Graphs

This is a collection of Biomarker Analysis graph templates.

AG1

In this page we collect standard utilities for plotting which can be applied in principle to all graphs. Then we don’t need to repeat explaining these in each of the other…

DG1

We will use the cadsl data set from the random.cdisc.data package and ggplot2 to create the plots. In this example, we will plot histograms of one or multiple numeric…

DG1A

We will use the cadsl data set from the random.cdisc.data package and ggplot2 to create the plots. In this example, we will plot histograms of one or multiple numeric…

DG1B

We will use the cadsl data set from the random.cdisc.data package and ggplot2 to create the plots. In this example, we will plot histograms of one or multiple numeric…

DG2

The graph below plots the distribution of a biomarker variable (on a continuous scale) as a boxplot by one or more categorical clinical variables with overlaid points.

DG3

The graphs below summarize the distribution of a categorical biomarker variable as barplots, either in the overall population or by one or more categorical clinical variables.

DG3A

The graphs below summarize the distribution of a categorical biomarker variable as barplots, either in the overall population or by one or more categorical clinical variables.

DG4

The graph below plots two continuous (biomarker) variables against each other.

KG1

We will use the cadtte data set from the random.cdisc.data package to create the Kaplan-Meier (KM) plots. We start by filtering the time-to-event dataset for the overall…

KG1A

We will use the cadtte data set from the random.cdisc.data package to create the Kaplan-Meier (KM) plots. We start by filtering the time-to-event dataset for the overall…

KG1B

We will use the cadtte data set from the random.cdisc.data package to create the Kaplan-Meier (KM) plots. We start by filtering the time-to-event dataset for the overall…

KG2

The same setup as in KG1 is used.

KG2A

The same setup as in KG1 is used.

KG3

The same data set as in KG1A is used. The difference is that here we use the categorical biomarker variable BMRKR2 as the treatment arm in variables which is then used by g_k…

KG4

A setup similar to KG1 is used, with some additional data manipulation steps to first binarize the continuous biomarker and the treatment arm variables, and then combine…

KG4A

A setup similar to KG1 is used, with some additional data manipulation steps to first binarize the continuous biomarker and the treatment arm variables, and then combine…

KG4B

A setup similar to KG1 is used, with some additional data manipulation steps to first binarize the continuous biomarker and the treatment arm variables, and then combine…

KG5

A setup similar to KG4 is used. The difference here is that we create the initial binary biomarker variable BMRKR1_BIN from comparing the continuous biomarker variable BMRKR1…

KG5A

A setup similar to KG4 is used. The difference here is that we create the initial binary biomarker variable BMRKR1_BIN from comparing the continuous biomarker variable BMRKR1…

KG5B

A setup similar to KG4 is used. The difference here is that we create the initial binary biomarker variable BMRKR1_BIN from comparing the continuous biomarker variable BMRKR1…

RFG1

These templates are helpful when we are interested in the odds ratios between two groups, usually two treatment arms. We would like to assess how the odds ratio changes when…

RFG1A

These templates are helpful when we are interested in the odds ratios between two groups, usually two treatment arms. We would like to assess how the odds ratio changes when…

RFG2A

These templates are helpful when we are interested in modelling the effects of continuous biomarker variables on the binary response outcome, conditional on covariates…

RFG2B

These templates are helpful when we are interested in modelling the effects of continuous biomarker variables on the binary response outcome, conditional on covariates…

RFG2C

These templates are helpful when we are interested in modelling the effects of continuous biomarker variables on the binary response outcome, conditional on covariates…

RFG3

For response endpoints it is good to show how to obtain within-treatment-arms comparisons of biomarker subgroups. This is similar to SFG04.

RG1

We will use the cadrs data set from the random.cdisc.data package to create the response plots. We transform the response variable into an ordered factor to ensure that the…

RG1A

We will use the cadrs data set from the random.cdisc.data package to create the response plots. We transform the response variable into an ordered factor to ensure that the…

RG1B

We will use the cadrs data set from the random.cdisc.data package to create the response plots. We transform the response variable into an ordered factor to ensure that the…

RG1C

We will use the cadrs data set from the random.cdisc.data package to create the response plots. We transform the response variable into an ordered factor to ensure that the…

RG2

The same setup as in RG1 is used.

