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This commit is contained in:
Bryan Roessler
2024-08-13 15:27:53 -04:00
parent 724b292dab
commit f190967383
7 changed files with 367 additions and 298 deletions
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1
.gitignore vendored
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@@ -2,3 +2,4 @@ old/
manual.odt
mwe
centos-upgrade-plan.txt
workflow/out/

81
workflow/README.md
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@@ -34,6 +34,7 @@ Insert a general description of Q-HTCP and the Q-HTCP process here.
* [py_gtf_concat](#pygtfconcat)
* [r_compile_gtf](#rcompilegtf)
* [get_studies](#getstudies)
* [choose_easy_results](#chooseeasyresults)
## Notes
@@ -94,7 +95,7 @@ Insert a general description of Q-HTCP and the Q-HTCP process here.
### parse_input
`--project`, `--module`, `--nomodule`, and `--submodule` can be passed multiple times or with a comma-separated string
`--project`, `--module`, `--nomodule`, and `--wrapper` can be passed multiple times or with a comma-separated string
#### Options
@@ -106,9 +107,9 @@ Insert a general description of Q-HTCP and the Q-HTCP process here.
One or more modules to run (default: all), can be passed multiple times or with a comma-separated string
* **-s\<value\>** | **--submodule=\<value\>**
* **-w\<value\>** | **--wrapper=\<value\>**
Requires two arguments: the name of the submodule and its arguments, can be passed multiple times
Requires two arguments: the name of the wrapper and its arguments, can be passed multiple times
* **-n\<value\>** | **--nomodule=\<value\>**
@@ -134,7 +135,7 @@ Insert a general description of Q-HTCP and the Q-HTCP process here.
* **PROJECTS** (array): List of projects to cycle through
* **MODULES** (array): List of modules to run on each project
* **SUBMODULES** (array): List of submodules and their arguments to run on each project
* **WRAPPERS** (array): List of wrappers and their arguments to run on each project
* **EXCLUDE_MODULES** (array): List of modules not to run on each project
* **DEBUG** (int): Turn debugging on
* **YES** (int): Turn assume yes on
@@ -147,10 +148,10 @@ Use a module to:
* Build a new type of analysis from scratch
* Generate project directories
* Group multiple submodules (and modules) into a larger task
* Dictate the ordering of multiple submodules
* Competently handle pushd and popd for their submodules if they do not reside in the SCANS/PROJECT_DIR
* Call their submodules with the appropriate arguments
* Group multiple wrappers (and modules) into a larger task
* Dictate the ordering of multiple wrappers
* Competently handle pushd and popd for their wrappers if they do not reside in the SCANS/PROJECT_DIR
* Call their wrappers with the appropriate arguments
### install_dependencies
@@ -204,6 +205,7 @@ TODO
* MasterPlate_ file **should not be an xlsx file**, no portability
* We can keep the existing xlsx code for old style fallback
* But moving forward should switch to csv or something open
* Do we need to sync a QHTCP template?
NOTES
@@ -218,6 +220,15 @@ NOTES
Run the EASY matlab program
INPUT FILES
* MasterPlate_.xls
* DrugMedia_.xls
OUTPUT FILES
* !!ResultsStd_.txt
* !!ResultsELr_.txt
TODO
* Don't create output in the scans folder, put it in an output directory
@@ -612,11 +623,9 @@ TODO
* **$5** (string): All_SGD_GOTerms_for_QHTCPtk.csv
* **$6** (string): zscores_interaction.csv
## Submodules
## Wrappers
Submodules are shell wrappers for workflow components in external languages
Submodules:
Wrappers:
* Allow scripts to be called by the main workflow script using input and output arguments as a translation mechanism.
* Only run by default if called by a module.
@@ -665,7 +674,7 @@ Output
*
This submodule:
This wrapper:
* Will perform both L and K comparisons for the specified experiment folders.
* The code uses the naming convention of PairwiseCompare_Exp’#’-Exp’#’ to standardize and keep simple the structural naming (where ‘X’ is either K or L and ‘Y’ is the number of the experiment GTA results to be found in ../GTAresult/Exp_).
@@ -697,7 +706,7 @@ Output
*
This submodule:
This wrapper:
* The Term Specific Heatmaps are produced directly from the ../ExpStudy/Exp_/ZScores/ZScores_Interaction.csv file generated by the user modified interaction… .R script.
* The heatmap labeling is per the names the user wrote into the StudyInfo.txt spreadsheet.
@@ -716,11 +725,18 @@ This submodule:
### r_interactions
Run the R interactions analysis (Z_InteractionTemplate.R)
Run the R interactions analysis (deprecates Z_InteractionTemplate.R)
SCRIPT: interactions.R
TODO
* Don't want to rename Z_InteractionTemplate.R because that will break logic, just edit in place instead
* Parallelization (need to consult with Sean)
* Needs more loops to reduce verbosity, but don't want to limit flexibility
* Replace 1:length() with seq_along()
* Re-enable disabled linter checks
* Reduce cyclomatic complexity of some of the for loops
* There needs to be one point of truth for the SD factor
NOTES
@@ -729,10 +745,11 @@ NOTES
#### Arguments
* **$1** (string): The input directory
* **$2** (string): The study info file
* **$3** (string): The zscores directory
* **$2** (string): The zscores directory
* **$3** (string): The study info file
* **$4** (string): SGD_features.tab
* **$5** (integer): delta SD background value (default: 5)
* **$6** (integer): experiment number
### r_join_interactions
@@ -826,7 +843,7 @@ Output
### pl_gtf_analyze
Perl analyze submodule
Perl analyze wrapper
This seems weird to me because we're just overwriting the same data for all set2 members
https://metacpan.org/dist/GO-TermFinder/view/examples/analyze.pl
Is there a reason you need a custom version and not the original from cpan?
@@ -840,7 +857,7 @@ Is there a reason you need a custom version and not the original from cpan?
### pl_gtf_terms2tsv
Perl terms2tsv submodule
Perl terms2tsv wrapper
Probably should be translated to shell/python
#### Arguments
@@ -849,7 +866,7 @@ Probably should be translated to shell/python
### py_gtf_concat
Python concat submodule for GTF
Python concat wrapper for GTF
Concat the process ontology outputs from the /REMcReady_lm_only folder
Probably should be translated to bash
@@ -872,14 +889,14 @@ Parse study names from StudyInfo.csv files
TODO
* This whole submodule should eventually be either
* This whole wrapper should eventually be either
* Removed
* Expanded into a file that stores all project/study settings (database)
* I had to had a new line to the end of StudyInfo.csv, may break things?
