Package 'fcfdr' reference manual

Title:	Flexible cFDR
Description:	Provides functions to implement the Flexible cFDR (Hutchinson et al. (2021) <doi:10.1371/journal.pgen.1009853>) and Binary cFDR (Hutchinson et al. (2021) <doi:10.1101/2021.10.21.465274>) methodologies to leverage auxiliary data from arbitrary distributions, for example functional genomic data, with GWAS p-values to generate re-weighted p-values.
Authors:	Anna Hutchinson [aut, cre] , Chris Wallace [aut], Thomas Willis [ctb, aut], James Liley [ctb]
Maintainer:	Anna Hutchinson <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.0
Built:	2025-01-03 05:12:36 UTC
Source:	https://github.com/annahutch/fcfdr

Perform cFDR leveraging binary auxiliary covariates

Description

Perform cFDR leveraging binary auxiliary covariates

Usage

binary_cfdr(p, q, group)
binary_cfdr(p, q, group)

Arguments

`p`	p-values for principal trait (vector of length n)
`q`	binary auxiliary data values (vector of length n)
`group`	group membership of each SNP for leave-one-out procedure (vector of length n) (e.g. chromosome number or LD block)

Value

data.frame of p, q and v values

Examples


# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the parameters_in_locfdr() function to extract the parameters estimated by
# the locfdr function.

# generate p
set.seed(2)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
q <- rbinom(n, 1, 0.1)

group <- c(rep("A", n/2), rep("B", n/2)) 

binary_cfdr(p, q, group)

# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the parameters_in_locfdr() function to extract the parameters estimated by
# the locfdr function.

# generate p
set.seed(2)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
q <- rbinom(n, 1, 0.1)

group <- c(rep("A", n/2), rep("B", n/2)) 

binary_cfdr(p, q, group)

Violin plot of p-values for quantiles of q

Description

Violin plot of p-values for quantiles of q

Usage

corr_plot(p, q, ylim = c(0, 1.5))
corr_plot(p, q, ylim = c(0, 1.5))

Arguments

`p`	p values for principal trait (vector of length n)
`q`	auxiliary data values (vector of length n)
`ylim`	y-axis limits (-log10)

Details

Can be used to investigate the relationship between p and q

If this shows a non-monotonic relationship then the cFDR framework should not be used

(because e.g. cFDR cannot simultaneously shrink v-values for high p and low p)

Value

ggplot object

Examples


# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the corr_plot() function to visualise the relationship between p and q.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

corr_plot(p, q)

# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the corr_plot() function to visualise the relationship between p and q.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

corr_plot(p, q)

Perform Flexible cFDR

Description

Performs Flexible cFDR for continuous auxiliary covariates

Usage

flexible_cfdr(
  p,
  q,
  indep_index,
  res_p = 300,
  res_q = 500,
  nxbin = 1000,
  gridp = 50,
  splinecorr = TRUE,
  dist_thr = 0.5,
  locfdr_df = 10,
  plot = TRUE,
  maf = NULL,
  check_indep_cor = TRUE,
  enforce_p_q_cor = TRUE
)
flexible_cfdr(
  p,
  q,
  indep_index,
  res_p = 300,
  res_q = 500,
  nxbin = 1000,
  gridp = 50,
  splinecorr = TRUE,
  dist_thr = 0.5,
  locfdr_df = 10,
  plot = TRUE,
  maf = NULL,
  check_indep_cor = TRUE,
  enforce_p_q_cor = TRUE
)

Arguments

`p`	p-values for principal trait (vector of length n)
`q`	continuous auxiliary data values (vector of length n)
`indep_index`	indices of independent SNPs
`res_p`	number of grid points in x-direction (`p`) for KDE estimation
`res_q`	number of grid points in y-direction (`q`) for KDE estimation
`nxbin`	number of bins in x-direction (`p`) for hex-binning
`gridp`	number of data points required in a KDE grid point for left-censoring
`splinecorr`	logical value for whether spline correction should be implemented
`dist_thr`	distance threshold for spline correction
`locfdr_df`	`df` parameter in locfdr function
`plot`	logical value for whether to produce plots to assess KDE fit
`maf`	minor allele frequencies for SNPs to which `p` and `q` relate (optional and used to perform MAF matching)
`check_indep_cor`	check that sign of the correlation between `p` and `q` is the same in the independent subset as in the whole
`enforce_p_q_cor`	if `p` and `q` are negatively correlated, flip the sign on `q` values

Details

If maf is specified, then the independent SNPs will be down-sampled to match the minor allele frequency distribution.

