Package 'Elja'

Title: Linear, Logistic and Generalized Linear Models Regressions for the EnvWAS/EWAS Approach
Description: Tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. It includes three functions. One function for linear regression, a second for logistic regression and a last one for generalized linear models.
Authors: Marwan El Homsi [aut, cre, cph] (<https://orcid.org/0000-0002-5782-6757>, Desbrest Institute of Epidemiology and Public Health, Univ Montpellier, INSERM, Montpellier, France.), Isabella Annesi-Maesano [ctb, ths, fnd] (<https://orcid.org/0000-0002-6340-9300>, Desbrest Institute of Epidemiology and Public Health, Univ Montpellier, INSERM, Montpellier, France., Department of Allergic and Respiratory Diseases, Montpellier University Hospital, Montpellier.)
Maintainer: Marwan El Homsi <[email protected]>
License: GPL (>= 3)
Version: 1.0.0
Built: 2024-10-26 03:56:12 UTC
Source: https://github.com/ehmarwan/elja

Help Index

Generalized Linear Models regression for EnvWAS/EWAS analysis

Description

A tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. This function is espacially for generalized linear models 'glm' and allows the addition of adjustment variables.

Usage

ELJAglm(
  var,
  var_adjust = NULL,
  family = binomial(link = "logit"),
  data,
  manplot = TRUE,
  nbvalmanplot = 100,
  Bonferroni = FALSE,
  FDR = FALSE,
  manplotsign = FALSE
)

Arguments

var

A categorical and binary variable. It is generally your outcome.
var_adjust

A vector containing the names of the fixed adjustment variables for all the models.
family

The family and the link use for the glm function.
data

A dataframe containing all the variables needed for the analysis.
manplot

Generate a Manhattan plot of the results of the analysis.
nbvalmanplot

The number of variables to include in each Manhattan plot.
Bonferroni

Add a dashed bar to the Manhattan plot showing the Bonferroni significance threshold.
FDR

Add a dashed bar to the Manhattan plot showing the False Discovery Rate (Benjamini-Hochberg method) significance threshold. NA if all p-values > FDR corrected p-values.
manplotsign

Generates a Manhattan plot with only significant results (p<0.05).

Value

A Dataframe with results for each variable of the model.

References

Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300. MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/ Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.

Examples

### Loading the PIMA dataset contained in the mlbench package

library(mlbench)
data(PimaIndiansDiabetes)

### Using ELJAlinear to perform EWAS analysis

ELJAglm(var = 'diabetes',data = PimaIndiansDiabetes,
family = binomial(link = "logit"), manplot = TRUE, Bonferroni = TRUE,
FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results

Linear regression for EnvWAS/EWAS analysis

Description

A tool for Environment-Wide Association Studies (EnvWAS / EWAS) namely repeated analyses allowing to estimate the relationships between several environmental factors and a health events. This function is especially for linear regressions and allows the addition of adjustment variables.

Usage

ELJAlinear(
  var,
  var_adjust = NULL,
  data,
  manplot = TRUE,
  nbvalmanplot = 100,
  Bonferroni = FALSE,
  FDR = FALSE,
  manplotsign = FALSE
)

Arguments

var

A categorical and binary variable. It is generally your outcome.
var_adjust

A vector containing the names of the fixed adjustment variables for all the models.
data

A dataframe containing all the variables needed for the analysis.
manplot

Generate a Manhattan plot of the results of the analysis.
nbvalmanplot

The number of variables to include in each Manhattan plot.
Bonferroni

Add a dashed bar to the Manhattan plot showing the Bonferroni significance level.
FDR

Add a dashed bar to the Manhattan plot showing the False Discovery Rate (Benjamini-Hochberg method) significance threshold. NA if all p-values > FDR corrected p-values.
manplotsign

Generates a Manhattan plot with only significant results (p<0.05).

Value

A Dataframe with results for each variable of the model.

References

Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300. MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/ Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.

Examples

### Loading the PIMA dataset contained in the mlbench package

library(mlbench)
data(PimaIndiansDiabetes)

### Using ELJAlinear to perform EWAS analysis

ELJAlinear(var = 'pregnant',data = PimaIndiansDiabetes,manplot = TRUE,
Bonferroni = TRUE,FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results

Logistic regression tool for EnvWAS/EWAS analysis

Description

A tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. This function is espacially for logistic regression based on the glm function with a binomial family with a logit link and allows the addition of adjustment variables.

Usage

ELJAlogistic(
  var,
  var_adjust = NULL,
  data,
  manplot = TRUE,
  nbvalmanplot = 100,
  Bonferroni = FALSE,
  FDR = FALSE,
  manplotsign = FALSE
)

Arguments

var

A categorical and binary variable. It is generally your outcome.
var_adjust

A vector containing the names of the fixed adjustment variables for all the models.
data

A dataframe containing all the variables needed for the analysis.
manplot

Generate a Manhattan plot of the results of the analysis.
nbvalmanplot

The number of variables to include in each Manhattan plot.
Bonferroni

Add a dashed bar to the Manhattan plot showing the Bonferroni significance level.
FDR

Add a dashed bar to the Manhattan plot showing the False Discovery Rate (Benjamini-Hochberg method) significance threshold. NA if all p-values > FDR corrected p-values.
manplotsign

Generates a Manhattan plot with only significant results (p<0.05).

Value

A Dataframe with results for each variable of the model.

References

Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300. MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/ Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.

Examples

### Loading the PIMA dataset contained in the mlbench package

library(mlbench)
data(PimaIndiansDiabetes)

### Using ELJAlinear to perform EWAS analysis

ELJAlogistic(var = 'diabetes',data = PimaIndiansDiabetes,manplot = TRUE,
Bonferroni = TRUE,FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results