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dive_phe2mash function v1 to test on several plant species

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Alice MacQueen
2021-03-30 18:33:06 -05:00
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^snpdiver\.Rproj$
^\.Rproj\.user$
^_pkgdown\.yml$
^docs$
^pkgdown$
^LICENSE\.md$

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# R Environment Variables # R Environment Variables
.Renviron .Renviron
.Rproj.user
docs
# Test files manually produced when checking functions
tests/

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Package: snpdiver
Title: Explore SNP Effects on Multiple Phenotypes
Version: 0.0.0.9000
Authors@R:
person(given = "Alice",
family = "MacQueen",
role = c("aut", "cre"),
email = "alice.macqueen@gmail.com",
comment = c(ORCID = "0000-0002-4606-1832"))
Description: What the package does (one paragraph).
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Imports:
ashr,
bigsnpr,
bigstatsr,
cowplot,
dplyr,
ggplot2,
mashr,
magrittr,
matrixStats,
purrr,
readr,
rlang (>= 0.1.2),
tibble,
tidyr,
tidyselect
Suggests:
roxygen2

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LICENSE
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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
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GNU General Public License
==========================
_Version 3, 29 June 2007_
_Copyright © 2007 Free Software Foundation, Inc. &lt;<http://fsf.org/>&gt;_
Everyone is permitted to copy and distribute verbatim copies of this license
document, but changing it is not allowed.
## Preamble
The GNU General Public License is a free, copyleft license for software and other
kinds of works.
The licenses for most software and other practical works are designed to take away
your freedom to share and change the works. By contrast, the GNU General Public
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Software Foundation, use the GNU General Public License for most of our software; it
applies also to any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not price. Our General
Public Licenses are designed to make sure that you have the freedom to distribute
copies of free software (and charge for them if you wish), that you receive source
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asking you to surrender the rights. Therefore, you have certain responsibilities if
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respect the freedom of others.
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## TERMS AND CONDITIONS
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means any tangible personal property which is normally used for personal, family, or
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“Installation Information” for a User Product means any methods,
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functioning of the modified object code is in no case prevented or interfered with
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If you convey an object code work under this section in, or with, or specifically for
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the right of possession and use of the User Product is transferred to the recipient
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this section must be in a format that is publicly documented (and with an
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special password or key for unpacking, reading or copying.
### 7. Additional Terms
“Additional permissions” are terms that supplement the terms of this
License by making exceptions from one or more of its conditions. Additional
permissions that are applicable to the entire Program shall be treated as though they
were included in this License, to the extent that they are valid under applicable
law. If additional permissions apply only to part of the Program, that part may be
used separately under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option remove any
additional permissions from that copy, or from any part of it. (Additional
permissions may be written to require their own removal in certain cases when you
modify the work.) You may place additional permissions on material, added by you to a
covered work, for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you add to a
covered work, you may (if authorized by the copyright holders of that material)
supplement the terms of this License with terms:
* **a)** Disclaiming warranty or limiting liability differently from the terms of
sections 15 and 16 of this License; or
* **b)** Requiring preservation of specified reasonable legal notices or author
attributions in that material or in the Appropriate Legal Notices displayed by works
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* **c)** Prohibiting misrepresentation of the origin of that material, or requiring that
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* **d)** Limiting the use for publicity purposes of names of licensors or authors of the
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* **f)** Requiring indemnification of licensors and authors of that material by anyone
who conveys the material (or modified versions of it) with contractual assumptions of
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All other non-permissive additional terms are considered “further
restrictions” within the meaning of section 10. If the Program as you received
it, or any part of it, contains a notice stating that it is governed by this License
along with a term that is a further restriction, you may remove that term. If a
license document contains a further restriction but permits relicensing or conveying
under this License, you may add to a covered work material governed by the terms of
that license document, provided that the further restriction does not survive such
relicensing or conveying.
If you add terms to a covered work in accord with this section, you must place, in
the relevant source files, a statement of the additional terms that apply to those
files, or a notice indicating where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the form of a
separately written license, or stated as exceptions; the above requirements apply
either way.
### 8. Termination
You may not propagate or modify a covered work except as expressly provided under
this License. Any attempt otherwise to propagate or modify it is void, and will
automatically terminate your rights under this License (including any patent licenses
granted under the third paragraph of section 11).
However, if you cease all violation of this License, then your license from a
particular copyright holder is reinstated **(a)** provisionally, unless and until the
copyright holder explicitly and finally terminates your license, and **(b)** permanently,
if the copyright holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is reinstated permanently
if the copyright holder notifies you of the violation by some reasonable means, this
is the first time you have received notice of violation of this License (for any
work) from that copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the licenses of
parties who have received copies or rights from you under this License. If your
rights have been terminated and not permanently reinstated, you do not qualify to
receive new licenses for the same material under section 10.
### 9. Acceptance Not Required for Having Copies
You are not required to accept this License in order to receive or run a copy of the
Program. Ancillary propagation of a covered work occurring solely as a consequence of
using peer-to-peer transmission to receive a copy likewise does not require
acceptance. However, nothing other than this License grants you permission to
propagate or modify any covered work. These actions infringe copyright if you do not
accept this License. Therefore, by modifying or propagating a covered work, you
indicate your acceptance of this License to do so.
### 10. Automatic Licensing of Downstream Recipients
Each time you convey a covered work, the recipient automatically receives a license
from the original licensors, to run, modify and propagate that work, subject to this
License. You are not responsible for enforcing compliance by third parties with this
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An “entity transaction” is a transaction transferring control of an
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merging organizations. If propagation of a covered work results from an entity
transaction, each party to that transaction who receives a copy of the work also
receives whatever licenses to the work the party's predecessor in interest had or
could give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if the predecessor
has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the rights granted or
affirmed under this License. For example, you may not impose a license fee, royalty,
or other charge for exercise of rights granted under this License, and you may not
initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging
that any patent claim is infringed by making, using, selling, offering for sale, or
importing the Program or any portion of it.
### 11. Patents
A “contributor” is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The work thus
licensed is called the contributor's “contributor version”.
A contributor's “essential patent claims” are all patent claims owned or
controlled by the contributor, whether already acquired or hereafter acquired, that
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selling its contributor version, but do not include claims that would be infringed
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sublicenses in a manner consistent with the requirements of this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free patent license
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In the following three paragraphs, a “patent license” is any express
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express permission to practice a patent or covenant not to sue for patent
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such an agreement or commitment not to enforce a patent against the party.
If you convey a covered work, knowingly relying on a patent license, and the
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Source to be so available, or **(2)** arrange to deprive yourself of the benefit of the
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the requirements of this License, to extend the patent license to downstream
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for the patent license, your conveying the covered work in a country, or your
recipient's use of the covered work in a country, would infringe one or more
identifiable patents in that country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or arrangement, you
convey, or propagate by procuring conveyance of, a covered work, and grant a patent
license to some of the parties receiving the covered work authorizing them to use,
propagate, modify or convey a specific copy of the covered work, then the patent
license you grant is automatically extended to all recipients of the covered work and
works based on it.
A patent license is “discriminatory” if it does not include within the
scope of its coverage, prohibits the exercise of, or is conditioned on the
non-exercise of one or more of the rights that are specifically granted under this
License. You may not convey a covered work if you are a party to an arrangement with
a third party that is in the business of distributing software, under which you make
payment to the third party based on the extent of your activity of conveying the
work, and under which the third party grants, to any of the parties who would receive
the covered work from you, a discriminatory patent license **(a)** in connection with
copies of the covered work conveyed by you (or copies made from those copies), or **(b)**
primarily for and in connection with specific products or compilations that contain
the covered work, unless you entered into that arrangement, or that patent license
was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting any implied
license or other defenses to infringement that may otherwise be available to you
under applicable patent law.
