Last updated: 2022-12-21

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Rmd 7b1c552 nklimko 2022-12-21 wflow_publish("analysis/multiReg.Rmd")

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

Depending on what is run, the system is not able to converge asa 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.

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 <- 525
#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)

# 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:"     "56.48"      "55.88"     
[1] "Iteration:" "2"          "Theta:"     "113.42"     "109.86"    
[1] "Iteration:" "3"          "Theta:"     "228.63"     "212.22"    
[1] "Iteration:" "4"          "Theta:"     "464.08"     "395.55"    
[1] "Iteration:" "5"          "Theta:"     "953.03"     "683.97"    
[1] "Iteration:" "6"          "Theta:"     "1986.92"    "1000"      
[1] "Iteration:" "7"          "Theta:"     "4165.83"    "922.12"    
[1] "Iteration:" "8"          "Theta:"     "8295.5"     "0"         
[1] "Iteration:" "9"          "Theta:"     "13497.65"   "0"         
[1] "Iteration:" "10"         "Theta:"     "18798.51"   "0"         
[1] "Iteration:" "11"         "Theta:"     "24464.08"   "0"         
[1] "Iteration:" "12"         "Theta:"     "11416.97"   "0"         
[1] "Iteration:" "13"         "Theta:"     "15184.66"   "0"         
[1] "Iteration:" "14"         "Theta:"     "21280.78"   "0"         
[1] "Iteration:" "15"         "Theta:"     "20079.34"   "0"         
[1] "Iteration:" "16"         "Theta:"     "21753.12"   "0"         
[1] "Iteration:" "17"         "Theta:"     "15461.88"   "0"         
[1] "Iteration:" "18"         "Theta:"     "16622.59"   "0"         
[1] "Iteration:" "19"         "Theta:"     "21829.12"   "0"         
[1] "Iteration:" "20"         "Theta:"     "0"          "8673.05"   
[1] "Iteration:" "21"         "Theta:"     "179.57"     "10062.57"  
[1] "Iteration:" "22"         "Theta:"     "851.22"     "8750.5"    
[1] "Iteration:" "23"         "Theta:"     "3861.07"    "2978.94"   
[1] "Iteration:" "24"         "Theta:"     "9205.44"    "0"         
[1] "Iteration:" "25"         "Theta:"     "15045.31"   "0"         
[1] "Iteration:" "26"         "Theta:"     "15221.25"   "0"         
[1] "Iteration:" "27"         "Theta:"     "22591.51"   "0"         
[1] "Iteration:" "28"         "Theta:"     "26538.09"   "0"         
[1] "Iteration:" "29"         "Theta:"     "0"          "3191.93"   
[1] "Iteration:" "30"         "Theta:"     "24.32"      "5397.35"   
[1] "Iteration:" "31"         "Theta:"     "130.59"     "7869.76"   
[1] "Iteration:" "32"         "Theta:"     "547.17"     "8856.37"   
[1] "Iteration:" "33"         "Theta:"     "2455.76"    "5345.19"   
[1] "Iteration:" "34"         "Theta:"     "7451.95"    "33.24"     
[1] "Iteration:" "35"         "Theta:"     "12677.86"   "0"         
[1] "Iteration:" "36"         "Theta:"     "18556.27"   "0"         
[1] "Iteration:" "37"         "Theta:"     "22523.37"   "0"         
[1] "Iteration:" "38"         "Theta:"     "17425.75"   "0"         
[1] "Iteration:" "39"         "Theta:"     "21327.84"   "0"         
[1] "Iteration:" "40"         "Theta:"     "21600.06"   "0"         
[1] "Iteration:" "41"         "Theta:"     "29564.68"   "0"         
[1] "Iteration:" "42"         "Theta:"     "0"          "0"         
[1] "Iteration:" "43"         "Theta:"     "0"          "0"         
[1] "Converged at Iteration:" "43"                     
[3] "Theta:"                  "0"                      
[5] "0"                      
stat <- glma(fit = fit, W = W)

#qqplot
qq(stat[,4], main="Female Gene p-values")