#### Arguments
* **$1** (string): File to read
* **$1** (string): Study info file
#### Variables set
@@ -890,3 +907,21 @@ TODO
* **0**: If one or more studies found
* **1**: If no studies found
### choose_easy_results
Chooses an EASY scans directory if the information is undefined
TODO Standardize EASY output, it's hard to understand
TODO eventually we could run this on multiple results dirs simultaneously with some refactoring
#### Arguments
* **$1** (string): directory containing EASY results dirs
#### Variables set
* **EASY_RESULTS_DIR** (string): The working EASY output directory
#### Exit codes
* **0**: if successfully choose anEASY results dir

2
workflow/apps/r/CompileGTF.R
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@@ -45,7 +45,7 @@ Function <- file.path(outDir, "Function", "FunctionResults.txt")
Process <- file.path(outDir, "Process", "ProcessResults.txt")
# Specify the list of input files (both CSV and TXT)
file_list <- c(Component,Process,Function)
file_list <- c(Component, Process, Function)
# Specify the output file name
output_file <- file.path(outDir, "GTFCombined.xlsx")

79
workflow/apps/r/addShiftVals.R
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@@ -7,7 +7,7 @@ library(plyr)
library(dplyr)
library(sos)
args=commandArgs(TRUE)
args <- commandArgs(TRUE)
if (length(args) >= 1) {
finalTable <- args[1]
@@ -30,62 +30,61 @@ if (length(args) >= 3) {
if (length(args) >= 4) {
output <- args[4]
} else {
output<- "REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
output <- "REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
}
# Read in the REMc finalTable data
X = data.frame(read.csv(file=finalTable,header=TRUE,stringsAsFactors = FALSE))
X <- data.frame(read.csv(file = finalTable, header = TRUE, stringsAsFactors = FALSE))
# Read in the shift data From ../JoinInteractions
Y = data.frame(read.csv(file=shiftFile,header=TRUE,stringsAsFactors = FALSE))
Labels <- read.delim(studyInfo,skip=0,as.is=T,row.names=1,strip.white=TRUE)
Y <- data.frame(read.csv(file = shiftFile, header = TRUE, stringsAsFactors = FALSE))
Labels <- read.delim(studyInfo, skip = 0, as.is = TRUE, row.names = 1, strip.white = TRUE)
# Determine the number of cols - needed to create the correct number of new cols
Xcolnum <- length(X[1,])
ADDnum <- Xcolnum + length(Y[1,]) - 2
Xcolnum <- length(X[1, ])
ADDnum <- Xcolnum + length(Y[1, ]) - 2
# Create new columns filled with NAs to be filled with data
Xtemp= X
Xtemp[,(Xcolnum+1):ADDnum] <- NA
Xtemp <- X
Xtemp[, (Xcolnum + 1):ADDnum] <- NA
# Match the orf names in each row to a orf name in the shift data file and then add the shift data to the finalTable file
shiftTbl <-as.data.frame(matrix(nrow=1,ncol=length(Y)-2)) #the df shiftTbl must be initialized before for loop
shiftTbl < - as.data.frame(matrix(nrow = 1, ncol = length(Y) - 2)) #the df shiftTbl must be initialized before for loop
for(i in 1:length(X[,1])){
Shiftrownum = match(X[i,2],Y[,1])
shiftTbl[i,]= Y[Shiftrownum,3:length(Y[1,])]
Xtemp[i,(Xcolnum+1):ADDnum] <- Y[Shiftrownum,3:length(Y[1,])]
for (i in 1:length(X[, 1])) {
Shiftrownum <- match(X[i, 2], Y[, 1])
shiftTbl[i, ] <- Y[Shiftrownum, 3:length(Y[1, ])]
Xtemp[i, (Xcolnum + 1):ADDnum] <- Y[Shiftrownum, 3:length(Y[1, ])]
}
headerX= colnames(Xtemp)
headerY= colnames(Y)
shfHdr= headerY[3:length(headerY)]
combTbl<- X[,1:3]
lmTbl= select(Xtemp, contains('Z_lm')) #X[,(4:Xcolnum-2)]
shiftTbl<- select(Xtemp, contains('V'))
clustTbl<- select(Xtemp, contains('cluster.'))
headerX <- colnames(Xtemp)
headerY <- colnames(Y)
shfHdr <- headerY[3:length(headerY)]
combTbl <- X[, 1:3]
lmTbl <- select(Xtemp, contains("Z_lm")) #X[,(4:Xcolnum-2)]
shiftTbl <- select(Xtemp, contains("V"))
clustTbl <- select(Xtemp, contains("cluster."))
# Give the new column names the same names as in the shift file
Xcols = colnames(X)
Ycols = colnames(Y)[3:length(Y[1,])]
newCols = c(Xcols[1:Xcolnum],Ycols)
Xcols <- colnames(X)
Ycols <- colnames(Y)[3:length(Y[1, ])]
newCols <- c(Xcols[1:Xcolnum], Ycols)
# Reorder columns for generating heatmaps
combI= combTbl #Starting Template orf, Genename columns
headersRemc<-newCols #colnames(X)
newHeaders= newCols[1:3]
lmHdr= colnames(lmTbl) #newCols[4:(length(Xcols)-2)]
clstHdr= colnames(clustTbl) #select(newCols, contains('cluster.')) #newCols[3+length(lmHdr):2]
combI <- combTbl #Starting Template orf, Genename columns
headersRemc <- newCols #colnames(X)
newHeaders <- newCols[1:3]
lmHdr <- colnames(lmTbl) #newCols[4:(length(Xcols)-2)]
clstHdr <- colnames(clustTbl) #select(newCols, contains('cluster.')) #newCols[3+length(lmHdr):2]
intLvHdr= vector()
intLvHdr <- vector()
#Reorder columns to produce an interleaved set of Z_lm and Shift data for all the cpps.