### 12. No Surrender of Others' Freedom
If conditions are imposed on you (whether by court order, agreement or otherwise)
that contradict the conditions of this License, they do not excuse you from the
conditions of this License. If you cannot convey a covered work so as to satisfy
simultaneously your obligations under this License and any other pertinent
obligations, then as a consequence you may not convey it at all. For example, if you
agree to terms that obligate you to collect a royalty for further conveying from
those to whom you convey the Program, the only way you could satisfy both those terms
and this License would be to refrain entirely from conveying the Program.
### 13. Use with the GNU Affero General Public License
Notwithstanding any other provision of this License, you have permission to link or
combine any covered work with a work licensed under version 3 of the GNU Affero
General Public License into a single combined work, and to convey the resulting work.
The terms of this License will continue to apply to the part which is the covered
work, but the special requirements of the GNU Affero General Public License, section
13, concerning interaction through a network will apply to the combination as such.
### 14. Revised Versions of this License
The Free Software Foundation may publish revised and/or new versions of the GNU
General Public License from time to time. Such new versions will be similar in spirit
to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies that
a certain numbered version of the GNU General Public License “or any later
version” applies to it, you have the option of following the terms and
conditions either of that numbered version or of any later version published by the
Free Software Foundation. If the Program does not specify a version number of the GNU
General Public License, you may choose any version ever published by the Free
Software Foundation.
If the Program specifies that a proxy can decide which future versions of the GNU
General Public License can be used, that proxy's public statement of acceptance of a
version permanently authorizes you to choose that version for the Program.
Later license versions may give you additional or different permissions. However, no
additional obligations are imposed on any author or copyright holder as a result of
your choosing to follow a later version.
### 15. Disclaimer of Warranty
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER
EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE
QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
### 16. Limitation of Liability
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY
COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS
PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL,
INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE
OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE
WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
### 17. Interpretation of Sections 15 and 16
If the disclaimer of warranty and limitation of liability provided above cannot be
given local legal effect according to their terms, reviewing courts shall apply local
law that most closely approximates an absolute waiver of all civil liability in
connection with the Program, unless a warranty or assumption of liability accompanies
a copy of the Program in return for a fee.
_END OF TERMS AND CONDITIONS_
## How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to
the public, the best way to achieve this is to make it free software which everyone
can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them
to the start of each source file to most effectively state the exclusion of warranty;
and each file should have at least the “copyright” line and a pointer to
where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short notice like this
when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type 'show c' for details.
The hypothetical commands `show w` and `show c` should show the appropriate parts of
the General Public License. Of course, your program's commands might be different;
for a GUI interface, you would use an “about box”.
You should also get your employer (if you work as a programmer) or school, if any, to
sign a “copyright disclaimer” for the program, if necessary. For more
information on this, and how to apply and follow the GNU GPL, see
&lt;<http://www.gnu.org/licenses/>&gt;.
The GNU General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may consider it
more useful to permit linking proprietary applications with the library. If this is
what you want to do, use the GNU Lesser General Public License instead of this
License. But first, please read
&lt;<http://www.gnu.org/philosophy/why-not-lgpl.html>&gt;.

69
NAMESPACE Normal file
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@@ -0,0 +1,69 @@
# Generated by roxygen2: do not edit by hand
export("%>%")
export(":=")
export(.data)
export(as_label)
export(as_name)
export(div_gwas)
export(div_lambda_GC)
export(dive_phe2mash)
export(enquo)
export(enquos)
export(expr)
export(get_lambdagc)
export(get_qqplot)
export(sym)
export(syms)
import(bigsnpr)
import(bigstatsr)
import(ggplot2)
import(mashr)
importFrom(ashr,get_fitted_g)
importFrom(bigsnpr,snp_autoSVD)
importFrom(cowplot,save_plot)
importFrom(dplyr,arrange)
importFrom(dplyr,case_when)
importFrom(dplyr,filter)
importFrom(dplyr,full_join)
importFrom(dplyr,group_by)
importFrom(dplyr,left_join)
importFrom(dplyr,mutate)
importFrom(dplyr,mutate_if)
importFrom(dplyr,rename)
importFrom(dplyr,select)
importFrom(dplyr,slice)
importFrom(dplyr,slice_max)
importFrom(dplyr,slice_sample)
importFrom(dplyr,summarise)
importFrom(dplyr,top_n)
importFrom(magrittr,"%>%")
importFrom(matrixStats,colMaxs)
importFrom(matrixStats,rowMaxs)
importFrom(purrr,as_vector)
importFrom(readr,write_csv)
importFrom(rlang,"!!")
importFrom(rlang,":=")
importFrom(rlang,.data)
importFrom(rlang,as_label)
importFrom(rlang,as_name)
importFrom(rlang,enquo)
importFrom(rlang,enquos)
importFrom(rlang,expr)
importFrom(rlang,sym)
importFrom(rlang,syms)
importFrom(stats,median)
importFrom(stats,ppoints)
importFrom(stats,predict)
importFrom(stats,qbeta)
importFrom(stats,uniroot)
importFrom(tibble,add_column)
importFrom(tibble,add_row)
importFrom(tibble,as_tibble)
importFrom(tibble,enframe)
importFrom(tibble,rownames_to_column)
importFrom(tibble,tibble)
importFrom(tidyr,gather)
importFrom(tidyr,replace_na)
importFrom(tidyselect,all_of)
importFrom(utils,tail)

4
R/snpdiver-package.R Normal file
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@@ -0,0 +1,4 @@
## usethis namespace: start
#' @importFrom tibble tibble
## usethis namespace: end
NULL

11
R/utils-pipe.R Normal file
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@@ -0,0 +1,11 @@
#' Pipe operator
#'
#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
NULL

47
R/utils-tidy-eval.R Normal file
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@@ -0,0 +1,47 @@
#' Tidy eval helpers
#'
#' @description
#'
#' * \code{\link[rlang]{sym}()} creates a symbol from a string and
#' \code{\link[rlang:sym]{syms}()} creates a list of symbols from a
#' character vector.
#'
#' * \code{\link[rlang:nse-defuse]{enquo}()} and
#' \code{\link[rlang:nse-defuse]{enquos}()} delay the execution of one or
#' several function arguments. \code{enquo()} returns a single quoted
#' expression, which is like a blueprint for the delayed computation.
#' \code{enquos()} returns a list of such quoted expressions.
#'
#' * \code{\link[rlang:nse-defuse]{expr}()} quotes a new expression _locally_. It
#' is mostly useful to build new expressions around arguments
#' captured with [enquo()] or [enquos()]:
#' \code{expr(mean(!!enquo(arg), na.rm = TRUE))}.
#'
#' * \code{\link[rlang]{as_name}()} transforms a quoted variable name
#' into a string. Supplying something else than a quoted variable
#' name is an error.
#'
#' That's unlike \code{\link[rlang]{as_label}()} which also returns
#' a single string but supports any kind of R object as input,
#' including quoted function calls and vectors. Its purpose is to
#' summarise that object into a single label. That label is often
#' suitable as a default name.
#'
#' If you don't know what a quoted expression contains (for instance
#' expressions captured with \code{enquo()} could be a variable
#' name, a call to a function, or an unquoted constant), then use
#' \code{as_label()}. If you know you have quoted a simple variable
#' name, or would like to enforce this, use \code{as_name()}.
#'
#' To learn more about tidy eval and how to use these tools, visit
#' \url{https://tidyeval.tidyverse.org} and the
#' \href{https://adv-r.hadley.nz/metaprogramming.html}{Metaprogramming
#' section} of \href{https://adv-r.hadley.nz}{Advanced R}.
