Help for package AEenrich

Version:

1.1.1

Title:

Adverse Event Enrichment Tests

Type:

Package

Description:

We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) <doi:10.48550/arXiv.2007.02266>.

License:

GPL-2

Encoding:

UTF-8

LazyData:

true

Biarch:

true

Depends:

R (≥ 3.5.0)

Imports:

dplyr, magrittr, qvalue, doParallel, tidyr, modelr, foreach, rlang, utils

URL:

https://github.com/umich-biostatistics/AEenrich

BugReports:

https://github.com/umich-biostatistics/AEenrich/issues

RoxygenNote:

7.3.3

Suggests:

testthat

NeedsCompilation:

Packaged:

2026-01-22 14:17:55 UTC; mk

Author:

Shuoran Li [aut], Hongfan Chen [aut], Lili Zhao [aut], Michael Kleinsasser [aut, cre]

Maintainer:

Michael Kleinsasser <mkleinsa@umich.edu>

Repository:

CRAN

Date/Publication:

2026-01-22 14:30:02 UTC

AEenrich: Adverse Event Enrichment Tests

Description

The count_cases function converts report-level data into aggregated data by grouping on specified covariates. Use count_cases to summarize adverse event reports into analysis-ready tables. See the GitHub home page at https://github.com/umich-biostatistics/AEenrich or run ?count_cases for examples.

Perform adverse event enrichment tests. The enrich function implements enrichment analysis for adverse event count data, which contain many zeros and ties. Two methods are provided: AEFisher, a modified Fisher’s exact test based on pre-selected significant adverse events, and AEKS, a modified Kolmogorov–Smirnov statistic. See the GitHub home page at https://github.com/umich-biostatistics/AEenrich or run ?enrich for examples.

Author(s)

Maintainer: Michael Kleinsasser mkleinsa@umich.edu

Authors:

Shuoran Li shuoranl@umich.edu
Hongfan Chen chenhf@umich.edu
Lili Zhao zhaolili@med.umich.edu

Convert data on the report level to aggregated data.

Description

The count_cases function is used to convert data on the report level to aggregated data, grouping by specified covariates.

Usage

count_cases(
  data,
  drug.case = drug.case,
  drug.control = NULL,
  covar_disc = NULL,
  covar_cont = NULL,
  breaks = NULL,
  cores = detectCores(),
  min_AE = 10
)

Arguments

data

a data.frame with at least 3 columns, consisting data on the report level, having ID, Drug type and AE name as the first 3 columns with covariates(optional) followed. The order of columns is not interchangeable.

drug.case

a character string for the target drug of interest.

drug.control

a character string for the reference drug. If NULL(default), all other drugs combined are the reference.

covar_disc

a character vector of categorical covariates.

covar_cont

a character vector of continuous covariates.

breaks

a list consists of vectors used for creating specific bins to transform continuous covariates into categorical. Breaks Should have the same length as covar_cont. Given a vector of non-decreasing breakpoints in breaks[i], find the interval containing each element of covar_cont[i]; i.e., for each index j in breaks[i], value j is assigned to covar_cont[i] if and only if breaks[i][j] ⁠<= covar_cont[i] < breaks[i][j+1]⁠.

cores

the number of cores to use for parallel execution.

min_AE

the minimum number of cases required to start counting for a specific AE. Default 10.

Value

A data.frame consists of aggregated data.

The returned data.frame contains the following columns:

DRUG_TYPE:: type of the drug, DrugYes for target drug and DrugNo for referenced drug
AE_NAME:: the name of the adverse event
AEyes:: number of observations that have this AE
AEno:: number of observations that do not have this AE
covariates:: covariates specifed by user

Examples


# count_cases(data = covid1, drug.case = "COVID19", drug.control = "OTHER",
#             covar_cont = c("AGE"), covar_disc = c("SEX"),
#             breaks = list(c(16,30,50,65,120)))

Covid Vaccine Adverse Event Data

Description

Adverse event data in the long format. Each row is a single adverse event, along with covariates.

Usage

covid1

Format

An object of class tbl_df (inherits from tbl, data.frame) with 12500 rows and 5 columns.

Value

A data frame with the following columns:

VAERS_ID

Event ID

VAX_LABEL

Vaccine type

AE_NAME

Adverse event name

AGE

Covariate

SEX

Covariate

Covid Vaccine Adverse Event Data

Description

Adverse event data in the short format. Each row is a count of adverse events with the given name.

Usage

covid2

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2656 rows and 6 columns.

Value

A data frame with the following columns:

DRUG_TYPE

Vaccine type

AE_NAME

Adverse event name

AEYes

Number of observations that have this adverse event

AENo

Number of observations that do not have this adverse event

AGE

Covariate

SEX

Covariate

Perform Adverse Event Enrichment Tests

Description

The enrich function is used to perform Adverse event (AE) enrichment analysis. Unlike the continuous gene expression data, AE data are counts. Therefore, AE data has many zeros and ties. We propose two enrichment tests. AEFisher is a modified Fisher's exact test based on pre-selected significant AEs, while AEKS is based on a modified Kolmogorov-Smirnov statistic.

