% -*- TeX:Rnw:UTF-8 -*-
% ----------------------------------------------------------------
% .R knitr file  ************************************************
% ----------------------------------------------------------------
%%
<<echo=FALSE>>=
###############################################
##                                           ##
## (c) Andrej Blejec (andrej.blejec@nib.si)  ##
##                                           ##
###############################################
@
<<echo=FALSE,results='hide'>>=
options(width=60)
@


<<>>=
testvar <- "Vitvi01g01391"
testvar <- rownames(expr)[11]

@

\clearpage
\subsection{Overall Model}

Simple model with water potential for all data:
<<>>=
design1 <- with(pd, model.matrix(~ swp))
#design1[,2] <- (-design1[,2])
head(design1)
tail(design1)
@
Linear model
<<>>=
fit1 <- lmFit(exprs, design1)
fit1[testvar,]$coefficients
fit1 <- eBayes(fit1)
fit1[testvar,]$coefficients
head(fit1[1:6,]$coefficients)
fit <- fit1
fit[testvar,]$coef
@

Models for each variety

<<>>=
coefs <- fit1$coefficients
plot(coefs[,1],coefs[,2])
@

<<>>=
dim(coefs[abs(coefs[,2])>2,])

@

Top table

<<>>=
which <- 2
  tt <- topTable(fit1,coef=which, number=Inf)
  head(tt)
@

\clearpage
\subsection{Models for variety}

Model with water potential by variety (slopes are of interest).
<<>>=
design2 <- with(pd, model.matrix(~ swp*variety))
head(design2)
tail(design2)

@
Linear model
<<>>=
fit2 <- lmFit(exprs, design2)
fit2[testvar,]$coefficients
@

<<>>=
varname <- testvar
par(mfrow=c(1,3))
p <- plot.ewp(varname,model="")
plot(pd$swp,exprs[testvar,],  xlim=c(0,-1.6),
   ,pch=16, col=6-as.numeric(pd$variety)*2,cex=pd$day/2)
c(p$C$coef[2],p$F$coef[2])
fit2$coef[testvar,]
p <- plot.ewp(varname,model="", exprs=exprs, pd=pd)

@

<<>>=
#
fit2 <- eBayes(fit2)
fit2[testvar,]$coefficients
head(fit2[1:6,]$coefficients)
@

For comparison, here is the beginning of coefficients for overall model.
<<>>=
head(fit1[1:6,]$coefficients)
@
<<>>=
fita[2]
fit1[testvar,]$coefficients[2]
fitc[2]
fit2[testvar,]$coefficients[2]
fitf[2]
fit2[testvar,]$coefficients[2]+fit2[testvar,]$coefficients[4]
@

Plots of coefficients

<<>>=
oldpar <- par(mfrow=c(2,2), mar=c(4,4,1,1))
coefs1 <- fit1$coefficients
coefs2 <- fit2$coefficients
plot(coefs1[,2],coefs2[,2], xlab="Slope All",ylab="Slope C")
abline(c(0,1),col=2)
cf24 <- coefs2[,2] + coefs2[,4]
plot(coefs2[,2], cf24, xlab="Slope C",ylab="Slope F")
abline(c(0,1),col=2)
plot(coefs1[,2], cf24, xlab="Slope All",ylab="Slope F")
abline(c(0,1),col=2)
par(oldpar)
@

<<warnings=FALSE,echo=FALSE,include=FALSE>>=
source("../doc/Graph3d.r")
persp.3d(coefs2[,2],cf24, coefs1[,2],
phi=45, zlab="All",xlab="C",ylab="F")
@

\subsection{Mean expressions}

<<>>=
my.predict <- function(fit, x=1){
cf <- fit$coefficients
mc <- cf%*%c(1,x,0,0)
mf <- cf%*%c(1,x,1,x)
return(data.frame(mc=mc,mf=mf))
}
fit2$coefficients[1:2,]
my.predict(fit2)[1:2,]
my.predict(fit2)[testvar,]
ma <- fit1$coefficients%*%c(1,mean(swp))
macf <- data.frame(ma=ma,my.predict(fit2, x=mean(swp)))
head(macf)
@

<<>>=
macf[testvar,]
@

Another way is using a separate model for varieties. It will provide opportunity to test the differences between varieties:

<<>>=
design3 <- with(pd, model.matrix(~ 0+variety))
head(design3)
tail(design3)
fit3 <- lmFit(exprs, design3)
fit3[testvar,]$coefficients
mswps <- with(pd, aggregate(swp, list(variety), mean) )$x
my.predict(fit2, mswps[1])[testvar,1]
my.predict(fit2, mswps[2])[testvar,2]

@

Object \code{fit3} holds mean expressions for varieties.

Predicting expression for given SWP value

<<>>=
p <- plot.ewp(varname,model="", exprs=exprs, pd=pd)
x <- 0
points(rep(x,2) ,my.predict(fit2,x=x)[varname,], cex=1.5)
x <- -1.5
points(rep(x,2) ,my.predict(fit2,x=x)[varname,], cex=1.5)
@

\subsubsection{ Plots of variety coefficients}

I will try to set the variety slopes compared to slope of complete data


For comparison, here is the beginning of coefficients for overall model.

