Last updated: 2023-01-22

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Knit directory: dgrp-starve/

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Multiple regression is the process of determining the coefficients of a model with each coefficient corresponding to the effect of one explanatory variable.

With Genomic Reduced Maximum Likelihood, or GREML, a design matrix is calculated using a Genomic Relationship matrix and a vector of 1s to find the intercepts.

Multiplying the vector for starvation resistance by the inverse of the design matrix solves for the coefficients of the multivariate model. The residuals must also be subtracted and consitute the random effect of the mixed model.

For each data set, the expression data matrix(line x gene) and the starvation resistance vector(line x 1) were taken from prior lab data. From the PCA Project, a dataframe containing line, starvation resistance, and expression data per gene was repurposed for this model.

The Transcriptomic Relation Matrix was calculated using the cross product of a scaled expression matrix and multiplied by the inverse of the number of columns.

#wolb infection and inversion status data with phenotype adjustment function

#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")
Type III ANOVA table for covariates: starvation
                 Df Sum of Sq   RSS    AIC F value  Pr(>F)  
<none>                        28767 1009.8                  
factor(wolba)     1    483.10 29250 1011.1  3.1236 0.07881 .
factor(In_2L_t)   2     60.22 28827 1006.2  0.1947 0.82325  
factor(In_2R_NS)  2    300.22 29067 1007.8  0.9706 0.38078  
factor(In_3R_P)   2     85.92 28853 1006.4  0.2778 0.75777  
factor(In_3R_K)   2    959.43 29726 1012.3  3.1017 0.04731 *
factor(In_3R_Mo)  2    416.65 29184 1008.6  1.3470 0.26255  
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Estimated effects
                    Estimate Std. Error    t value     Pr(>|t|)
(Intercept)       58.7465186   1.553929 37.8051442 3.103130e-89
factor(wolba)y     3.2450739   1.836101  1.7673724 7.880567e-02
factor(In_2L_t)1  -0.8506378   3.101713 -0.2742477 7.841986e-01
factor(In_2L_t)2  -1.8583916   3.151661 -0.5896547 5.561378e-01
factor(In_2R_NS)1  0.8630221   4.621570  0.1867379 8.520697e-01
factor(In_2R_NS)2  6.7057598   4.825270  1.3897169 1.662760e-01
factor(In_3R_P)1  -1.8513843   5.206148 -0.3556150 7.225319e-01
factor(In_3R_P)2   4.0596759   6.331413  0.6411959 5.221847e-01
factor(In_3R_K)1   7.4870256   4.084124  1.8332024 6.837092e-02
factor(In_3R_K)2  15.5762633   8.948152  1.7407240 8.338564e-02
factor(In_3R_Mo)1 -5.4712766   4.279569 -1.2784644 2.026787e-01
factor(In_3R_Mo)2 -3.6074434   3.224295 -1.1188318 2.646547e-01
#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)
[1] "Iteration:" "1"          "Theta:"     "5.99"       "5.89"      
[1] "Iteration:" "2"          "Theta:"     "11.78"      "11.21"     
[1] "Iteration:" "3"          "Theta:"     "22.76"      "20.3"      
[1] "Iteration:" "4"          "Theta:"     "42.49"      "33.49"     
[1] "Iteration:" "5"          "Theta:"     "74.03"      "46.61"     
[1] "Iteration:" "6"          "Theta:"     "113.62"     "49.79"     
[1] "Iteration:" "7"          "Theta:"     "145.01"     "41.46"     
[1] "Iteration:" "8"          "Theta:"     "158.34"     "34.69"     
[1] "Iteration:" "9"          "Theta:"     "162.39"     "32.33"     
[1] "Iteration:" "10"         "Theta:"     "163.58"     "31.62"     
[1] "Iteration:" "11"         "Theta:"     "163.93"     "31.42"     
[1] "Iteration:" "12"         "Theta:"     "164.03"     "31.36"     
[1] "Iteration:" "13"         "Theta:"     "164.06"     "31.34"     
[1] "Iteration:" "14"         "Theta:"     "164.07"     "31.34"     
[1] "Iteration:" "15"         "Theta:"     "164.07"     "31.34"     
[1] "Iteration:" "16"         "Theta:"     "164.07"     "31.34"     
[1] "Iteration:" "17"         "Theta:"     "164.07"     "31.34"     
[1] "Iteration:" "18"         "Theta:"     "164.07"     "31.34"     
[1] "Iteration:" "19"         "Theta:"     "164.07"     "31.34"     
[1] "Iteration:" "20"         "Theta:"     "164.07"     "31.34"     
[1] "Converged at Iteration:" "20"                     
[3] "Theta:"                  "164.07"                 
[5] "31.34"                  
#general linear model analysis
statTemp <- glma(fit = fitG, W = W)


#histogram of coefficients
hist(statTemp[,1], main="Female Coefficients")

#qq plot of p-values
qq(statTemp[,4], main="Female Gene p-values")

statF <- statTemp
# k-fold parameters
n <- length(y_adj)
fold <- 10
nvalid <- 50

#validate set creation
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)

#summary statistics of correlation

gg <- vector(mode='list', length=6)

histData <- data.table(cor = cvTB$accuracy$Corr)

#female mean
gg[[1]] <- ggplot(histData, aes(x=cor)) +
  geom_histogram(bins=8, fill='red') +
  labs(x="CV Trial", y="Correlation Coefficient") +
  ggtitle("Female CV Correlations")


[1] 0.1329747
[1] 51.72705

The mean Correlation coefficient for all trials was 0.32968 with variance 0.0176823.

The mean of Mean Square Predicted Error for all trials was 130.60358 with variance 2675.6878958.

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/

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 [7] LC_PAPER=en_US.utf-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=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     labeling_0.4.2    
[25] splines_4.0.3      statmod_1.4.37     stringr_1.4.0      munsell_0.5.0     
[29] compiler_4.0.3     httpuv_1.6.5       xfun_0.30          pkgconfig_2.0.3   
[33] mcmc_0.9-7         htmltools_0.5.2    tidyselect_1.1.2   tibble_3.1.6      
[37] fansi_1.0.3        calibrate_1.7.7    crayon_1.5.1       withr_2.5.0       
[41] later_1.3.0        MASS_7.3-56        grid_4.0.3         jsonlite_1.8.0    
[45] gtable_0.3.0       lifecycle_1.0.1    DBI_1.1.2          git2r_0.30.1      
[49] magrittr_2.0.3     scales_1.2.0       cli_3.3.0          stringi_1.7.6     
[53] farver_2.1.0       fs_1.5.2           promises_1.2.0.1   bslib_0.3.1       
[57] ellipsis_0.3.2     generics_0.1.2     vctrs_0.4.1        tools_4.0.3       
[61] glue_1.6.2         purrr_0.3.4        parallel_4.0.3     processx_3.5.3    
[65] fastmap_1.1.0      survival_3.3-1     yaml_2.3.5         colorspace_2.0-3  
[69] knitr_1.38         sass_0.4.1         quantreg_5.94      MCMCpack_1.6-3