#'
#' @md
#' @name tidyeval
#' @keywords internal
#' @importFrom rlang expr enquo enquos sym syms .data := as_name as_label
#' @aliases expr enquo enquos sym syms .data := as_name as_label
#' @export expr enquo enquos sym syms .data := as_name as_label
NULL

814
R/wrapper.R Normal file
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@@ -0,0 +1,814 @@
#' @title Wrapper to run mash given a phenotype data frame
#'
#' @description Though step-by-step GWAS, preparation of mash inputs, and mash
#' allows you the most flexibility and opportunities to check your results
#' for errors, once those sanity checks are complete, this function allows
#' you to go from a phenotype data.frame of a few phenotypes you want to
#' compare to a mash result. Some exception handling has been built into
#' this function, but the user should stay cautious and skeptical of any
#' results that seem 'too good to be true'.
#'
#' @param df Dataframe containing phenotypes for mash where the first column is
#' 'sample.ID', which should match values in the snp$fam$sample.ID column.
#' @param snp A "bigSNP" object; load with \code{snp_attach()}.
#' @param type Character string, or a character vector the length of the number
#' of phenotypes. Type of univarate regression to run for GWAS.
#' Options are "linear" or "logistic".
#' @param svd A "big_SVD" object; Optional covariance matrix to use for
#' population structure correction.
#' @param suffix Optional character vector to give saved files a unique search string/name.
#' @param outputdir Optional file path to save output files.
#' @param min.phe Integer. Minimum number of individuals phenotyped in order to
#' include that phenotype in GWAS. Default is 200. Use lower values with
#' caution.
#' @param save.plots Logical. Should Manhattan and QQ-plots be generated and
#' saved to the working directory for univariate GWAS? Default is TRUE.
#' @param thr.r2 Value between 0 and 1. Threshold of r2 measure of linkage
#' disequilibrium. Markers in higher LD than this will be subset using clumping.
#' @param thr.m "sum" or "max". Type of threshold to use to clump values for
#' mash inputs. "sum" sums the -log10pvalues for each phenotype and uses
#' the maximum of this value as the threshold. "max" uses the maximum
#' -log10pvalue for each SNP across all of the univariate GWAS.
#' @param num.strong Integer. Number of SNPs used to derive data-driven covariance
#' matrix patterns, using markers with strong effects on phenotypes.
#' @param num.random Integer. Number of SNPs used to derive the correlation structure
#' of the null tests, and the mash fit on the null tests.
#' @param scale.phe Logical. Should effects for each phenotype be scaled to fall
#' between -1 and 1? Default is TRUE.
#' @param roll.size Integer. Used to create the svd for GWAS.
#' @param U.ed Mash data-driven covariance matrices. Specify these as a list or a path
#' to a file saved as an .rds. Creating these can be time-consuming, and
#' generating these once and reusing them for multiple mash runs can save time.
#' @param U.hyp Other covariance matrices for mash. Specify these as a list. These
#' matrices must have dimensions that match the number of phenotypes where
#' univariate GWAS ran successfully.
#'
#' @return A mash object made up of all phenotypes where univariate GWAS ran
#' successfully.
#'
#' @importFrom ashr get_fitted_g
#' @importFrom tibble tibble enframe add_row add_column rownames_to_column
#' @importFrom bigsnpr snp_autoSVD
#' @importFrom dplyr group_by summarise left_join select slice slice_max slice_sample mutate filter
#' @import bigstatsr
#' @import mashr
#' @importFrom cowplot save_plot
#' @importFrom tidyr replace_na
#' @importFrom matrixStats colMaxs rowMaxs
#' @importFrom stats predict
#'
#' @export
dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
outputdir = ".",
min.phe = 200, save.plots = TRUE, thr.r2 = 0.2,
thr.m = c("sum", "max"), num.strong = 1000,
num.random = NA,
scale.phe = TRUE, roll.size = 50, U.ed = NA,
U.hyp = NA){
# 1. Stop if not functions. ----
if (attr(snp, "class") != "bigSNP") {
stop("snp needs to be a bigSNP object, produced by the bigsnpr package.")
}
if (colnames(df)[1] != "sample.ID") {
stop("First column of phenotype dataframe (df) must be 'sample.ID'.")
}
if (length(type) > 1) {
if (length(type) != ncol(df) - 1) {
stop(paste0("Specify either one GWAS type (type = 'linear' or type = ",
"'logistic'), or one type for each phenotype in 'df'."))
}
} else {
type <- rep(type, ncol(df) - 1)
}
## 1a. Generate useful values ----
G <- snp$genotypes
nSNP_M <- round(snp$genotypes$ncol/1000000, digits = 1)
nSNP <- paste0(nSNP_M, "_M")
if (nSNP_M < 1) {
nSNP_K <- round(snp$genotypes$ncol/1000, digits = 1)
nSNP <- paste0(nSNP_K, "_K")
}
# nInd <- snp$genotypes$nrow
plants <- snp$fam$sample.ID
bonferroni <- -log10(0.05/length(snp$map$physical.pos))
markers <- tibble(CHR = snp$map$chromosome, POS = snp$map$physical.pos,
marker.ID = snp$map$marker.ID) %>%
mutate(CHRN = as.numeric(as.factor(.data$CHR)))
# 2. Pop Structure Correction ----
if (is.null(svd)) {
message(paste0("Covariance matrix (svd) was not supplied - this will be",
" generated using snp_autoSVD()."))
svd <- snp_autoSVD(G = G, infos.chr = markers$CHRN, infos.pos = markers$POS,
k = 10, thr.r2 = thr.r2, roll.size = roll.size)
} else {
stopifnot(attr(svd, "class") == "big_SVD")
}
pc_max <- ncol(svd$u)
gwas_ok <- c()
for (i in 2:ncol(df)) {
df1 <- df %>%
dplyr::select(.data$sample.ID, all_of(i))
phename <- names(df1)[2]
df1 <- df1 %>%
group_by(.data$sample.ID) %>%
filter(!is.na(.data[[phename]])) %>%
summarise(phe = mean(.data[[phename]]), .groups = "drop_last")
df1 <- plants %>%
enframe(name = NULL, value = "sample.ID") %>%
mutate(sample.ID = as.character(.data$sample.ID)) %>%
left_join(df1, by = "sample.ID")
nPhe <- length(which(!is.na(df1[,2])))
nLev <- nrow(unique(df1[which(!is.na(df1[,2])),2]))
# Checks for correct combinations of phenotypes and GWAS types.
gwas_ok[i-1] <- check_gwas(df1 = df1, phename = phename, type = type[i-1],
nPhe = nPhe, minphe = min.phe, nLev = nLev)
# Find best # PCs to correct for population structure for each phenotype.
if(gwas_ok[i-1]){
lambdagc_df <- div_lambda_GC(df = df1, type = type[i-1], snp = snp,
svd = svd, npcs = c(0:pc_max))
PC_df <- get_best_PC_df(lambdagc_df)
PC_df <- PC_df[1,]
# 3. GWAS ----
# run gwas using best npcs from step 2 (best pop structure correction)
gwas <- div_gwas(df = df1, snp = snp, type = type[i - 1], svd = svd,
npcs = PC_df$NumPCs)
gwas <- gwas %>% mutate(pvalue = predict(gwas, log10 = FALSE),
log10p = -log10(.data$pvalue))
gwas_data <- tibble(phe = phename, type = type[i - 1],
nsnp = nSNP, npcs = PC_df$NumPCs, nphe = nPhe,
nlev = nLev, lambda_GC = PC_df$lambda_GC,
bonferroni = bonferroni)
# plot Manhattan and QQ if save.plots == TRUE
if(save.plots == TRUE){
qqplot <- get_qqplot(ps = gwas$pvalue, lambdaGC = TRUE)
manhattan <- get_manhattan(log10p = gwas$log10p, snp = snp,
thresh = bonferroni) # could round these too
plotname <- paste0(gwas_data$phe, "_", gwas_data$type, "_model_",
gwas_data$nphe, "g_", gwas_data$nsnp, "_SNPs_",
gwas_data$npcs, "_PCs.png")
save_plot(filename = file.path(outputdir, paste0("QQplot_", plotname)),
plot = qqplot, base_asp = 1, base_height = 4)
save_plot(filename = file.path(outputdir, paste0("Manhattan_", plotname)),
plot = manhattan, base_asp = 2.1, base_height = 3.5)
}
# save gwas outputs together in a fbm
gwas <- gwas %>%
select(.data[["estim"]], .data[["std.err"]], .data[["log10p"]])
if(i == 2){ # save .bk and .rds file the first time through the loop.