<<>>=
head(fit1[1:6,]$coefficients)

@

Extract slopes

<<>>=
A <- fit1$coefficients[,2]
C <- fit2$coefficients[,2]
F <- fit2$coefficients[,2]+fit2$coefficients[,4]
ma <- macf[,"ma"]
mc <- fit3$coefficients[,1]
mf <- fit3$coefficients[,2]
coefs <- data.frame(A,C,F,ma,mc,mf)
head(coefs)
ca <- C-A
fa <- F-A
fc <- F-C
coefs <- data.frame(mc,mf,C,F)
head(coefs)
@

<<>>=
par(mfrow=c(2,3))
hist(A,xlim=range(c(A,C,F)))
hist(C,xlim=range(c(A,C,F)))
hist(F,xlim=range(c(A,C,F)))
hist(ma)
hist(mc)
hist(mf)
@


\subsubsection{Types by effects}
<<>>=
delta <- 2
large_abs_A <- abs(A)> delta
small_fc <- abs(fc) < delta/5
small_ca <- abs(ca) < delta/5
oldpar <- par(mfrow=c(2,3), mar=c(4,4,1,1))
plot(A,ca, xlab="Slope All",ylab="Slope C - slope All")
points(A[large_abs_A],ca[large_abs_A],col=2,pch=16)
abline(h=0,v=0,col=2)
plot(A,fa, xlab="Slope All",ylab="Slope F - slope All")
points(A[large_abs_A],fa[large_abs_A],col=2,pch=16)
abline(h=0,v=0,col=2)
plot(ca,fa, xlab="Slope C - slope All",ylab="Slope F - slope All")
points(ca[large_abs_A],fa[large_abs_A],col=2,pch=16)
abline(h=0,v=0,col=2)
#
plot(A,fc, xlab="slope All",ylab="Slope F - slope C")
points(A[large_abs_A],fc[large_abs_A],col=2,pch=16)
points(A[large_abs_A&small_fc],fc[large_abs_A&small_fc],col=4,pch=16)
abline(h=0,v=0,col=2)
#
plot(C,fc, xlab="slope C",ylab="Slope F - slope C")
points(C[large_abs_A],fc[large_abs_A],col=2,pch=16)
points(C[large_abs_A&small_fc],fc[large_abs_A&small_fc],col=4,pch=16)
abline(h=0,v=0,col=2)
#
plot(C,F, xlab="slope C",ylab="Slope F ")
points(C[large_abs_A],F[large_abs_A],col=2,pch=16)
points(C[large_abs_A&small_fc],F[large_abs_A&small_fc],col=4,pch=16)
type1 <- large_abs_A&small_fc&small_ca
points(
   C[type1],
   F[type1],
   col=3,pch=16)

abline(h=0,v=0,col=2)
abline(c(0,1),col=3)
par(oldpar)
@

\section{Situation types and Type selection criteria}

Boundaries:
\begin{description}
  \item[minX] X at least ... ( > )
  \item[maxX] X at most ... ( < )
  \item[deltaxy] difference of mean x-y treshold ( < )
\end{description}

<<>>=
.deltam     # <- 2
.min.slope       # <-  2
.max.slope       # <- 0.5
.diff.slope      # <- 1
.maxfc      # <- 0.5
.diff01
.deltam2    # <- .deltam/2
@

\subsection{Old Types}

Type1

<<>>=
# type1 <- (large_abs_C|large_abs_F) & small_fc & (abs(mc-mf)<deltam)
@

Absolute slope C or F larger than \Sexpr{.min.slope} with difference of slopes smaller than \Sexpr{.maxfc} and difference of means below \Sexpr{.deltam}.

Type2

<<>>=
#type2 <- (large_abs_C & large_abs_F) & (abs(fc) > diff.slope)&(abs(mc-mf)>deltam2)
#type2 <- type2 & ( (C*F >0) )
@

Both slopes C and F larger than \Sexpr{.min.slope} , both positive or negative, with difference of slopes larger than \Sexpr{.diff.slope} and difference of means above \Sexpr{.deltam2}.