Value

List of length two: (1) data.frame of p-values, q-values and v-values (2) data.frame of auxiliary data (q_low used for left censoring, how many data-points were left censored and/or spline corrected)

Examples


# this is a long running example
 
# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the flexible_cfdr() function to generate v-values using default parameter values.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

flexible_cfdr(p, q, indep_index = 1:n_indep)


# this is a long running example
 
# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the flexible_cfdr() function to generate v-values using default parameter values.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

flexible_cfdr(p, q, indep_index = 1:n_indep)

Plot -log10(p) against -log10(v) and colour by q

Description

Plot -log10(p) against -log10(v) and colour by q

Usage

log10pv_plot(p, q, v, axis_lim = c(0, 20))
log10pv_plot(p, q, v, axis_lim = c(0, 20))

Arguments

`p`	p values for principal trait (vector of length n)
`q`	auxiliary data values (vector of length n)
`v`	v values from cFDR
`axis_lim`	Optional axis limits

Details

Can be used to visualise the results from Flexible cFDR

Value

ggplot object

Examples


# this is a long running example

# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the flexible_cfdr() function to generate v-values and then the log10pv_plot() function
# to visualise the results.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

res <- flexible_cfdr(p, q, indep_index = 1:n_indep)

log10pv_plot(p = res[[1]]$p, q = res[[1]]$q, v = res[[1]]$v)


# this is a long running example

# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the flexible_cfdr() function to generate v-values and then the log10pv_plot() function
# to visualise the results.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

res <- flexible_cfdr(p, q, indep_index = 1:n_indep)

log10pv_plot(p = res[[1]]$p, q = res[[1]]$q, v = res[[1]]$v)

Function to downsample independent SNPs to match MAF distribution of whole set.

Description

Matches MAF distribution of independent set of SNPs to MAF distribution of whole set of SNPs to avoid MAF-based confounding.

Usage

match_ind_maf(maf, indep_index)
match_ind_maf(maf, indep_index)

Arguments

`maf`	minor allele frequencies of (all) SNPs
`indep_index`	indices of independent SNPs

Details

Must supply maf values from the whole data set, not just the independent SNPs.

Value

indices of independent SNP in chosen in sample

parameters_in_locfdr

Description

parameters_in_locfdr

Usage

parameters_in_locfdr(
  p,
  q,
  indep_index,
  res_p = 300,
  res_q = 500,
  maf = NULL,
  check_indep_cor = TRUE,
  enforce_p_q_cor = TRUE
)
parameters_in_locfdr(
  p,
  q,
  indep_index,
  res_p = 300,
  res_q = 500,
  maf = NULL,
  check_indep_cor = TRUE,
  enforce_p_q_cor = TRUE
)

Arguments

`p`	p values for principal trait (vector of length n)
`q`	continuous auxiliary data values (vector of length n)
`indep_index`	indices of independent SNPs
`res_p`	resolution for p
`res_q`	resolution for q
`maf`	minor allele frequencies for SNPs to which `p` and `q` relate (optional and used to perform MAF matching)
`check_indep_cor`	check that sign of the correlation between `p` and `q` is the same in the independent subset as in the whole
`enforce_p_q_cor`	if `p` and `q` are negatively correlated, flip the sign on `q` values

Value

list of values used as input into locfdr::locfdr function intrinsically in flexible_cfdr

Examples


# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the parameters_in_locfdr() function to extract the parameters estimated by
# the locfdr function.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

parameters_in_locfdr(p, q, indep_index = 1:n_indep)


# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the parameters_in_locfdr() function to extract the parameters estimated by
# the locfdr function.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

parameters_in_locfdr(p, q, indep_index = 1:n_indep)

Plot p against v and colour by q

Description

Plot p against v and colour by q

Usage

pv_plot(p, q, v, axis_lim = c(0, 1))
pv_plot(p, q, v, axis_lim = c(0, 1))

Arguments

`p`	p values for principal trait (vector of length n)
`q`	auxiliary data values (vector of length n)
`v`	v values from cFDR
`axis_lim`	Optional axis limits

Details

Can be used to visualise the results from Flexible cFDR

Value

ggplot object

Examples


 # this is a long running example
 
# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the flexible_cfdr() function to generate v-values and then the pv_plot() function
# to visualise the results.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

res <- flexible_cfdr(p, q, indep_index = 1:n_indep)

pv_plot(p = res[[1]]$p, q = res[[1]]$q, v = res[[1]]$v)


# this is a long running example
 
# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing functional genomic data).
# We use the flexible_cfdr() function to generate v-values and then the pv_plot() function
# to visualise the results.

