Male Prediction WIP
Last updated: 2023-01-22
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Knit directory: dgrp-starve/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a65dfd2 | nklimko | 2023-01-22 | secretariat shutdown files |
Female Data
#wolb infection and inversion status data with phenotype adjustment function
load("/data/morgante_lab/data/dgrp/misc/adjustData.RData")
#read in expression data
fMeans <- fread("data/fMeans.txt")
#create matrix of only gene expression, trims line and starvation
X <- as.matrix(fMeans[,3:11340])
rownames(X) <- fMeans[,line]
#extract and adjust phenotype(starvation)
y <- fMeans[,starvation]
dat <- data.frame(id=fMeans[,line], y=y)
y_adj <- adjustPheno(dat, "starvation")
#scale matrix and compute TRM using crossproduct and number of markers(genes)
W <- scale(X)
TRM <- tcrossprod(W)/ncol(W)
#convert TRM structure to list
listTRM <- list(A=TRM)
#model to solve for, vector of ones
mu <- matrix(rep(1, length(y_adj)), ncol=1)
# REML analyses
fitG <- greml(y = y_adj, X = mu, GRM = listTRM, verbose = TRUE)
# Create marker sets
#setsTB <- list(A = colnames(X)) # gblup model
# grm computes crossproduct
#TB <- lapply(setsTB, function(x) {grm(W = X[, x])})
# k-fold parameters
n <- length(y_adj)
fold <- 10
nvalid <- 50
#validate sets
validate <- replicate(nvalid, sample(1:n, as.integer(n / fold)))
#cross-validation greml
cvTB <- greml(y = y_adj, X = mu, GRM = listTRM, validate = validate, verbose=FALSE)
hist(cvTB$accuracy$Corr, main = "Female Correlations")
statTemp <- glma(fit = fitG, W = W)
#summary(cvTB$accuracy$Corr)
par(mfrow=c(1,2))
hist(statTemp[,1], main="Female Coefficients")
qq(statTemp[,4], main="Female Gene p-values")
statF <- statTemp
summary(statF)Male Data
rm(list=ls())
#wolb infection and inversion status data with phenotype adjustment function
load("/data/morgante_lab/data/dgrp/misc/adjustData.RData")
#read in expression data
mMeans <- fread("data/mMeans.txt")
#create matrix of only gene expression, trims line and starvation
X <- as.matrix(mMeans[,3:11340])
rownames(X) <- mMeans[,line]
#extract and adjust phenotype(starvation)
y <- mMeans[,starvation]
dat <- data.frame(id=mMeans[,line], y=y)
y_adj <- adjustPheno(dat, "starvation")Type III ANOVA table for covariates: starvation
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 15975 893.32
factor(wolba) 1 3.041 15978 891.35 0.0354 0.8509
factor(In_2L_t) 2 299.141 16274 892.99 1.7415 0.1781
factor(In_2R_NS) 2 243.044 16218 892.31 1.4149 0.2455
factor(In_3R_P) 2 230.105 16205 892.15 1.3396 0.2645
factor(In_3R_K) 2 288.050 16263 892.85 1.6770 0.1898
factor(In_3R_Mo) 2 207.445 16182 891.87 1.2077 0.3012
Estimated effects
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.6614374 1.157976 39.4321227 2.909220e-92
factor(wolba)y -0.2574606 1.368248 -0.1881681 8.509500e-01
factor(In_2L_t)1 1.7198329 2.311372 0.7440744 4.577704e-01
factor(In_2L_t)2 -3.7780449 2.348592 -1.6086422 1.093906e-01
factor(In_2R_NS)1 -1.0811449 3.443957 -0.3139252 7.539297e-01
factor(In_2R_NS)2 5.8594026 3.595753 1.6295343 1.048923e-01
factor(In_3R_P)1 -2.0731924 3.879580 -0.5343858 5.937128e-01
factor(In_3R_P)2 7.1994131 4.718118 1.5259077 1.287315e-01
