Simple Regression
Last updated: 2023-01-08
Checks: 7 0
Knit directory: dgrp-starve/
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Methodology
The lowest p-values were chosen to represent the fixed effect as better correlations assist in prediction of starvation resistance.
Random effect was a normal distribution with a mean of 1 and sd of 0.25. Default norm resulted in errors with negative value affecting matrix computations or creating singular matrices.
Additionally, I left verbose on TRUE to observe iteration steps as while randomness is seeded, iteration steps were not the same every time.
Depending on what is run, the system is not able to converge as it either goes to positive infinity and overflows to a negative value or reaches zero and “converges” at zero. If this webpage renders, then both runs will have converged on zero.
Note: data shown in this instance is wrong as highest p-values were chosen as the prediction almost always converged to zero, allowing compilation with workflowr
Female
#read in p values
fReg <- fread("data/fRegress.txt")
#read in expression data
fMeans <- fread("data/fMeans.txt")
#create matrix of only gene expression, trims line and starvation
Y <- as.matrix(fMeans[,3:11340], row.names=1)
#dimensions and number of fixed effect genes
n <- dim(Y)[1]
p <- dim(Y)[2]
p_effect <- 260
#p_effect <- round(p / 15)
#400, 250 maxit
#keep
if(TRUE){
#error is seeded rnorm, number of rows(lines)
e <- rnorm(n, 1, 0.25)
#e <- rnorm(n)
# add gene names to p val list
geneNames <- colnames(fMeans)[3:11340]
fReg <- fReg[, gene:=geneNames]
###sorted p values
# LOW pval
pSort <- fReg[order(pvalList)]
#HIGH pval
#pSort <- fReg[order(-pvalList)]
}
#rerun
if(TRUE){
# fixed effect vector USELESS, must be matched to certain values
b <- c(rnorm(p_effect, mean=6), rep(0, p-p_effect))
#affix vector to sorted p values
pSort[,b:=b]
#restore sort order to id/alphabetical gene, matches expression data order
fFin <- pSort[order(id)]
# PROPER fixed effect vector PROPER with proper indexing
b <- fFin[,b]
#matrix multiplication of the data and p
y <- drop(Y%*%b) + e
###Create model for covariates to adjust for (only an intercept in our case)
mu <- rep(1, length(y))
names(mu) <- names(y)
###Compute transcriptomic relationship matrix (accounts for structure based on expression levels)
W <- scale(Y)
TRM <- tcrossprod(W)/ncol(W)
}
###Fit mixed model
fit <- greml(y = y, X = mu, GRM = list(TRM), verbose = TRUE, maxit = 100)[1] "Iteration:" "1" "Theta:" "55.73" "54.66"
[1] "Iteration:" "2" "Theta:" "112.48" "106.07"
[1] "Iteration:" "3" "Theta:" "228.94" "199.48"
[1] "Iteration:" "4" "Theta:" "473.32" "350.66"
[1] "Iteration:" "5" "Theta:" "1004.25" "526.12"
[1] "Iteration:" "6" "Theta:" "2204.95" "479.76"
[1] "Iteration:" "7" "Theta:" "4915.96" "0"
[1] "Iteration:" "8" "Theta:" "10576.46" "0"
[1] "Iteration:" "9" "Theta:" "24499.85" "0"
[1] "Iteration:" "10" "Theta:" "33377.59" "0"
[1] "Iteration:" "11" "Theta:" "64233.75" "0"
[1] "Iteration:" "12" "Theta:" "21974.94" "0"
[1] "Iteration:" "13" "Theta:" "54027.91" "0"
[1] "Iteration:" "14" "Theta:" "79643.02" "0"
[1] "Iteration:" "15" "Theta:" "0" "0"
[1] "Iteration:" "16" "Theta:" "0" "0"
