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% .R knitr file  ************************************************
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<<echo=FALSE>>=
###############################################
##                                           ##
## (c) Andrej Blejec (andrej.blejec@nib.si)  ##
##                                           ##
###############################################
@
<<echo=FALSE,results='hide'>>=
options(width=70)
@

\hfill{Entered 10a-ReadData.Rnw}

\clearpage
\subsection{Phenodata}

Two incomplete lines in original phenodata were manually completed.
Sample ids (\code{ID}) were parsed in Excel to form factors of interest:
\code{variety},\code{year} and \code{day}.



<<>>=
(pfn <- getDesc(.adesc
    , "Phenodata"))
dir(file.path(.iroot), pattern = pfn)
phdata <- read.table(file.path(.iroot, pfn)
  , header = TRUE
  , sep = "\t"
  , stringsAsFactors = FALSE
  )
rownames(phdata) <- phdata[,1]
my.summary(phdata)
dim(phdata)
names(phdata)
@

\subsection{Declare factor types}

In 2018, day 11 is present only for C and day 10 only for F.
The two day points coincide and are changed to a common value 11.

<<>>=
for(yr in levels(phdata$year)){
cat("\n\nYear:",yr,"\n")
print(
with(phdata[phdata$year==yr,], ftable(treat,variety,factor(day)))
)
}
phdata[phdata$day==10,"day"] <- 11
@

Factors

<<>>=
factors <- colnames(phdata)[4:8]
factors
for (varname in factors)
phdata[,varname] <- factor(phdata[,varname])
#
# Change the order of levels for treatment
phdata$treat <- factor(phdata$treat, levels=c("WW","WS"))
str(phdata[,factors])
@

Date of sampling might be useful as well

<<warnings=FALSE>>=
library(lubridate)
pdata$date <- as_date(pdata$Date, format="%d.%m.%Y")
table(data.frame(pdata$date,"Count"=1))
@

\subsection{Select samples}

Check for assay specific sample selection column (assay name)
<<>>=
.aName
selectId <- substr(gsub("-",".",.aName),2,nchar(.aName))
selectId <- .vzorci
selectId
#
if(selectId %in% names(phdata)){
pdata <- phdata[!is.na(phdata[,selectId]),]
cat("Sample selection column found (", selectId,"),\n",
nrow(pdata), "samples will be used.\n")
} else {
pdata <- phdata
cat("No sample selection column found,\n",
     nrow(pdata), "samples will be used.\n")
}
dim(pdata)
@

Table of sample conditions

<<>>=
head(pdata[,factors])
for(yr in levels(pdata$year)){
cat("\n\nYear:",yr,"\n")
print(
with(pdata[pdata$year==yr,], ftable(treat,variety,factor(day)))
)
}
@


<<>>=
my.summary(pdata)
@

For separate yearly analyses, it will be handy to have two, separate phenodata objects.

<<>>=
dim(pdata)
table(pdata$year)
pdata18 <- pdata[pdata$year==18,]
dim(pdata18)
pdata18[1:5,1:6]
#
pdata19 <- pdata[pdata$year==19,]
dim(pdata19)
pdata19[1:5,1:6]
@

\clearpage
\subsection{Featuredata}

<<>>=
(ffn <- getDesc(.adesc,"Featuredata"))
@

<<results='asis'>>=
if(ffn=="") {
cat("No feature data declared in the assay metadata file.\\\\")
fdata <- NULL
}
@

<<eval=(ffn!=""), echo=(ffn!="")>>=
fdata <- read.table(file.path(.iroot,ffn)
    , sep = "\t"
    , header = TRUE
    , na.strings = c("", "-")
    , stringsAsFactors = FALSE
    , row.names = 1
    )
if(interactive()) my.summary(fdata)
@

A glimpse into feature data (or \code{NULL} if nonexistent)
<<>>=
fdata[,1:2]
@

\clearpage
\subsection{Transcripts measurements}

Normalised transcript measurements for each year are available in two separate files. The first few lines contain factor values and are commented out (by a hash). The first effecitve line is the table header line with sample ids.

\subsubsection{Normalized values}

Data for year 2018:

<<>>=
(t18fn <- getDesc(.adesc,"Transcript data 18"))
td  <- read.table(file.path(.aroot,t18fn),
    header = TRUE,
    sep = "\t",
    row.names = 1
    )
head(td)
dim(td)
t18 <- td
@

<<>>=
(t19fn <- getDesc(.adesc,"Transcript data 19"))
td  <- read.table(file.path(.aroot,t19fn),
    header = TRUE,
    sep = "\t",
    row.names = 1
    )
head(td)
dim(td)
t19 <- td
@

<<>>=
dim(t18)-dim(t19)
@

Data for year 2018 have more genes than data for year 2019.

