## ---- echo=TRUE , eval=FALSE , results="hide" , message=FALSE , warning=FALSE---- # #Given a LowMACA object 'lm' # lm <- newLowMACA(genes=c("TP53" , "TP63" , "TP73")) # lmParams(lm)$clustal_cmd <- "/your/path/to/clustalo" ## ---- echo=TRUE , eval=TRUE,results="hide" , message=FALSE , warning=FALSE---- library(LowMACA) #User Input Genes <- c("ADNP","ALX1","ALX4","ARGFX","CDX4","CRX" ,"CUX1","CUX2","DBX2","DLX5","DMBX1","DRGX" ,"DUXA","ESX1","EVX2","HDX","HLX","HNF1A" ,"HOXA1","HOXA2","HOXA3","HOXA5","HOXB1","HOXB3" ,"HOXD3","ISL1","ISX","LHX8") Pfam <- "PF00046" ## ---- echo=TRUE------------------------------------------------------------ #Construct the object lm <- newLowMACA(genes=Genes, pfam=Pfam) str(lm , max.level=3) ## ---- echo=TRUE------------------------------------------------------------ #See default parameters lmParams(lm) #Change some parameters #Accept sequences even with no mutations lmParams(lm)$min_mutation_number <- 0 #Changing selected tumor types #Check the available tumor types in cBioPortal available_tumor_types <- showTumorType() head(available_tumor_types) #Select melanoma, stomach adenocarcinoma, uterine cancer, lung adenocarcinoma, #lung squamous cell carcinoma, colon rectum adenocarcinoma and breast cancer lmParams(lm)$tumor_type <- c("skcm" , "stad" , "ucec" , "luad" , "lusc" , "coadread" , "brca") ## ---- fourthchunk, echo=TRUE , eval=TRUE----------------------------------- lm <- alignSequences(lm) ## ---- fourthchunkBis, echo=TRUE , eval=TRUE , message=FALSE , warning=FALSE---- lm <- alignSequences(lm , mail="lowmaca@gmail.com") ## ---- fifthchunck, echo=TRUE, eval=TRUE------------------------------------ #Access to the slot alignment myAlignment <- lmAlignment(lm) str(myAlignment , max.level=2 , vec.len=2) ## ---- sixthchunk, echo=TRUE , eval=TRUE------------------------------------ lm <- getMutations(lm) lm <- mapMutations(lm) ## ---- seventhchunk2, echo=TRUE,eval=TRUE----------------------------------- #Access to the slot mutations myMutations <- lmMutations(lm) str(myMutations , vec.len=3 , max.level=1) ## ---- seventhchunk, echo=TRUE,eval=TRUE------------------------------------ myMutationFreqs <- myMutations$freq #Showing the first genes myMutationFreqs[ , 1:10] ## ---- eighthchunk, echo=TRUE , eval=FALSE , message=FALSE , warning=FALSE---- # #Local Installation of clustalo # lm <- setup(lm) # #Web Service # lm <- setup(lm , mail="lowmaca@gmail.com") ## ---- ninthchunk_pre , echo=TRUE , eval=TRUE------------------------------- #Reuse the downloaded data as a toy example myOwnData <- myMutations$data #How myOwnData should look like for the argument repos str(myMutations$data , vec.len=1) #Read the mutation data repository instead of using cgdsr package #Following the process step by step lm <- getMutations(lm , repos=myOwnData) #Setup in one shot lm <- setup(lm , repos=myOwnData) ## ---- tenthchunk, echo=TRUE , eval=TRUE------------------------------------ lm <- entropy(lm) #Global Score myEntropy <- lmEntropy(lm) str(myEntropy) #Per position score head(myAlignment$df) ## ---- eleventhchunk, echo=TRUE--------------------------------------------- significant_muts <- lfm(lm) #Display original mutations that formed significant clusters (column Multiple_Aln_pos) head(significant_muts) #What are the genes mutated in position 4 in the consensus? genes_mutated_in_pos4 <- significant_muts[ significant_muts$Multiple_Aln_pos==4 , 'Gene_Symbol'] ## ---- eleventh_2chunck , echo=TRUE----------------------------------------- sort(table(genes_mutated_in_pos4)) ## ---- echo=TRUE, eval=TRUE, results="hide"--------------------------------- bpAll(lm) ## ---- echo=TRUE, eval=TRUE, results="hide"-------------------------------- lmPlot(lm) ## ---- protterChunk, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE----- #This plot is saved as a png image on a temporary file tmp <- tempfile(pattern = "homeobox_protter" , fileext = ".png") protter(lm , filename=tmp) ## ---- out.width = "400px" , echo=FALSE , eval=TRUE------------------------- knitr::include_graphics(tmp) ## ---- allPfamAnalysis, eval=TRUE------------------------------------------- #Load Homeobox example data(lmObj) #Extract the data inside the object as a toy example myData <- lmMutations(lmObj)$data #Run allPfamAnalysis on every mutations significant_muts <- allPfamAnalysis(repos=myData) #Show the result of alignment based analysis head(significant_muts$AlignedSequence) #Show all the genes that harbor significant mutations unique(significant_muts$AlignedSequence$Gene_Symbol) #Show the result of the Single Gene based analysis head(significant_muts$SingleSequence) #Show all the genes that harbor significant mutations unique(significant_muts$SingleSequence$Gene_Symbol) ## ---- summary, eval=FALSE , echo=TRUE-------------------------------------- # library(LowMACA) # Genes <- c("ADNP","ALX1","ALX4","ARGFX","CDX4","CRX" # ,"CUX1","CUX2","DBX2","DLX5","DMBX1","DRGX" # ,"DUXA","ESX1","EVX2","HDX","HLX","HNF1A" # ,"HOXA1","HOXA2","HOXA3","HOXA5","HOXB1","HOXB3" # ,"HOXD3","ISL1","ISX","LHX8") # Pfam <- "PF00046" # lm <- newLowMACA(genes=Genes , pfam=Pfam) # lmParams(lm)$tumor_type <- c("skcm" , "stad" , "ucec" , "luad" # , "lusc" , "coadread" , "brca") # lmParams(lm)$min_mutation_number <- 0 # lm <- setup(lm , mail="lowmaca@gmail.com") # lm <- entropy(lm) # lfm(lm) # bpAll(lm) # lmPlot(lm) # protter(lm) ## ---- info,echo=TRUE------------------------------------------------------- sessionInfo()