('lmregrepeat'))
)
)
)
),
column(width=12,style="margin-top:10px",
column(width=6,style='padding-left:0;',numericInput(width="350px","pvalue_thresh", "P-value threshold:", 0.05, min = 0, max = 1)),
column(width=6,numericInput(width="350px","foldchangethresh", "Fold change threshold (0-100 limit):", 0, min = 0, max = 100))
),
column(width=12,style="margin-top:10px",
column(width=6,style='padding-left:0;',numericInput(width="350px","fdrthresh", "False discovery threshold:", 0.05, min = 0, max = 1)),
column(width=6,selectInput(width="350px","fdr_method","Choose FDR correction method:",
c("BH (Benjamini-Hochberg 1995)"="BH","ST (Storey & Tibshirani 2001)"="ST","Strimmer (Strimmer 2008)"="Strimmer",
"BY (Benjamini-Yekutieli 2001)"="BY", "Bonferroni"="bonferroni","none"="none")))
),
column(width=12,style="margin-top:10px",
column(width=6,numericInput(width="350px","pls_vip_thresh", "VIP threshold (1-100 limit):", 2, min = 1, max = 100)),
column(width=6,style='margin-top:25px;padding-left:0;',actionButton("argumentbutton", "More arguments"))
),
bsModal("argument_modal", "More arguments for feature selection", "argumentbutton", size = "large",
tags$div(
width=12,
style="height:575px",
column(width=6,numericInput(width="350px","kfold", "k for k-fold Cross Validation (1-10000 limit):", 10, min = 1, max = 10000)),
bsTooltip("pls_vip_thresh", "This argument only works for PLS or O1PLS models","bottom", options = list(container = "body")),
column(width=6,numericInput(width="350px","pls_ncomp", "Max number of components to consider:", 5, min = 1, max = 100000)),
bsTooltip("pls_ncomp", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,numericInput(width="350px","max_comp_sel", "Number of components to use for VIP selection:", 1, min = 1, max = 100000)),
bsTooltip("max_comp_sel", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,selectInput(width="350px","optselect","Find optimal number of components:",c("TRUE","FALSE"))),
bsTooltip("optselect", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,selectInput(width="350px","pls.vip.selection","VIP summarization across multiple components:",c("max","mean"))),
bsTooltip("optselect", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,
tags$label("Number of permutations for calculating p-values:", `for` = "permu_switch"),
div(style="display: inline-block;vertical-align:top; width: 100px;", switchInput(inputId = "permu_switch",value = FALSE)),
div(style="display: inline-block;vertical-align:top; width: 250px;", numericInput(inputId = "pls_permut_count", label = NULL, value = 1000, min = 1, max = 100000))
),
bsTooltip("pls_permut_count", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,conditionalPanel(
condition = "output.checkmax_varsel",
numericInput(width="350px","max_varsel", "Max number of variables to be used:", 100, min = 1, max = 100000),
bsTooltip("max_varsel", "This argument only works for sPLS, spls1wayrepeat, spls2wayrepeat, rfesvm, and Random Forest","bottom", options = list(container = "body"))
)),
column(width=12,style="margin-top:10px",
column(width=6,radioButtons("limmadecideTests", "Should the LIMMA decide tests [-1 (down),0 (no change),1 (up)] be performed?", inline=TRUE,c(True = "TRUE",False = "FALSE"),
selected = "FALSE")),
column(width=6,selectInput(width="350px","limma.contrasts.type","Select contrast type:",c("Sum contrats as in ANOVA (contr.sum)"="contr.sum",
"Use the first group as the reference (contr.treatment)"="contr.treatment"),
selected="contr.sum"))
),
column(width=12,style="margin-top:10px",
# column(width=6,radioButtons("limmadecideTests", "Should the LIMMA decide tests [-1 (down),0 (no change),1 (up)] be performed?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "TRUE")),
column(width=6,selectInput(width="350px","similarity.matrix","Select similarity matrix for hierarchical clustering:",c("correlation (similarity.matrix)"="correlation",
"topological overlap (TOM)"="TOM"),
selected="correlation (similarity.matrix)")),
column(width=6,#style='padding-left:10;',
tags$label("Network based feature ranking (differential network analysis):", `for` = "netbasedfeatranking_switch"),
div(style="width: 100px;", switchInput(inputId = "netbasedfeatranking_switch",value = FALSE))
)
),
column(width=12,style="margin-top:10px",
column(width=6,
#conditionalPanel(
# condition = "output.checkaggregationmethod",
selectInput(width="350px","aggregation_method","Method for aggregating results:",