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
mixture_comp1 <- function(x) rnorm(x, mean = -0.5, sd = 0.5)
mixture_comp2 <- function(x) rnorm(x, mean = 2, sd = 1)
q <- c(mixture_comp1(n1p), mixture_comp2(n-n1p))

n_indep <- n

res <- flexible_cfdr(p, q, indep_index = 1:n_indep)

pv_plot(p = res[[1]]$p, q = res[[1]]$q, v = res[[1]]$v)

Stratified Q-Q plot.

Description

Stratified Q-Q plot.

Usage

stratified_qqplot(
  data_frame,
  prin_value_label,
  cond_value_label = NULL,
  thresholds = c(1, 0.1, 0.01, 0.001, 1e-04)
)
stratified_qqplot(
  data_frame,
  prin_value_label,
  cond_value_label = NULL,
  thresholds = c(1, 0.1, 0.01, 0.001, 1e-04)
)

Arguments

`data_frame`	`data.frame` containing p-values and auxiliary data values
`prin_value_label`	label of principal p-value column in `data_frame`
`cond_value_label`	label of conditional trait column in `data_frame`
`thresholds`	threshold values to define strata

Details

Can be used to investigate the relationship between p and q

Note that this function does not do the heavy lifting of styling the plot's aesthetics.

Value

ggplot object

Examples


# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing GWAS p-values for a related trait).
# We use the stratified_qqplot() function to examine the relationship between p and q

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
zq <- c(rnorm(n1p, sd=4), rnorm(n-n1p, sd=1.2))
q <- 2*pnorm(-abs(zq))

df <- data.frame(p, q)

stratified_qqplot(data_frame = df, prin_value_label = "p", cond_value_label = "q")

# In this example, we generate some p-values (representing GWAS p-values)
# and some arbitrary auxiliary data values (e.g. representing GWAS p-values for a related trait).
# We use the stratified_qqplot() function to examine the relationship between p and q

# generate p
set.seed(1)
n <- 1000
n1p <- 50 
zp <- c(rnorm(n1p, sd=5), rnorm(n-n1p, sd=1))
p <- 2*pnorm(-abs(zp))

# generate q
zq <- c(rnorm(n1p, sd=4), rnorm(n-n1p, sd=1.2))
q <- 2*pnorm(-abs(zq))

df <- data.frame(p, q)

stratified_qqplot(data_frame = df, prin_value_label = "p", cond_value_label = "q")

A data.frame containing the rsID, chromosome (CHR19) and base pair position (BP19) in hg19, reference allele (REF), alternative allele (ALLT), type 1 diabetes GWAS p-value (T1D_pval), minor allele frequency (MAF), LDAK weight (LDAK_weight), rheumatoid arthritis GWAS p-value (RA_pval), binary regulatory factor binding site overlap (DGF), average H3K27ac fold change value in T1D-relevant cell types (H3K27ac) for 113,543 SNPs in the T1D GWAS (https://www.nature.com/articles/ng.3245)

Usage

T1D_application_data
T1D_application_data

Format

A data frame with 113543 rows and 11 variables:

Details

Minor allele frequencies estimated from the CEU sub-population samples in the 1000 Genomes Project Phase 3 data set. Missing values were replaced by drawing samples from the empirical distribution of MAFs

Package 'fcfdr'

Help Index

Perform cFDR leveraging binary auxiliary covariates

Description

Usage

Arguments

Value

Examples

Violin plot of p-values for quantiles of q

Description

Usage

Arguments

Details

Value

Examples

Perform Flexible cFDR

Description

Usage

Arguments

Details

Value

Examples

Plot -log10(p) against -log10(v) and colour by q

Description

Usage

Arguments

Details

Value

Examples

Function to downsample independent SNPs to match MAF distribution of whole set.

Description

Usage

Arguments

Details

Value

parameters_in_locfdr

Description

Usage

Arguments

Value

Examples

Plot p against v and colour by q

Description

Usage

Arguments

Details

Value

Examples

Stratified Q-Q plot.

Description

Usage

Arguments

Details

Value

Examples

Data for T1D application

Description

Usage

Format

Details