for(i in 1:(length(shiftTbl[1,]))){
combI=cbind.data.frame(combI, shiftTbl[i])
combI=cbind.data.frame(combI, lmTbl[i])
intLvHdrx= c(shfHdr[i],lmHdr[i] )
intLvHdr= c(intLvHdr,intLvHdrx)
for (i in 1:(length(shiftTbl[1, ]))) {
combI <- cbind.data.frame(combI, shiftTbl[i])
combI <- cbind.data.frame(combI, lmTbl[i])
intLvHdrx <- c(shfHdr[i], lmHdr[i])
intLvHdr <- c(intLvHdr, intLvHdrx)
}
combIHdr= c(colnames(combTbl),intLvHdr,clstHdr)
combI=cbind.data.frame(combI, clustTbl)
colnames(combI)= combIHdr
write.csv(combI,file=output, row.names=FALSE)
combIHdr <- c(colnames(combTbl), intLvHdr, clstHdr)
combI <- cbind.data.frame(combI, clustTbl)
colnames(combI) <- combIHdr
write.csv(combI, file = output, row.names = FALSE)

321
workflow/apps/r/createHeatMaps.R
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@@ -9,18 +9,18 @@ args <- commandArgs(TRUE)
# Set output dir
if (length(args) >= 1) {
input_finalTable <- args[1]
input_finalTable <- file.path(args[1])
} else {
input_finalTable <- "/REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
}
if (length(args) >= 2) {
outDir <- args[2]
outDir <- file.path(args[2])
} else {
outDir <- "/REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
}
hmapfile <- data.frame(read.csv(file=input_finalTable,header=TRUE,sep=",",stringsAsFactors = FALSE))
hmapfile <- data.frame(read.csv(file = input_finalTable, header = TRUE, sep = ",", stringsAsFactors = FALSE))
# set NAs to NA
hmapfile[hmapfile == -100] <- NA
hmapfile[hmapfile == 100] <- NA
@@ -28,19 +28,20 @@ hmapfile[hmapfile == 0.001] <- NA
hmapfile[hmapfile == -0.001] <- NA
# select the number of rows based on the number of genes
num_total_genes <- length(hmapfile[,1])
num_total_genes <- length(hmapfile[, 1])
# Break out the cluster names so each part of the cluster origin can be accessed
# line below removed because it adds to many genes to clusters when going past 1-0-10 since it cannot differentiate between 1-0-1 and 1-0-10 when using grepl.
# line below removed because it adds to many genes to clusters when going past 1-0-10
# since it cannot differentiate between 1-0-1 and 1-0-10 when using grepl.
# hmapfile$cluster.origin = gsub(" ","",x=hmapfile$cluster.origin)
hmapfile$cluster.origin = gsub(";"," ;",x=hmapfile$cluster.origin)
hmapfile$cluster.origin = strsplit(hmapfile$cluster.origin,';')
hmapfile$cluster.origin <- gsub(";", " ;", x = hmapfile$cluster.origin)
hmapfile$cluster.origin <- strsplit(hmapfile$cluster.origin, ";")
#use tail(x,n) for accessing the outward most cluster
clust_rounds <- 0
for(i in 1:num_total_genes){
if(length(hmapfile$cluster.origin[[i]]) > clust_rounds){
for (i in 1:num_total_genes) {
if (length(hmapfile$cluster.origin[[i]]) > clust_rounds) {
clust_rounds <- length(hmapfile$cluster.origin[[i]])
}
}
@@ -49,12 +50,13 @@ unique_clusts <- unique(hmapfile$cluster.origin[1:num_total_genes])
unique_clusts <- unique_clusts[unique_clusts != " "]
# Select only the unique cluster names
unique_clusts <- sort(unique(unlist(unique_clusts,use.names= FALSE)),decreasing=FALSE)
unique_clusts <- sort(unique(unlist(unique_clusts, use.names = FALSE)), decreasing = FALSE)
num_unique_clusts <- length(unique_clusts)
# Base the color key on a statistical analysis of the L and K data
# need to create "breaks" to set the color key, need to have 12 different breaks (for 11 colors)
# scale() will calculate the mean and standard deviation of the entire vector, then "scale" each element by those values by subtracting the mean and dividing by the sd.
# scale() will calculate the mean and standard deviation of the entire vector,
# then "scale" each element by those values by subtracting the mean and dividing by the sd.
# hmapfile[,4:(length(hmapfile[1,]) - 2)] <- scale(hmapfile[,4:(length(hmapfile[1,]) - 2)])
# change so that the L data is multiplied to be on the same scale as the K data
@@ -65,12 +67,12 @@ L_MAX <- 0
KcolumnValues <- vector()
LcolumnValues <- vector()
for(i in 4:(length(hmapfile[1,]) - 2)){
if(grepl("_Z_lm_K",colnames(hmapfile)[i],fixed=TRUE) == TRUE){
KcolumnValues <- append(KcolumnValues,i)
for (i in 4:(length(hmapfile[1, ]) - 2)){
if (grepl("_Z_lm_K", colnames(hmapfile)[i], fixed = TRUE) == TRUE) {
KcolumnValues <- append(KcolumnValues, i)
}
if(grepl("_Z_lm_L",colnames(hmapfile)[i],fixed=TRUE) == TRUE){
LcolumnValues <- append(LcolumnValues,i)
if (grepl("_Z_lm_L", colnames(hmapfile)[i], fixed = TRUE) == TRUE) {
LcolumnValues <- append(LcolumnValues, i)
}
}
@@ -101,38 +103,41 @@ print(KEY_MIN)
print(L_MAX)
# print(L_Multiplier)
colormapbreaks <- c(KEY_MIN,KEY_MIN*(5/6),KEY_MIN*(4/6),KEY_MIN*(3/6),KEY_MIN*(2/6),KEY_MIN*(1/6),KEY_MAX*(1/6),KEY_MAX*(2/6),KEY_MAX*(3/6),KEY_MAX*(4/6),KEY_MAX*(5/6),KEY_MAX)
colormapbreaks <- c(KEY_MIN, KEY_MIN * (5 / 6), KEY_MIN * (4 / 6), KEY_MIN * (3 / 6), KEY_MIN * (2 / 6),
KEY_MIN * (1 / 6), KEY_MAX * (1 / 6), KEY_MAX * (2 / 6), KEY_MAX * (3 / 6), KEY_MAX * (4 / 6), KEY_MAX * (5 / 6), KEY_MAX)
# print(colormapbreaks)
# Probably should give a way to detect shift in case that is is not in the first row... (maybe just grepl for the whole column name?)
# However since also using this to amend the first part. Could possibly identify all the ones that contain the word shift and then create an object containing just those numbers
# then could just use these values and create spaces only between interaction values - possibly could get rid of redundant shift values if we don't want to view these
