Package 'clinical' reference manual

Title:	Clinical Metadata Exploration and Feature Matching
Description:	A collection of tools to easily analyze clinical data, including functions for correlation analysis, and statistical testing. The package facilitates the integration of clinical metadata with other omics layers, enabling exploration of quantitative variables. It also includes the utility for frequency matching samples across a dataset based on patient variables.
Authors:	Stefano Cacciatore [aut, trl, cre]
Maintainer:	Stefano Cacciatore <[email protected]>
License:	GPL (>= 2)
Version:	0.1
Built:	2026-05-07 07:51:45 UTC
Source:	https://github.com/tkcaccia/clinical

Add analysis results to a clinical object

Description

This function appends the results of an analysis to a clinical object.

Usage

add_analysis(ma, name, x)
add_analysis(ma, name, x)

Arguments

ma

A clinical object to which results will be attached.

name

The name (string) of the analysis.

x

The result object (e.g., data frame, matrix) to attach.

Value

Returns the updated clinical object with the new analysis appended.

Author(s)

Stefano Cacciatore

Examples

data(prostate)


ma=initialization(prostate[,"Hospital"])
ma=add_analysis(ma,"Gender",prostate[,"Gender"])
ma=add_analysis(ma,"Gleason score",prostate[,"Gleason score"])
ma=add_analysis(ma,"BMI",prostate[,"BMI"])
ma=add_analysis(ma,"Age",prostate[,"Age"])
ma
data(prostate)


ma=initialization(prostate[,"Hospital"])
ma=add_analysis(ma,"Gender",prostate[,"Gender"])
ma=add_analysis(ma,"Gleason score",prostate[,"Gleason score"])
ma=add_analysis(ma,"BMI",prostate[,"BMI"])
ma=add_analysis(ma,"Age",prostate[,"Age"])
ma

Convert Character Matrix to Numeric Matrix

Description

Converts a matrix of character or factor entries to a numeric matrix, preserving row and column names.

Usage

as.data.matrix(x)
as.data.matrix(x)

Arguments

x

A matrix containing character or factor values that represent numeric entries.

Details

This function is useful when a matrix has been read as character or factor and needs to be transformed into numeric format for analysis. It preserves both the row and column names of the input matrix.

Value

A numeric matrix of the same dimensions as x, with the same row and column names.

Author(s)

Stefano Cacciatore

Examples

# Create a character matrix
ma <- matrix(c("1", "2", "3", "4"), ncol = 2)

# Convert to numeric matrix
ma_numeric <- as.data.matrix(ma)

# Print result
ma_numeric
# Create a character matrix
ma <- matrix(c("1", "2", "3", "4"), ncol = 2)

# Convert to numeric matrix
ma_numeric <- as.data.matrix(ma)

# Print result
ma_numeric

Categorical Information

Description

Summarization of the categorical information.

Usage

categorical.test (name,x,y,total.column=FALSE,...)
categorical.test (name,x,y,total.column=FALSE,...)

Arguments

name

the name of the feature.

x

the information to summarize.

y

the classification of the cohort.

total.column

option to visualize the total (by default = "FALSE").

...

further arguments to be passed to the function.

Value

The function returns a table with the summarized information and The p-value computated using the Fisher's test.

Author(s)

Stefano Cacciatore

Examples


data(prostate)

hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

rbind(A,B,C,D)


data(prostate)

hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

rbind(A,B,C,D)

Continuous Information

Description

Summarization of the continuous information.

Usage

continuous.test (name,
                 x,    
                 y,
                 center = c("median", "mean"),
                 digits = 3,
                 scientific = FALSE, 
                 range = c("IQR","95%CI","range","sd"), 
                 logchange = FALSE,
                 pos=2, 
                 method=c("non-parametric","parametric"),
                 total.column=FALSE, ...) 
continuous.test (name,
                 x,    
                 y,
                 center = c("median", "mean"),
                 digits = 3,
                 scientific = FALSE, 
                 range = c("IQR","95%CI","range","sd"), 
                 logchange = FALSE,
                 pos=2, 
                 method=c("non-parametric","parametric"),
                 total.column=FALSE, ...)

Arguments

name

the name of the feature.

x

the information to summarize.

y

the classification of the cohort.

center

A character string specifying the measure of central tendency to report: either "median" or "mean".

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.

pos

a value indicating the position of range to be visualized. 1 for column, 2 for row.

method

a character string indicating which test method is to be computed. "non-parametric" (default), or "parametric".

total.column

option to visualize the total (by default = "FALSE")

...

further arguments to be passed to or from methods.

Value

The function returns a table with the summarized information and the relative p-value. For non-parametric method, if the number of group is equal to two, the p-value is computed using the Wilcoxon rank-sum test, Kruskal-Wallis test otherwise. For parametric method, if the number of group is equal to two, the p-value is computed using the Student's t-Test, ANOVA one-way otherwise.

