In hanqiluo/BRINDA: Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
BRINDA
The BRINDA R package is a user-friendly all-in-one R package that uses a series
of functions to implement BRINDA adjustment method .
Installation
install.packages("BRINDA")
Development version
Alternatively, you can install the development version of BRINDA from GitHub with:
install.packages("devtools")
devtools::install_github("hanqiluo/BRINDA")
Example
This is a basic example which shows you how to use the BRINDA package to adjust retinol binding protein,
retinol, ferritin, soluble transferrin receptor, and zinc using AGP and/or
CRP.
library(BRINDA)
## basic example code
data(sample_data)
#
# Example 1: Preschool-age children (PSC) -----------------------------
# calculate BRINDA inflammation adjustment values for preschool-age children (Assuming the dataset contain information of preschool-age children)
#
# Example 1.1 PSC: Both AGP and CRP are available
#
final_data_psc <- BRINDA(
# Enter the name of the dataset
dataset = sample_data,
# Enter the variable name of retinol binding protein in your dataset (if available)
retinol_binding_protein_varname = rbp,
# Enter the variable name of serum/plasma retinol in your dataset (if available)
retinol_varname = sr,
# Enter the variable name of serum/plasma ferritin in your dataset (if available)
ferritin_varname = sf,
# Enter the variable name of serum/plasma soluble transferrin receptor in your dataset (if available)
soluble_transferrin_receptor_varname = stfr,
# Enter the variable name of serum/plasma zinc in your dataset (if available)
zinc_varname = zinc,
# Enter the variable name of CRP (unit must be mg/L) in your dataset (if available)
crp_varname = crp,
# Enter the variable name of AGP (unit must be g/L) in your dataset (if available)
agp_varname = agp,
# Please write WRA, PSC, Other, or Manual.
population = Psc,
# leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for PSC
crp_ref_value_manual = ,
# leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for PSC
agp_ref_value_manual = ,
# Please write FULL or SIMPLE (default output is SIMPLE, if users leave output_format empty)
output_format = full)
#
# Example 1.2 PSC: Only AGP is available
#
final_data_psc <- BRINDA(
# Enter the name of the dataset
dataset = sample_data,
# Enter the variable name of retinol binding protein in your dataset (if available)
retinol_binding_protein_varname = rbp,
# Enter the variable name of serum/plasma retinol in your dataset (if available)
retinol_varname = sr,
# Enter the variable name of serum/plasma ferritin in your dataset (if available)
ferritin_varname = sf,
# Enter the variable name of serum/plasma soluble transferrin receptor in your dataset (if available)
soluble_transferrin_receptor_varname = stfr,
# Enter the variable name of serum/plasma zinc in your dataset (if available)
zinc_varname = zinc,
# Enter the variable name of CRP (unit must be mg/L) in your dataset (if available)
# crp_varname = crp,
# Enter the variable name of AGP (unit must be g/L) in your dataset (if available)
agp_varname = agp,
# Please write WRA, PSC, Other, or Manual.
population = Psc,
# leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for PSC
crp_ref_value_manual = ,
# leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for PSC
agp_ref_value_manual = ,
# Please write FULL or SIMPLE (default output is SIMPLE, if users leave output_format empty)
output_format = full)
#
# Example 1.3 PSC: Only CRP is available
#
final_data_psc <- BRINDA(
# Enter the name of the dataset
dataset = sample_data,
# Enter the variable name of retinol binding protein in your dataset (if available)
retinol_binding_protein_varname = rbp,
# Enter the variable name of serum/plasma retinol in your dataset (if available)
retinol_varname = sr,
# Enter the variable name of serum/plasma ferritin in your dataset (if available)
ferritin_varname = sf,
# Enter the variable name of serum/plasma soluble transferrin receptor in your dataset (if available)
soluble_transferrin_receptor_varname = stfr,
# Enter the variable name of serum/plasma zinc in your dataset (if available)
zinc_varname = zinc,
# Enter the variable name of CRP (unit must be mg/L) in your dataset (if available)
crp_varname = crp,
# Enter the variable name of AGP (unit must be g/L) in your dataset (if available)
# agp_varname = agp,
# Please write WRA, PSC, Other, or Manual.
population = Psc,
# leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for PSC
crp_ref_value_manual = ,
# leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for PSC
agp_ref_value_manual = ,
# Please write FULL or SIMPLE (default output is SIMPLE, if users leave output_format empty)
output_format = full)
#
# Example 2: Women of reproductive age (WRA) -----------------------------
# calculate BRINDA inflammation adjustment values for non-pregnant women of reproductive age assuming the sample dataset contain information of women of reproductive age).
