Linear Regression - Adjusted Phenotypes+
Last updated: 2023-01-22
Checks: 5 2
Knit directory: dgrp-starve/
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| absolute | relative |
|---|---|
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/fRegress_adj.txt | data/fRegress_adj.txt |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/f_adj.txt | data/f_adj.txt |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/mRegress_adj.txt | data/mRegress_adj.txt |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/m_adj.txt | data/m_adj.txt |
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Prep
#load mgl data and custom function
load("/data/morgante_lab/data/dgrp/misc/adjustData.RData")
#read in expression data
f_data <- fread("data/fMeans.txt")
#grab starvation
y <- f_data[,starvation]
#store starvation with id
dat <- data.frame(id=f_data[,line], y=y)
#adjust data with given function
f_adjPheno <- 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#overwrite starvation column
f_adj <- f_data[,starvation:=f_adjPheno]
#save to txt file
fwrite(f_adj, "data/f_adj.txt")#load mgl data and custom function
load("/data/morgante_lab/data/dgrp/misc/adjustData.RData")
#read in expression data
m_data <- fread("data/mMeans.txt")
#grab starvation
y <- m_data[,starvation]
#store starvation with id
dat <- data.frame(id=m_data[,line], y=y)
#adjust data with given function
m_adjPheno <- 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#overwrite starvation column
m_adj <- m_data[,starvation:=m_adjPheno]
#save to txt file
fwrite(m_adj, "data/m_adj.txt")Correlations
In order to save time, I used secretariat’s computational resources to perform the correlation calculations in parallel. The following two sections of code were submission scripts performed in secretariat.
library(dplyr)
library(data.table)
library(doParallel)
registerDoParallel(cores = 12)
# file path for final table
filePath <- "/data/morgante_lab/nklimko/rep/dgrp-starve/data/fRegress_adj.txt"
# gene expression data from earlier bound to line and starvation
f_adj <- fread("/data/morgante_lab/nklimko/rep/dgrp-starve/data/f_adj.txt")
start <- 3
end <- 11340
#f 11340
#m 13577
# foreach creates a list with index indicating position in loop
# function grabs the p-value from correlation of starvation to expression of trait
pvalList <- foreach(i=start:end) %dopar% {
temp <- summary(lm(f_adj[,c(2,i), with=FALSE], na.action=na.omit))[[4]][8]
}
# converts list to vector
part <- unlist(pvalList, use.names = FALSE)
# binds p-vals to column index
id <- start:end
part <- data.table(id,pvalList)
# write table to file
fwrite(part, filePath)Both of these are available by clicking the drop down buttons on the right ->
library(dplyr)
library(data.table)
library(doParallel)
registerDoParallel(cores = 12)
# file path for final table
filePath <- "/data/morgante_lab/nklimko/rep/dgrp-starve/data/mRegress_adj.txt"
# gene expression data from earlier bound to line and starvation
m_adj <- fread("/data/morgante_lab/nklimko/rep/dgrp-starve/data/m_adj.txt")
start <- 3
end <- 13577
#f 11340
#m 13577
# foreach creates a list with index indicating position in loop
# function grabs the p-value from correlation of starvation to expression of trait
pvalList <- foreach(i=start:end) %dopar% {
temp <- summary(lm(m_adj[,c(2,i), with=FALSE], na.action=na.omit))[[4]][8]
}
# converts list to vector
part <- unlist(pvalList, use.names = FALSE)
# binds p-vals to column index
id <- start:end
part <- data.table(id,pvalList)