if (!grepl("_$", suffix) & suffix != ""){
suffix <- paste0("_", suffix)
}
fbm.name <- paste0(outputdir, "gwas_effects", suffix)
colnames_fbm <- c(paste0(phename, "_Effect"), paste0(phename, "_SE"),
paste0(phename, "_log10p"))
as_FBM(gwas, backingfile = fbm.name)$save()
gwas2 <- big_attach(paste0(fbm.name, ".rds"))
gwas_metadata <- gwas_data
} else {
colnames_fbm <- c(colnames_fbm, paste0(phename, "_Effect"),
paste0(phename, "_SE"), paste0(phename, "_log10p"))
gwas2$add_columns(ncol_add = 3)
gwas2[,c(i*3-5, i*3-4, i*3-3)] <- gwas
gwas2$save()
gwas_metadata <- add_row(gwas_metadata, phe = phename, type = type[i-1],
nsnp = nSNP, npcs = PC_df$NumPCs, nphe = nPhe,
nlev = nLev, lambda_GC = PC_df$lambda_GC,
bonferroni = bonferroni)
}
rm(gwas)
message(paste0("Finished phenotype ", i-1, ": ", names(df)[i]))
}
}
# 4. mash input ----
## prioritize effects with max(log10p) or max(sum(log10p))
## make a random set of relatively unlinked SNPs
ind_estim <- (1:sum(gwas_ok))*3 - 2
ind_se <- (1:sum(gwas_ok))*3 - 1
ind_p <- (1:sum(gwas_ok))*3
if(thr.m == "sum"){
thr_log10p <- big_apply(gwas2,
a.FUN = function(X, ind) rowSums(X[, ind]),
ind = ind_p,
a.combine = 'plus')
} else if(thr.m == "max"){
log10pmax_f <- function(X, ind) rowMaxs(as.matrix(X[, ind]))
thr_log10p <- big_apply(gwas2,
a.FUN = log10pmax_f,
ind = ind_p, a.combine = 'c')
}
gwas2$add_columns(ncol_add = 1)
colnames_fbm <- c(colnames_fbm, paste0(thr.m, "_thr_log10p"))
gwas2[,(sum(gwas_ok)*3+1)] <- thr_log10p
gwas2$save()
## replace NA or Nan values
# Replace SE with 1's, estimates and p values with 0's.
replace_na_1 <- function(X, ind) replace_na(X[, ind], 1)
replace_na_0 <- function(X, ind) replace_na(X[, ind], 0)
gwas2[,ind_se] <- big_apply(gwas2, a.FUN = replace_na_1, ind = ind_se,
a.combine = 'plus')
gwas2[,ind_estim] <- big_apply(gwas2, a.FUN = replace_na_0, ind = ind_estim,
a.combine = 'plus')
gwas2[,ind_p] <- big_apply(gwas2, a.FUN = replace_na_0, ind = ind_p,
a.combine = 'plus')
gwas2[,(sum(gwas_ok)*3+1)] <- big_apply(gwas2, a.FUN = replace_na_0,
ind = (sum(gwas_ok)*3+1),
a.combine = 'plus')
gwas2$save()
strong_clumps <- snp_clumping(G, infos.chr = markers$CHRN, thr.r2 = thr.r2,
infos.pos = markers$POS, S = thr_log10p)
random_clumps <- snp_clumping(G, infos.chr = markers$CHRN, thr.r2 = thr.r2,
infos.pos = markers$POS)
# this should be a top_n (slice_min/slice_max/slice_sample) with numSNPs, not a quantile
strong_sample <- add_column(markers, thr_log10p) %>%
rownames_to_column(var = "value") %>%
mutate(value = as.numeric(.data$value)) %>%
filter(.data$value %in% strong_clumps) %>%
slice_max(order_by = .data$thr_log10p, n = num.strong) %>%
arrange(.data$value)
if (is.na(num.random)[1]) {
num.random <- num.strong*2
}
random_sample <- add_column(markers, thr_log10p) %>%
rownames_to_column(var = "value") %>%
mutate(value = as.numeric(.data$value)) %>%
filter(!is.na(.data$thr_log10p)) %>%
filter(.data$value %in% random_clumps) %>%
slice_sample(n = num.random) %>%
arrange(.data$value)
## scaling
if (scale.phe == TRUE) {
colmaxes <- function(X, ind) colMaxs(abs(as.matrix(X[, ind])))
scale.effects <- big_apply(gwas2, a.FUN = colmaxes,
ind = ind_estim, a.combine = 'c')
colstand <- function(X, ind, v) X[,ind] / v
for (j in seq_along(scale.effects)) { # standardize one gwas at a time.
gwas2[,c(ind_estim[j], ind_se[j])] <-
big_apply(gwas2, a.FUN = colstand, ind = c(ind_estim[j], ind_se[j]),
v = scale.effects[j], a.combine = 'plus')
}
gwas2$save()
gwas_metadata <- gwas_metadata %>% mutate(scaled = TRUE)
} else {
gwas_metadata <- gwas_metadata %>% mutate(scaled = FALSE)
}
write_csv(tibble(colnames_fbm), file.path(outputdir,
paste0("gwas_effects", suffix,
"_column_names.csv")))
write_csv(gwas_metadata, file.path(outputdir,
paste0("gwas_effects", suffix,
"_associated_metadata.csv")))
## make mash input data.frames (6x or more)
Bhat_strong <- as.matrix(gwas2[strong_sample$value, ind_estim], )
Shat_strong <- as.matrix(gwas2[strong_sample$value, ind_se])
Bhat_random <- as.matrix(gwas2[random_sample$value, ind_estim])
Shat_random <- as.matrix(gwas2[random_sample$value, ind_se])
## Full data: Both Bhat and Shat are zero (or near zero) for some input data.
## Filter this data from the input, or set Shat to a positive number to
## avoid numerical issues. which rowSums are 0, filter these out or make +.
## Eventually want to batch process SNPs through this, not make a full set.
Bhat_full <- as.matrix(gwas2[, ind_estim])
Shat_full <- as.matrix(gwas2[, ind_se])
## name the columns for these conditions (usually the phenotype)
colnames(Bhat_strong) <- gwas_metadata$phe
colnames(Shat_strong) <- gwas_metadata$phe
colnames(Bhat_random) <- gwas_metadata$phe
colnames(Shat_random) <- gwas_metadata$phe
colnames(Bhat_full) <- gwas_metadata$phe
colnames(Shat_full) <- gwas_metadata$phe
# 5. mash ----
data_r <- mashr::mash_set_data(Bhat_random, Shat_random)
message(paste0("Estimating the correlation structure in the null tests from ",
"the random data.
(not the strong data because it will not necessarily contain
any null tests)."))
Vhat <- mashr::estimate_null_correlation_simple(data = data_r)
message(paste0("Setting up the main data objects with this correlation ",
"structure in place."))
data_strong <- mashr::mash_set_data(Bhat_strong, Shat_strong, V = Vhat)
data_random <- mashr::mash_set_data(Bhat_random, Shat_random, V = Vhat)
data_full <- mashr::mash_set_data(Bhat_full, Shat_full, V = Vhat)
U_c <- mashr::cov_canonical(data_random)
if (!is.na(U.ed[1])) {
message(paste0("Now estimating data-driven covariances using the strong",
" tests.