<<>>=
#
delta <- 2
#
(deltam <- as.numeric(.deltam)) #2
(deltam2 <- as.numeric(.deltam2)) # delta mean for type 2
(min.slope <- as.numeric(.min.slope))   # 2       # slope C larger
(max.slope <- as.numeric(.max.slope))    # slope C smaller
min.slope <- min.slope    # slope F larger
max.slope <- max.slope    # slope F smaller
minA <- min.slope    # slope A larger
(diff.slope <- as.numeric(.diff.slope)) # 1      # difference F - C larger
(diff.slope5 <- as.numeric(.diff.slope5)) # difference for F - C larger for type 5
(maxfc <- as.numeric(.maxfc))    # difference F - C smaller
maxca <- maxfc  # difference C - A smaller
maxfa <- maxfc  # difference F - A smaller
#
large_abs_A <- abs(A)> minA
large_abs_C <- abs(C)> min.slope
large_abs_F <- abs(F)> min.slope
#
small_fc <- abs(fc) < maxfc
small_ca <- abs(ca) < maxca
small_fa <- abs(fa) < maxfa
plot(C,F, xlab="slope C",ylab="Slope F ")
#points(C[large_abs_A],F[large_abs_A],col=2,pch=16)
#
type10 <- large_abs_A & small_fc & small_ca
type1 <- (large_abs_C|large_abs_F) & small_fc & (abs(mc-mf)<deltam)
table(type10,type1)
points(
   C[type1],
   F[type1],
   col=2, pch=16)
#
type2 <- (large_abs_C & large_abs_F) & (abs(fc) > diff.slope)&(abs(mc-mf)>deltam2)
type2 <- type2 & ( (C*F >0) )
points(
   C[type2],
   F[type2],
   col=3,pch=16)
#
type30 <- ((abs(C)<max.slope)|(abs(F)<max.slope)) & (abs(fc)> diff.slope)
type3 <- ((abs(C)<max.slope)|(abs(F)<max.slope)) & (abs(fc)> diff.slope)&(abs(mc-mf)>deltam)
table(type3,type30)
points(
   C[type3],
   F[type3],
   col=4,pch=16)
#
type4 <- (large_abs_C|large_abs_F)&(abs(mc-mf)>deltam)&(C*F>0)
points(
   C[type4],
   F[type4],
   col=5,pch=16)
#
type5 <- ((abs(C)<max.slope)|(abs(F)<max.slope)) & (abs(fc)> diff.slope5)&(abs(mc-mf)<deltam)
points(
   C[type5],
   F[type5],
   col=6,pch=16)

table(type3,type5)
points(
   C[type3],
   F[type3],
   col=4,pch=16)
# type 3 is a subset of type5, type3 & large mean difference

#
abline(h=0,v=0,col=2)
abline(c(0,1),col=3)
types <- data.frame(type1,type2,type3,type4,type5)
nt <- apply(types,2,sum)
legend("bottomright",pch=rep(16,5),col=2:6,legend=paste0("type",1:5," | ",nt),bty="n")

@

\subsection{New type definitions}
After discussion with Maruša on Feb 3, new type definitions were sumarized in the excel file.

Function to plot the overview for types

<<>>=
#' Plot selected expression swp graphs for selected genes
#'
#' @param filt logical vector to select genes, typically type identification
#' @param
#' @return Plot first six exprs-swp situations
#' @export
#' @seealso \code{\link{funkcija}}
#' @note
#' @references
#' @keywords package
#' @title
#' @author Andrej Blejec \email{andrej.blejec@nib.si}
#' @examples
#' plot.type() # TRUE most of the time

plot.type <- function( filt=rep(TRUE,6), ...){
par(mfrow=c(2,3), oma=c(0,0,1,0))
#filt <- type1
print(sum(filt))
for(i in 1:min(sum(filt),6)){
varname <- rownames(exprs)[filt][i]
p <- plot.ewp(varname,model="", exprs=exprs, pd=pd, ylim=range(exprs)
     , cex= 1.5
     , pch = c(16,16)
     , all = FALSE
     )
mtext(paste(round(coefs[varname,],1),collapse=" | "),cex=0.75)
mtext(paste(round(c(p$C$coefficients[2],p$F$coefficients[2]),1)
,collapse=" | "),1,cex=0.75)
}
}
plot.type()
@

<<>>=
(min.slope <- as.numeric(.min.slope))         # slope C larger
(max.slope <- as.numeric(.max.slope))    # slope C smaller
(diff.slope <- as.numeric(.diff.slope))    # difference of slopes
(diff01 <- as.numeric(.diff01))    # difference at extremes

@

\clearpage
\subsubsection{Parameter explanation}

\begin{description}
  \item[.min.slope  (\Sexpr{.min.slope})] Lower level for large slope
  \item[.max.slope (\Sexpr{.max.slope})] Upper level for small slope
  \item[.diff.slope (\Sexpr{.diff.slope})] Threshold for slope difference
  \item[.diff01 (\Sexpr{.diff01})] Threshold for differences of predicted values at 0 and -1.5
\end{description}

Preparation for decision rules
<<>>=
# predicted values
left <- my.predict(fit2, x = -0.2)
right <- my.predict(fit2, x = -1.4)
left[varname,]
right[varname,]
p <- plot.ewp(varname,model="", exprs=exprs, pd=pd, ylim=range(exprs)
     , cex= 1.5
     , pch = c(16,16)
     , all = FALSE
     )
# differences at the no-crossing borders
diffl <- apply(left,1,diff)
diffr <- apply(right,1,diff)
nocross <- (diffl*diffr>0)
# differences of predicted values at extremes
diff0 <- apply(my.predict(fit2,x=0),1,diff)
diff1 <- apply(my.predict(fit2,x=-1.5),1,diff)
@

<<>>=
plot(diffl,diffr)
points(diffl[nocross],diffr[nocross], col=2)
abline(h=c(-.diff01,.diff01),v=c(-.diff01,.diff01))
@
Function to plot coefficients for types
<<>>=
largeslope <- ( abs(C) > min.slope) & (abs(F) > min.slope)
plot.type(largeslope&nocross)
@

