Title: Flexible cFDR
Version: 1.0.0
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.
Imports: locfdr, MASS, ggplot2, cowplot, fields, dplyr, spatstat.geom, polyCub, hexbin, bigsplines, data.table, grDevices, Hmisc
Suggests: stats, knitr, rmarkdown, digest, testthat (≥ 3.0.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
VignetteBuilder: knitr
Depends: R (≥ 3.5.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2022-02-03 14:23:05 UTC; anna
Author: Anna Hutchinson ORCID iD [aut, cre], Chris Wallace [aut], Thomas Willis [ctb, aut], James Liley [ctb]
Maintainer: Anna Hutchinson <annahutchinson1995@gmail.com>
Repository: CRAN
Date/Publication: 2022-02-07 09:00:05 UTC

Data for T1D application

Description

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

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

Perform cFDR leveraging binary auxiliary covariates

Description

Perform cFDR leveraging binary auxiliary covariates

Usage

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)

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))

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)

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
)

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)

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))

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)

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)

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
)

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)

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))

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)

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)
)

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")