AJWillsey — Mar 18, 2013, 3:56 PM
###AJ Willsey, March 18 2013
##Script: Analyse_shLoF_KvsTADA.R (same location as setwd)
setwd("~/Dropbox/SSC_July_2012/Co-expression/coexp_paper_Nov-12/Paper_Network_files")
mydata=read.table("TADA_vs_K.txt", sep="\t", head=T)
mydata=na.omit(mydata)
mynewdata=cbind(mydata, qnorm(mydata[,4], lower.tail=F))
##Fit linear model to predict Z as a function of K
#periods3-5 in Frontal Cortex, all pTADA.LoF results
summary(lm(formula = mynewdata[,5] ~ mynewdata[,2]))
Call:
lm(formula = mynewdata[, 5] ~ mynewdata[, 2])
Residuals:
Min 1Q Median 3Q Max
-3.143 -1.193 0.249 1.347 2.625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2235 0.4384 2.79 0.0062 **
mynewdata[, 2] 0.0238 0.0233 1.02 0.3080
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.46 on 111 degrees of freedom
Multiple R-squared: 0.00936, Adjusted R-squared: 0.000436
F-statistic: 1.05 on 1 and 111 DF, p-value: 0.308
#periods13-15 in Frontal Cortex, all pTADA.LoF results
summary(lm(formula = mynewdata[,5] ~ mynewdata[,3]))
Call:
lm(formula = mynewdata[, 5] ~ mynewdata[, 3])
Residuals:
Min 1Q Median 3Q Max
-3.229 -1.127 0.225 1.309 2.521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.53538 0.44577 3.44 0.00081 ***
mynewdata[, 3] 0.00741 0.02745 0.27 0.78763
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.47 on 111 degrees of freedom
Multiple R-squared: 0.000656, Adjusted R-squared: -0.00835
F-statistic: 0.0729 on 1 and 111 DF, p-value: 0.788
#periods3-5 in Frontal Cortex, only pTADA.LoF <=0.01
summary(lm(formula = mynewdata[,5][mynewdata[,4]<=0.01] ~ mynewdata[,2][mynewdata[,4]<=0.01]))
Call:
lm(formula = mynewdata[, 5][mynewdata[, 4] <= 0.01] ~ mynewdata[,
2][mynewdata[, 4] <= 0.01])
Residuals:
Min 1Q Median 3Q Max
-0.6924 -0.1497 -0.0332 0.1338 1.0478
Coefficients:
Estimate Std. Error t value
(Intercept) 3.19228 0.14385 22.19
mynewdata[, 2][mynewdata[, 4] <= 0.01] -0.00694 0.00733 -0.95
Pr(>|t|)
(Intercept) <2e-16 ***
mynewdata[, 2][mynewdata[, 4] <= 0.01] 0.35
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.322 on 48 degrees of freedom
Multiple R-squared: 0.0184, Adjusted R-squared: -0.0021
F-statistic: 0.897 on 1 and 48 DF, p-value: 0.348
#periods13-15 in Frontal Cortex, only pTADA.LoF <=0.01
summary(lm(formula = mynewdata[,5][mynewdata[,4]<=0.01] ~ mynewdata[,3][mynewdata[,4]<=0.01]))
Call:
lm(formula = mynewdata[, 5][mynewdata[, 4] <= 0.01] ~ mynewdata[,
3][mynewdata[, 4] <= 0.01])
Residuals:
Min 1Q Median 3Q Max
-0.6853 -0.1574 -0.0679 0.1370 1.0859
Coefficients:
Estimate Std. Error t value
(Intercept) 3.07706 0.13479 22.83
mynewdata[, 3][mynewdata[, 4] <= 0.01] -0.00087 0.00786 -0.11
Pr(>|t|)
(Intercept) <2e-16 ***
mynewdata[, 3][mynewdata[, 4] <= 0.01] 0.91
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.325 on 48 degrees of freedom
Multiple R-squared: 0.000255, Adjusted R-squared: -0.0206
F-statistic: 0.0123 on 1 and 48 DF, p-value: 0.912
#plot periods3-5 in Frontal Cortex, all pTADA.LoF results
t=summary(lm(formula = mynewdata[,5] ~ mynewdata[,2]))
plot(formula = mynewdata[,5] ~ mynewdata[,2], main="Periods 3-5 in Frontal Cortex", xlab="Sum of Connectivity with all network nodes", ylab="PTADA LoF", pch=19, col="blue")
abline(a=c(t$coefficients[1], t$coefficients[2]))
mtext(paste("Adjusted R-squared: ", signif(t$adj.r.squared, digits=2), ", p-value: 0.31", sep=""), side=3)