<<>>=
head(types)
plot.types <- function(x=C, y=F, type=types,title,...){
xlab <- deparse(substitute(x))
ylab <- deparse(substitute(y))
plot(x,y, xlab=xlab,ylab=ylab, type="n", ...)
   abline(h=0,v=0,col=2, lty=2)
abline(c(0,1),col=3)
points(x,y,col=8)
m <- ncol(type)
sek <- 1:ncol(type)

for( i in sek){
points(
   x[type[,i]],
   y[type[,i]],
   col=i+1,pch=16)
   }

if(!missing(title)) title(title)
nt <- apply(type,2,sum)
pre <- "" # substr(rep("         ",length(nt)),1,(3-nchar(nt))*2.3)
legend("bottomright",pch=rep(16,m),col=2:(m+1),legend=paste0(colnames(type)," | ",pre,nt),bty="n")
}
#plot.types(type=Types)
@

\clearpage
\subsubsection{Type1}

<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
   abline(h=0,v=0,col=8)
#points(C[large_abs_A],F[large_abs_A],col=2,pch=16)
#
type10 <- large_abs_A & small_fc & small_ca
type1 <- (large_abs_C|large_abs_F) & small_fc & (abs(mc-mf)<deltam)
#
Type1 <- C*F > 0
Type1 <- Type1 & ( abs(C) > min.slope) & (abs(F) > min.slope)
Type1 <- Type1 & ( abs(F-C) < diff.slope)
Type1 <- Type1 & ((abs(diff0) < diff01) & (abs(diff1) < diff01))
#
table(Type1,type1)
points(
   C[Type1],
   F[Type1],
   bg=2, pch=21, col=1)
   abline(c(0,1),col=3)
Types <- data.frame(Type1)
nt <- apply(Types,2,sum)
legend("bottomright",pch=rep(16,5),col=2:7,legend=paste0("Type",1:ncol(Types)," | ",nt),bty="n")
nt
plot.type(Type1)
plot.types(type=Types)
@

\clearpage
\subsubsection{Type1, extended}

After manual inspection, Maruša extended the definition of Type1.

\begin{itemize}
  \item Both slopes > \Sexpr{min.slope} (Type1.0)
  \item One slope > \Sexpr{min.slope}, the other between \Sexpr{max.slope} and \Sexpr{min.slope} (Type1.0C and Type1.0F)
\end{itemize}


<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
   abline(h=0,v=0,col=8)
#points(C[large_abs_A],F[large_abs_A],col=2,pch=16)
#
type10 <- large_abs_A & small_fc & small_ca
type1 <- (large_abs_C|large_abs_F) & small_fc & (abs(mc-mf)<deltam)
#
Type1.0 <- C*F > 0
sum(Type1.0)
Type1.0CF <- ( abs(C) > min.slope) & (abs(F) > min.slope)
sum(Type1.0CF)
Type1.0C <- (abs(C) > min.slope) & (abs(F) > max.slope & abs(F) < min.slope)
sum(Type1.0C)
Type1.0F <- (abs(F) > min.slope) & (abs(C) > max.slope & abs(C) < min.slope)
sum(Type1.0F)
Type1.0CF <- Type1.0CF | Type1.0C | Type1.0F
sum(Type1.0CF)
Type1.0diff <- ((abs(diff0) < diff01) | (abs(diff1) < diff01))
sum(Type1.0diff)
#
Type1.0 <- Type1.0 & Type1.0CF & Type1.0diff
#
table(Type1,Type1.0)
points(
   C[Type1.0],
   F[Type1.0],
   bg=2, pch=21, col=1)
   abline(c(0,1),col=3)
Types <- data.frame(Type1.0, Type1)
nt <- apply(Types,2,sum)
legend("bottomright",pch=rep(16,5),col=2:7,legend=paste0("Type",1:ncol(Types)," | ",nt),bty="n")
nt
plot.type(Type1.0)
plot.types(type=Types)
@

<<>>=
table(Type1,Type1.0)
@



\clearpage
\subsubsection{Type2}

<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
      abline(h=0,v=0,col=8)
#
type2 <- (large_abs_C & large_abs_F) & (abs(fc) > diff.slope)&(abs(mc-mf)>deltam2)
type2 <- type2 & ( (C*F >0) )
#

#
Type2 <- C*F > 0
Type2 <- Type2 & ( abs(C) > min.slope) & (abs(F) > min.slope )
Type2 <- Type2 & ( abs(F-C) > diff.slope )
Type2 <- Type2 & ( abs(diff1) < diff01 )
Type2 <- Type2 & nocross
#
table(Type2,type2)
points(
   C[Type2],
   F[Type2],
   bg=3, pch=21, col=1)
   abline(c(0,1),col=3)
Type1.2 <- Type2
Types <- data.frame(Types,Type1.2)
(nt <- apply(Types,2,sum))
legend("bottomright",pch=rep(16,5),col=2:7,legend=paste0("Type",1:ncol(Types)," | ",nt),bty="n")
nt

#

plot.type(Type1.2)
plot.types(type=Types)

@
\clearpage
\subsubsection{Type3}

<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
      abline(h=0,v=0,col=8)
type30 <- ((abs(C)<max.slope)|(abs(F)<max.slope)) & (abs(fc)> diff.slope)
type3 <- ((abs(C)<max.slope)|(abs(F)<max.slope)) & (abs(fc)> diff.slope)&(abs(mc-mf)>deltam)
#
table(( abs(C) > min.slope) , (abs(F) < max.slope))