Author(s)

Stefano Cacciatore

Examples

data(prostate)

hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

rbind(A,B,C,D)


data(prostate)

hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

rbind(A,B,C,D)

Continuous Information

Description

Summarization of the continuous information.

Usage

correlation.test (x,
                  y,
                  method = c("pearson", "spearman","MINE"), 
                  name=NA, 
                  perm=100, ...)
correlation.test (x,
                  y,
                  method = c("pearson", "spearman","MINE"), 
                  name=NA, 
                  perm=100, ...)

Arguments

x

a numeric vector.

y

a numeric vector.

method

a character string indicating which correlation method is to be computed. "pearson" (default), "spearman", or "MINE".

name

the name of the feature.

perm

number of permutation needed to estimate the p-value with MINE correlation.

...

further arguments to be passed to or from methods.

Value

The function returns a table with the summarized information.

Author(s)

Stefano Cacciatore

Examples

data(prostate)

correlation.test(prostate[,"Age"],prostate[,"BMI"],name="correlation between Age and BMI")

data(prostate)

correlation.test(prostate[,"Age"],prostate[,"BMI"],name="correlation between Age and BMI")

Frequency Matching

Description

A method to select unbalanced groupd in a cohort.

Usage

frequency_matching (data,label,times=5,seed=1234)
frequency_matching (data,label,times=5,seed=1234)

Arguments

data

a data.frame of data.

label

a classification of the groups.

times

The ratio between the two groups.

seed

a single number for random number generation.

Value

The function returns a list with 2 items or 4 items (if a test data set is present):

data

the data after the frequency matching.

label

the label after the frequency matching.

selection

the rows selected for the frequency matching.

Author(s)

Stefano Cacciatore

Examples

data(prostate)



hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

# Analysis without matching
rbind(A,B,C,D)



# The order is important. Right is more important than left in the vector
# So, Ethnicity will be more important than Age
var=c("Age","BMI","Gleason score")
data.categorized=prostate[,var]

# Extract the Age vector
x <- data.categorized[["Age"]]

# Compute quantiles (0%, 25%, 50%, 75%, 100%) with NA handling
breaks <- quantile(x, probs = c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE)

# Apply the cut and update the Age column with labeled bins
data.categorized[["Age"]] <- cut(x, breaks = breaks, include.lowest = TRUE)

# Extract the Age vector
x <- data.categorized[["BMI"]]

# Compute quantiles (0%, 25%, 50%, 75%, 100%) with NA handling
breaks <- quantile(x, probs = c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE)

# Apply the cut and update the Age column with labeled bins
data.categorized[["BMI"]] <- cut(x, breaks = breaks, include.lowest = TRUE)

times=c(1,1)
names(times)=c("Hospital A","Hospital B")
t=frequency_matching(data.categorized,prostate[,"Hospital"],times=times)



newdata=prostate[t$selection,]

hosp.new=newdata[,"Hospital"]
gender.new=newdata[,"Gender"]
GS.new=newdata[,"Gleason score"]
BMI.new=newdata[,"BMI"]
age.new=newdata[,"Age"]

A=categorical.test("Gender",gender.new,hosp.new)
B=categorical.test("Gleason score",GS.new,hosp.new)

C=continuous.test("BMI",BMI.new,hosp.new,digits=2)
D=continuous.test("Age",age.new,hosp.new,digits=1)

# Analysis with matching
rbind(A,B,C,D)
data(prostate)



hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

# Analysis without matching
rbind(A,B,C,D)



# The order is important. Right is more important than left in the vector
# So, Ethnicity will be more important than Age
var=c("Age","BMI","Gleason score")
data.categorized=prostate[,var]

# Extract the Age vector
x <- data.categorized[["Age"]]

# Compute quantiles (0%, 25%, 50%, 75%, 100%) with NA handling
breaks <- quantile(x, probs = c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE)

# Apply the cut and update the Age column with labeled bins
data.categorized[["Age"]] <- cut(x, breaks = breaks, include.lowest = TRUE)

# Extract the Age vector
x <- data.categorized[["BMI"]]

# Compute quantiles (0%, 25%, 50%, 75%, 100%) with NA handling
breaks <- quantile(x, probs = c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE)

# Apply the cut and update the Age column with labeled bins
data.categorized[["BMI"]] <- cut(x, breaks = breaks, include.lowest = TRUE)

times=c(1,1)
names(times)=c("Hospital A","Hospital B")
t=frequency_matching(data.categorized,prostate[,"Hospital"],times=times)



newdata=prostate[t$selection,]

hosp.new=newdata[,"Hospital"]
gender.new=newdata[,"Gender"]
GS.new=newdata[,"Gleason score"]
BMI.new=newdata[,"BMI"]
age.new=newdata[,"Age"]

A=categorical.test("Gender",gender.new,hosp.new)
B=categorical.test("Gleason score",GS.new,hosp.new)

C=continuous.test("BMI",BMI.new,hosp.new,digits=2)
D=continuous.test("Age",age.new,hosp.new,digits=1)

# Analysis with matching
rbind(A,B,C,D)

Initialize a Clinical Table Object

Description

Initializes a new object of class "clinical.table", setting up the grouping variable and a flag for whether to include a total column.