NB: This step may take some time to complete."))
if (length(ind_p) < 6) {
cov_npc <- ind_p - 1
} else {
cov_npc <- 5
}
U_pca = mashr::cov_pca(data_strong, npc = cov_npc)
U_ed = mashr::cov_ed(data_strong, U_pca)
saveRDS(U_ed, file = paste0(outputdir, "Mash_U_ed", suffix, ".rds"))
} else if (typeof(U.ed) == "list") {
U_ed <- U.ed
} else if (typeof(U.ed) == "character") {
U_ed <- readRDS(file = U.ed)
} else {
stop("U.ed should be NA, a list created using 'mashr::cov_ed', ",
"or a file path of a U_ed saved as an .rds")
}
if (typeof(U.hyp) == "list") {
m = mashr::mash(data_random, Ulist = c(U_ed, U_c, U.hyp), outputlevel = 1)
} else {
m = mashr::mash(data_random, Ulist = c(U_ed, U_c), outputlevel = 1)
}
message(paste0("Compute posterior matrices for all effects",
" using the mash fit from the
random tests."))
m2 = mashr::mash(data_full, g = ashr::get_fitted_g(m), fixg = TRUE)
return(m2)
}
#' Wrapper for bigsnpr for GWAS
#'
#' @description Given a dataframe of phenotypes associated with sample.IDs, this
#' function is a wrapper around bigsnpr functions to conduct linear or
#' logistic regression on wheat. The main advantages of this
#' function over just using the bigsnpr functions is that it automatically
#' removes individual genotypes with missing phenotypic data
#' and that it can run GWAS on multiple phenotypes sequentially.
#'
#' @param df Dataframe of phenotypes where the first column is sample.ID
#' @param type Character string. Type of univarate regression to run for GWAS.
#' Options are "linear" or "logistic".
#' @param snp Genomic information to include for wheat.
#' @param svd Optional covariance matrix to include in the regression. You
#' can generate these using \code{bigsnpr::snp_autoSVD()}.
#' @param npcs Integer. Number of PCs to use for population structure correction.
#'
#' @import bigsnpr
#' @import bigstatsr
#' @importFrom dplyr mutate rename case_when
#' @importFrom purrr as_vector
#' @importFrom tibble as_tibble enframe
#' @importFrom rlang .data
#'
#' @return The gwas results for the last phenotype in the dataframe. That
#' phenotype, as well as the remaining phenotypes, are saved as RDS objects
#' in the working directory.
#'
#' @export
div_gwas <- function(df, snp, type, svd, npcs){
stopifnot(type %in% c("linear", "logistic"))
if(attr(snp, "class") != "bigSNP"){
stop("snp needs to be a bigSNP object, produced by the bigsnpr package.")
}
if(colnames(df)[1] != "sample.ID"){
stop("First column of phenotype dataframe (df) must be 'sample.ID'.")
}
G <- snp$genotypes
pc_max = ncol(svd$u)
for(i in seq_along(names(df))[-1]){
y1 <- as_vector(df[which(!is.na(df[,i])), i])
ind_y <- which(!is.na(df[,i]))
if(type == "linear"){
if(npcs > 0){
ind_u <- matrix(svd$u[which(!is.na(df[,i])),1:npcs], ncol = npcs)
gwaspc <- big_univLinReg(G, y.train = y1, covar.train = ind_u,
ind.train = ind_y, ncores = 1)
} else {
gwaspc <- big_univLinReg(G, y.train = y1, ind.train = ind_y,
ncores = 1)
}
} else if(type == "logistic"){
message(paste0("For logistic models, if convergence is not reached by ",
"the main algorithm for any SNP, the corresponding `niter` element ",
"is set to NA, and glm is used instead. If glm can't ",
"converge either, those SNP estimations are set to NA."))
if(npcs > 0){
ind_u <- matrix(svd$u[which(!is.na(df[,i])),1:npcs], ncol = npcs)
gwaspc <- suppressMessages(big_univLogReg(G, y01.train = y1,
covar.train = ind_u,
ind.train = ind_y,
ncores = 1))
} else {
gwaspc <- suppressMessages(big_univLogReg(G, y01.train = y1,
ind.train = ind_y,
ncores = 1))
}
} else {
stop(paste0("Type of GWAS not recognized: please choose one of 'linear'",
" or 'logistic'"))
}
}
return(gwaspc)
}
#' Create a quantile-quantile plot with ggplot2.
#'
#' @description Assumptions for this quantile quantile plot:
#' Expected P values are uniformly distributed.
#' Confidence intervals assume independence between tests.
#' We expect deviations past the confidence intervals if the tests are
#' not independent.
#' For example, in a genome-wide association study, the genotype at any
#' position is correlated to nearby positions. Tests of nearby genotypes
#' will result in similar test statistics.
#'
#' @param ps Numeric vector of p-values.
#' @param ci Numeric. Size of the confidence interval, 0.95 by default.
#' @param lambdaGC Logical. Add the Genomic Control coefficient as subtitle to
#' the plot?
#'
#' @import ggplot2
#' @importFrom tibble as_tibble
#' @importFrom rlang .data
#' @importFrom stats qbeta ppoints
#' @param tol Numeric. Tolerance for optional Genomic Control coefficient.
#'
#' @return A ggplot2 plot.
#'
#' @export
get_qqplot <- function(ps, ci = 0.95, lambdaGC = FALSE, tol = 1e-8) {
ps <- ps[which(!is.na(ps))]
n <- length(ps)
df <- data.frame(
observed = -log10(sort(ps)),
expected = -log10(ppoints(n)),
clower = -log10(qbeta(p = (1 - ci) / 2, shape1 = 1:n, shape2 = n:1)),
cupper = -log10(qbeta(p = (1 + ci) / 2, shape1 = 1:n, shape2 = n:1))
)
df_round <- round_xy(df$expected, df$observed, cl = df$clower, cu = df$cupper)
log10Pe <- expression(paste("Expected -log"[10], plain("("), italic(p-value),
plain(")")))
log10Po <- expression(paste("Observed -log"[10], plain("("), italic(p-value),
plain(")")))
p1 <- ggplot(as_tibble(df_round)) +
geom_point(aes(.data$expected, .data$observed), shape = 1, size = 1) +
geom_abline(intercept = 0, slope = 1, size = 1.5, color = "red") +
geom_line(aes(.data$expected, .data$cupper), linetype = 2) +
geom_line(aes(.data$expected, .data$clower), linetype = 2) +
xlab(log10Pe) +
ylab(log10Po) +
theme_classic() +
theme(axis.title = element_text(size = 10),
axis.text = element_text(size = 10),
axis.line.x = element_line(size = 0.35, colour = 'grey50'),
axis.line.y = element_line(size = 0.35, colour = 'grey50'),
axis.ticks = element_line(size = 0.25, colour = 'grey50'),
legend.justification = c(1, 0.75), legend.position = c(1, 0.9),
legend.key.size = unit(0.35, 'cm'),
legend.title = element_blank(),
legend.text = element_text(size = 9),
legend.text.align = 0, legend.background = element_blank(),
plot.subtitle = element_text(size = 10, vjust = 0),
strip.background = element_blank(),
strip.text = element_text(hjust = 0.5, size = 10 ,vjust = 0),
strip.placement = 'outside', panel.spacing.x = unit(-0.4, 'cm'))
if (lambdaGC) {
lamGC <- get_lambdagc(ps = ps, tol = tol)
expr <- substitute(expression(lambda[GC] == l), list(l = lamGC))
p1 + labs(subtitle = eval(expr))
} else {
p1
}
}
#' Return a number rounded to some number of digits
#'
#' @description Given some x, return the number rounded to some number of
#' digits.
#'
#' @param x A number or vector of numbers
#' @param at Numeric. Rounding factor or size of the bin to round to.
#'
#' @return A number or vector of numbers
round2 <- function(x, at) ceiling(x / at) * at
#' Return a dataframe binned into 2-d bins by some x and y.
#'
#' @description Given a dataframe of x and y values (with some optional
#' confidence intervals surrounding the y values), return only the unique
#' values of x and y in some set of 2-d bins.
