| Title: | A Suite of Packages for Analysis of Big Genomic Data |
|---|---|
| Description: | An umbrella package providing a phenotype/genotype data structure and scalable and efficient computational methods for large genomic datasets in combination with several other packages: 'BEDMatrix', 'LinkedMatrix', and 'symDMatrix'. |
| Authors: | Gustavo de los Campos [aut], Alexander Grueneberg [aut, cre], Paulino Perez [ctb], Ana Vazquez [ctb] |
| Maintainer: | Alexander Grueneberg <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 2.4.1 |
| Built: | 2024-11-19 06:29:49 UTC |
| Source: | CRAN |
Modern genomic datasets are big (large n), high-dimensional (large p), and multi-layered. The challenges that need to be addressed are memory requirements and computational demands. Our goal is to develop software that will enable researchers to carry out analyses with big genomic data within the R environment.
We have identified several approaches to tackle those challenges within R:
File-backed matrices: The data is stored in on the hard drive and users can read in smaller chunks when they are needed.
Linked arrays: For very large datasets a single file-backed array may not be enough or convenient. A linked array is an array whose content is distributed over multiple file-backed nodes.
Multiple dispatch: Methods are presented to users so that they can treat these arrays pretty much as if they were RAM arrays.
Multi-level parallelism: Exploit multi-core and multi-node computing.
Inputs: Users can create these arrays from standard formats (e.g., PLINK .bed).
The BGData package is an umbrella package that comprises several
packages: BEDMatrix, LinkedMatrix, and symDMatrix. It
features scalable and efficient computational methods for large genomic
datasets such as genome-wide association studies (GWAS) or genomic
relationship matrices (G matrix). It also contains a container class called
BGData that holds genotypes, sample information, and variant
information.
The extdata folder contains example files that were generated from
the 250k SNP and phenotype data in
Atwell et al. (2010).
Only the first 300 SNPs of chromosome 1, 2, and 3 were included to keep the
size of the example dataset small.
PLINK was used to convert the
data to .bed and
.raw files.
FT10 has been chosen as a phenotype and is provided as an
alternate phenotype
file. The file is intentionally shuffled to demonstrate that the
additional phenotypes are put in the same order as the rest of the
phenotypes.
BEDMatrix-package,
LinkedMatrix-package, and
symDMatrix-package for an introduction to the
respective packages.
file-backed-matrices for more information on file-backed
matrices. multi-level-parallelism for more information on
multi-level parallelism.
Converts other objects to BGData objects by loading supplementary
phenotypes and map files referenced by the object to be used for the sample
information and variant information, respectively.
Currently supported are BEDMatrix objects, plain or nested in
ColumnLinkedMatrix objects.
as.BGData(x, alternatePhenotypeFile = NULL, ...) ## S3 method for class 'BEDMatrix' as.BGData(x, alternatePhenotypeFile = NULL, ...) ## S3 method for class 'ColumnLinkedMatrix' as.BGData(x, alternatePhenotypeFile = NULL, ...) ## S3 method for class 'RowLinkedMatrix' as.BGData(x, alternatePhenotypeFile = NULL, ...)as.BGData(x, alternatePhenotypeFile = NULL, ...) ## S3 method for class 'BEDMatrix' as.BGData(x, alternatePhenotypeFile = NULL, ...) ## S3 method for class 'ColumnLinkedMatrix' as.BGData(x, alternatePhenotypeFile = NULL, ...) ## S3 method for class 'RowLinkedMatrix' as.BGData(x, alternatePhenotypeFile = NULL, ...)
x |
An object. Currently supported are |
alternatePhenotypeFile |
Path to an alternate phenotype file. |
... |
Additional arguments to the |
The .ped and .raw formats only allows for a single phenotype. If more
phenotypes are required it is possible to store them in an
alternate phenotype
file. The path to such a file can be provided with
alternatePhenotypeFile and will be merged with the existing sample
information. The first and second columns of that file must contain family
and within-family IDs, respectively.
For BEDMatrix objects: If a .fam file (which corresponds to the
first six columns of a .ped or .raw file) of the same name and in the same
directory as the .bed file exists, the sample information will be populated
with the data stored in that file. Otherwise a stub that only contains an
IID column populated with the rownames of geno(x) will be
generated. The same will happen for a .bim file for the variant
information.
For ColumnLinkedMatrix objects: See the case for BEDMatrix
objects, but only the .fam file of the first node of the
LinkedMatrix will be read and used for the sample information, and
the .bim files of all nodes will be combined and used for the variant
information.
A BGData object.
readRAW() to convert text files to BGData
objects. BGData-class,
BEDMatrix-class,
ColumnLinkedMatrix-class for more information
on the above mentioned classes. read.table and
fread to learn more about extra arguments that
can be passed via ....