Type3 <- ((abs(diff0) < diff01) | (abs(diff1) < diff01))
Type3 <- Type3 & nocross
Type3.C <-  Type3 & (( abs(C) > min.slope) & (abs(F) < max.slope))
Type3.F <-  Type3 & (( abs(F) > min.slope) & (abs(C) < max.slope))
Type3 <- Type3 & ((( abs(C) > min.slope) & (abs(F) < max.slope)) |
                 (( abs(F) > min.slope) & (abs(C) < max.slope)))
all(Type3==(Type3.C|Type3.F))
cbind(Type3,Type3.C,Type3.F)[((Type3 != (Type3.C|Type3.F))),]
sum((Type3 != (Type3.C|Type3.F)))
#
table(Type3,type3)
points(
   C[Type3.C],
   F[Type3.C],
   bg=4, pch=21, col=1)
   points(
   C[Type3.F],
   F[Type3.F],
   bg=5, pch=21, col=1)
abline(c(0,1),col=3)
Types <- data.frame(Types,Type3.C, Type3.F)
(nt <- apply(Types,2,sum))
legend("bottomright",pch=rep(16,ncol(Types)),col=c(4,5),legend=paste0(colnames(Types)," | ",nt),bty="n")
nt
#

plot.type(Type3.C)
plot.type(Type3.F)
plot.types(type=Types)

@

\clearpage
\subsubsection{Type4}

<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
      abline(h=0,v=0,col=8)
#
type4 <- (large_abs_C|large_abs_F)&(abs(mc-mf)>deltam)&(C*F>0)
#
Type4 <- C*F > 0
Type4 <- Type4 & ( abs(C) > min.slope) & (abs(F) > min.slope )
Type4 <- Type4 & ( abs(F-C) > diff.slope )
Type4 <- Type4 & ( abs(diff0) < diff01 )
Type4 <- Type4 & nocross
#
Type1.4 <- Type4
table(Type4,type4)
points(
   C[Type4],
   F[Type4],
   bg=5, pch=21, col=1)
   abline(h=0,v=0,col=2)
abline(c(0,1),col=3)
Types <- data.frame(Types, Type1.4)
(nt <- apply(Types,2,sum))
legend("bottomright",pch=rep(16,5),col=2:7,legend=paste0("Type",1:ncol(Types)," | ",nt),bty="n")
nt

plot.type(Type1.4)
plot.types(type=data.frame(Type1.4))
plot.types(type=Types)

@

\clearpage
\subsubsection{Type5}

<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
      abline(h=0,v=0,col=8)
#
type5 <- ((abs(C)<max.slope)|(abs(F)<max.slope)) & (abs(fc)> diff.slope5)&(abs(mc-mf)<deltam)
#
Type5 <- C*F > 0
Type5 <- Type5 & ( abs(C) > min.slope) & (abs(F) > min.slope )
Type5 <- Type5 & ( ( abs(diff0) > diff01 ) & ( abs(diff1) > diff01 ) )
Type5 <- Type5 & nocross
#
Type5.0 <- C*F > 0
sum(Type5.0)
Type5.0CF <- ( abs(C) > min.slope) & (abs(F) > min.slope)
sum(Type5.0CF)
Type5.0C <- (abs(C) > min.slope) & (abs(F) > max.slope & abs(F) < min.slope)
sum(Type5.0C)
Type5.0F <- (abs(F) > min.slope) & (abs(C) > max.slope & abs(C) < min.slope)
sum(Type5.0F)
Type5.0CF <- Type5.0CF | Type5.0C | Type5.0F
sum(Type5.0CF)
Type5.0diff <- ((abs(diff0) > diff01) & (abs(diff1) > diff01))
sum(Type5.0diff)
#
Type5.0 <- Type5.0 & Type5.0CF & Type5.0diff & nocross
#
table(Type5,Type5.0)
# type 3 is a subset of type5, type3 & large mean difference
points(
   C[Type5.0],
   F[Type5.0],
   bg=6, pch=21, col=1)
#
abline(h=0,v=0,col=2)
abline(c(0,1),col=3)
Types <- data.frame(Types, Type5.0)
(nt <- apply(Types,2,sum))
legend("bottomright",pch=rep(16,5),col=2:7,legend=paste0("Type",1:ncol(Types)," | ",nt),bty="n")
nt
plot.type(Type5)
plot.types(type=data.frame(Type5.0,Type5))

@

\clearpage
\subsubsection{Type6}

<<>>=
plot(C,F, xlab="slope C",ylab="Slope F ")
      abline(h=0,v=0,col=8)
#
#
Type6 <- ((abs(diff0) > diff01) & (abs(diff1) > diff01))
Type6 <- Type6 & nocross
Type6.C <-  Type6 & (( abs(C) > min.slope) & (abs(F) < max.slope))
Type6.F <-  Type6 & (( abs(F) > min.slope) & (abs(C) < max.slope))
Type6 <- Type6 & ((( abs(C) > min.slope) & (abs(F) < max.slope)) |
                 (( abs(F) > min.slope) & (abs(C) < max.slope)))
#
table(Type6,Type6.C, Type6.F)
# type 3 is a subset of Type6, type3 & large mean difference
table(Type3,Type6)
points(
   C[Type6.C],
   F[Type6.C],
   bg=7, pch=21, col=1)
points(
   C[Type6.F],
   F[Type6.F],
   bg=7, pch=21, col=1)
#
abline(h=0,v=0,col=2)
abline(c(0,1),col=3)
Types <- data.frame(Types, Type6.C, Type6.F)
(nt <- apply(Types,2,sum))
legend("bottomright",pch=rep(16,5),col=2:7,legend=paste0("Type",1:ncol(Types)," | ",nt),bty="n")
nt
plot.type(Type6.C)
plot.type(Type6.F)
plot.types(type=cbind(Type6.C, Type6.F))
@