# However since also using this to amend the first part.
# Could possibly identify all the ones that contain the word shift and then create an object containing just those numbers
# then could just use these values and create spaces only between interaction values
# possibly could get rid of redundant shift values if we don't want to view these
# could we pool all the shift data/average it?
if(grepl("Shift",colnames(hmapfile)[4],fixed=TRUE) == TRUE){
even_columns <- seq(from= 2, to= (length(hmapfile[1,]) - 7),by=2)
#ev_repeat = rep("white",length(even_columns))
#ev_repeat = rep("red",(length(hmapfile[1,]) - 5))
#middle_col <- (length(hmapfile[1,]) - 5)/2
#ev_repeat[(middle_col/2)] <- "black"
#print(ev_repeat)
if (grepl("Shift", colnames(hmapfile)[4], fixed = TRUE) == TRUE) {
even_columns <- seq(from = 2, to = (length(hmapfile[1, ]) - 7), by = 2)
# ev_repeat = rep("white",length(even_columns))
# ev_repeat = rep("red",(length(hmapfile[1,]) - 5))
# middle_col <- (length(hmapfile[1,]) - 5)/2
# ev_repeat[(middle_col/2)] <- "black"
# print(ev_repeat)
}
if(grepl("Shift",colnames(hmapfile)[4],fixed=TRUE) == FALSE){
even_columns <- seq(from= 2, to= (length(hmapfile[1,]) - 7),by=1)
if (grepl("Shift", colnames(hmapfile)[4], fixed = TRUE) == FALSE) {
even_columns <- seq(from = 2, to = (length(hmapfile[1, ]) - 7), by = 1)
print("NO SHIFT VALS FOUND")
}
#FOR THIS SCRIPT ONLY (rap tem hu script)
#even_columns <- c(2,5,7,10,12,15,17)
# FOR THIS SCRIPT ONLY (rap tem hu script)
# even_columns <- c(2,5,7,10,12,15,17)
#m <- 0
colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1,]) - 2)])
#print(colnames_edit)
for(i in 1:length(colnames_edit)){
if(grepl("Shift",colnames_edit[i],fixed=TRUE) == TRUE){
# m <- 0
colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1, ]) - 2)])
# print(colnames_edit)
for (i in 1:length(colnames_edit)) {
if (grepl("Shift", colnames_edit[i], fixed = TRUE) == TRUE) {
colnames_edit[i] <- ""
colnames_edit[i+1] <- gsub(pattern = "_Z_lm_",replacement = " ",x = colnames_edit[i+1])
try(colnames_edit[i+1] <- gsub(pattern = "_",replacement = " ",x = colnames_edit[i+1]))
colnames_edit[i + 1] <- gsub(pattern = "_Z_lm_", replacement = " ", x = colnames_edit[i + 1])
try(colnames_edit[i + 1] <- gsub(pattern = "_", replacement = " ", x = colnames_edit[i + 1]))
# INT_store <- strsplit(colnames_edit[i+1], "Z_lm")
# print(length(unlist(INT_store)))
@@ -149,140 +154,152 @@ for(i in 1:length(colnames_edit)){
# if(length(unlist(INT_store)) == 6){
# colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[6],sep=" ")
# }
}
}
print(colnames_edit)
#break()
#colnames_edit[5] <- "TEM HLEG K"
#colnames_edit[10] <- "TEM HL K"
#colnames_edit[15] <- "TEM HLEG L"
#colnames_edit[20] <- "TEM HL L"
# break()
# colnames_edit[5] <- "TEM HLEG K"
# colnames_edit[10] <- "TEM HL K"
# colnames_edit[15] <- "TEM HLEG L"
# colnames_edit[20] <- "TEM HL L"
# Create the heatmaps
for(i in 1:num_unique_clusts){
for (i in 1:num_unique_clusts) {
cluster <- unique_clusts[i]
cluster_data <- subset(hmapfile,grepl(cluster,cluster.origin))
cluster_length <- length(cluster_data[,1])
if(cluster_length != 1){
X0 <- as.matrix(cluster_data[,4:(length(hmapfile[1,]) - 2)])
if(cluster_length >= 2001){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,height=20,width=15)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor= "white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
#cellnote = round(X0,digits=0), notecex = 0.1, key=TRUE,
keysize=0.7, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
#ColSideColors=ev_repeat,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
#abline(v=0.5467,col="black")
cluster_data <- subset(hmapfile, grepl(cluster, cluster.origin))
cluster_length <- length(cluster_data[, 1])
if (cluster_length != 1) {
X0 <- as.matrix(cluster_data[, 4:(length(hmapfile[1, ]) - 2)])
if (cluster_length >= 2001) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, height = 20, width = 15)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
# zlim=c(-132, 132),
xlab = "Type of Media", ylab = "Gene Name",
# cellnote = round(X0,digits = 0), notecex = 0.1, key = TRUE,
keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
# ColSideColors=ev_repeat,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
# abline(v=0.5467,col="black")
dev.off()
}
if(cluster_length >= 201 && cluster_length <= 2000){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,height=15,width=12)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0,digits=0), notecex = 0.1, key=TRUE,
keysize=0.7, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
#abline(v=0.5316,col="black")
if (cluster_length >= 201 && cluster_length <= 2000) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, height = 15, width = 12)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
# zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0, digits = 0), notecex = 0.1, key = TRUE,
keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
# abline(v=0.5316,col="black")
dev.off()
}
if(cluster_length >= 150 && cluster_length <= 200){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,height=12,width=12)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0,digits=0), notecex = 0.2, key=TRUE,
keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
if (cluster_length >= 150 && cluster_length <= 200) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, height = 12, width = 12)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
# zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0, digits = 0), notecex = 0.2, key = TRUE,
keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
dev.off()
}
if(cluster_length >= 101 && cluster_length <= 149){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,mypath,height=12,width=12)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.2, scale = "none",
breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0,digits=0), notecex = 0.3, key=TRUE,
keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
if (cluster_length >= 101 && cluster_length <= 149) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, mypath, height = 12, width = 12)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.2, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
dev.off()
}
if(cluster_length >= 60 && cluster_length <= 100){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,height=12,width=12)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.4, scale = "none",
breaks=colormapbreaks,symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0,digits=0), notecex = 0.3, key=TRUE,
keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
if (cluster_length >= 60 && cluster_length <= 100) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, height = 12, width = 12)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.4, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
# zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
dev.off()
}
if(cluster_length <= 59 && cluster_length >= 30){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,height=9,width=12)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.6, scale = "none",
breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0,digits=0), notecex = 0.4, key=TRUE,
keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
if (cluster_length <= 59 && cluster_length >= 30) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, height = 9, width = 12)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.6, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
# zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
dev.off()
}
if(cluster_length <= 29){
mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
pdf(file=mypath,height=7,width=12)
heatmap.2(x=X0,
Rowv=TRUE, Colv=NA,
distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.9, scale = "none",
breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
#zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0,digits=0), notecex = 0.4, key=TRUE,
keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
na.color="red", col=brewer.pal(11,"PuOr"),
main=cluster,
labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
if (cluster_length <= 29) {
mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
pdf(file = mypath, height = 7, width = 12)
heatmap.2(
x = X0,
Rowv = TRUE, Colv = NA,
distfun = dist, hclustfun = hclust,
dendrogram = "row", cexCol = 0.8, cexRow = 0.9, scale = "none",
breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
# zlim=c(-132,132),
xlab = "Type of Media", ylab = "Gene Name",
cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
na.color = "red", col = brewer.pal(11, "PuOr"),
main = cluster,
labRow = as.character(cluster_data$Gene), labCol = colnames_edit
)
dev.off()
}
}
#print(paste("FINISHED", "CLUSTER",cluster,sep=" "))
# print(paste("FINISHED", "CLUSTER",cluster,sep=" "))
}

136
workflow/apps/r/interactions.R
View File

@@ -20,25 +20,25 @@ library(htmlwidgets)
# Parse arguments
args <- commandArgs(TRUE)
inputFile <- args[1]
inputFile <- file.path(args[1])
# Set output dir
if (length(args) >= 2) {
outDir <- args[2]
outDir <- file.path(args[2])
} else {
outDir <- "/ZScores/" # for legacy workflow
}
# Set StudyInfo file path
if (length(args) >= 3) {
studyInfo <- args[3]
studyInfo <- file.path(args[3])
} else {
studyInfo <- "../Code/StudyInfo.csv" # for legacy workflow
}
# Set SGDgeneList file path
if (length(args) >= 4) {
SGDgeneList <- args[4]
SGDgeneList <- file.path(args[4])
} else {
SGDgeneList <- "../Code/SGD_features.tab" # for legacy workflow
}
@@ -54,7 +54,7 @@ if (length(args) >= 5) {
}
delBGFactor <- as.numeric(delBGFactor)
if (is.na(delBGFactor)) {
delBGFactor <- 3 # Recommended by Sean
delBGFactor <- 3 # recommended by Sean
}
print(paste("The Standard Deviation Value is:", delBGFactor))
@@ -68,21 +68,25 @@ if (length(args) >= 6) {
}
expNumber <- as.numeric(expNumber)
outDir_QC <- paste(outDir, "QC/", sep = "")
outDir_QC <- file.path(outDir, "QC")
if (!file.exists(outDir)) {
dir.create(file.path(outDir))
dir.create(outDir)
}
if (!file.exists(outDir_QC)) {
dir.create(file.path(outDir_QC))
}
# Capture Exp_ number, use it to Save args[2]{std}to Labels field and then
# write to Labels to studyInfo.txt for future reference
Labels <- read.csv(file = studyInfo, stringsAsFactors = FALSE) # sep = ","
Labels[expNumber, 3] <- delBGFactor
write.csv(Labels, file = studyInfo, row.names = FALSE)
options(width = 1000)
ls.str()
# Write delBJFactor to the StudyInfo file
# TODO we probably shouldn't be doing this, need one source of truth
# TODO disabling this for now
# Labels <- read.csv(file = studyInfo, stringsAsFactors = FALSE) # sep = ","
# Labels[expNumber, 3] <- delBGFactor
# write.csv(Labels, file = studyInfo, row.names = FALSE)
# Begin User Data Selection Section
@@ -366,19 +370,19 @@ Raw_l_vs_K_beforeQC <-
ggtitle("Raw L vs K before QC") +
theme_publication_legend_right()
pdf(paste(outDir_QC, "Raw_L_vs_K_beforeQC.pdf", sep = ""), width = 12, height = 8)
pdf(file.path(outDir_QC, "Raw_L_vs_K_beforeQC.pdf"), width = 12, height = 8)
Raw_l_vs_K_beforeQC
dev.off()
pgg <- ggplotly(Raw_l_vs_K_beforeQC)
plotly_path <- paste(outDir_QC, "Raw_L_vs_K_beforeQC.html", sep = "")
plotly_path <- file.path(outDir_QC, "Raw_L_vs_K_beforeQC.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
# Set delta background tolerance based on 3 sds from the mean delta background
Delta_Background_Tolerance <- mean(X$Delta_Backgrd) + (delBGFactor * sd(X$Delta_Backgrd))
# Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
print(paste("Delta_Background_Tolerance is", Delta_Background_Tolerance, sep = " "))
sprintf("Delta_Background_Tolerance is %f", Delta_Background_Tolerance)
Plate_Analysis_Delta_Backgrd <-
ggplot(X, aes(Scan, Delta_Backgrd, color = as.factor(Conc_Num))) +
@@ -417,13 +421,13 @@ X_Delta_Backgrd_above_Tolerance_L_vs_K <-
) +
theme_publication_legend_right()
pdf(paste(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf", sep = ""), width = 12, height = 8)
pdf(file.path(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf"), width = 12, height = 8)
X_Delta_Backgrd_above_Tolerance_L_vs_K
dev.off()
pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
plotly_path <- paste(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html", sep = "")
plotly_path <- file.path(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
# Frequency plot for all data vs. the delta_background
@@ -434,7 +438,7 @@ DeltaBackground_Frequency_Plot <- ggplot(X, aes(Delta_Backgrd, color = as.factor
DeltaBackground_Bar_Plot <- ggplot(X, aes(Delta_Backgrd, color = as.factor(Conc_Num))) + geom_bar() +
ggtitle("Bar plot for Delta Background by Conc All Data") + theme_publication_legend_right()
pdf(file = paste(outDir_QC, "Frequency_Delta_Background.pdf", sep = ""), width = 12, height = 8)
pdf(file.path(outDir_QC, "Frequency_Delta_Background.pdf"), width = 12, height = 8)
print(DeltaBackground_Frequency_Plot)
print(DeltaBackground_Bar_Plot)
dev.off()
@@ -568,7 +572,7 @@ Plate_Analysis_Delta_Backgrd_Box_afterQC <-
theme_publication()
# Print the plate analysis data before and after QC
pdf(file = paste(outDir_QC, "Plate_Analysis.pdf", sep = ""), width = 14, height = 9)
pdf(file.path(outDir_QC, "Plate_Analysis.pdf"), width = 14, height = 9)
Plate_Analysis_L
Plate_Analysis_L_afterQC
Plate_Analysis_K
@@ -582,7 +586,7 @@ Plate_Analysis_Delta_Backgrd_afterQC
dev.off()
# Print the plate analysis data before and after QC
pdf(file = paste(outDir_QC, "Plate_Analysis_Boxplots.pdf", sep = ""), width = 18, height = 9)
pdf(file.path(outDir_QC, "Plate_Analysis_Boxplots.pdf"), width = 18, height = 9)
Plate_Analysis_L_Box
Plate_Analysis_L_Box_afterQC
Plate_Analysis_K_Box