final_data_wra <- BRINDA(dataset = sample_data,
retinol_binding_protein_varname = rbp,
retinol_varname = sr,
ferritin_varname = sf,
soluble_transferrin_receptor_varname = stfr,
zinc_varname = zinc,
crp_varname = crp,
agp_varname = agp,
# Please write WRA, PSC, Other, or Manual.
population = WRA,
# leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for WRA
crp_ref_value_manual = ,
# leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for WRA
agp_ref_value_manual = ,
output_format = simple)
#
# Example 3: Other population groups ----------------------------------
# calculate BRINDA inflammation adjustment values for other population assuming the study population is neither women of reproductive age nor preschool-age children
#
final_data_other <- BRINDA(dataset = sample_data,
retinol_binding_protein_varname = rbp,
retinol_varname = sr,
ferritin_varname = sf,
soluble_transferrin_receptor_varname = stfr,
zinc_varname = zinc,
crp_varname = crp,
agp_varname = agp,
population = OTHER,
# leave crp_ref_value_manual empty, BRINDA R package will calculate the lowest decile of CRP and use it as the crp reference value
crp_ref_value_manual = ,
# leave agp_ref_value_manual empty, BRINDA R package will calculate the lowest decile of AGP and use it as the agp reference value
agp_ref_value_manual = ,
output_format = FULL)
#
# Example 4: User-defined CRP and AGP -------------------------------
# calculate BRINDA inflammation adjustment values for a population when users would like to apply user-defined CRP and AGP reference values
final_data_manual <- BRINDA(dataset = sample_data,
retinol_binding_protein_varname = rbp,
retinol_varname = sr,
ferritin_varname = sf,
soluble_transferrin_receptor_varname = stfr,
zinc_varname = zinc,
crp_varname = crp,
agp_varname = agp,
# If users select Manual as the population group,
# users can select their own AGP and CRP reference values for the BRINDA adjustment.
population = MANUAL,
# Enter a user-specified CRP reference value
crp_ref_value_manual = 0.2,
# Enter a user-specified AGP reference value
agp_ref_value_manual = 1.4,
output_format = SIMPLE)
Citation
Luo, H.; Addo, Y.; Geng, J (2022) BRINDA: Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation. R package version 0.1.5, https://CRAN.R-project.org/package=BRINDA
Contributing
If you would like to report bugs, suggest features, or leave comments, please create issues.
If you would like to contribute code, please fork the source code, modify, and issue a pull request.
Acknowledgements
The Authors thank all members of the BRINDA working group who helped in developing the BRINDA adjustment method. The authors also thank Charles D. Arnold, Fanny Sandalinas, Kevin Tang, and Lucas Gosdin for the extensive testing of the package, Joanne Arsenault and Christine McDonald for their editing and comments, and Jae Yeon Kim for his assistance with CRAN submission.
hanqiluo/BRINDA documentation built on Oct. 16, 2022, 8:33 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
BRINDA
The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method .
Installation
install.packages("BRINDA")
Development version
Alternatively, you can install the development version of BRINDA from GitHub with:
install.packages("devtools") devtools::install_github("hanqiluo/BRINDA")
Example
This is a basic example which shows you how to use the BRINDA package to adjust retinol binding protein, retinol, ferritin, soluble transferrin receptor, and zinc using AGP and/or CRP.
library(BRINDA) ## basic example code data(sample_data) # # Example 1: Preschool-age children (PSC) ----------------------------- # calculate BRINDA inflammation adjustment values for preschool-age children (Assuming the dataset contain information of preschool-age children) # # Example 1.1 PSC: Both AGP and CRP are available # final_data_psc <- BRINDA( # Enter the name of the dataset dataset = sample_data, # Enter the variable name of retinol binding protein in your dataset (if available) retinol_binding_protein_varname = rbp, # Enter the variable name of serum/plasma retinol in your dataset (if available) retinol_varname = sr, # Enter the variable name of serum/plasma ferritin in your dataset (if available) ferritin_varname = sf, # Enter the variable name of serum/plasma soluble transferrin receptor in your dataset (if available) soluble_transferrin_receptor_varname = stfr, # Enter the variable name of serum/plasma zinc in your dataset (if available) zinc_varname = zinc, # Enter the variable name of CRP (unit must be mg/L) in your dataset (if available) crp_varname = crp, # Enter the variable name of AGP (unit must be g/L) in your dataset (if available) agp_varname = agp, # Please write WRA, PSC, Other, or Manual. population = Psc, # leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for PSC crp_ref_value_manual = , # leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for PSC agp_ref_value_manual = , # Please write FULL or SIMPLE (default output is SIMPLE, if users leave output_format empty) output_format = full) # # Example 1.2 PSC: Only AGP is available # final_data_psc <- BRINDA( # Enter the name of the dataset dataset = sample_data, # Enter the variable name of retinol binding protein in your dataset (if available) retinol_binding_protein_varname = rbp, # Enter the variable name of serum/plasma retinol in your dataset (if available) retinol_varname = sr, # Enter the variable name of