Type2 and Type4 are merged with Type1 + some mixed types

<<>>=
Types <- data.frame(Type1.0, Type5.0, Type3.C, Type3.F,
      Type6.C, Type6.F)
@

<<fig.width=8,fig.height=8>>=
plot.types(type=Types, pty="s")
abline(v=c(min.slope,max.slope),h=c(min.slope,max.slope), col=8)
abline(v=-c(min.slope,max.slope),h=-c(min.slope,max.slope),col=8)

@

<<fig.width=8,fig.height=8>>=
plot.types(diff0, diff1, type=Types, pty="s")

@


<<>>=
nts <- apply(Types,2,sum)
sum(nts)
@

<<results='asis'>>=
xtable( data.frame(Count=t(t(addmargins(apply(Types,2,table),2)[2,]))), auto=TRUE, caption="Table of new types.")
@



Override old types

<<>>=
types.old <- types
types.new <- Types
types <- types.new

@

<<>>=
head(types)
plot.types()
plot.types(diff0,diff1)
plot.types(C,diff0)
plot.types(C+F,diff0-diff1)
@

<<>>=
#persp.3d(C+F,diff0,diff1, col=8)
#persp(1:6,1:6,6:1, col=8)

@


<<>>=
x <- coefs[Type1,]
ylim <- range(x)
my.parallelplot <- function(x, ylim = range(x), title,  ...){
if(missing(title)) title <- deparse(substitute(x))
m <- ncol(x)
plot(0,0, type = "n"
    , xlim = c(1,m)
    , ylim = ylim
    , axes = FALSE
    , ann=FALSE
    )
title(title)
mtext(paste("n of profiles:", nrow(x)),cex=0.75)
axis(2)
axis(1, at=1:m,labels=gsub("\\.\\.\\.","-",colnames(x)),cex=0.9,xpd=TRUE)
abline(v=1:m,col=8)
x$col <- 1:nrow(x)
apply(x,1,function(y) lines(1:m,y[-(m+1)],col=y[m+1]))
invisible(nrow(x))
}
if(interactive()) my.parallelplot(coefs[Type1,])
@

<<fig.height=8>>=
par(mfrow=c(3,2))
tpar <- data.frame(C,F,F-C,diff0,diff1)
ylim <- range(tpar)
for(tip in colnames(types))
my.parallelplot(tpar[types[,tip],], ylim=ylim, title=tip)
@


<<>>=
plot.types()
@

<<fig.height=8>>=
par(mfrow=c(3,2))
tpar <- data.frame(C,F,F-C,diff0,diff1)
abstpar <- abs(tpar)
ylim <- range(abstpar)
for(tip in colnames(types))
my.parallelplot(abstpar[types[,tip],], ylim=ylim, title=tip)


@

<<>>=
plot.types(abs(C),abs(F),title="Absolute slopes")
@

<<>>=
plot.types(C,F,title="Slopes")
plot.types(mc,mf,title="Mean expression")
@

<<>>=
varname <- testvar
plot.ewp(varname,model="", exprs=exprs, pd=pd)
@

<<>>=
apply(types,2,sum)
@

<<>>=
for(i in 1:ncol(types)) {
plot.type(types[,i])
mtext(names(types[i]), outer=TRUE, line=-1)
}
@



<<>>=
which <- 2
  tt <- topTable(fit,coef=which, number=Inf)
  head(tt)
@

\clearpage
\subsection{Export output files}

Export fit in a form:

<<>>=
fit <- fit2
fitfn <- "lm-fit-swpXvariety-statistics.txt"

write.fit(head(fit),file="", adjust="BH")
write.fit(fit,file=file.path(.oroot,fitfn), adjust="BH")
@

Export top table

<<write top table, results='asis'>>=
ttfn <- "TopTable.txt"
tt <- topTable(fit, number=Inf)
#
types <- types.new
tt$Type <- ""
types <- types[rownames(tt),]
all(rownames(tt)==rownames(types))
#ind <- sapply(rownames(tt),FUN=function(x) match(x,rownames(types)))
for(i in 1:ncol(types)) tt$Type[types[,i]] <- colnames(types)[i]
#
ind <- sapply(rownames(tt),FUN=function(x) match(x,fdata$geneID))
ttext <- data.frame(tt,fdata[ind,2:5] )
my.write.table(ttext,file=file.path(.oroot,ttfn))
types <- types.new
@


<<>>=
.topn <- 100
topn <- .topn
@



\clearpage
\section{Types and significance of contrasts}

Starting point is fit2



Model with water potential by variety.
<<>>=
design2 <- with(pd, model.matrix(~ swp*variety))
head(design2)
tail(design2)