#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:"     "67.12"      "66.26"     
[1] "Iteration:" "2"          "Theta:"     "134.91"     "129.78"    
[1] "Iteration:" "3"          "Theta:"     "272.45"     "248.79"    
[1] "Iteration:" "4"          "Theta:"     "555.04"     "456.14"    
[1] "Iteration:" "5"          "Theta:"     "1147.56"    "758.93"    
[1] "Iteration:" "6"          "Theta:"     "2419.77"    "995.89"    
[1] "Iteration:" "7"          "Theta:"     "5131.9"     "519.48"    
[1] "Iteration:" "8"          "Theta:"     "9959.11"    "0"         
[1] "Iteration:" "9"          "Theta:"     "16310.79"   "0"         
[1] "Iteration:" "10"         "Theta:"     "20358.71"   "0"         
[1] "Iteration:" "11"         "Theta:"     "24474.35"   "0"         
[1] "Iteration:" "12"         "Theta:"     "13600.55"   "0"         
[1] "Iteration:" "13"         "Theta:"     "18941.17"   "0"         
[1] "Iteration:" "14"         "Theta:"     "28096.15"   "0"         
[1] "Iteration:" "15"         "Theta:"     "23638.18"   "0"         
[1] "Iteration:" "16"         "Theta:"     "29428.87"   "0"         
[1] "Iteration:" "17"         "Theta:"     "20912.7"    "0"         
[1] "Iteration:" "18"         "Theta:"     "24153.18"   "0"         
[1] "Iteration:" "19"         "Theta:"     "30225.63"   "0"         
[1] "Iteration:" "20"         "Theta:"     "20537.07"   "0"         
[1] "Iteration:" "21"         "Theta:"     "25064.16"   "0"         
[1] "Iteration:" "22"         "Theta:"     "27891.38"   "0"         
[1] "Iteration:" "23"         "Theta:"     "29893.94"   "0"         
[1] "Iteration:" "24"         "Theta:"     "34179.96"   "0"         
[1] "Iteration:" "25"         "Theta:"     "0"          "6389.34"   
[1] "Iteration:" "26"         "Theta:"     "165"        "8818.54"   
[1] "Iteration:" "27"         "Theta:"     "714.36"     "9334.69"   
[1] "Iteration:" "28"         "Theta:"     "2253.96"    "6761.17"   
[1] "Iteration:" "29"         "Theta:"     "6603.69"    "1212.43"   
[1] "Iteration:" "30"         "Theta:"     "13450.58"   "0"         
[1] "Iteration:" "31"         "Theta:"     "19797.88"   "0"         
[1] "Iteration:" "32"         "Theta:"     "25555.02"   "0"         
[1] "Iteration:" "33"         "Theta:"     "0"          "6838.35"   
[1] "Iteration:" "34"         "Theta:"     "189.01"     "9140.41"   
[1] "Iteration:" "35"         "Theta:"     "800.24"     "9276.18"   
[1] "Iteration:" "36"         "Theta:"     "2500.69"    "6358.49"   
[1] "Iteration:" "37"         "Theta:"     "7248.68"    "588.32"    
[1] "Iteration:" "38"         "Theta:"     "13664.31"   "0"         
[1] "Iteration:" "39"         "Theta:"     "20969.31"   "0"         
[1] "Iteration:" "40"         "Theta:"     "26751.79"   "0"         
[1] "Iteration:" "41"         "Theta:"     "0"          "7381.46"   
[1] "Iteration:" "42"         "Theta:"     "220.22"     "9477.46"   
[1] "Iteration:" "43"         "Theta:"     "907.81"     "9155.03"   
[1] "Iteration:" "44"         "Theta:"     "2809.38"    "5872.14"   
[1] "Iteration:" "45"         "Theta:"     "8016.1"     "0"         
[1] "Iteration:" "46"         "Theta:"     "13956.18"   "0"         
[1] "Iteration:" "47"         "Theta:"     "22143.48"   "0"         
[1] "Iteration:" "48"         "Theta:"     "30798.36"   "0"         
[1] "Iteration:" "49"         "Theta:"     "25500.08"   "0"         
[1] "Iteration:" "50"         "Theta:"     "28702.26"   "0"         
[1] "Iteration:" "51"         "Theta:"     "29545.3"    "0"         
[1] "Iteration:" "52"         "Theta:"     "21718.96"   "0"         
[1] "Iteration:" "53"         "Theta:"     "29634.62"   "0"         
[1] "Iteration:" "54"         "Theta:"     "34044.94"   "0"         
[1] "Iteration:" "55"         "Theta:"     "24358.5"    "0"         
[1] "Iteration:" "56"         "Theta:"     "29489.91"   "0"         
[1] "Iteration:" "57"         "Theta:"     "0"          "0"         
[1] "Iteration:" "58"         "Theta:"     "0"          "0"         
[1] "Converged at Iteration:" "58"                     
[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