@@ -713,7 +717,7 @@ Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <-
theme_publication()
# Print the plate analysis data before and after QC
pdf(file = paste(outDir_QC, "Plate_Analysis_noZeros.pdf", sep = ""), width = 14, height = 9)
pdf(file.path(outDir_QC, "Plate_Analysis_noZeros.pdf"), width = 14, height = 9)
Plate_Analysis_L_afterQC_Z
Plate_Analysis_K_afterQC_Z
Plate_Analysis_r_afterQC_Z
@@ -722,7 +726,7 @@ Plate_Analysis_Delta_Backgrd_afterQC_Z
dev.off()
# Print the plate analysis data before and after QC
pdf(file = paste(outDir_QC, "Plate_Analysis_noZeros_Boxplots.pdf", sep = ""), width = 18, height = 9)
pdf(file.path(outDir_QC, "Plate_Analysis_noZeros_Boxplots.pdf"), width = 18, height = 9)
Plate_Analysis_L_Box_afterQC_Z
Plate_Analysis_K_Box_afterQC_Z
Plate_Analysis_r_Box_afterQC_Z
@@ -768,7 +772,7 @@ X_stats_ALL <- ddply(
)
# print(X_stats_ALL_L)
write.csv(X_stats_ALL, file = paste(outDir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)
write.csv(X_stats_ALL, file.path(outDir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)
# Part 3 - Generate summary statistics and calculate the max theoretical L value
# Calculate the Z score at each drug conc for each deletion strain
@@ -885,7 +889,7 @@ for (s in Background_Strains) {
se_AUC = sd_AUC / sqrt(N - 1)
)
write.csv(X_stats_BY, file = paste(outDir, "SummaryStats_BackgroundStrains.csv"), row.names = FALSE)
write.csv(X_stats_BY, file.path(outDir, "SummaryStats_BackgroundStrains.csv"), row.names = FALSE)
# Calculate the max theoretical L values
# Only look for max values when K is within 2SD of the ref strain
@@ -964,7 +968,7 @@ for (s in Background_Strains) {
write.csv(
X_stats_BY_L_within_2SD_K,
file = paste(outDir_QC, "Max_Observed_L_Vals_for_spots_within_2SD_K.csv", sep = ""),
file.path(outDir_QC, "Max_Observed_L_Vals_for_spots_within_2SD_K.csv"),
row.names = FALSE
)
@@ -991,13 +995,13 @@ for (s in Background_Strains) {
ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") +
theme_publication_legend_right()
pdf(paste(outDir_QC, "Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf", sep = ""), width = 10, height = 8)
pdf(file.path(outDir_QC, "Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf"), width = 10, height = 8)
print(Outside_2SD_K_L_vs_K)
dev.off()
pgg <- ggplotly(Outside_2SD_K_L_vs_K)
plotly_path <- paste(outDir_QC, "RawL_vs_K_for_strains_outside_2SD_K.html", sep = "")
plotly_path <- file.path(outDir_QC, "RawL_vs_K_for_strains_outside_2SD_K.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
Outside_2SD_K_delta_background_vs_K <-
@@ -1006,13 +1010,13 @@ for (s in Background_Strains) {
ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") +
theme_publication_legend_right()
pdf(paste(outDir_QC, "DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf", sep = ""), width = 10, height = 8)
pdf(file.path(outDir_QC, "DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf"), width = 10, height = 8)
Outside_2SD_K_delta_background_vs_K
dev.off()
pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
# pgg
plotly_path <- paste(outDir_QC, "DeltaBackground_vs_K_for_strains_outside_2SD_K.html", sep = "")
plotly_path <- file.path(outDir_QC, "DeltaBackground_vs_K_for_strains_outside_2SD_K.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
# Get the background strain mean values at the no drug conc to calculate shift
@@ -1046,7 +1050,7 @@ for (s in Background_Strains) {
X2_temp <- X2[X2$Conc_Num == Concentration, ]
if (Concentration == 0) {
X2_new <- X2_temp
print(paste("Check loop order, conc =", Concentration, sep = " "))
sprintf("Check loop order, conc = %f", Concentration)
}
if (Concentration > 0) {
try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
@@ -1055,7 +1059,7 @@ for (s in Background_Strains) {
# X2_temp[X2_temp$K == 0, ]$K <- X_stats_ALL_K$max[i]
# X2_temp[X2_temp$r == 0, ]$r <- X_stats_ALL_r$max[i]
# X2_temp[X2_temp$AUC == 0, ]$AUC <- X_stats_ALL_AUC$max[i]
print(paste("Check loop order, conc =", Concentration, sep = " "))
sprintf("Check loop order, conc = %f", Concentration)
X2_new <- rbind(X2_new, X2_temp)
}
}
@@ -1075,13 +1079,13 @@ for (s in Background_Strains) {
X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration, ]
if (Concentration == 0) {
X2_RF_new <- X2_RF_temp
print(paste("Check loop order, conc =", Concentration, sep = " "))
sprintf("Check loop order, conc = %f", Concentration)
}
if (Concentration > 0) {
try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l), ]$SM <- 1)
try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
print(paste("If error, refs have no L values outside theoretical max L, for REFs, conc =", Concentration, sep = " "))
sprintf("If error, refs have no L values outside theoretical max L, for REFs, conc = %f", Concentration)
X2_RF_new <- rbind(X2_RF_new, X2_RF_temp)
}
}
@@ -1446,7 +1450,7 @@ for (s in Background_Strains) {
InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC) / (lm_sd_AUC)
InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L, decreasing = TRUE), ]
InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG, decreasing = TRUE), ]
write.csv(InteractionScores_RF, paste(outDir, "RF_ZScores_Interaction.csv", sep = ""), row.names = FALSE)
write.csv(InteractionScores_RF, file.path(outDir, "RF_ZScores_Interaction.csv"), row.names = FALSE)
for (i in 1:num_genes_RF) {
Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
@@ -1548,7 +1552,7 @@ for (s in Background_Strains) {
}
}
print("Pass RF ggplot loop")
write.csv(X_stats_interaction_ALL_RF_final, paste(outDir, "RF_ZScore_Calculations.csv", sep = ""), row.names = FALSE)
write.csv(X_stats_interaction_ALL_RF_final, file.path(outDir, "RF_ZScore_Calculations.csv"), row.names = FALSE)
# Part 5 - Get Zscores for Gene deletion strains
@@ -1904,7 +1908,7 @@ for (s in Background_Strains) {
InteractionScores <- InteractionScores[order(InteractionScores$NG, decreasing = TRUE), ]
df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
# X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG, decreasing = TRUE), ]
write.csv(InteractionScores, paste(outDir, "ZScores_Interaction.csv", sep = ""), row.names = FALSE)
write.csv(InteractionScores, file.path(outDir, "ZScores_Interaction.csv"), row.names = FALSE)
InteractionScores_deletion_enhancers_L <-
InteractionScores[InteractionScores$Avg_Zscore_L >= 2, ]
@@ -1957,25 +1961,25 @@ for (s in Background_Strains) {
InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[
!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep), ]
write.csv(InteractionScores_deletion_enhancers_L,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_L.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_L.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_K,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_K.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_K.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_suppressors_L,