c("None"="none","RankAggreg (cross entropy)"="RankAggreg","RankAggregGA (genetic algorithm)"="RankAggregGA","Consensus"="consensus"),selected="None")
),
#column(width=6,radioButtons("limmadecideTests", "Should the LIMMA decide tests be performed?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "TRUE")),
column(width=6,radioButtons("balance.classes", "Should the ROSE method be used for balancing classes?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "FALSE"))
),
column(width=12,style="margin-top:10px",
column(width=6,
#conditionalPanel(
# condition = "output.checkaggregationmethod",
selectInput(width="350px","outlier.method","Outlier detection method:",
c("Tukey's 1.5*interquartile range rule on summed values"="sumtukey","Tukey's 1.5*interquartile range rule on PCA scores"="pcatukey",
"Using the multivariate outliers detection method (pcout)"="pcout","Using robust Mahalanobis distance with a Chi-square test (pcachisq)"="pcachisq"),
selected="pcout")),
column(width=6,
#conditionalPanel(
# condition = "output.checkaggregationmethod",
selectInput(width="350px","vcovHC.type","Estimation type for the robust covariance matrix estimation:",
c("HC0"="HC0","HC3"="HC3"),selected="HC3")
)
),
column(width=12,style="margin-top:10px",
column(width=6,style='padding-left:0;',selectInput(width="350px","lme.modeltype","Model type for mixed effects model:",
c("Random Intercept"="RI","Random Intercept & Random Slope"="RIRS"),selected="Random Intercept")),
column(width=6,numericInput(width="350px","log2.transform.constant", "Small constant for log2 transformation:", 1, min = 0))
)
)
)
)
))
kuppal2/xmsPANDA documentation built on May 15, 2021, 5:48 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
source("R/load_packages.R")
workflow<-fluidRow(
tags$div(
id="maindiv2",
column(12,textOutput("txt")),
column(width=12,
id="inputarea",
column(width=6,selectInput(width="350px","analysismode","Select analysis mode:",c("classification","regression"))),
column(width=6,radioButtons("pairedanalysis", "Is this a repeated-measurement design?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "FALSE")),
column(width=12,
conditionalPanel(
condition = "input.analysismode == 'classification' && input.pairedanalysis == 'FALSE'",
column(width=12,
# div(style="display: inline-block;vertical-align:top",tags$p(style="margin:0;padding-top:4px;padding-bottom:4px;font-weight:bold","Choose feature selection methods: ")),
#div(style="display: inline-block;vertical-align:top",actionButton("methodbuttoni", "View")),
#bsModal("method_modali", "Method List", "methodbuttoni", size = "large",
column(width=12,selectInput(width="800px","featselmethodi", "Select feature selection methods for classification (click in the box to edit or select more options)", multiple = TRUE,c('limma (one-way ANOVA using LIMMA)'='limma',
'pls (partial least squares using VIP)'='pls',
'limma2way (two-way ANOVA using LIMMA)'='limma2way',
'limmarobust (one-way ANOVA using LIMMA robust)'='limmarobust',
'limma2wayrobust (two-way ANOVA using LIMMA robust)'='limma2wayrobust',
'lm1wayanova (one-way ANOVA using linear model)'='lm1wayanova',
'lm2wayanova (two-way ANOVA using linear model)'='lm2wayanova',
'lmreg (linear regression)'='lmreg',
'lmregrobust (linear regression using robust sandwich estimator)'='lmregrobust',
'logitreg (logistic regression)'='logitreg',
'logitregrobust (logistic regression using robust sandwich estimator)'='logitregrobust',
'poissonreg (poisson regression)'='poissonreg',
'poissonregrobust (poisson regression using robust sandwich estimator)'='poissonregrobust',
'rfesvm (recursive feature elimination SVM)'='rfesvm',
'ttest (simple t-test)'='ttest',
'wilcox (wilcoxon test)'='wilcox',
'RF (random forest using Boruta algorithm)'='RF',
'MARS (multiple adaptive regression splines)'='MARS',
'spls (sparse partial least squares)'='spls',
'spls2way (two-factor analysis using sparse partial least squares)'='spls2way',
'o1pls (orthogonal partial least squares)'='o1pls',
'pamr (microarrays algorithm based on the nearest shrunked centroid method)'='pamr'),
selected=c('limma','pls')
)
)
#)
)
),
conditionalPanel(
condition = "input.analysismode == 'classification' && input.pairedanalysis == 'TRUE'",
column(width=12,
