continuous_table: Continuous Information

Description

Summarization of the continuous information.

Usage

continuous_table (name,
                  num,
                  label,
                  digits=0,
                  scientific=FALSE,
                  range=c("IQR","95%CI"),
                  logchange=FALSE)

Arguments

name

the name of the feature.

num

the information to summarize.

label

the classification of the cohort.

digits

how many significant digits are to be used.

scientific

either a logical specifying whether result should be encoded in scientific format.

range

the range to be visualized.

logchange

either a logical specifying whether log2 of fold change should be visualized.

Value

The function returns a table with the summarized information. If the number of group is equal to two, the p-value is computed using the Wilcoxon rank-sum test, Kruskal-Wallis test otherwise.

References

Cacciatore S, Luchinat C, Tenori L Knowledge discovery by accuracy maximization. Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA KODAMA: an updated R package for knowledge discovery and data mining. Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link

Examples

Run this code

# NOT RUN {
data(clinical)

A=categorical_table("Gender",clinical[,"Gender"],clinical[,"Hospital"])
B=categorical_table("Gleason score",clinical[,"Gleason score"],clinical[,"Hospital"])
C=categorical_table("Ethnicity",clinical[,"Ethnicity"],clinical[,"Hospital"])

D=continuous_table("BMI",clinical[,"BMI"],clinical[,"Hospital"],digits=2)
E=continuous_table("Age",clinical[,"Age"],clinical[,"Hospital"],digits=1)

rbind(A,B,C,D,E)

# }