RG2A

The same setup as in RG1 is used.

RG3

The same setup as in RG1 is used.

RG3A

The same setup as in RG1 is used.

RG3B

The same setup as in RG1 is used.

RNAG1

This page can be used as a template of how to use the available hermes functions for simple QC analyses of RNA-seq gene expression data and to create interactive QC graphs…

RNAG10

This page can be used as a template of how to produce survival forest graphs for RNA-seq gene expression analysis using available tern and hermes functions, and to create an…

RNAG2

This page can be used as a template of how to use the available hermes functions to derive and plot top genes for RNA-seq RNA-seq gene expression data and to create an…

RNAG3

This page can be used as a template of how to use the available hermes functions for principal components analysis and plots of RNAseq data sets.

RNAG4

This page can be used as a template of how to use the available hermes functions to calculate the correlation between samples in HermesData, visualize them in a heatmap, and…

RNAG5

This page can be used as a template of how to use the available hermes functions to take differential gene expression analysis between samples in HermesData, visualize them…

RNAG6

This page can be used as a template of how to use the available hermes functions to produce a barplot of the dichotomized gene expression counts into two or three categories…

RNAG7

This page can be used as a template of how to create boxplots for RNA-seq gene expression data using available hermes, and to create interactive boxplot for RNA-seq gene…

RNAG8

This page can be used as a template of how to use the available hermes functions to produce a scatterplot of two genes or gene signatures and to create an interactive…

RNAG9

This page can be used as a template of how to produce Kaplan-Meier graphs for RNA-seq gene expression analysis using available tern and hermes functions, and to create an…

SFG1

We will use the cadtte data set from the random.cdisc.data package to create the survival forest graph. We start by filtering the adtte data set for the overall survival…

SFG1A

We will use the cadtte data set from the random.cdisc.data package to create the survival forest graph. We start by filtering the adtte data set for the overall survival…

SFG1B

We will use the cadtte data set from the random.cdisc.data package to create the survival forest graph. We start by filtering the adtte data set for the overall survival…

SFG2

We prepare the data similarly as in SFG1. In particular we use again the cut_quantile_bins() function, here to obtain quartile bins of the continuous biomarker BMRKR1.

SFG2A

We prepare the data similarly as in SFG1. In particular we use again the cut_quantile_bins() function, here to obtain quartile bins of the continuous biomarker BMRKR1.

SFG2B

We prepare the data similarly as in SFG1. In particular we use again the cut_quantile_bins() function, here to obtain quartile bins of the continuous biomarker BMRKR1.

SFG2C

We prepare the data similarly as in SFG1. In particular we use again the cut_quantile_bins() function, here to obtain quartile bins of the continuous biomarker BMRKR1.

SFG2D

We prepare the data similarly as in SFG1. In particular we use again the cut_quantile_bins() function, here to obtain quartile bins of the continuous biomarker BMRKR1.

SFG3

We prepare the data similarly as in SFG1, focusing on a single arm in the biomarker evaluable population.

SFG3A

We prepare the data similarly as in SFG1, focusing on a single arm in the biomarker evaluable population.

SFG4

We prepare the data similarly as in SFG1.

SFG5

We prepare the data similarly as in SFG1.

SFG5A

We prepare the data similarly as in SFG1.

SFG5B

We prepare the data similarly as in SFG1.

SFG5C

We prepare the data similarly as in SFG1.

SFG6A

These templates are helpful when we are interested in modelling the effects of continuous biomarker variables on a time-to-event (survival) outcome, conditional on…

SFG6B

These templates are helpful when we are interested in modelling the effects of continuous biomarker variables on a time-to-event (survival) outcome, conditional on…

SFG6C

These templates are helpful when we are interested in modelling the effects of continuous biomarker variables on a time-to-event (survival) outcome, conditional on…

SPG1

We will use the cadtte data set from the random.cdisc.data package to create the STEP survival graph. We start by filtering the adtte data set for the overall survival…

SPG2

We will use the cadrs data set from the random.cdisc.data package to create the STEP response graph. We start by filtering the adrs data set for response evaluable patients…

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This is a collection of Biomarker Analysis graph templates.

:::{#graphs}
:::