[1] "Converged at Iteration:" "16"
[3] "Theta:" "0"
[5] "0" stat <- glma(fit = fit, W = W)
#qqplot
qq(stat[,4], main="Female Gene p-values")
Male
rm(list=ls())
#read in p values
mReg <- fread("data/mRegress.txt")
#read in expression data
mMeans <- fread("data/mMeans.txt")
#create matrix of only gene expression, trims line and starvation
Y <- as.matrix(mMeans[,3:13577], row.names=1)
#dimensions and number of fixed effect genes
n <- dim(Y)[1]
p <- dim(Y)[2]
p_effect <- 525
#p_effect <- round(p / 15)
# add gene names to p val list
geneNames <- colnames(mMeans)[3:13577]
mReg <- mReg[, gene:=geneNames]
###sorted p values
# LOW pval
pSort <- mReg[order(pvalList)]
#HIGH pval
#pSort <- mReg[order(-pvalList)]
# fixed effect vector USELESS, must be matched to certain values
b <- c(rnorm(p_effect, mean=6), rep(0, p-p_effect))
#affix vector to sorted p values
pSort[,b:=b]
#restore sort order to id/alphabetical gene, matches expression data order
mFin <- pSort[order(id)]
# PROPER fixed effect vector PROPER with proper indexing
b <- mFin[,b]
#error is seeded rnorm, number of rows(lines)
e <- rnorm(n, 1, 0.25)
#e <- rnorm(n)
#matrix multiplication of the data and p
y <- drop(Y%*%b) + e
###Create model for covariates to adjust for (only an intercept in our case)
mu <- rep(1, length(y))
names(mu) <- names(y)
###Compute transcriptomic relationship matrix (accounts for structure based on expression levels)
W <- scale(Y)
TRM <- tcrossprod(W)/ncol(W)
###Fit mixed model
fit <- greml(y = y, X = mu, GRM = list(TRM), verbose = TRUE)[1] "Iteration:" "1" "Theta:" "91.15" "88.84"
[1] "Iteration:" "2" "Theta:" "183.8" "170.11"
[1] "Iteration:" "3" "Theta:" "373.54" "310.86"
[1] "Iteration:" "4" "Theta:" "769.98" "511.57"
[1] "Iteration:" "5" "Theta:" "1624.31" "637.37"
[1] "Iteration:" "6" "Theta:" "3520.08" "117.94"
[1] "Iteration:" "7" "Theta:" "7519.51" "0"
[1] "Iteration:" "8" "Theta:" "16410.1" "0"
[1] "Iteration:" "9" "Theta:" "31164.21" "0"
[1] "Iteration:" "10" "Theta:" "60969.74" "0"
[1] "Iteration:" "11" "Theta:" "8515.65" "0"
[1] "Iteration:" "12" "Theta:" "19284.7" "0"
[1] "Iteration:" "13" "Theta:" "47341.63" "0"
[1] "Iteration:" "14" "Theta:" "95237.84" "0"
[1] "Iteration:" "15" "Theta:" "45481.58" "0"
[1] "Iteration:" "16" "Theta:" "109238.51" "0"
[1] "Iteration:" "17" "Theta:" "208252.63" "0"
[1] "Iteration:" "18" "Theta:" "92623.61" "0"
[1] "Iteration:" "19" "Theta:" "0" "68299.02"
[1] "Iteration:" "20" "Theta:" "50566.26" "0"
[1] "Iteration:" "21" "Theta:" "63615.84" "0"
[1] "Iteration:" "22" "Theta:" "143982.95" "0"
[1] "Iteration:" "23" "Theta:" "145265.04" "0"
[1] "Iteration:" "24" "Theta:" "0" "5808.2"
[1] "Iteration:" "25" "Theta:" "365.69" "7491.58"
[1] "Iteration:" "26" "Theta:" "1580.34" "6062.89"
[1] "Iteration:" "27" "Theta:" "4522.6" "1158.62"
[1] "Iteration:" "28" "Theta:" "10692.21" "0"
[1] "Iteration:" "29" "Theta:" "24363.01" "0"
[1] "Iteration:" "30" "Theta:" "52929.41" "0"
[1] "Iteration:" "31" "Theta:" "0" "0"
[1] "Iteration:" "32" "Theta:" "0" "0"
[1] "Converged at Iteration:" "32"
[3] "Theta:" "0"
[5] "0" stat <- glma(fit = fit, W = W)
#qqplot
qq(stat[,4], main="Male Gene p-values")
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