# Path to example data path <- system.file("extdata", package = "BGData") # Convert a single BEDMatrix object to a BGData object chr1 <- BEDMatrix::BEDMatrix(paste0(path, "/chr1.bed")) bg1 <- as.BGData(chr1) # Convert multiple BEDMatrix objects in a ColumnLinkedMatrix to a BGData object chr2 <- BEDMatrix::BEDMatrix(paste0(path, "/chr2.bed")) chr3 <- BEDMatrix::BEDMatrix(paste0(path, "/chr3.bed")) clm <- ColumnLinkedMatrix(chr1, chr2, chr3) bg2 <- as.BGData(clm) # Load additional (alternate) phenotypes bg3 <- as.BGData(clm, alternatePhenotypeFile = paste0(path, "/pheno.txt"))# Path to example data path <- system.file("extdata", package = "BGData") # Convert a single BEDMatrix object to a BGData object chr1 <- BEDMatrix::BEDMatrix(paste0(path, "/chr1.bed")) bg1 <- as.BGData(chr1) # Convert multiple BEDMatrix objects in a ColumnLinkedMatrix to a BGData object chr2 <- BEDMatrix::BEDMatrix(paste0(path, "/chr2.bed")) chr3 <- BEDMatrix::BEDMatrix(paste0(path, "/chr3.bed")) clm <- ColumnLinkedMatrix(chr1, chr2, chr3) bg2 <- as.BGData(clm) # Load additional (alternate) phenotypes bg3 <- as.BGData(clm, alternatePhenotypeFile = paste0(path, "/pheno.txt"))
This function constructs a new BGData object.
BGData(geno, pheno = NULL, map = NULL)BGData(geno, pheno = NULL, map = NULL)
geno |
A |
pheno |
A |
map |
A |
BGData-class and geno-class for more
information on the above mentioned classes.
The BGData class is a container for genotypes, sample information, and
variant information. The class is inspired by the .bed/.fam/.bim
(binary) and .ped/.fam/.map (text) phenotype/genotype file formats
of PLINK. It is used by several
functions of this package such as GWAS for performing a Genome Wide
Association Study or getG for calculating a genomic relationship
matrix.
There are several ways to create an instance of this class:
from arbitrary phenotype/genotype data using the BGData
constructor function.
from a .bed file using as.BGData and BEDMatrix.
from a previously saved BGData object using
load.BGData.
from multiple files (even a mixture of different file types)
using LinkedMatrix.
from a .raw file (or a .ped-like file) using
readRAW, readRAW_matrix, or
readRAW_big.matrix.
A .ped file can be recoded to a .raw file in
PLINK using plink --file
myfile --recodeA, or converted to a .bed file using plink --file
myfile --make-bed. Conversely, a .bed file can be transformed back to a
.ped file using plink --bfile myfile --recode or to a .raw file
using plink --bfile myfile --recodeA without losing information.
In the following code snippets, x is a BGData object.
geno(x), geno(x) <- value:Get or set genotypes.
pheno(x), pheno(x) <- value:Get or set sample information.
map(x), map(x) <- value:Get or set variant information.
BGData, as.BGData, load.BGData,
readRAW to create BGData objects.
LinkedMatrix-class and
BEDMatrix-class for more information on the above
mentioned classes.
X <- matrix(data = rnorm(100), nrow = 10, ncol = 10) Y <- data.frame(y = runif(10)) MAP <- data.frame(means = colMeans(X), freqNA = colMeans(is.na(X))) DATA <- BGData(geno = X, pheno = Y, map = MAP) dim(geno(DATA)) head(pheno(DATA)) head(map(DATA))X <- matrix(data = rnorm(100), nrow = 10, ncol = 10) Y <- data.frame(y = runif(10)) MAP <- data.frame(means = colMeans(X), freqNA = colMeans(is.na(X))) DATA <- BGData(geno = X, pheno = Y, map = MAP) dim(geno(DATA)) head(pheno(DATA)) head(map(DATA))
Similar to apply, but designed for file-backed matrices. The
function brings chunks of an object into physical memory by taking subsets,
and applies a function on either the rows or the columns of the chunks
using an optimized version of apply. If nCores is greater
than 1, the function will be applied in parallel using mclapply. In
that case the subsets of the object are taken on the slaves.
chunkedApply(X, MARGIN, FUN, i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)chunkedApply(X, MARGIN, FUN, i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)
X |
A file-backed matrix, typically the genotypes of a |
MARGIN |
The subscripts which the function will be applied over. 1 indicates rows, 2 indicates columns. |
FUN |
The function to be applied. |
i |
Indicates which rows of |
j |
Indicates which columns of |
chunkSize |
The number of rows or columns of |
nCores |
The number of cores (passed to |
verbose |
Whether progress updates will be posted. Defaults to |
... |
Additional arguments to be passed to the |
file-backed-matrices for more information on file-backed
matrices. multi-level-parallelism for more information on
multi-level parallelism. BGData-class for more information on
the BGData class.
# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Compute standard deviation of columns chunkedApply(X = geno(bg), MARGIN = 2, FUN = sd)# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Compute standard deviation of columns chunkedApply(X = geno(bg), MARGIN = 2, FUN = sd)
Similar to lapply, but designed for file-backed matrices. The
function brings chunks of an object into physical memory by taking subsets,
and applies a function on them. If nCores is greater than 1, the
function will be applied in parallel using mclapply. In that case
the subsets of the object are taken on the slaves.
chunkedMap(X, FUN, i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkBy = 2L, chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)chunkedMap(X, FUN, i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkBy = 2L, chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)
X |
A file-backed matrix, typically the genotypes of a |
FUN |
The function to be applied on each chunk. |
i |
Indicates which rows of |
j |
Indicates which columns of |
chunkBy |
Whether to extract chunks by rows (1) or by columns (2). Defaults to columns (2). |
chunkSize |
The number of rows or columns of |
nCores |
The number of cores (passed to |
verbose |
Whether progress updates will be posted. Defaults to |
... |
Additional arguments to be passed to the
|
file-backed-matrices for more information on file-backed
matrices. multi-level-parallelism for more information on
multi-level parallelism. BGData-class for more information on
the BGData class.
# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Compute column sums of each chunk chunkedMap(X = geno(bg), FUN = colSums)# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Compute column sums of each chunk chunkedMap(X = geno(bg), FUN = colSums)
Functions with the chunkSize parameter work best with file-backed
matrices such as BEDMatrix objects. To avoid loading the whole,
potentially very large matrix into memory, these functions will load chunks
of the file-backed matrix into memory and perform the operations on one
chunk at a time. The size of the chunks is determined by the
chunkSize parameter. Care must be taken to not set chunkSize
too high to avoid memory shortage, particularly when combined with parallel
computing.
BEDMatrix-class as an example of a file-backed
matrix.
Find related individuals in a relationship matrix.
findRelated(x, ...) ## S3 method for class 'matrix' findRelated(x, cutoff = 0.03, ...) ## S3 method for class 'symDMatrix' findRelated(x, cutoff = 0.03, verbose = FALSE, ...)findRelated(x, ...) ## S3 method for class 'matrix' findRelated(x, cutoff = 0.03, ...) ## S3 method for class 'symDMatrix' findRelated(x, cutoff = 0.03, verbose = FALSE, ...)
x |
A matrix-like object with dimnames. |
... |
Additional arguments for methods. |
cutoff |
The cutoff between 0 and 1 for related individuals to be included in the output. Defaults to 0.03. |
verbose |
Whether progress updates will be posted. Defaults to |
A vector of names or indices of related individuals.
matrix: Find related individuals in matrices
symDMatrix: Find related individuals in symDMatrix objects
# Load example data bg <- BGData:::loadExample() G <- getG(geno(bg)) findRelated(G)# Load example data bg <- BGData:::loadExample() G <- getG(geno(bg)) findRelated(G)
Performs forward regression of y on the columns of X.
Predictors are added, one at a time, each time adding the one that produces
the largest reduction in the residual sum of squares (RSS). The function
returns estimates and summaries for the entire forward search. This
function performs a similar search than that of step(,
direction='forward'), however, FWD() is optimized for
computational speed for linear models with very large sample size. To
achieve fast computations, the software first computes the sufficient
statistics X'X and X'y. At each step, the function first finds the
predictor that produces the largest reduction in the sum of squares (this
can be derived from X'X, X'y and the current solution of effects), and then
updates the estimates of effects for the resulting model using Gauss Seidel
iterations performed on the linear system (X'X)b=X'y, iterating only over
the elements of b that are active in the model.
FWD(y, X, df = 20, tol = 1e-7, maxIter = 1000, centerImpute = TRUE, verbose = TRUE)FWD(y, X, df = 20, tol = 1e-7, maxIter = 1000, centerImpute = TRUE, verbose = TRUE)
y |
The response vector (numeric nx1). |
X |
An (nxp) numeric matrix. Columns are the features (aka predictors)
considered in the forward search. The rows of |
df |
Defines the maximum number of predictors to be included in the model.
For complete forward search, set |
tol |
A tolerance parameter to control when to stop the Gauss Seidel algorithm. |
maxIter |
The maximum number of iterations for the Gauss Seidel algorithm (only used when the algorithm is not stopped by the tolerance parameter). |
centerImpute |
Whether to center the columns of |
verbose |
Use |
A list with two entries:
B: (pxdf+1) includes the estimated effects for each
predictor (rows) at each step of the forward search (df, in columns).
path: A data frame providing the order in which variables
were added to the model (variable) and statistics for each step
of the forward search (RSS, LogLik, VARE (the
residual variance), DF, AIC, and BIC).
A set of generic functions for getting/setting the genotypes, sample information, and variant information.
geno(x) geno(x) <- value pheno(x) pheno(x) <- value map(x) map(x) <- valuegeno(x) geno(x) <- value pheno(x) pheno(x) <- value map(x) map(x) <- value
x |
The object from/on which to get/set genotypes, sample information, and
variant information. Typically a |
value |
Typically a Typically a Typically a |
# Load example data bg <- BGData:::loadExample() # Access genotypes geno(bg) # Access sample information pheno(bg) # Access variant information map(bg)# Load example data bg <- BGData:::loadExample() # Access genotypes geno(bg) # Access sample information pheno(bg) # Access variant information map(bg)
geno is a class union of several matrix-like types, many of
them suitable for very large datasets.
Currently supported are LinkedMatrix, BEDMatrix,
big.matrix,ff_matrix, and matrix.