factor(In_3R_K)1 5.3513957 3.043456 1.7583284 8.033635e-02
factor(In_3R_K)2 3.7673764 6.668091 0.5649857 5.727643e-01
factor(In_3R_Mo)1 -3.0569154 3.189101 -0.9585510 3.390293e-01
factor(In_3R_Mo)2 -3.0977753 2.402719 -1.2892793 1.989020e-01#scale matrix and compute TRM using crossproduct and number of markers(genes)
W <- scale(X)
TRM <- tcrossprod(W)/ncol(W)
listTRM <- list(A=TRM)
#model to solve for, vector of ones
mu <- matrix(rep(1, length(y_adj)), ncol=1)
# REML analyses
fitG <- greml(y = y_adj, X = mu, GRM = listTRM, verbose = TRUE, maxit=1000)[1] "Iteration:" "1" "Theta:" "4.48" "4.31"
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[5] "0" # Create marker sets
#setsTB <- list(A = colnames(X)) # gblup model
# grm computes crossproduct
#TB <- lapply(setsTB, function(x) {grm(W = X[, x])})
# k-fold parameters
n <- length(y_adj)
fold <- 10
nvalid <- 50
#validate sets
validate <- replicate(nvalid, sample(1:n, as.integer(n / fold)))
cvTB <- greml(y = y_adj, X = mu, GRM = listTRM, validate = validate, verbose=FALSE)
hist(cvTB$accuracy$Corr, main = "Male Correlations")
statTemp <- glma(fit = fitG, W = W)
#summary(cvTB$accuracy$Corr)
par(mfrow=c(1,2))
hist(statTemp[,1], main="Male Coefficients")
qq(statTemp[,4], main="Male Gene p-values")
statM <- statTemp
summary(statM) coef se stat p
Min. :-2.69683 Min. :0.5455 Min. : 0.00000 Min. :0.00122
1st Qu.:-0.44988 1st Qu.:0.5455 1st Qu.: 0.05792 1st Qu.:0.37266
Median : 0.01846 Median :0.5455 Median : 0.26271 Median :0.60826
Mean : 0.01882 Mean :0.5455 Mean : 0.62653 Mean :0.58162
3rd Qu.: 0.48516 3rd Qu.:0.5455 3rd Qu.: 0.79479 3rd Qu.:0.80981
Max. : 2.94910 Max. :0.5455 Max. :10.45937 Max. :0.99996
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/Software/openblas_0.3.10/lib/libopenblas_haswellp-r0.3.10.dev.so
locale:
[1] LC_CTYPE=en_US.utf-8 LC_NUMERIC=C
[3] LC_TIME=en_US.utf-8 LC_COLLATE=en_US.utf-8
[5] LC_MONETARY=en_US.utf-8 LC_MESSAGES=en_US.utf-8
[7] LC_PAPER=en_US.utf-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.utf-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] qgg_1.1.1 qqman_0.1.8 cowplot_1.1.1 ggplot2_3.3.5
[5] data.table_1.14.2 dplyr_1.0.8 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 lattice_0.20-45 getPass_0.2-2 ps_1.6.0
[5] assertthat_0.2.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2
[9] R6_2.5.1 MatrixModels_0.5-1 evaluate_0.15 coda_0.19-4
[13] highr_0.9 httr_1.4.2 pillar_1.7.0 rlang_1.0.4
[17] rstudioapi_0.13 SparseM_1.81 whisker_0.4 callr_3.7.0
[21] jquerylib_0.1.4 Matrix_1.5-3 rmarkdown_2.16 splines_4.0.3
[25] statmod_1.4.37 stringr_1.4.0 munsell_0.5.0 compiler_4.0.3
[29] httpuv_1.6.5 xfun_0.30 pkgconfig_2.0.3 mcmc_0.9-7
[33] htmltools_0.5.2 tidyselect_1.1.2 tibble_3.1.6 fansi_1.0.3
[37] calibrate_1.7.7 crayon_1.5.1 withr_2.5.0 later_1.3.0
[41] MASS_7.3-56 grid_4.0.3 jsonlite_1.8.0 gtable_0.3.0
[45] lifecycle_1.0.1 DBI_1.1.2 git2r_0.30.1 magrittr_2.0.3
[49] scales_1.2.0 cli_3.3.0 stringi_1.7.6 fs_1.5.2
[53] promises_1.2.0.1 bslib_0.3.1 ellipsis_0.3.2 generics_0.1.2
[57] vctrs_0.4.1 tools_4.0.3 glue_1.6.2 purrr_0.3.4
[61] parallel_4.0.3 processx_3.5.3 fastmap_1.1.0 survival_3.3-1
[65] yaml_2.3.5 colorspace_2.0-3 knitr_1.38 sass_0.4.1
[69] quantreg_5.94 MCMCpack_1.6-3