#'
#' @param x Numeric vector. The first vector for binning.
#' @param y Numeric vector. the second vector for binning
#' @param cl Numeric vector. Optional confidence interval for the y vector,
#' lower bound.
#' @param cu Numeric vector. Optional confidence interval for the y vector,
#' upper bound.
#' @param roundby Numeric. The amount to round the x and y vectors by for 2d
#' binning.
#'
#' @return A dataframe containing the 2-d binned values for x and y, and their
#' confidence intervals.
round_xy <- function(x, y, cl = NA, cu = NA, roundby = 0.001){
expected <- round2(x, at = roundby)
observed <- round2(y, at = roundby)
if(!is.na(cl[1]) & !is.na(cu[1])){
clower <- round2(cl, at = roundby)
cupper <- round2(cu, at = roundby)
tp <- cbind(expected, observed, clower, cupper)
return(tp[!duplicated(tp),])
} else {
tp <- cbind(expected, observed)
return(tp[!duplicated(tp),])
}
}
get_manhattan <- function(log10p, snp, thresh){
plot_data <- tibble(CHR = snp$map$chromosome, POS = snp$map$physical.pos,
marker.ID = snp$map$marker.ID, log10p = log10p) %>%
mutate(observed = round2(.data$log10p, at = 0.001)) %>%
group_by(.data$CHR, .data$POS, .data$observed) %>%
slice(1)
nchr <- length(unique(plot_data$CHR))
p1 <- plot_data %>%
ggplot(aes(x = .data$POS, y = .data$log10p)) +
geom_point(aes(color = .data$CHR, fill = .data$CHR)) +
geom_hline(yintercept = thresh, color = "black", linetype = 2,
size = 1) +
facet_wrap(~ .data$CHR, nrow = 1, scales = "free_x",
strip.position = "bottom") +
scale_color_manual(values = rep(c("#1B0C42FF", "#48347dFF",
"#95919eFF"), ceiling(nchr/3)),
guide = FALSE) +
theme_classic() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
panel.background = element_rect(fill=NA),
legend.position = "none",
axis.title = element_text(size = 10),
axis.text = element_text(size = 10),
axis.line.x = element_line(size = 0.35, colour = 'grey50'),
axis.line.y = element_line(size = 0.35, colour = 'grey50'),
axis.ticks = element_line(size = 0.25, colour = 'grey50'),
legend.justification = c(1, 0.75),
legend.key.size = unit(0.35, 'cm'),
legend.title = element_blank(),
legend.text = element_text(size = 9),
legend.text.align = 0, legend.background = element_blank(),
plot.subtitle = element_text(size = 10, vjust = 0),
strip.background = element_blank(),
strip.text = element_text(hjust = 0.5, size = 10 ,vjust = 0),
strip.placement = 'outside', panel.spacing.x = unit(-0.1, 'cm')) +
labs(x = "Chromosome", y = "-log10(p value)") +
scale_x_continuous(expand = c(0.2, 0.2))
return(p1)
}
#' Return lambda_GC for different numbers of PCs for GWAS on Panicum virgatum.
#'
#' @description Given a dataframe of phenotypes associated with sample.IDs and
#' output from a PCA to control for population structure, this function will
#' return a .csv file of the lambda_GC values for the GWAS upon inclusion
#' of different numbers of PCs. This allows the user to choose a number of
#' PCs that returns a lambda_GC close to 1, and thus ensure that they have
#' done adequate correction for population structure.
#'
#' @param df Dataframe of phenotypes where the first column is sample.ID and each
#' sample.ID occurs only once in the dataframe.
#' @param type Character string. Type of univarate regression to run for GWAS.
#' Options are "linear" or "logistic".
#' @param snp A bigSNP object with sample.IDs that match the df.
#' @param svd big_SVD object; Covariance matrix to include in the regression.
#' Generate these using \code{bigsnpr::snp_autoSVD()}.
#' @param ncores Number of cores to use. Default is one.
#' @param npcs Integer vector of principle components to use.
#' Defaults to c(0:10).
#' @param saveoutput Logical. Should output be saved as a csv to the
#' working directory?
#'
#' @import bigsnpr
#' @import bigstatsr
#' @importFrom dplyr mutate rename case_when mutate_if
#' @importFrom purrr as_vector
#' @importFrom tibble as_tibble enframe
#' @importFrom rlang .data
#' @importFrom readr write_csv
#' @importFrom utils tail
#'
#' @return A dataframe containing the lambda_GC values for each number of PCs
#' specified. This is also saved as a .csv file in the working directory.
#'
#' @export
div_lambda_GC <- function(df, type = c("linear", "logistic"), snp,
svd = NA, ncores = 1, npcs = c(0:10),
saveoutput = FALSE){
if(attr(snp, "class") != "bigSNP"){
stop("snp needs to be a bigSNP object, produced by the bigsnpr package.")
}
if(colnames(df)[1] != "sample.ID"){
stop("First column of phenotype dataframe (df) must be 'sample.ID'.")
}
if(length(svd) == 1){
stop(paste0("Need to specify covariance matrix (svd) and a vector of",
" PC #'s to test (npcs)."))
}
G <- snp$genotypes
LambdaGC <- as_tibble(matrix(data =
c(npcs, rep(NA, (ncol(df) - 1)*length(npcs))),
nrow = length(npcs), ncol = ncol(df),
dimnames = list(npcs, colnames(df))))
LambdaGC <- LambdaGC %>%
dplyr::rename("NumPCs" = .data$sample.ID) %>%
mutate_if(is.integer, as.numeric)
for (i in seq_along(names(df))[-1]) {
for (k in c(1:length(npcs))) {
if (type == "linear") {
y1 <- as_vector(df[which(!is.na(df[,i])), i])
ind_y <- which(!is.na(df[,i]))
if (npcs[k] == 0) {
gwaspc <- big_univLinReg(G, y.train = y1, ind.train = ind_y,
ncores = ncores)
} else {
ind_u <- matrix(svd$u[which(!is.na(df[,i])),1:npcs[k]],
ncol = npcs[k])
gwaspc <- big_univLinReg(G, y.train = y1, covar.train = ind_u,
ind.train = ind_y, ncores = ncores)
}
} else if(type == "logistic"){
message(paste0("For logistic models, if convergence is not reached by ",
"the main algorithm for some SNPs, the corresponding `niter` element ",
"is set to NA, and glm is used instead. If glm can't ",
"converge either, those SNP estimations are set to NA."))
y1 <- as_vector(df[which(!is.na(df[,i])), i])
ind_y <- which(!is.na(df[,i]))
if(npcs[k] == 0){
gwaspc <- suppressMessages(big_univLogReg(G, y01.train = y1,
ind.train = ind_y,
ncores = ncores))
} else {
ind_u <- matrix(svd$u[which(!is.na(df[,i])),1:npcs[k]],
ncol = npcs[k])
gwaspc <- suppressMessages(big_univLogReg(G, y01.train = y1,
covar.train = ind_u,
ind.train = ind_y,
ncores = ncores))
}
}
ps <- predict(gwaspc, log10 = FALSE)
LambdaGC[k,i] <- get_lambdagc(ps = ps)
#message(paste0("Finished Lambda_GC calculation for ", names(df)[i],
# " using ", npcs[k], " PCs."))
}
if(saveoutput == TRUE){
write_csv(LambdaGC, path = paste0("Lambda_GC_", names(df)[i], ".csv"))
}
#message(paste0("Finished phenotype ", i-1, ": ", names(df)[i]))
}
if(saveoutput == TRUE){
write_csv(LambdaGC, path = paste0("Lambda_GC_", names(df)[2], "_to_",
tail(names(df), n = 1), "_Phenotypes_",
npcs[1], "_to_", tail(npcs, n = 1),
"_PCs.csv"))
best_LambdaGC <- get_best_PC_df(df = LambdaGC)
write_csv(best_LambdaGC, path = paste0("Best_Lambda_GC_", names(df)[2],
"_to_", tail(names(df), n = 1),
"_Phenotypes_", npcs[1], "_to_",
tail(npcs, n = 1), "_PCs.csv"))
}
return(LambdaGC)
}
#' Find lambda_GC value for non-NA p-values
#'
#' @description Finds the lambda GC value for some vector of p-values.