paste(outDir, "ZScores_Interaction_DeletionSuppressors_L.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionSuppressors_L.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_suppressors_K,
paste(outDir, "ZScores_Interaction_DeletionSuppressors_K.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionSuppressors_K.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,
paste(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,
paste(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,
paste(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,
paste(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv"), row.names = FALSE)
# Get enhancers and suppressors for linear regression
InteractionScores_deletion_enhancers_L_lm <-
@@ -2010,19 +2014,19 @@ for (s in Background_Strains) {
!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep), ]
write.csv(InteractionScores_deletion_enhancers_L_lm,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_L_lm.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_L_lm.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_K_lm,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_K_lm.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_K_lm.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_suppressors_L_lm,
paste(outDir, "ZScores_Interaction_DeletionSuppressors_L_lm.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionSuppressors_L_lm.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_suppressors_K_lm,
paste(outDir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv", sep = ""), row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv"), row.names = FALSE)
write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,
paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv", sep = ""), row.names = FALSE)
write.csv(Labels, file = paste(studyInfo), row.names = FALSE)
# write.table(Labels, file = paste("../Code/StudyInfo.txt"), sep = "\t", row.names = FALSE)
file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv"), row.names = FALSE)
# write.csv(Labels, studyInfo, row.names = FALSE)
# write.table(Labels, file.path("../Code/StudyInfo.txt"), sep = "\t", row.names = FALSE)
for (i in 1:num_genes) {
Gene_Sel <- unique(InteractionScores$OrfRep)[i]
@@ -2113,11 +2117,11 @@ for (s in Background_Strains) {
}
}
print("Pass Int ggplot loop")
write.csv(X_stats_interaction_ALL_final, paste(outDir, "ZScore_Calculations.csv", sep = ""), row.names = FALSE)
write.csv(X_stats_interaction_ALL_final, file.path(outDir, "ZScore_Calculations.csv"), row.names = FALSE)
Blank <- ggplot(X2_RF) + geom_blank()
pdf(paste(outDir, "InteractionPlots.pdf", sep = ""), width = 16, height = 16, onefile = TRUE)
pdf(file.path(outDir, "InteractionPlots.pdf"), width = 16, height = 16, onefile = TRUE)
X_stats_X2_RF <- ddply(
X2_RF,
@@ -2344,7 +2348,7 @@ for (s in Background_Strains) {
dev.off()
pdf(paste(outDir, "RF_InteractionPlots.pdf", sep = ""), width = 16, height = 16, onefile = TRUE)
pdf(file.path(outDir, "RF_InteractionPlots.pdf"), width = 16, height = 16, onefile = TRUE)
X_stats_X2_RF <- ddply(
X2_RF,
@@ -2732,7 +2736,7 @@ for (s in Background_Strains) {
geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
theme_publication()
pdf(paste(outDir, "RankPlots.pdf", sep = ""), width = 18, height = 12, onefile = TRUE)
pdf(file.path(outDir, "RankPlots.pdf"), width = 18, height = 12, onefile = TRUE)
grid.arrange(
Rank_L_1SD,
@@ -2866,7 +2870,7 @@ for (s in Background_Strains) {
geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
theme_publication()
pdf(paste(outDir, "RankPlots_lm.pdf", sep = ""), width = 18, height = 12, onefile = TRUE)
pdf(file.path(outDir, "RankPlots_lm.pdf"), width = 18, height = 12, onefile = TRUE)
grid.arrange(
Rank_L_1SD_lm,
@@ -2916,7 +2920,7 @@ for (s in Background_Strains) {
get_lm_AUC <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Avg_Zscore_AUC)
AUC_lm <- summary(get_lm_AUC)
pdf(paste(outDir, "Avg_Zscore_vs_lm_NA_rm.pdf", sep = ""), width = 16, height = 12, onefile = TRUE)
pdf(file.path(outDir, "Avg_Zscore_vs_lm_NA_rm.pdf"), width = 16, height = 12, onefile = TRUE)
print(ggplot(X_NArm, aes(Avg_Zscore_L, Z_lm_L)) +
geom_point(aes(color = Overlap), shape = 3) +
@@ -2955,7 +2959,7 @@ for (s in Background_Strains) {
annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm$r.squared, 2))) + theme_publication_legend_right()
pgg <- ggplotly(lm_v_Zscore_L)
plotly_path <- paste(outDir, "Avg_Zscore_vs_lm_NA_rm.html", sep = "")
plotly_path <- file.path(outDir, "Avg_Zscore_vs_lm_NA_rm.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
@@ -2978,7 +2982,7 @@ for (s in Background_Strains) {
AUC_lm2 <- summary(get_lm_AUC2)
num_genes_NArm2 <- (dim(X_NArm)[1]) / 2
pdf(paste(outDir, "Avg_Zscore_vs_lm_ranked_NA_rm.pdf", sep = ""), width = 16, height = 12, onefile = TRUE)
pdf(file.path(outDir, "Avg_Zscore_vs_lm_ranked_NA_rm.pdf"), width = 16, height = 12, onefile = TRUE)
print(
ggplot(X_NArm, aes(L_Rank, L_Rank_lm)) +
@@ -3114,7 +3118,7 @@ for (s in Background_Strains) {
geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
theme_publication()
pdf(paste(outDir, "RankPlots_naRM.pdf", sep = ""), width = 18, height = 12, onefile = TRUE)
pdf(file.path(outDir, "RankPlots_naRM.pdf"), width = 18, height = 12, onefile = TRUE)
grid.arrange(
Rank_L_1SD,
@@ -3234,7 +3238,7 @@ for (s in Background_Strains) {
geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
theme_publication()
pdf(paste(outDir, "RankPlots_lm_naRM.pdf", sep = ""), width = 18, height = 12, onefile = TRUE)
pdf(file.path(outDir, "RankPlots_lm_naRM.pdf"), width = 18, height = 12, onefile = TRUE)
grid.arrange(
Rank_L_1SD_lm,
@@ -3273,7 +3277,7 @@ L_lm_5 <- summary(get_lm_5)
get_lm_6 <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Z_lm_r)
L_lm_6 <- summary(get_lm_6)
pdf(file = paste(outDir, "Correlation_CPPs.pdf", sep = ""), width = 10, height = 7, onefile = TRUE)
pdf(file.path(outDir, "Correlation_CPPs.pdf"), width = 10, height = 7, onefile = TRUE)
ggplot(X_NArm, aes(Z_lm_L, Z_lm_K)) +
geom_point(shape = 3, color = "gray70") +
@@ -3482,5 +3486,5 @@ ggplot(X_NArm, aes(Z_lm_r, Z_lm_AUC)) +
dev.off()
# write.csv(Labels, file = paste("../Code/Parameters.csv"), row.names = FALSE)
# write.csv(Labels, file.path("../Code/Parameters.csv"), row.names = FALSE)
timestamp()

43
workflow/qhtcp-workflow
View File

@@ -14,7 +14,7 @@
# Insert a general description of Q-HTCP and the Q-HTCP process here.
shopt -s extglob # Turn on extended globbing
DEBUG=1 # Turn debugging ON by default during development
DEBUG=${DEBUG:-1} # Turn debugging ON by default during development
# @description Use `--help` to print the help message.
# @internal
@@ -25,13 +25,13 @@ print_help() {
cat <<-EOF
USAGE:
script-run-workflow [[OPTION] [VALUE]]...
qhtcp-workflow [[OPTION] [VALUE]]...