serum/plasma ferritin in your dataset (if available) ferritin_varname = sf, # Enter the variable name of serum/plasma soluble transferrin receptor in your dataset (if available) soluble_transferrin_receptor_varname = stfr, # Enter the variable name of serum/plasma zinc in your dataset (if available) zinc_varname = zinc, # Enter the variable name of CRP (unit must be mg/L) in your dataset (if available) # crp_varname = crp, # Enter the variable name of AGP (unit must be g/L) in your dataset (if available) agp_varname = agp, # Please write WRA, PSC, Other, or Manual. population = Psc, # leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for PSC crp_ref_value_manual = , # leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for PSC agp_ref_value_manual = , # Please write FULL or SIMPLE (default output is SIMPLE, if users leave output_format empty) output_format = full) # # Example 1.3 PSC: Only CRP is available # final_data_psc <- BRINDA( # Enter the name of the dataset dataset = sample_data, # Enter the variable name of retinol binding protein in your dataset (if available) retinol_binding_protein_varname = rbp, # Enter the variable name of serum/plasma retinol in your dataset (if available) retinol_varname = sr, # Enter the variable name of serum/plasma ferritin in your dataset (if available) ferritin_varname = sf, # Enter the variable name of serum/plasma soluble transferrin receptor in your dataset (if available) soluble_transferrin_receptor_varname = stfr, # Enter the variable name of serum/plasma zinc in your dataset (if available) zinc_varname = zinc, # Enter the variable name of CRP (unit must be mg/L) in your dataset (if available) crp_varname = crp, # Enter the variable name of AGP (unit must be g/L) in your dataset (if available) # agp_varname = agp, # Please write WRA, PSC, Other, or Manual. population = Psc, # leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for PSC crp_ref_value_manual = , # leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for PSC agp_ref_value_manual = , # Please write FULL or SIMPLE (default output is SIMPLE, if users leave output_format empty) output_format = full) # # Example 2: Women of reproductive age (WRA) ----------------------------- # calculate BRINDA inflammation adjustment values for non-pregnant women of reproductive age assuming the sample dataset contain information of women of reproductive age). final_data_wra <- BRINDA(dataset = sample_data, retinol_binding_protein_varname = rbp, retinol_varname = sr, ferritin_varname = sf, soluble_transferrin_receptor_varname = stfr, zinc_varname = zinc, crp_varname = crp, agp_varname = agp, # Please write WRA, PSC, Other, or Manual. population = WRA, # leave crp_ref_value_manual empty, BRINDA R package will use an external crp reference value for WRA crp_ref_value_manual = , # leave agp_ref_value_manual empty, BRINDA R package will use an external agp reference value for WRA agp_ref_value_manual = , output_format = simple) # # Example 3: Other population groups ---------------------------------- # calculate BRINDA inflammation adjustment values for other population assuming the study population is neither women of reproductive age nor preschool-age children # final_data_other <- BRINDA(dataset = sample_data, retinol_binding_protein_varname = rbp, retinol_varname = sr, ferritin_varname = sf, soluble_transferrin_receptor_varname = stfr, zinc_varname = zinc, crp_varname = crp, agp_varname = agp, population = OTHER, # leave crp_ref_value_manual empty, BRINDA R package will calculate the lowest decile of CRP and use it as the crp reference value crp_ref_value_manual = , # leave agp_ref_value_manual empty, BRINDA R package will calculate the lowest decile of AGP and use it as the agp reference value agp_ref_value_manual = , output_format = FULL) # # Example 4: User-defined CRP and AGP ------------------------------- # calculate BRINDA inflammation adjustment values for a population when users would like to apply user-defined CRP and AGP reference values final_data_manual <- BRINDA(dataset = sample_data, retinol_binding_protein_varname = rbp, retinol_varname = sr, ferritin_varname = sf, soluble_transferrin_receptor_varname = stfr, zinc_varname = zinc, crp_varname = crp, agp_varname = agp, # If users select Manual as the population group, # users can select their own AGP and CRP reference values for the BRINDA adjustment. population = MANUAL, # Enter a user-specified CRP reference value crp_ref_value_manual = 0.2, # Enter a user-specified AGP reference value agp_ref_value_manual = 1.4, output_format = SIMPLE)
Citation
Luo, H.; Addo, Y.; Geng, J (2022) BRINDA: Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation. R package version 0.1.5, https://CRAN.R-project.org/package=BRINDA
Contributing
If you would like to report bugs, suggest features, or leave comments, please create issues.
If you would like to contribute code, please fork the source code, modify, and issue a pull request.
Acknowledgements
The Authors thank all members of the BRINDA working group who helped in developing the BRINDA adjustment method. The authors also thank Charles D. Arnold, Fanny Sandalinas, Kevin Tang, and Lucas Gosdin for the extensive testing of the package, Joanne Arsenault and Christine McDonald for their editing and comments, and Jae Yeon Kim for his assistance with CRAN submission.
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