#'
#' @param ps Numeric vector of p-values. Can have NA's.
#' @param tol Numeric. Tolerance for optional Genomic Control coefficient.
#'
#' @importFrom stats median uniroot
#'
#' @return A lambda GC value (some positive number, ideally ~1)
#'
#' @export
get_lambdagc <- function(ps, tol = 1e-8){
ps <- ps[which(!is.na(ps))]
xtr <- log10(ps)
MEDIAN <- log10(0.5)
f.opt <- function(x) (x - MEDIAN)
xtr_p <- median(xtr) / uniroot(f.opt, interval = range(xtr),
check.conv = TRUE,
tol = tol)$root
lamGC <- signif(xtr_p)
return(lamGC)
}
#' Return best number of PCs in terms of lambda_GC for Panicum virgatum.
#' Return best number of PCs in terms of lambda_GC for the CDBN.
#'
#' @description Given a dataframe created using pvdiv_lambda_GC, this function
#' returns the first lambda_GC less than 1.05, or the smallest lambda_GC,
#' for each column in the dataframe.
#'
#' @param df Dataframe of phenotypes where the first column is NumPCs and
#' subsequent column contains lambda_GC values for some phenotype.
#'
#' @importFrom dplyr filter top_n select full_join arrange
#' @importFrom tidyr gather
#' @importFrom rlang .data sym !!
#' @importFrom tidyselect all_of
#'
#' @return A dataframe containing the best lambda_GC value and number of PCs
#' for each phenotype in the data frame.
get_best_PC_df <- function(df){
column <- names(df)[ncol(df)]
bestPCs <- df %>%
filter(!! sym(column) < 1.05| !! sym(column) == min(!! sym(column))) %>%
top_n(n = -1, wt = .data$NumPCs) %>%
select(.data$NumPCs, all_of(column))
if(ncol(df) > 2){
for(i in c((ncol(df)-2):1)){
column <- names(df)[i+1]
bestPCs <- df %>%
filter(!! sym(column) < 1.05 | !! sym(column) == min(!! sym(column))) %>%
top_n(n = -1, wt = .data$NumPCs) %>%
select(.data$NumPCs, all_of(column)) %>%
full_join(bestPCs, by = c("NumPCs", (column)))
}
}
bestPCdf <- bestPCs %>%
arrange(.data$NumPCs) %>%
gather(key = "trait", value = "lambda_GC", 2:ncol(bestPCs)) %>%
filter(!is.na(.data$lambda_GC))
return(bestPCdf)
}
div_mash <- function(){}
check_gwas <- function(df1, phename, type, nPhe, minphe, nLev){
if(nPhe < minphe){
message(paste0("The phenotype ", phename, " does not have the minimum ",
"number of phenotyped sample.ID's, (", minphe, ") and so ",
"will not be used for GWAS."))
gwas_ok <- FALSE
} else if(nLev < 2){
message(paste0("The phenotype ", phename, " does not have two or more ",
"distinct non-NA values and will not be used for GWAS."))
gwas_ok <- FALSE
} else if(nLev > 2 & type == "logistic"){
message(paste0("The phenotype ", phename, " has more than two distinct ",
"non-NA values and will not be used for GWAS with 'type=",
"logistic'."))
gwas_ok <- FALSE
} else if(!(unique(df1[which(!is.na(df1[,2])),2])[1,1] %in% c(0,1)) &
!(unique(df1[which(!is.na(df1[,2])),2])[2,1] %in% c(0,1)) &
type == "logistic"){
message(paste0("The phenotype ", phename, " has non-NA values that are ",
"not 0 or 1 and will not be used for GWAS with 'type=",
"logistic'."))
gwas_ok <- FALSE
} else {
gwas_ok <- TRUE
}
return(gwas_ok)
}

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https://github.com/Alice-MacQueen/snpdiver

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{div_gwas}
\alias{div_gwas}
\title{Wrapper for bigsnpr for GWAS}
\usage{
div_gwas(df, snp, type, svd, npcs)
}
\arguments{
\item{df}{Dataframe of phenotypes where the first column is sample.ID}
\item{snp}{Genomic information to include for wheat.}
\item{type}{Character string. Type of univarate regression to run for GWAS.
Options are "linear" or "logistic".}
\item{svd}{Optional covariance matrix to include in the regression. You
can generate these using \code{bigsnpr::snp_autoSVD()}.}
\item{npcs}{Integer. Number of PCs to use for population structure correction.}
}
\value{
The gwas results for the last phenotype in the dataframe. That
phenotype, as well as the remaining phenotypes, are saved as RDS objects
in the working directory.
}
\description{
Given a dataframe of phenotypes associated with sample.IDs, this
function is a wrapper around bigsnpr functions to conduct linear or
logistic regression on wheat. The main advantages of this
function over just using the bigsnpr functions is that it automatically
removes individual genotypes with missing phenotypic data
and that it can run GWAS on multiple phenotypes sequentially.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{div_lambda_GC}
\alias{div_lambda_GC}
\title{Return lambda_GC for different numbers of PCs for GWAS on Panicum virgatum.}
\usage{
div_lambda_GC(
df,
type = c("linear", "logistic"),
snp,
svd = NA,
ncores = 1,
npcs = c(0:10),
saveoutput = FALSE
)
}
\arguments{
\item{df}{Dataframe of phenotypes where the first column is sample.ID and each
sample.ID occurs only once in the dataframe.}
\item{type}{Character string. Type of univarate regression to run for GWAS.
Options are "linear" or "logistic".}
\item{snp}{A bigSNP object with sample.IDs that match the df.}
\item{svd}{big_SVD object; Covariance matrix to include in the regression.
Generate these using \code{bigsnpr::snp_autoSVD()}.}
\item{ncores}{Number of cores to use. Default is one.}
\item{npcs}{Integer vector of principle components to use.
Defaults to c(0:10).}
\item{saveoutput}{Logical. Should output be saved as a csv to the
working directory?}
}
\value{
A dataframe containing the lambda_GC values for each number of PCs
specified. This is also saved as a .csv file in the working directory.
}
\description{
Given a dataframe of phenotypes associated with sample.IDs and
output from a PCA to control for population structure, this function will
return a .csv file of the lambda_GC values for the GWAS upon inclusion
of different numbers of PCs. This allows the user to choose a number of
PCs that returns a lambda_GC close to 1, and thus ensure that they have
done adequate correction for population structure.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{dive_phe2mash}
\alias{dive_phe2mash}
\title{Wrapper to run mash given a phenotype data frame}
\usage{
dive_phe2mash(
df,
snp,
type = "linear",
svd = NULL,
suffix = "",
outputdir = ".",
min.phe = 200,
save.plots = TRUE,
thr.r2 = 0.2,
thr.m = c("sum", "max"),
num.strong = 1000,
num.random = NA,
scale.phe = TRUE,
roll.size = 50,
U.ed = NA,
U.hyp = NA
)
}
\arguments{
\item{df}{Dataframe containing phenotypes for mash where the first column is
'sample.ID', which should match values in the snp$fam$sample.ID column.}
\item{snp}{A "bigSNP" object; load with \code{snp_attach()}.}
\item{type}{Character string, or a character vector the length of the number
of phenotypes. Type of univarate regression to run for GWAS.