LinkedMatrix-class,
BEDMatrix-class,
big.matrix-class, ff, and
matrix for more information on each matrix-like type.
BGData-class for more information on the BGData class,
in particular its geno accessor that accepts geno objects.
Computes a positive semi-definite symmetric genomic relation matrix G=XX'
offering options for centering and scaling the columns of X
beforehand.
getG(X, center = TRUE, scale = TRUE, impute = TRUE, scaleG = TRUE, minVar = 1e-05, i = seq_len(nrow(X)), j = seq_len(ncol(X)), i2 = NULL, chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE)getG(X, center = TRUE, scale = TRUE, impute = TRUE, scaleG = TRUE, minVar = 1e-05, i = seq_len(nrow(X)), j = seq_len(ncol(X)), i2 = NULL, chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE)
X |
A matrix-like object, typically the genotypes of a |
center |
Either a logical value or a numeric vector of length equal to the
number of columns of |
scale |
Either a logical value or a numeric vector of length equal to the
number of columns of |
impute |
Indicates whether missing values should be imputed. Defaults to
|
scaleG |
Whether XX' should be scaled. Defaults to |
minVar |
Columns with variance lower than this value will not be used in the
computation (only if |
i |
Indicates which rows of |
j |
Indicates which columns of |
i2 |
Indicates which rows should be used to compute a block of the genomic
relationship matrix. Will compute XY' where X is determined by |
chunkSize |
The number of columns of |
nCores |
The number of cores (passed to |
verbose |
Whether progress updates will be posted. Defaults to |
If center = FALSE, scale = FALSE and scaleG = FALSE,
getG produces the same outcome than tcrossprod.
A positive semi-definite symmetric numeric matrix.
file-backed-matrices for more information on file-backed
matrices. multi-level-parallelism for more information on
multi-level parallelism. BGData-class for more information on
the BGData class.
# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Compute a scaled genomic relationship matrix from centered and scaled # genotypes g1 <- getG(X = geno(bg)) # Disable scaling of G g2 <- getG(X = geno(bg), scaleG = FALSE) # Disable centering of genotypes g3 <- getG(X = geno(bg), center = FALSE) # Disable scaling of genotypes g4 <- getG(X = geno(bg), scale = FALSE) # Provide own scales scales <- chunkedApply(X = geno(bg), MARGIN = 2, FUN = sd) g4 <- getG(X = geno(bg), scale = scales) # Provide own centers centers <- chunkedApply(X = geno(bg), MARGIN = 2, FUN = mean) g5 <- getG(X = geno(bg), center = centers) # Only use the first 50 individuals (useful to account for population structure) g6 <- getG(X = geno(bg), i = 1:50) # Only use the first 100 markers (useful to ignore some markers) g7 <- getG(X = geno(bg), j = 1:100) # Compute unscaled G matrix by combining blocks of $XX_{i2}'$ where $X_{i2}$ is # a horizontal partition of X. This is useful for distributed computing as each # block can be computed in parallel. Centers and scales need to be precomputed. block1 <- getG(X = geno(bg), i2 = 1:100, center = centers, scale = scales) block2 <- getG(X = geno(bg), i2 = 101:199, center = centers, scale = scales) g8 <- cbind(block1, block2) # Compute unscaled G matrix by combining blocks of $X_{i}X_{i2}'$ where both # $X_{i}$ and $X_{i2}$ are horizontal partitions of X. Similarly to the example # above, this is useful for distributed computing, in particular to compute # very large G matrices. Centers and scales need to be precomputed. This # approach is similar to the one taken by the symDMatrix package, but the # symDMatrix package adds memory-mapped blocks, only stores the upper side of # the triangular matrix, and provides a type that allows for indexing as if the # full G matrix is in memory. block11 <- getG(X = geno(bg), i = 1:100, i2 = 1:100, center = centers, scale = scales) block12 <- getG(X = geno(bg), i = 1:100, i2 = 101:199, center = centers, scale = scales) block21 <- getG(X = geno(bg), i = 101:199, i2 = 1:100, center = centers, scale = scales) block22 <- getG(X = geno(bg), i = 101:199, i2 = 101:199, center = centers, scale = scales) g9 <- rbind( cbind(block11, block12), cbind(block21, block22) )# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Compute a scaled genomic relationship matrix from centered and scaled # genotypes g1 <- getG(X = geno(bg)) # Disable scaling of G g2 <- getG(X = geno(bg), scaleG = FALSE) # Disable centering of genotypes g3 <- getG(X = geno(bg), center = FALSE) # Disable scaling of genotypes g4 <- getG(X = geno(bg), scale = FALSE) # Provide own scales scales <- chunkedApply(X = geno(bg), MARGIN = 2, FUN = sd) g4 <- getG(X = geno(bg), scale = scales) # Provide own centers centers <- chunkedApply(X = geno(bg), MARGIN = 2, FUN = mean) g5 <- getG(X = geno(bg), center = centers) # Only use the first 50 individuals (useful to account for population structure) g6 <- getG(X = geno(bg), i = 1:50) # Only use the first 100 markers (useful to ignore some markers) g7 <- getG(X = geno(bg), j = 1:100) # Compute unscaled G matrix by combining blocks of $XX_{i2}'$ where $X_{i2}$ is # a horizontal partition of X. This is useful for distributed computing as each # block can be computed in parallel. Centers and scales need to be precomputed. block1 <- getG(X = geno(bg), i2 = 1:100, center = centers, scale = scales) block2 <- getG(X = geno(bg), i2 = 101:199, center = centers, scale = scales) g8 <- cbind(block1, block2) # Compute unscaled G matrix by combining blocks of $X_{i}X_{i2}'$ where both # $X_{i}$ and $X_{i2}$ are horizontal partitions of X. Similarly to the example # above, this is useful for distributed computing, in particular to compute # very large G matrices. Centers and scales need to be precomputed. This # approach is similar to the one taken by the symDMatrix package, but the # symDMatrix package adds memory-mapped blocks, only stores the upper side of # the triangular matrix, and provides a type that allows for indexing as if the # full G matrix is in memory. block11 <- getG(X = geno(bg), i = 1:100, i2 = 1:100, center = centers, scale = scales) block12 <- getG(X = geno(bg), i = 1:100, i2 = 101:199, center = centers, scale = scales) block21 <- getG(X = geno(bg), i = 101:199, i2 = 1:100, center = centers, scale = scales) block22 <- getG(X = geno(bg), i = 101:199, i2 = 101:199, center = centers, scale = scales) g9 <- rbind( cbind(block11, block12), cbind(block21, block22) )
Computes a positive semi-definite symmetric genomic relation matrix G=XX'
offering options for centering and scaling the columns of X
beforehand.