Usage

enrich(
  data,
  dd.group,
  drug.case,
  drug.control = NULL,
  method = "aeks",
  n_perms = 1000,
  covar = NULL,
  p = 0,
  q.cut = 0.1,
  or.cut = 1.5,
  zero = FALSE,
  min_size = 5,
  min_AE = 10,
  cores = detectCores()
)

Arguments

data

a data.frame. Two data types are allowed. Type I data consisting data on the report level, having ID, Drug type and AE name as the first 3 columns with covariates(optional) followed. Type II data have drug type and AE name as the first two columns, with the 3rd and 4th Columns giving the numbers of successes(have AE) and failures(Do not have AE) respectively, then followed by covariates. See example data for details.

dd.group

a data.frame with AE name and Group name. This data.frame have the group information for each individual AE.

drug.case

a character string for the target drug of interest.

drug.control

a character string for the reference drug. If NULL(default), all other drugs combined are the reference.

method

a character string specifying the method for the enrichment test. It must take "aeks" (default) or "aefisher"; "aeks" is the rank-based enrichment test, and "aefisher" is the Fisher enrichment test. See details described in the paper (see reference section of this document).

n_perms

an integer value specifying the number of permutations in permutation test.

covar

a character vector specifying the columns of covariates, default NULL.

p

a numerical value to control the weight of the step, can take any value between 0 and 1. If 0(default), reduces to the standard Kolmogorov-Smirnov statistics.

q.cut

a numerical value specifying the significance cut for q value of AEs in aefisher.

or.cut

a numerical value specifying the significance cut for odds ratio of AEs in aefisher.

zero

logical, default FALSE.If TRUE, add zero indicator to enrichment score.

min_size

the minimum size of group required for enrichment analysis.

min_AE

the minimum number of cases required to start counting for a specific AE.

cores

the number of cores to use for parallel execution.

Value

A list containing 2 data.frames named Final_result and AE_info.

The Final_result data.frame contains the following columns:

GROUP_NAME:: AE group names
ES:: enrichment score
p_value:: p value of the enrichment test
GROUP_SIZE:: number of AEs per group

The AE_info contains the following columns:

AE_NAME:: AE names
OR:: odds ratio for each individual AE
p_value:: p value for AE-drug association
95Lower:: lower bound of 95 percent confidence interval of odds ratio
95Lower:: upper bound of 95 percent confidence interval of odds ratio
se(logOR):: standard error of log odds ratio

References

Li, S. and Zhao, L. (2020). Adverse event enrichment tests using VAERS. doi:10.48550/arXiv.2007.02266.

Subramanian, A.e.a. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. Proceedings of the National Academy of Sciences. 102. 15545-15550.

Tian, Lu & Greenberg, Steven & Kong, Sek Won & Altschuler, Josiah & Kohane, Isaac & Park, Peter. (2005). Discovering statistically significant pathways in expression profiling studies. Proceedings of the National Academy of Sciences of the United States of America. 102. 13544-9. 10.1073/pnas.0506577102.

Examples



# AEKS

### Type I data: data on report level
# enrich(data = covid1, covar = c("SEX", "AGE"), p = 0, method = "aeks",
#        n_perms = 1000, drug.case = "COVID19", dd.group = group, cores = 2,
#        drug.control = "OTHER", min_size = 5, min_AE = 10, zero = FALSE)
      
## Type II data: aggregated data
# enrich(data = covid2, covar = c("SEX", "AGE"), p = 0, method = "aeks",
#        n_perms = 1000, drug.case = "DrugYes", dd.group = group, cores = 2,
#        drug.control = "DrugNo", min_size = 5, min_AE = 10)
      
# AEFISHER
## Type I data: data on report level
# enrich(data = covid1, covar = c("SEX", "AGE"), p = 0, method = "aefisher",
#        n_perms = 1000, drug.case = "COVID19", dd.group = group,
#        drug.control = "OTHER", min_size = 5, min_AE = 10, q.cut = 0.05, 
#        or.cut = 1.5, cores = 2)
      
## Type II data: aggregated data
# enrich(data = covid2, covar = c("SEX", "AGE"), p = 0, method = "aefisher",
#        n_perms = 1000, drug.case = "DrugYes", dd.group = group,
#        drug.control = "DrugNo", min_size = 5, min_AE = 10, cores = 2)

Group Structure Data

Description

Identifies which group each set of adverse events belongs.

Usage

group

Format

An object of class tbl_df (inherits from tbl, data.frame) with 35339 rows and 2 columns.

Value

A data frame with the following columns:

AE_NAME

Adverse event name

GROUP_NAME

Group name

AEenrich: Adverse Event Enrichment Tests

Description

Author(s)

See Also

Convert data on the report level to aggregated data.

Description

Usage

Arguments

Value

Examples

Covid Vaccine Adverse Event Data

Description

Usage

Format

Value

Covid Vaccine Adverse Event Data

Description

Usage

Format

Value

Perform Adverse Event Enrichment Tests

Description

Usage

Arguments

Value

References

Examples

Group Structure Data

Description

Usage

Format

Value