# div(style="display: inline-block;vertical-align:top",tags$p(style="margin:0;padding-top:4px;padding-bottom:4px;font-weight:bold","Choose feature selection methods: ")),
# div(style="display: inline-block;vertical-align:top",actionButton("methodbuttonii", "View")),
# bsModal("method_modalii", "Method List", "methodbuttonii", size = "large",
column(width=12,selectInput(width="800px","featselmethodii", "Select feature selection methods for classification with repeat measures (click in the box to edit or select more options)", multiple = TRUE,
c('limma1wayrepeat (one-way ANOVA repeated measures using LIMMA)'='limma1wayrepeat',
'limma2wayrepeat (two-way ANOVA repeated measures using LIMMA)'='limma2wayrepeat',
'limma1wayrepeatrobust (one-way ANOVA repeated measures using LIMMA robust)'='limma1wayrepeatrobust',
'limma2wayrepeatrobust (two-way ANOVA repeated measures using LIMMA robust)'='limma2wayrepeatrobust',
'lm1wayanovarepeat (one-way ANOVA repeated measures using linear model)'='lm1wayanovarepeat',
'lm2wayanovarepeat (two-way ANOVA repeated measures using linear model)'='lm2wayanovarepeat',
'spls1wayrepeat (one-way ANOVA repeated measures using sparse partial least squares)'='spls1wayrepeat',
'spls2wayrepeat (two-way ANOVA repeated measures using sparse partial least squares)'='spls2wayrepeat',
'ttestrepeat (simple t-test repeated measures)'='ttestrepeat',
'wilcoxrepeat (wilcoxon test repeated measures)'='wilcoxrepeat'),
selected=c('lm1wayanovarepeat'))
)
# )
)
),
conditionalPanel(
condition = "input.analysismode == 'regression' && input.pairedanalysis == 'FALSE'",
column(width=12,
column(width=12,selectInput(width="800px","featselmethodiii", "Select feature selection methods for regression", multiple = TRUE,
c('lmreg (linear regression)'='lmreg',
'lmregrobust (linear regression using robust sandwich estimator)'='lmregrobust',
'poissonreg (poisson regression)'='poissonreg',
'poissonregrobust (poisson regression using robust sandwich estimator)'='poissonregrobust',
'RF (random forest using boruta algorithm)'='RF',
'MARS (multiple adaptive regression splines)'='MARS',
'pls (partial least squares using VIP)'='pls',
'spls (sparse partial least squares)'='spls',
'o1pls (orthogonal partial least squares)'='o1pls'),
selected=c('lmreg'))
)
)
),
conditionalPanel(
condition = "input.analysismode == 'regression' && input.pairedanalysis == 'TRUE'",
column(width=12,
column(width=12,selectInput(width="800px","featselmethodiv", "Select feature selection methods for regression", multiple = TRUE,
c('lmregrepeat (linear regression with mixed effects model)'='lmregrepeat',
'plsrepeat (multilevel partial least squares using VIP)'='plsrepeat',
'splsrepeat (multilevel sparse partial least squares)'='splsrepeat'),
selected=c('lmregrepeat'))
)
)
)
),
column(width=12,style="margin-top:10px",
column(width=6,style='padding-left:0;',numericInput(width="350px","pvalue_thresh", "P-value threshold:", 0.05, min = 0, max = 1)),
column(width=6,numericInput(width="350px","foldchangethresh", "Fold change threshold (0-100 limit):", 0, min = 0, max = 100))
),
column(width=12,style="margin-top:10px",
column(width=6,style='padding-left:0;',numericInput(width="350px","fdrthresh", "False discovery threshold:", 0.05, min = 0, max = 1)),
column(width=6,selectInput(width="350px","fdr_method","Choose FDR correction method:",
c("BH (Benjamini-Hochberg 1995)"="BH","ST (Storey & Tibshirani 2001)"="ST","Strimmer (Strimmer 2008)"="Strimmer",
"BY (Benjamini-Yekutieli 2001)"="BY", "Bonferroni"="bonferroni","none"="none")))
),
column(width=12,style="margin-top:10px",
column(width=6,numericInput(width="350px","pls_vip_thresh", "VIP threshold (1-100 limit):", 2, min = 1, max = 100)),
column(width=6,style='margin-top:25px;padding-left:0;',actionButton("argumentbutton", "More arguments"))
),
bsModal("argument_modal", "More arguments for feature selection", "argumentbutton", size = "large",
tags$div(
width=12,
style="height:575px",
column(width=6,numericInput(width="350px","kfold", "k for k-fold Cross Validation (1-10000 limit):", 10, min = 1, max = 10000)),
bsTooltip("pls_vip_thresh", "This argument only works for PLS or O1PLS models","bottom", options = list(container = "body")),
column(width=6,numericInput(width="350px","pls_ncomp", "Max number of components to consider:", 5, min = 1, max = 100000)),
bsTooltip("pls_ncomp", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,numericInput(width="350px","max_comp_sel", "Number of components to use for VIP selection:", 1, min = 1, max = 100000)),