@

Linear model
<<>>=
fit2 <- lmFit(exprs, design2)
fit2[testvar,]$coefficients
@

<<>>=
design3 <- with(pd, model.matrix(~ 0+variety))
head(design3)
tail(design3)
fit3 <- lmFit(exprs, design3)
fit3[testvar,]$coefficients
@

Combine design2 and design3:
Replace interceps with means in coefficients and standard deviations

<<>>=
fit4 <- fit2
fit4$coefficients[,1] <- fit3$coefficients[,1]
fit4$coefficients[,3] <- fit3$coefficients[,2]
fit4$stdev.unscaled[,1] <- fit3$stdev.unscaled[,1]
fit4$stdev.unscaled[,3] <- fit3$stdev.unscaled[,2]
dimnames(fit4$coefficients)[[2]] <- c("mc","c1","mf","f1.c1")
fit4[testvar,]$coefficients
fit2[testvar,]$coefficients
fit3[testvar,]$coefficients
@

<<>>=
fit4<- eBayes(fit4)
head(fit4)
fit4[testvar,]
@

<<>>=
tt <- topTable(fit4,2,number=Inf)
tt[testvar,]
@

With difference of means

<<>>=
fit4 <- fit2
fit4$coefficients[,1] <- fit3$coefficients[,1]
fit4$coefficients[,3] <- fit3$coefficients[,2]-fit3$coefficients[,1]
fit4$stdev.unscaled[,1] <- fit3$stdev.unscaled[,1]
fit4$stdev.unscaled[,3] <- apply(fit3$stdev.unscaled[,1:2], 1
     , function(x) sqrt(sum(x^2 )) )
dimnames(fit4$coefficients)[[2]] <- c("mc","c","mf.mc","f.c")
fit4[testvar,]$coefficients
fit3[testvar,]$stdev.unscaled
fit4[testvar,]$stdev.unscaled
#
fit2[testvar,]$coefficients
fit3[testvar,]$coefficients
@

<<>>=
fit4<- eBayes(fit4)
head(fit4)
fit4[testvar,]
@

<<>>=
fit4[testvar,]$coef
fit4[testvar,]$p.value
#
tt.mc <- topTable(fit4,1,number=Inf, sort.by="none", confint=TRUE)
all(rownames(fit4)==rownames(tt.mc))
tt.mc[testvar,]
alpha <- as.numeric(.alpha)
with(tt.mc, table(p=P.Value < alpha, adj.p=adj.P.Val < alpha))
#
tt.c <- topTable(fit4,2,number=Inf, sort.by="none", confint=TRUE)
tt.c[testvar,]
with(tt.c, table(p=P.Value < alpha, adj.p=adj.P.Val < alpha))
#
tt.mf.mc <- topTable(fit4,3,number=Inf, sort.by="none", confint=TRUE)
tt.mf.mc[testvar,]
with(tt.mf.mc, table(p=P.Value < alpha, adj.p=adj.P.Val < alpha))
#
tt.f.c <- topTable(fit4,4,number=Inf, sort.by="none", confint=TRUE)
tt.f.c[testvar,]
with(tt.f.c, table(p=P.Value < alpha, adj.p=adj.P.Val < alpha))
@

\clearpage
\subsection{Export fit statistics, types and descriptions}

<<>>=
fit4fn <- "lm-fit-means-swpXvariety-statistics.txt"
fit <- fit4
decide0 <- decideTests(fit,lfc=0 )
vennDiagram(decide0)
#ind <- sapply(rownames(fit),FUN=function(x) match(x,fdata$geneID))
ind <- match(rownames(fit),fdata$geneID)
df4 <- as.data.frame(fit)
write.fit(fit,file="../doc/tmp2.txt", adjust="BH")
df4 <-  read.table("../doc/tmp2.txt")
colnames(df4)
Koef.f <- df4$Coef.f.c+df4$Coef.c
ins <- grep("t.",colnames(df4))[1]
df4 <- data.frame(df4[,1:(ins-1)],Koef.f,diff0,diff1,df4[,ins:ncol(df4)])
#
## df4$type <- ""
## types <- types.old
## types <- types[rownames(df4),]
## all(rownames(df4)==rownames(types))
## #ind <- sapply(rownames(tt),FUN=function(x) match(x,rownames(types)))
## for(i in 1:ncol(types)) {
##       df4$type[types[,i]] <- paste(df4$type[types[,i]], colnames(types)[i], sep="|")
##       print(table(df4$type))
##       }
## df4$type <- gsub("^\\|","", df4$type)
#
df4$Type <- ""
types <- types.new
types <- types[rownames(df4),]
all(rownames(df4)==rownames(types))
#ind <- sapply(rownames(tt),FUN=function(x) match(x,rownames(types)))
for(i in 1:ncol(types)) {
      df4$Type[types[,i]] <- paste(df4$Type[types[,i]], colnames(types)[i], sep="|")
      print(table(df4$Type))
      }
df4$Type <- gsub("^\\|","", df4$Type)
types <- types.new
#
# Mark interesting genes:
stat4 <- data.frame(df4,fdata[ind,2:5] )
pc <- stat4[,"P.value.c"]
pfc <- stat4[,"P.value.f.c"]
inc <- c("","*")[(pc < alpha)+1]
incf <- c("","+")[((pc > alpha)&(pfc < alpha))+1]
df4$interestingC <- inc
df4$interestingFC <- incf
#
stat4 <- data.frame(df4,fdata[ind,2:5] )
table(stat4$interestingC,stat4$interestingFC)
#print(table(stat4$type,stat4$Type), zero.print=".")
@