getG_symDMatrix(X, center = TRUE, scale = TRUE, impute = TRUE, scaleG = TRUE, minVar = 1e-05, blockSize = 5000L, folderOut = paste0("symDMatrix_", randomString()), vmode = "double", i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE)getG_symDMatrix(X, center = TRUE, scale = TRUE, impute = TRUE, scaleG = TRUE, minVar = 1e-05, blockSize = 5000L, folderOut = paste0("symDMatrix_", randomString()), vmode = "double", i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE)
X |
A matrix-like object, typically the genotypes of a |
center |
Either a logical value or a numeric vector of length equal to the
number of columns of |
scale |
Either a logical value or a numeric vector of length equal to the
number of columns of |
impute |
Indicates whether missing values should be imputed. Defaults to
|
scaleG |
TRUE/FALSE whether xx' must be scaled. |
minVar |
Columns with variance lower than this value will not be used in the
computation (only if |
blockSize |
The number of rows and columns of each block. If |
folderOut |
The path to the folder where to save the |
vmode |
vmode of |
i |
Indicates which rows of |
j |
Indicates which columns of |
chunkSize |
The number of columns of |
nCores |
The number of cores (passed to |
verbose |
Whether progress updates will be posted. Defaults to |
Even very large genomic relationship matrices are supported by partitioning
X into blocks and calling getG on these blocks. This function
performs the block computations sequentially, which may be slow. In an HPC
environment, performance can be improved by manually distributing these
operations to different nodes.
A symDMatrix object.
multi-level-parallelism for more information on multi-level
parallelism. symDMatrix-class and
BGData-class for more information on the BGData class.
getG to learn more about the underlying method.
Implements single marker regressions. The regression model includes all the
covariates specified in the right-hand-side of the formula plus one
column of the genotypes at a time. The data from the association tests is
obtained from a BGData object.
GWAS(formula, data, method = "lsfit", i = seq_len(nrow(geno(data))), j = seq_len(ncol(geno(data))), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)GWAS(formula, data, method = "lsfit", i = seq_len(nrow(geno(data))), j = seq_len(ncol(geno(data))), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE, ...)
formula |
The formula for the GWAS model without the variant, e.g. |
data |
A |
method |
The regression method to be used. Currently, the following methods are
implemented: |
i |
Indicates which rows of the genotypes should be used. Can be integer, boolean, or character. By default, all rows are used. |
j |
Indicates which columns of the genotypes should be used. Can be integer, boolean, or character. By default, all columns are used. |
chunkSize |
The number of columns of the genotypes that are brought into physical
memory for processing per core. If |
nCores |
The number of cores (passed to |
verbose |
Whether progress updates will be posted. Defaults to |
... |
Additional arguments for chunkedApply and regression method. |
The rayOLS method is a regression through the origin that can only
be used with a y ~ 1 formula, i.e. it only allows for one
quantitative response variable y and one variant at a time as an
explanatory variable (the variant is not included in the formula, hence
1 is used as a dummy). If covariates are needed, consider
preadjustment of y. Among the provided methods, it is by far the
fastest.
Some regression methods may require the data to not contain columns with
variance 0 or too many missing values. We suggest running summarize
to detect variants that do not clear the desired minor-allele frequency and
rate of missing genotype calls, and filtering these variants out using the
j parameter of the GWAS function (see example below).
The same matrix that would be returned by coef(summary(model)).
file-backed-matrices for more information on file-backed
matrices. multi-level-parallelism for more information on
multi-level parallelism. BGData-class for more information on
the BGData class. lsfit,
lm, lm.fit,
glm, lmer, and
SKAT for more information on regression methods.
# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Detect variants that do not pass MAF and missingness thresholds summaries <- summarize(geno(bg)) maf <- ifelse(summaries$allele_freq > 0.5, 1 - summaries$allele_freq, summaries$allele_freq) exclusions <- maf < 0.01 | summaries$freq_na > 0.05 # Perform a single marker regression res1 <- GWAS(formula = FT10 ~ 1, data = bg, j = !exclusions) # Draw a Manhattan plot plot(-log10(res1[, 4])) # Use lm instead of lsfit (the default) res2 <- GWAS(formula = FT10 ~ 1, data = bg, method = "lm", j = !exclusions) # Use glm instead of lsfit (the default) y <- pheno(bg)$FT10 pheno(bg)$FT10.01 <- y > quantile(y, 0.8, na.rm = TRUE) res3 <- GWAS(formula = FT10.01 ~ 1, data = bg, method = "glm", j = !exclusions) # Perform a single marker regression on the first 50 markers (useful for # distributed computing) res4 <- GWAS(formula = FT10 ~ 1, data = bg, j = 1:50)# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Detect variants that do not pass MAF and missingness thresholds summaries <- summarize(geno(bg)) maf <- ifelse(summaries$allele_freq > 0.5, 1 - summaries$allele_freq, summaries$allele_freq) exclusions <- maf < 0.01 | summaries$freq_na > 0.05 # Perform a single marker regression res1 <- GWAS(formula = FT10 ~ 1, data = bg, j = !exclusions) # Draw a Manhattan plot plot(-log10(res1[, 4])) # Use lm instead of lsfit (the default) res2 <- GWAS(formula = FT10 ~ 1, data = bg, method = "lm", j = !exclusions) # Use glm instead of lsfit (the default) y <- pheno(bg)$FT10 pheno(bg)$FT10.01 <- y > quantile(y, 0.8, na.rm = TRUE) res3 <- GWAS(formula = FT10.01 ~ 1, data = bg, method = "glm", j = !exclusions) # Perform a single marker regression on the first 50 markers (useful for # distributed computing) res4 <- GWAS(formula = FT10 ~ 1, data = bg, j = 1:50)
This function is similar to load, but also initializes the different
types of objects that can be used as genotypes in a BGData object.
Currently supported are ff_matrix, big.matrix, and
BEDMatrix objects. If the object is of type LinkedMatrix, all
nodes will be initialized with their appropriate method.
load.BGData(file, envir = parent.frame())load.BGData(file, envir = parent.frame())
file |
The name of the .RData file to be loaded. |
envir |
The environment where to load the data. |
BGData-class, ff,
big.matrix-class,
BEDMatrix-class, and
LinkedMatrix-class for more information on the
above mentioned classes.
Functions with the nCores, i, and j parameters provide
capabilities for both parallel and distributed computing.
For parallel computing, nCores determines the number of cores the
code is run on. Memory usage can be an issue for higher values of
nCores as R is not particularly memory-efficient. As a rule of
thumb, at least around (nCores * object_size(chunk)) +
object_size(result) MB of total memory will be needed for operations
on file-backed matrices, not including potential copies of your data that
might be created (for example lsfit runs cbind(1, X)).
i and j can be used to include or exclude certain rows or
columns. Internally, the mclapply function is used and therefore
parallel computing will not work on Windows machines.
For distributed computing, i and j determine the subset of
the input matrix that the code runs on. In an HPC environment, this can be
used not just to include or exclude certain rows or columns, but also to
partition the task among many nodes rather than cores. Scheduler-specific
code and code to aggregate the results need to be written by the user. It
is recommended to set nCores to 1 as nodes are often cheaper
than cores.
mclapply to learn more about the function used to
implement parallel computing. detectCores to detect
the number of available cores.
This is a simplified version of merge useful for merging additional
data into a BGData object while keeping the order of the data in the
BGData object.
orderedMerge(x, y, by = c(1L, 2L))orderedMerge(x, y, by = c(1L, 2L))
x |
Data frame |
y |
Data frame |
by |
Specifications of the columns used for merging. Defaults to the first two columns of the data frame, which traditionally has the family ID and the individual ID. |
Merged data frame
BGData-class for more information on the BGData class.
A faster version of scale with a similar interface that
also allows for imputation. The main difference is that this version scales
by the standard deviation regardless of whether centering is enabled or
not. If centering is enabled, missing values are imputed by 0, otherwise by
the mean of the column that contains the value.
preprocess(X, center = FALSE, scale = FALSE, impute = FALSE, nCores = getOption("mc.cores", 2L))preprocess(X, center = FALSE, scale = FALSE, impute = FALSE, nCores = getOption("mc.cores", 2L))
X |
A numeric matrix. |
center |
Either a logical value or numeric vector of length equal to the number
of columns of |
scale |
Either a logical value or numeric vector of length equal to the number
of columns of |
impute |
Indicates whether missing values should be imputed. |
nCores |
The number of cores (passed to |
The centered, scaled, and imputed matrix.
scale, which this function tries to improve upon.