<<>>=
allNames <- unique(c(rownames(t18),rownames(t19)))
length(allNames)
sum(allNames %in% rownames(t18))
sum(allNames %in% rownames(t19))
tbl <- table(gene.in.18=allNames %in% rownames(t18),
             gene.in.19=allNames %in% rownames(t19))
tbl
@

We have complete yearly data on \Sexpr{tbl[2,2]} genes.
At the meeting on Nov. 19 we decided to use only the genes in the intersection.
This will allow us to use the combined set of data.

\subsubsection{Keep genes in the intersection}

<<>>=
gene.keep <- (allNames %in% rownames(t18))&(allNames %in% rownames(t19))
sum(gene.keep)
t18 <- t18[allNames[gene.keep],]
t19 <- t19[allNames[gene.keep],]
all(rownames(t18)==rownames(t19))
dim(t18)
dim(t19)
@

%% \subsubsection{Filter genes by expression}
%%
%%
%%
%% <<>>=
%% minLevel <- 0.7
%% minNumber <- 1
%% @
%%
%% Genes with normalized values that are not exceeding the minimal treshold expression of \Sexpr{minLevel} in less than \Sexpr{minNumber} sample(s) are excluded. Probably this should be performed on combined data, for both years.
%% But first we will do it separatey on each dataset and then on the combined intersection.
%%
%% <<>>=
%% minLevel
%% minNumber
%% expr18 <- apply(t18, 1,
%%        FUN = function(x) sum(x >= minLevel))
%% filter18 <- expr18 >= minNumber
%% nrow(t18)
%% sum(filter18)
%% par(mfrow=c(1,2))
%% plot(sort(expr18), ylab="N of samples")
%% expr19 <- apply(t19, 1,
%%        FUN = function(x) sum(x >= minLevel))
%%        plot(sort(expr19), ylab="N of samples")
%% filter19 <- expr19 >= minNumber
%% nrow(t19)
%% sum(filter19)
%% @
%%
%% Filtering didn't substantially reduce the number of genes:\\[12pt]
%% Year 2018: \Sexpr{nrow(t18)} $\rightarrow$ \Sexpr{sum(filter18)}\\
%% Year 2019: \Sexpr{nrow(t19)} $\rightarrow$ \Sexpr{sum(filter19)}

Order rows of yearly phenodata according to the sequence of samples (columns) in the measurements.

Year 2018
<<>>=
dim(pdata18)
dim(t18)
pdata18 <- pdata18[colnames(t18),]
dim(pdata18)
dim(t18)
pdata18[,4:8]
all(rownames(pdata18)==colnames(t18))
@

Get rid of unused factors

<<>>=
str(pdata18[,factors])
for (var in factors) pdata18[,var] <- factor(pdata18[,var])
str(pdata18[,factors])
@


Year 2019
<<>>=
dim(pdata19)
dim(t19)
pdata19 <- pdata19[colnames(t19),]
dim(pdata19)
dim(t19)
pdata19[,4:9]
all(rownames(pdata19)==colnames(t19))
@

Get rid of unused factors levels

<<>>=
str(pdata19[,factors])
for (var in factors) pdata19[,var] <- factor(pdata19[,var])
str(pdata19[,factors])
@

<<>>=
t1819 <- data.frame(t18, t19)
pdata1819 <- rbind(pdata18, pdata19)
str(pdata1819[,factors])
catln("Sample names ",
c("different","OK")[1+all(rownames(pdata1819)==colnames(t1819))])
@

\clearpage
\subsection{Created objects overview}

<<results='hide'>>=
#if(!exists("addObject")) {
addObject <- function(x=NULL,desc="",x0=my.objects){
if(is.null(x)) x0 <- data.frame(name="",description="",class="", lenght=NA,ncol=NA) else {
nr <- length(x)
nc <- ncol(x)
if(is.data.frame(x)) {
    nr <- nrow(x)
    nc <- ncol(x)
    }
if(is.null(nc)) nc <- NA
x0 <- rbind(x0,c(deparse(substitute(x)), desc, class(x), nr, nc))
x0 <- x0[x0$name!="",]
}
rownames(x0) <- 1:nrow(x0)
return(x0)
}
#}
(my.objects <- addObject())
(my.objects <- addObject(t18,"Transcripts for 2018"))
(my.objects <- addObject(t19,"Transcripts for 2019"))
(my.objects <- addObject(t1819,"Transcripts for 2018 and 2019"))
(my.objects <- addObject(pdata18,"Phenodata for 2018"))
(my.objects <- addObject(pdata19,"Phenodata for 2019"))
(my.objects <- addObject(pdata1819,"Phenodata for 2018 and 2019"))
@

<<results='markup'>>=
my.objects

@