Some genes are classified into two types:

<<>>=
t(t(table(df4$Type)))
@


<<>>=
summary(types)
@

Order stat4 and types by decreasing coefficient for C

<<>>=
ordr <- rev(order(stat4$Coef.c))
stat4 <- stat4[ordr,]
types <- types[rownames(stat4),]
all(rownames(stat4)==rownames(types))

@

Genes from interesting bins are marked by\\
 '*' (for significant slope C) and\\
 '+' (for significant difference in slopes).

<<write stat4, results="asis">>=
my.write.table(stat4,file=file.path(.oroot,fit4fn),
label="WP x variety model statistics, types and gene annotation")
@
\clearpage

Comparison with effects.

Large abs slope C

<<>>=
alpha
indp <- (tt.c$P.Value < alpha)
sum(indp)
table(rownames(tt.c)[indp]%in%rownames(tt.c)[large_abs_C])
table(indp,large_abs_C)
@

Small difference of slopes
<<>>=
alpha
indp <- (tt.f.c$P.Value > alpha)
sum(indp)
table(rownames(tt.c)[indp]%in%rownames(tt.c)[small_fc])
table(indp,small_fc)
@

Large difference of mean expressions

<<>>=
alpha
range(tt.mf.mc$P.Value)
indp <- (tt.mf.mc$P.Value < alpha)
sum(indp)
table(rownames(tt.c)[indp]%in%rownames(tt.c)[(abs(mc-mf)<deltam)])
table(indp,large.diff.means=(abs(mc-mf)<deltam))
@

\clearpage
\section{Detailed plots, stats and info of Genes by types}

Plots of situation for genes in determined types. Significance can be checked in individual plots.

Figures caption:

<<echo=FALSE>>=
fig.cap <- "Mean expression (E) and mean water potential (WP). C (blue) and all F (red). Gray line (if present) is regression of all data, regardles of treatment and variety."
@

\Sexpr{fig.cap}

\clearpage

Order stat4 and types by decreasing coefficient for C

<<>>=
ordr <- rev(order(stat4$Coef.c))
stat4 <- stat4[ordr,]
types <- types[rownames(stat4),]
all(rownames(stat4)==rownames(types))
@


<<>>=
par(mfrow=c(2,3))
ylim <- range(stat4[,"Coef.c"])
for(i in 1:ncol(types)){
varnames <- rownames(stat4)[types[,i]]
plot(stat4[varnames,"Coef.c"], ylab="Slope C",
  ,ylim=ylim, main=colnames(types)[i])
  abline(h=0,col=8)
}
@



<<50aa_one-gene-plots.Rnw>>=
# Report for selected genes
out <- ""
(cname <- "")
colnames(stat4)
i <- 1
types[,1] <- TRUE

(typevar <- substr(names(types)[1],1,4))
tipi <- sort(unique(stat4[,typevar]))[-1]
# reorder type names
types <- types[,sort(colnames(types))]
for(i in 1){
type.name <- colnames(types)[i]
out <- paste(out,"
\\clearpage
\\subsection{",type.name,"}\\")
varnames <- rownames(stat4)[types[,i]]
varname <- varnames[2]
fdata <- mfdata
varnames <- rownames(stat4)
for(varname in varnames)
 out <- paste(out,knit_child(file.path("../doc/","55aa_one-gene-plots.Rnw"),quiet=TRUE))
}
@
\Sexpr{out}


<<eval=FALSE,echo=FALSE>>=
varname <- testvar
par(mfrow=c(1,3))
p <- plot.ewp(varname,model="")
plot(pd$swp,exprs[testvar,],  xlim=c(0,-1.6),
   ,pch=16, col=6-as.numeric(pd$variety)*2,cex=pd$day/2)
c(p$C$coef[2],p$F$coef[2])
fit2$coef[testvar,]
p <- plot.ewp(varname,model="", exprs=exprs, pd=pd)
#
isC <- regexpr("C",colnames(exprs))>0
(mswpC <- mean(swp[isC]))
(mswpF <- mean(swp[!isC]))
(msC <- my.predict(fit2, mswpC)[testvar,][1])
(msF <- my.predict(fit2, mswpF)[testvar,][2])
points(mswpC, msC, cex=2, col=4)
points(mswpF, msF, cex=2,col=2)
ms2 <- my.predict(fit2,-1)[testvar,]
ms2
points(rep(-1,2), ms2, cex=2)
ms3 <- fit3[testvar,]$coefficients
ms3
abline(h=ms3,col=c(4,2))
abline(v=c(mswpC,mswpF), col=c(4,2))

@

<<>>=
#
fit2 <- eBayes(fit2)
fit2[testvar,]$coefficients
head(fit2[1:6,]$coefficients)
@