# Load example data bg <- BGData:::loadExample() # Center and scale genotypes W <- preprocess(as.matrix(geno(bg)), center = TRUE, scale = TRUE)# Load example data bg <- BGData:::loadExample() # Center and scale genotypes W <- preprocess(as.matrix(geno(bg)), center = TRUE, scale = TRUE)
Creates a BGData object from a .raw file (generated with
--recodeA in PLINK).
Other text-based file formats are supported as well by tweaking some of the
parameters as long as the records of individuals are in rows, and
phenotypes, covariates and markers are in columns.
readRAW(fileIn, header = TRUE, dataType = integer(), n = NULL, p = NULL, sep = "", na.strings = "NA", nColSkip = 6L, idCol = c(1L, 2L), nNodes = NULL, linked.by = "rows", folderOut = paste0("BGData_", sub("\\.[[:alnum:]]+$", "", basename(fileIn))), outputType = "byte", dimorder = if (linked.by == "rows") 2L:1L else 1L:2L, verbose = FALSE) readRAW_matrix(fileIn, header = TRUE, dataType = integer(), n = NULL, p = NULL, sep = "", na.strings = "NA", nColSkip = 6L, idCol = c(1L, 2L), verbose = FALSE) readRAW_big.matrix(fileIn, header = TRUE, dataType = integer(), n = NULL, p = NULL, sep = "", na.strings = "NA", nColSkip = 6L, idCol = c(1L, 2L), folderOut = paste0("BGData_", sub("\\.[[:alnum:]]+$", "", basename(fileIn))), outputType = "char", verbose = FALSE)readRAW(fileIn, header = TRUE, dataType = integer(), n = NULL, p = NULL, sep = "", na.strings = "NA", nColSkip = 6L, idCol = c(1L, 2L), nNodes = NULL, linked.by = "rows", folderOut = paste0("BGData_", sub("\\.[[:alnum:]]+$", "", basename(fileIn))), outputType = "byte", dimorder = if (linked.by == "rows") 2L:1L else 1L:2L, verbose = FALSE) readRAW_matrix(fileIn, header = TRUE, dataType = integer(), n = NULL, p = NULL, sep = "", na.strings = "NA", nColSkip = 6L, idCol = c(1L, 2L), verbose = FALSE) readRAW_big.matrix(fileIn, header = TRUE, dataType = integer(), n = NULL, p = NULL, sep = "", na.strings = "NA", nColSkip = 6L, idCol = c(1L, 2L), folderOut = paste0("BGData_", sub("\\.[[:alnum:]]+$", "", basename(fileIn))), outputType = "char", verbose = FALSE)
fileIn |
The path to the plaintext file. |
header |
Whether |
dataType |
The coding type of genotypes in |
n |
The number of individuals. Auto-detect if |
p |
The number of markers. Auto-detect if |
sep |
The field separator character. Values on each line of the file are
separated by this character. If |
na.strings |
The character string used in the plaintext file to denote missing
value. Defaults to |
nColSkip |
The number of columns to be skipped to reach the genotype information
in the file. Defaults to |
idCol |
The index of the ID column. If more than one index is given, both
columns will be concatenated with "_". Defaults to |
nNodes |
The number of nodes to create. Auto-detect if |
linked.by |
If |
folderOut |
The path to the folder where to save the binary files. Defaults to the
name of the input file ( |
outputType |
The |
dimorder |
The physical layout of the underlying |
verbose |
Whether progress updates will be posted. Defaults to |
The data included in the first couple of columns (up to nColSkip) is
used to populate the sample information of a BGData object, and the
remaining columns are used to fill the genotypes. If the first row contains
a header (header = TRUE), data in this row is used to determine the
column names for sample information and genotypes.
The genotypes can take several forms, depending on the function that is
called (readRAW, readRAW_matrix, or
readRAW_big.matrix). The following sections illustrate each function
in detail.
Genotypes are stored in a LinkedMatrix object where each node is an
ff instance. Multiple ff files are used because the array
size in ff is limited to the largest integer which can be
represented on the system (.Machine$integer.max) and for genetic
data this limitation is often exceeded. The LinkedMatrix package
makes it possible to link several ff files together by columns or by
rows and treat them similarly to a single matrix. By default a
ColumnLinkedMatrix is used for the genotypes, but the user can
modify this using the linked.by argument. The number of nodes to
generate is either specified by the user using the nNodes argument
or determined internally so that each ff object has a number of
cells that is smaller than .Machine$integer.max / 1.2. A folder (see
folderOut) that contains the binary flat files (named
geno_*.bin) and an external representation of the BGData
object in BGData.RData is created.
Genotypes are stored in a regular matrix object. Therefore, this
function will only work if the .raw file is small enough to fit into
memory.
Genotypes are stored in a filebacked big.matrix object. A folder
(see folderOut) that contains the binary flat file (named
BGData.bin), a descriptor file (named BGData.desc), and an
external representation of the BGData object in BGData.RData
are created.
To reload a BGData object, it is recommended to use the
load.BGData function instead of the load function as
load does not initialize ff objects or attach
big.matrix objects.
load.BGData() to load a previously saved
BGData object, as.BGData() to create
BGData objects from non-text files (e.g. .bed files).