Options are "linear" or "logistic".}
\item{svd}{A "big_SVD" object; Optional covariance matrix to use for
population structure correction.}
\item{suffix}{Optional character vector to give saved files a unique search string/name.}
\item{outputdir}{Optional file path to save output files.}
\item{min.phe}{Integer. Minimum number of individuals phenotyped in order to
include that phenotype in GWAS. Default is 200. Use lower values with
caution.}
\item{save.plots}{Logical. Should Manhattan and QQ-plots be generated and
saved to the working directory for univariate GWAS? Default is TRUE.}
\item{thr.r2}{Value between 0 and 1. Threshold of r2 measure of linkage
disequilibrium. Markers in higher LD than this will be subset using clumping.}
\item{thr.m}{"sum" or "max". Type of threshold to use to clump values for
mash inputs. "sum" sums the -log10pvalues for each phenotype and uses
the maximum of this value as the threshold. "max" uses the maximum
-log10pvalue for each SNP across all of the univariate GWAS.}
\item{num.strong}{Integer. Number of SNPs used to derive data-driven covariance
matrix patterns, using markers with strong effects on phenotypes.}
\item{num.random}{Integer. Number of SNPs used to derive the correlation structure
of the null tests, and the mash fit on the null tests.}
\item{scale.phe}{Logical. Should effects for each phenotype be scaled to fall
between -1 and 1? Default is TRUE.}
\item{roll.size}{Integer. Used to create the svd for GWAS.}
\item{U.ed}{Mash data-driven covariance matrices. Specify these as a list or a path
to a file saved as an .rds. Creating these can be time-consuming, and
generating these once and reusing them for multiple mash runs can save time.}
\item{U.hyp}{Other covariance matrices for mash. Specify these as a list. These
matrices must have dimensions that match the number of phenotypes where
univariate GWAS ran successfully.}
}
\value{
A mash object made up of all phenotypes where univariate GWAS ran
successfully.
}
\description{
Though step-by-step GWAS, preparation of mash inputs, and mash
allows you the most flexibility and opportunities to check your results
for errors, once those sanity checks are complete, this function allows
you to go from a phenotype data.frame of a few phenotypes you want to
compare to a mash result. Some exception handling has been built into
this function, but the user should stay cautious and skeptical of any
results that seem 'too good to be true'.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{get_best_PC_df}
\alias{get_best_PC_df}
\title{Return best number of PCs in terms of lambda_GC for Panicum virgatum.
Return best number of PCs in terms of lambda_GC for the CDBN.}
\usage{
get_best_PC_df(df)
}
\arguments{
\item{df}{Dataframe of phenotypes where the first column is NumPCs and
subsequent column contains lambda_GC values for some phenotype.}
}
\value{
A dataframe containing the best lambda_GC value and number of PCs
for each phenotype in the data frame.
}
\description{
Given a dataframe created using pvdiv_lambda_GC, this function
returns the first lambda_GC less than 1.05, or the smallest lambda_GC,
for each column in the dataframe.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{get_lambdagc}
\alias{get_lambdagc}
\title{Find lambda_GC value for non-NA p-values}
\usage{
get_lambdagc(ps, tol = 1e-08)
}
\arguments{
\item{ps}{Numeric vector of p-values. Can have NA's.}
\item{tol}{Numeric. Tolerance for optional Genomic Control coefficient.}
}
\value{
A lambda GC value (some positive number, ideally ~1)
}
\description{
Finds the lambda GC value for some vector of p-values.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{get_qqplot}
\alias{get_qqplot}
\title{Create a quantile-quantile plot with ggplot2.}
\usage{
get_qqplot(ps, ci = 0.95, lambdaGC = FALSE, tol = 1e-08)
}
\arguments{
\item{ps}{Numeric vector of p-values.}
\item{ci}{Numeric. Size of the confidence interval, 0.95 by default.}
\item{lambdaGC}{Logical. Add the Genomic Control coefficient as subtitle to
the plot?}
\item{tol}{Numeric. Tolerance for optional Genomic Control coefficient.}
}
\value{
A ggplot2 plot.
}
\description{
Assumptions for this quantile quantile plot:
Expected P values are uniformly distributed.
Confidence intervals assume independence between tests.
We expect deviations past the confidence intervals if the tests are
not independent.
For example, in a genome-wide association study, the genotype at any
position is correlated to nearby positions. Tests of nearby genotypes
will result in similar test statistics.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-pipe.R
\name{\%>\%}
\alias{\%>\%}
\title{Pipe operator}
\usage{
lhs \%>\% rhs
}
\description{
See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
}
\keyword{internal}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{round2}
\alias{round2}
\title{Return a number rounded to some number of digits}
\usage{
round2(x, at)
}
\arguments{
\item{x}{A number or vector of numbers}
\item{at}{Numeric. Rounding factor or size of the bin to round to.}
}
\value{
A number or vector of numbers
}
\description{
Given some x, return the number rounded to some number of
digits.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrapper.R
\name{round_xy}
\alias{round_xy}
\title{Return a dataframe binned into 2-d bins by some x and y.}
\usage{
round_xy(x, y, cl = NA, cu = NA, roundby = 0.001)
}
\arguments{
\item{x}{Numeric vector. The first vector for binning.}
\item{y}{Numeric vector. the second vector for binning}
\item{cl}{Numeric vector. Optional confidence interval for the y vector,
lower bound.}
\item{cu}{Numeric vector. Optional confidence interval for the y vector,
upper bound.}
\item{roundby}{Numeric. The amount to round the x and y vectors by for 2d
binning.}
}
\value{
A dataframe containing the 2-d binned values for x and y, and their
confidence intervals.
}
\description{
Given a dataframe of x and y values (with some optional
confidence intervals surrounding the y values), return only the unique
values of x and y in some set of 2-d bins.
}

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-tidy-eval.R
\name{tidyeval}
\alias{tidyeval}
\alias{expr}
\alias{enquo}
\alias{enquos}
\alias{sym}
\alias{syms}
\alias{.data}
\alias{:=}
\alias{as_name}
\alias{as_label}
\title{Tidy eval helpers}
\description{
\itemize{
\item \code{\link[rlang]{sym}()} creates a symbol from a string and
\code{\link[rlang:sym]{syms}()} creates a list of symbols from a
character vector.
\item \code{\link[rlang:nse-defuse]{enquo}()} and
\code{\link[rlang:nse-defuse]{enquos}()} delay the execution of one or
several function arguments. \code{enquo()} returns a single quoted
expression, which is like a blueprint for the delayed computation.
\code{enquos()} returns a list of such quoted expressions.
\item \code{\link[rlang:nse-defuse]{expr}()} quotes a new expression \emph{locally}. It
is mostly useful to build new expressions around arguments
captured with \code{\link[=enquo]{enquo()}} or \code{\link[=enquos]{enquos()}}:
\code{expr(mean(!!enquo(arg), na.rm = TRUE))}.
\item \code{\link[rlang]{as_name}()} transforms a quoted variable name
into a string. Supplying something else than a quoted variable
name is an error.
That's unlike \code{\link[rlang]{as_label}()} which also returns
a single string but supports any kind of R object as input,
including quoted function calls and vectors. Its purpose is to
summarise that object into a single label. That label is often
suitable as a default name.
If you don't know what a quoted expression contains (for instance
expressions captured with \code{enquo()} could be a variable
name, a call to a function, or an unquoted constant), then use
\code{as_label()}. If you know you have quoted a simple variable
name, or would like to enforce this, use \code{as_name()}.
}
To learn more about tidy eval and how to use these tools, visit
\url{https://tidyeval.tidyverse.org} and the
\href{https://adv-r.hadley.nz/metaprogramming.html}{Metaprogramming
section} of \href{https://adv-r.hadley.nz}{Advanced R}.
}
\keyword{internal}

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Version: 1.0
RestoreWorkspace: No
SaveWorkspace: No
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: pdfLaTeX
AutoAppendNewline: Yes
StripTrailingWhitespace: Yes
LineEndingConversion: Posix
BuildType: Package
PackageUseDevtools: Yes
PackageInstallArgs: --no-multiarch --with-keep.source
PackageRoxygenize: rd,collate,namespace