bsTooltip("max_comp_sel", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,selectInput(width="350px","optselect","Find optimal number of components:",c("TRUE","FALSE"))),
bsTooltip("optselect", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,selectInput(width="350px","pls.vip.selection","VIP summarization across multiple components:",c("max","mean"))),
bsTooltip("optselect", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,
tags$label("Number of permutations for calculating p-values:", `for` = "permu_switch"),
div(style="display: inline-block;vertical-align:top; width: 100px;", switchInput(inputId = "permu_switch",value = FALSE)),
div(style="display: inline-block;vertical-align:top; width: 250px;", numericInput(inputId = "pls_permut_count", label = NULL, value = 1000, min = 1, max = 100000))
),
bsTooltip("pls_permut_count", "This argument only works for PLS, sPLS, or O1PLS models","bottom", options = list(container = "body")),
column(width=6,conditionalPanel(
condition = "output.checkmax_varsel",
numericInput(width="350px","max_varsel", "Max number of variables to be used:", 100, min = 1, max = 100000),
bsTooltip("max_varsel", "This argument only works for sPLS, spls1wayrepeat, spls2wayrepeat, rfesvm, and Random Forest","bottom", options = list(container = "body"))
)),
column(width=12,style="margin-top:10px",
column(width=6,radioButtons("limmadecideTests", "Should the LIMMA decide tests [-1 (down),0 (no change),1 (up)] be performed?", inline=TRUE,c(True = "TRUE",False = "FALSE"),
selected = "FALSE")),
column(width=6,selectInput(width="350px","limma.contrasts.type","Select contrast type:",c("Sum contrats as in ANOVA (contr.sum)"="contr.sum",
"Use the first group as the reference (contr.treatment)"="contr.treatment"),
selected="contr.sum"))
),
column(width=12,style="margin-top:10px",
# column(width=6,radioButtons("limmadecideTests", "Should the LIMMA decide tests [-1 (down),0 (no change),1 (up)] be performed?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "TRUE")),
column(width=6,selectInput(width="350px","similarity.matrix","Select similarity matrix for hierarchical clustering:",c("correlation (similarity.matrix)"="correlation",
"topological overlap (TOM)"="TOM"),
selected="correlation (similarity.matrix)")),
column(width=6,#style='padding-left:10;',
tags$label("Network based feature ranking (differential network analysis):", `for` = "netbasedfeatranking_switch"),
div(style="width: 100px;", switchInput(inputId = "netbasedfeatranking_switch",value = FALSE))
)
),
column(width=12,style="margin-top:10px",
column(width=6,
#conditionalPanel(
# condition = "output.checkaggregationmethod",
selectInput(width="350px","aggregation_method","Method for aggregating results:",
c("None"="none","RankAggreg (cross entropy)"="RankAggreg","RankAggregGA (genetic algorithm)"="RankAggregGA","Consensus"="consensus"),selected="None")
),
#column(width=6,radioButtons("limmadecideTests", "Should the LIMMA decide tests be performed?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "TRUE")),
column(width=6,radioButtons("balance.classes", "Should the ROSE method be used for balancing classes?", inline=TRUE,c(True = "TRUE",False = "FALSE"),selected = "FALSE"))
),
column(width=12,style="margin-top:10px",
column(width=6,
#conditionalPanel(
# condition = "output.checkaggregationmethod",
selectInput(width="350px","outlier.method","Outlier detection method:",
c("Tukey's 1.5*interquartile range rule on summed values"="sumtukey","Tukey's 1.5*interquartile range rule on PCA scores"="pcatukey",
"Using the multivariate outliers detection method (pcout)"="pcout","Using robust Mahalanobis distance with a Chi-square test (pcachisq)"="pcachisq"),
selected="pcout")),
column(width=6,
#conditionalPanel(
# condition = "output.checkaggregationmethod",
selectInput(width="350px","vcovHC.type","Estimation type for the robust covariance matrix estimation:",
c("HC0"="HC0","HC3"="HC3"),selected="HC3")
)
),
column(width=12,style="margin-top:10px",
column(width=6,style='padding-left:0;',selectInput(width="350px","lme.modeltype","Model type for mixed effects model:",
c("Random Intercept"="RI","Random Intercept & Random Slope"="RIRS"),selected="Random Intercept")),
column(width=6,numericInput(width="350px","log2.transform.constant", "Small constant for log2 transformation:", 1, min = 0))
)
)
)
)
))
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