Some options (--project, --include, --exclude) can be passed multiple times or
by using comma-separated strings (see EXAMPLES below)
OPTIONS:
--project, -p PROJECT
--project, -p PROJECT[,PROJECT...]
PROJECT should follow the pattern ${PROJECT_PREFIX}_PROJECT_NAME
--module, -m MODULE[,MODULE...]
See MODULES section below for list of available modules
@@ -65,13 +65,13 @@ print_help() {
BiocManager: ${depends_bioc[@]}
EXAMPLES:
script-run-workflow --module ${ALL_MODULES[0]} --module ${ALL_MODULES[1]}
script-run-workflow --project ${PROJECT_PREFIX}_MY_PROJECT --module ${ALL_MODULES[0]} --module ${ALL_MODULES[1]}
script-run-workflow --project ${PROJECT_PREFIX}_MY_PROJECT --module ${ALL_MODULES[1]} --module ${ALL_MODULES[2]} --yes
script-run-workflow --module=${ALL_MODULES[0]},${ALL_MODULES[1]}
script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT,${PROJECT_PREFIX}_MY_OTHER_PROJECT
script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT,${PROJECT_PREFIX}_MY_OTHER_PROJECT --module=${ALL_MODULES[1]},${ALL_MODULES[2]} --yes --debug
script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT --wrapper ${ALL_WRAPPERS[2]} \"/path/to/genefile.txt,/path/to/output/dir\" --wrapper ${ALL_WRAPPERS[3]} \"/path/to/sgofile\"
qhtcp-workflow --module ${ALL_MODULES[0]} --module ${ALL_MODULES[1]}
qhtcp-workflow --project ${PROJECT_PREFIX}_MY_PROJECT --module ${ALL_MODULES[0]} --module ${ALL_MODULES[1]}
qhtcp-workflow --project ${PROJECT_PREFIX}_MY_PROJECT --module ${ALL_MODULES[1]} --module ${ALL_MODULES[2]} --yes
qhtcp-workflow --module=${ALL_MODULES[0]},${ALL_MODULES[1]}
qhtcp-workflow --project=${PROJECT_PREFIX}_MY_PROJECT,${PROJECT_PREFIX}_MY_OTHER_PROJECT
qhtcp-workflow --project=${PROJECT_PREFIX}_MY_PROJECT,${PROJECT_PREFIX}_MY_OTHER_PROJECT --module=${ALL_MODULES[1]},${ALL_MODULES[2]} --yes --debug
qhtcp-workflow --project=${PROJECT_PREFIX}_MY_PROJECT --wrapper ${ALL_WRAPPERS[2]} \"/path/to/genefile.txt,/path/to/output/dir\" --wrapper ${ALL_WRAPPERS[3]} \"/path/to/sgofile\"
EOF
}
@@ -506,6 +506,7 @@ interactive_header() {
printf "%d. %s\n" $((i+1)) "${ALL_WRAPPERS[i]}"
done
fi
echo ""
# Let user choose or add project(s)
if [[ ${#PROJECTS[@]} -eq 0 ]]; then
@@ -630,7 +631,7 @@ module install_dependencies
#
# #### Perl
#
# * `cpan File::Map ExtUtils::PkgConfig GD GO::TermFinder`
# * `cpan -I -i File::Map ExtUtils::PkgConfig GD GO::TermFinder`
#
# #### R
#
@@ -643,10 +644,14 @@ install_dependencies() {
debug "Running: ${FUNCNAME[0]} $*"
# Dependency arrays
depends_rpm=(graphviz pandoc pdftk-java gd-devel perl-CPAN shdoc nano rsync coreutils libcurl-devel openssl-devel harfbuzz-devel fribidi-devel R-core R-core-devel)
depends_deb=(graphviz pandoc pdftk-java libgd-dev perl shdoc nano rsync coreutils libcurl-dev openssl-dev libharfbuzz-dev libfribidi-dev r-base r-base-dev)
depends_rpm=(graphviz pandoc pdftk-java gd-devel perl-CPAN shdoc nano
rsync coreutils libcurl-devel openssl-devel harfbuzz-devel fribidi-devel
R-core R-core-devel)
depends_deb=(graphviz pandoc pdftk-java libgd-dev perl shdoc nano rsync
coreutils libcurl-dev openssl-dev libharfbuzz-dev libfribidi-dev r-base r-base-dev)
depends_brew=(graphiz pandoc gd pdftk-java shdoc nano perl rsync coreutils harfbuzz fribidi r)
depends_perl=(Test::Warnings Test::Fatal File::Map Sub::Uplevel ExtUtils::Config ExtUtils::PkgConfig IPC::Run Module::Build::Tiny IPC::Run GD GO::TermFinder)
depends_perl=(Test::Warnings Test::Fatal File::Map Sub::Uplevel ExtUtils::Config
ExtUtils::PkgConfig IPC::Run Module::Build::Tiny GD GO::TermFinder)
depends_r=(BiocManager ontologyIndex ggrepel tidyverse sos openxlsx ggplot2
plyr extrafont gridExtra gplots stringr plotly ggthemes pandoc rmarkdown
plotly htmlwidgets gplots gdata)
@@ -1791,6 +1796,8 @@ wrapper r_interactions
# * Replace 1:length() with seq_along()
# * Re-enable disabled linter checks
# * Reduce cyclomatic complexity of some of the for loops
# * There needs to be one point of truth for the SD factor
# * Replace most paste() functions with printf()
#
# NOTES
#
@@ -1930,6 +1937,12 @@ r_add_shift_values() {
wrapper r_create_heat_maps
# @description Execute createHeatMaps.R
#
# TODO
# * Needs more looping for brevity
#
#
#
# @arg $1 string The final shift table (REMcWithShift.csv)
# @arg $2 string The output directory
r_create_heat_maps() {
@@ -2151,7 +2164,7 @@ generate_markdown() {
}
# @description The main loop of script-run-workflow
# @description The main loop of qhtcp-workflow
# May eventually need to add git ops
# Passes on arguments
# Most variables in main() are user configurable or can be overriden by env