BGData-class,
ColumnLinkedMatrix-class,
RowLinkedMatrix-class,
big.matrix-class, and ff for
more information on the above mentioned classes.
# Path to example data path <- system.file("extdata", package = "BGData") # Convert RAW files of chromosome 1 to a BGData object bg <- readRAW(fileIn = paste0(path, "/chr1.raw")) unlink("BGData_chr1", recursive = TRUE)# Path to example data path <- system.file("extdata", package = "BGData") # Convert RAW files of chromosome 1 to a BGData object bg <- readRAW(fileIn = paste0(path, "/chr1.raw")) unlink("BGData_chr1", recursive = TRUE)
Given a summary statistic and a threshold, this function computes the
number of non-overlapping segments, each defined as a discovery (i.e.,
statistic[i] <= threshold) +/- a gap, in the same units as bp
(often base-pair position).
segments(statistic, chr, bp, threshold, gap, trim = FALSE, verbose = FALSE)segments(statistic, chr, bp, threshold, gap, trim = FALSE, verbose = FALSE)
statistic |
A statistic (e.g., BFDR or p-values). |
chr |
A vector containing the chromosome for each value of |
bp |
A vector containing the base-pair positions for each value of
|
threshold |
The threshold to determine 'significance' (e.g., |
gap |
1/2 of the length of the desired segments. |
trim |
Whether to collapse segments that were artifically inflated by
|
verbose |
Whether progress updates will be posted. Defaults to |
A data frame containing the following information:
chr |
Chromosome |
start |
Index where segment starts within |
end |
Index where segment ends within |
length |
Length of segment. |
bpStart |
Base-pair position where segment starts. |
bpEnd |
Base-pair position where segment ends. |
bpLength |
Length of segment in base-pair positions. |
minValue |
Smallest value of |
minValuePos |
Position of variant with the smallest value of |
library(BGData) # Load example data bg <- BGData:::loadExample() # Perform GWAS pValues <- GWAS( formula = FT10 ~ 1, data = bg, method = "rayOLS" ) # Determine segments within +/- 1MB from a significant variant segments <- segments( statistic = pValues[, 4], chr = map(bg)$chromosome, bp = map(bg)$base_pair_position, threshold = 1e-5, gap = 1e6, trim = FALSE, verbose = FALSE )library(BGData) # Load example data bg <- BGData:::loadExample() # Perform GWAS pValues <- GWAS( formula = FT10 ~ 1, data = bg, method = "rayOLS" ) # Determine segments within +/- 1MB from a significant variant segments <- segments( statistic = pValues[, 4], chr = map(bg)$chromosome, bp = map(bg)$base_pair_position, threshold = 1e-5, gap = 1e6, trim = FALSE, verbose = FALSE )
Computes the frequency of missing values, the (minor) allele frequency, and
standard deviation of each column of X.
summarize(X, i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE)summarize(X, i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L, nCores = getOption("mc.cores", 2L), verbose = FALSE)
X |
A matrix-like object, typically the genotypes of a |
i |
Indicates which rows of |
j |
Indicates which columns of |
chunkSize |
The number of columns of |
nCores |
The number of cores (passed to |
verbose |
Whether progress updates will be posted. Defaults to |
A data.frame with three columns: freq_na for frequencies of
missing values, allele_freq for allele frequencies of the counted
allele, and sd for standard deviations.
file-backed-matrices for more information on file-backed
matrices. multi-level-parallelism for more information on
multi-level parallelism. BGData-class for more information on
the BGData class.
# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Summarize the whole dataset sum1 <- summarize(X = geno(bg)) # Summarize the first 50 individuals sum2 <- summarize(X = geno(bg), i = 1:50) # Summarize the first 1000 markers (useful for distributed computing) sum3 <- summarize(X = geno(bg), j = 1:100) # Summarize the first 50 individuals on the first 1000 markers sum4 <- summarize(X = geno(bg), i = 1:50, j = 1:100) # Summarize by names sum5 <- summarize(X = geno(bg), j = c("snp81233_C", "snp81234_C", "snp81235_T")) # Convert to minor allele frequencies (useful if the counted alleles are not # the minor alleles) maf <- ifelse(sum1$allele_freq > 0.5, 1 - sum1$allele_freq, sum1$allele_freq)# Restrict number of cores to 1 on Windows if (.Platform$OS.type == "windows") { options(mc.cores = 1) } # Load example data bg <- BGData:::loadExample() # Summarize the whole dataset sum1 <- summarize(X = geno(bg)) # Summarize the first 50 individuals sum2 <- summarize(X = geno(bg), i = 1:50) # Summarize the first 1000 markers (useful for distributed computing) sum3 <- summarize(X = geno(bg), j = 1:100) # Summarize the first 50 individuals on the first 1000 markers sum4 <- summarize(X = geno(bg), i = 1:50, j = 1:100) # Summarize by names sum5 <- summarize(X = geno(bg), j = c("snp81233_C", "snp81234_C", "snp81235_T")) # Convert to minor allele frequencies (useful if the counted alleles are not # the minor alleles) maf <- ifelse(sum1$allele_freq > 0.5, 1 - sum1$allele_freq, sum1$allele_freq)