# write table to file
fwrite(part, filePath)QQ and Manthattan Data Prep ->
# Female qq prep
#f 11340
#m 13577
gg <- vector(mode='list', length=4)
#read in results from compute node
fReg <- fread("data/fRegress_adj.txt")
#number of data points
n <- dim(fReg)[1]
#Theoretical Quantiles
uniform <- 1:n/(n+1)
#sorted p values
empirical <- sort(fReg[,pvalList])
#-log 10
logPlot <- -log10(fReg[,pvalList])
#table for qqplot
qqdata <- data.table(uniform, empirical, logPlot)
qqdataF <- qqdata
# ggplot of data to fpr, qqplot
if(FALSE){
gg[[1]] <- ggplot(qqdata, aes(x=uniform, y=empirical)) +
geom_point(color="red") +
geom_segment(aes(x=0,y=0,xend=1,yend=1)) +
xlab("Theoretical Quantiles") +
ylab("Sorted p-values") +
ggtitle("Female Expression QQ Plot")
}
gg[[2]] <- ggplot(qqdata, aes(x=uniform, y=logPlot)) +
geom_point(color="red") +
geom_hline(yintercept= -log10(0.05/n), color="magenta") +
xlab("Theoretical Quantiles") +
ylab("-log10 p-values") +
ggtitle("Female Expression Manhattan Plot")
## Code for looking at specific genes
#fMeans <- fread("data/fMeans.txt")
#genes <- colnames(fMeans)
#genes <- genes[3:length(genes)]
#fReg <- fReg[, gene:=genes]# Male qq prep
#read in results from compute node
mReg <- fread("data/mRegress_adj.txt")
#number of data points
n <- dim(mReg)[1]
#Theoretical Quantiles
uniform <- 1:n/(n+1)
#sorted p values
empirical <- sort(mReg[,pvalList])
#-log 10
logPlot <- -log10(mReg[,pvalList])
#table for qqplot
qqdata <- data.table(uniform, empirical, logPlot)
qqdataM <- qqdata
# ggplot of data to fpr, qqplot
if(FALSE){
gg[[3]] <- ggplot(qqdata, aes(x=uniform, y=empirical)) +
geom_point(color="blue") +
geom_segment(aes(x=0,y=0,xend=1,yend=1), linewidth=1) +
xlab("Theoretical Quantiles") +
ylab("Sorted p-values") +
ggtitle("Male Expression QQ Plot")
}
gg[[4]] <- ggplot(qqdata, aes(x=uniform, y=logPlot)) +
geom_point(color="blue") +
geom_hline(yintercept= -log10(0.05/n), color="cyan") +
xlab("Theoretical Quantiles") +
ylab("-log10 p-values") +
ggtitle("Male Expression Manhattan Plot")
## Code for looking at specific genes
#mMeans <- fread("data/mMeans.txt")
#genes <- colnames(mMeans)
#genes <- genes[3:length(genes)]
#mReg <- mReg[, gene:=genes]QQ Plots
par(mfrow=c(1,2))
qq(qqdataF[,empirical], main="Female Gene p-values")
qq(qqdataM[,empirical], main="Male Gene p-values")
Manhattan Plots
par(mfrow=c(1,2))
plot_grid(gg[[2]],gg[[4]], ncol=2)
Part of the reason both plots deviate from linearity because we did not account for genes that were correlated with each other.
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] qqman_0.1.8 cowplot_1.1.1 ggplot2_3.3.5 data.table_1.14.2
[5] dplyr_1.0.8
loaded via a namespace (and not attached):
[1] tidyselect_1.1.2 xfun_0.30 bslib_0.3.1 purrr_0.3.4
[5] colorspace_2.0-3 vctrs_0.4.1 generics_0.1.2 htmltools_0.5.2
[9] yaml_2.3.5 utf8_1.2.2 rlang_1.0.4 jquerylib_0.1.4
[13] later_1.3.0 pillar_1.7.0 glue_1.6.2 withr_2.5.0
[17] DBI_1.1.2 calibrate_1.7.7 lifecycle_1.0.1 stringr_1.4.0
[21] munsell_0.5.0 gtable_0.3.0 workflowr_1.7.0 evaluate_0.15
[25] labeling_0.4.2 knitr_1.38 fastmap_1.1.0 httpuv_1.6.5
[29] fansi_1.0.3 highr_0.9 Rcpp_1.0.8.3 promises_1.2.0.1
[33] scales_1.2.0 jsonlite_1.8.0 farver_2.1.0 fs_1.5.2
[37] digest_0.6.29 stringi_1.7.6 rprojroot_2.0.3 grid_4.0.3
[41] cli_3.3.0 tools_4.0.3 magrittr_2.0.3 sass_0.4.1
[45] tibble_3.1.6 crayon_1.5.1 pkgconfig_2.0.3 ellipsis_0.3.2
[49] MASS_7.3-56 assertthat_0.2.1 rmarkdown_2.16 rstudioapi_0.13
[53] R6_2.5.1 git2r_0.30.1 compiler_4.0.3