## ----setup_latex, include=FALSE, cache=FALSE, echo=FALSE-----------------
Sys.setenv(TEXINPUTS=getwd(),
           BIBINPUTS=getwd(),
           BSTINPUTS=getwd())
library(xtable)


## ----dataload------------------------------------------------------------
library(rgsepd)
data("IlluminaBodymap" , package="rgsepd")
data("IlluminaBodymapMeta" , package="rgsepd")


## ----MyT1CodeBlock, echo = FALSE, results = 'hide'-----------------------
T1 <- head(IlluminaBodymap,n=15L)[,c(1,2,3,4,5,9,13)]
T2 <- head(IlluminaBodymapMeta)


## ----MyLaTeXT1Caption, echo = FALSE, results = 'asis'--------------------
xT<-xtable(T1, caption ="First few rows of the included IlluminaBodymap dataset. See \\texttt{?IlluminaBodymap} for more details.", label = 'MyT1')
print.xtable(xT, scalebox=0.60)
xtable(T2, caption ="First few rows of the included IlluminaBodymapMeta dataset. See \\texttt{?IlluminaBodymapMeta} for more details. These are easy to build with a spreadsheet, saved to csv and R's builtin \\texttt{?read.csv}", label = 'MyT2')


## ----setup---------------------------------------------------------------
set.seed(1000) #fixed randomness
isoform_ids <- Name_to_RefSeq(c("GAPDH","HIF1A","EGFR","MYH7","CD33","BRCA2"))
rows_of_interest <- unique( c( isoform_ids ,
                    sample(rownames(IlluminaBodymap),
                    size=1000,replace=FALSE)))
G <- GSEPD_INIT(Output_Folder="OUT",
          finalCounts=round(IlluminaBodymap[rows_of_interest , ]),
          sampleMeta=IlluminaBodymapMeta,
            COLORS=c(blue="#4DA3FF",black="#000000",gold="#FFFF4D"))
G <- GSEPD_ChangeConditions( G, c("A","B"))


## ----ParameterChanging, cache=FALSE--------------------------------------
  G$MAX_Genes_for_Heatmap <- 25
  G$MAX_GOs_for_Heatmap <- 30
  G$MaxGenesInSet <- 12
  G$LIMIT$LFC <- log( 2.50 , 2 )
  G$LIMIT$HARD <- FALSE


## ----GSEPD_Process, cache=FALSE------------------------------------------
  G <- GSEPD_Process( G )


## ----GSEPD_Heatmap_-_PCA-------------------------------------------------
  print(isoform_ids)
  GSEPD_Heatmap(G,isoform_ids)
  GSEPD_PCA_Plot(G)


## ----MyT3----------------------------------------------------------------
Annotated_Filtered <- read.csv("OUT/DESEQ.RES.Ax4.Bx8.Annote_Filter.csv",
                      header=TRUE,as.is=TRUE)


## ----MyLaTeXT3Caption, echo = FALSE, results = 'asis'--------------------
xT<-xtable(head(Annotated_Filtered, n=10L),
           caption ="First few rows of OUT/DESEQ.RES.Ax4.Bx8.Annote\\_Filter.csv which contains the DESeq results, cropped for significant results, and annotated with gene names (the HGNC Symbol).", label = 'Table_Annote')
print(xT, scalebox=0.70, include.rownames=FALSE)


## ----MyLaTeXT5Caption, echo = FALSE, results = 'asis'--------------------
Merge_File <- read.csv("OUT/GSEPD.RES.Ax4.Bx8.MERGE.csv",
                      header=TRUE,as.is=TRUE, nrows=20)
xT<-xtable(head(Merge_File, n=15L),
           caption ="First few rows of OUT/GSEPD.RES.Ax4.Bx8.MERGE.csv showing enriched GO Terms, and each terms' underlying gene expression averages per group. This data is central to the rgsepd package, defining the group centroids per GO-Term. It consists of the cross-product of the GO enrichment statistics and the DESeq differential expression and summarization. ", label = 'MyT5')
print(xT, scalebox=0.60, include.rownames=FALSE)


## ----AlphaBetaTables, echo = FALSE, results = 'asis'---------------------
Alpha_File <- read.csv("OUT/GSEPD.Alpha.Ax4.Bx8.csv",
                      header=TRUE,as.is=TRUE, nrows=20, row.names=1)
xT<-xtable(head(Alpha_File, n=10L)[,c(1,2,3,4,5,9,13)],
           caption ="First ten rows of OUT/GSEPD.Alpha.Ax4.Bx8.csv showing the group projection scores for each sample, these directly correspond to the colors in the HMA file. Where the HMA displays only significant sets, the Alpha table continues for all tested GO Terms. Both the Alpha table and Beta table are summarized in Figure \\ref{fig:HMA}.", label = 'TableAlpha')
print(xT, scalebox=0.80, include.rownames=TRUE)
Beta_File <- read.csv("OUT/GSEPD.Beta.Ax4.Bx8.csv",
                      header=TRUE,as.is=TRUE, nrows=20, row.names=1)
xT<-xtable(head(Beta_File, n=10L)[,c(1,2,3,4,5,9,13)],
           caption ="First ten rows of OUT/GSEPD.Beta.Ax4.Bx8.csv showing the linear divergence (distance to axis) for each sample, high values here would be annotated with white dots on the HMA file to indicate that a sample is not falling on the axis. Non-tested samples are expected to frequently have high values here, the C group was not part of the A vs B comparison.  Where the HMA displays only significant sets, the Beta table continues for all tested GO Terms. Both the Alpha table and Beta table are summarized in Figure \\ref{fig:HMA}.", label = 'TableBeta')
print(xT, scalebox=0.80, include.rownames=TRUE)


## ----FindFiles, echo = FALSE, results = 'asis'---------------------------
HM_File <- list.files("OUT",pattern="HM.A")
PScatter_File <- list.files("OUT//SCGO",pattern="Scatter.Ax")[1]
PGSEPD_File <- list.files("OUT//SCGO",pattern="GSEPD.Ax")[1]
PPairs_File <- list.files("OUT//SCGO",pattern="Pairs.Ax")[1]
#trim the .PDF
HM_File <- substring(HM_File,0,nchar(HM_File)-4)
PScatter_File <- substring(PScatter_File,0,nchar(PScatter_File)-4)
PGSEPD_File <- substring(PGSEPD_File,0,nchar(PGSEPD_File)-4)
PPairs_File <- substring(PPairs_File,0,nchar(PPairs_File)-4)



## ----sessionInfo---------------------------------------------------------
  sessionInfo()