Usage

initialization(y, total.column = FALSE)
initialization(y, total.column = FALSE)

Arguments

y

A numeric or factor vector representing the outcome or grouping variable.

total.column

Logical; whether to include a total column in the results (default is FALSE).

Value

Returns an S4 object of class "clinical.table".

Examples

y <- factor(c("A", "B", "A", "B"))
ma <- initialization(y)
y <- factor(c("A", "B", "A", "B"))
ma <- initialization(y)

Intersection of Multiple Vectors

Description

Returns the intersection of all vectors provided as arguments.

Usage

intersect(x, y, ...)
intersect(x, y, ...)

Arguments

x

A vector.

y

A vector.

...

Additional vectors to include in the intersection.

Details

This function computes the intersection of two or more vectors by identifying elements that are present in all of them. Unlike the base R intersect, which only compares two vectors, this version can take multiple vectors as input.

Value

A character vector (or NULL if no intersection exists) containing the elements that are common to all input vectors.

Author(s)

Stefano Cacciatore

Examples

x <- c("a", "b", "c")
y <- c("b", "c", "d")
z <- c("c", "b", "e")

intersect(x, y, z)  # Returns "b" and "c"
intersect(x, y, c("f", "g"))  # Returns NULL
x <- c("a", "b", "c")
y <- c("b", "c", "d")
z <- c("c", "b", "e")

intersect(x, y, z)  # Returns "b" and "c"
intersect(x, y, c("f", "g"))  # Returns NULL

Continuous Information

Description

Summarization of the continuous information.

Usage

multi_analysis  (data, 
                 y, 
                 FUN=c("continuous.test","correlation.test"), ...)                 
multi_analysis  (data, 
                 y, 
                 FUN=c("continuous.test","correlation.test"), ...)

Arguments

data

the matrix containing the continuous values. Each row corresponds to a different sample. Each column corresponds to a different variable.

y

the classification of the cohort.

FUN

function to be considered. Choices are "continuous.test" and "correlation.test"

...

further arguments to be passed to or from methods.

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.

Author(s)

Stefano Cacciatore

Examples

data(prostate)


multi_analysis(prostate[,c("BMI","Age")],prostate[,"Hospital"],FUN="continuous.test")

data(prostate)


multi_analysis(prostate[,c("BMI","Age")],prostate[,"Hospital"],FUN="continuous.test")

Clinical Data of a Cohort of Prostate Cancer Patiens

Description

The data belong to a cohort of 35 patients with prostate cancer from two different hospitals.

Usage

data(prostate)data(prostate)

Value

The data.frame "prostate" with the following elements: "Hospital", "Gender", "Gleason score", "BMI", and "Age".

Examples

data(prostate)

head(prostate)
data(prostate)

head(prostate)

Median and Coefficient Interval

Description

Summarization of a numeric vector.

Usage

txtsummary(x,
           f = c("median", "mean"),
           digits = 0,
           scientific = FALSE,
           range = c("IQR", "95%CI", "range", "sd"))
txtsummary(x,
           f = c("median", "mean"),
           digits = 0,
           scientific = FALSE,
           range = c("IQR", "95%CI", "range", "sd"))

Arguments

x

a numeric vector.

f

xxx.

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.

Value

The function returns the median and the range (interquartile or 95% coefficient interval) of numeric vetor.

Author(s)

Stefano Cacciatore

Examples

data(prostate)

txtsummary(prostate[,"BMI"])


data(prostate)

txtsummary(prostate[,"BMI"])

Package 'clinical'

Help Index

Add analysis results to a clinical object

Description

Usage

Arguments

Value

Author(s)

Examples

Convert Character Matrix to Numeric Matrix

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Categorical Information

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Continuous Information

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Continuous Information

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Frequency Matching

Description

Usage

Arguments

Value

Author(s)

Examples

Initialize a Clinical Table Object

Description

Usage

Arguments

Value

Examples

Intersection of Multiple Vectors

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Continuous Information

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Clinical Data of a Cohort of Prostate Cancer Patiens

Description

Usage

Value

Examples

Median and Coefficient Interval

Description

Usage

Arguments