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* UI cleanup. * Removed unused security * removed assignment * Made Jokes a global variable. Tidied up data inspection * Moved preprocessing away. Made some if/else statments clearer. TODO: "addWarning" seems quite unintuitive right now * Apply suggestions from code review Co-authored-by: Lea Seep <[email protected]> * Moved getCurrentVersion to util --------- Co-authored-by: Lea Seep <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| # preprocessing procedures | ||
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| prefiltering <- function(data, omic_type){ | ||
| # Filter out low abundant entities for Metabol- and Transcriptomics. | ||
| if(omic_type == "Transcriptomics"){ | ||
| print("Remove anything of rowCount <=10") | ||
| return(data[which(rowSums(assay(data)) > 10),]) | ||
| } | ||
| if(omic_type == "Metabolomics"){ | ||
| print("Remove anything which has a row median of 0") | ||
| return(data[which(apply(assay(data),1,median)!=0),]) | ||
| } | ||
| } | ||
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| simple_center_scaling <- function(data, omic_type){ | ||
| # Center and scale the data | ||
| print("Do chosen Preprocessing: simpleCenterScaling") | ||
| # prefilter the data | ||
| data <- prefiltering(data, omic_type) | ||
| # center and scale the data | ||
| processedData <- as.data.frame(t(scale( | ||
| x = as.data.frame(t(as.data.frame(assay(data)))), | ||
| scale = T, | ||
| center = T | ||
| ))) | ||
| assay(data) <- processedData | ||
| return(data) | ||
| } | ||
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| scaling_normalisation <- function(data, omic_type, scaling_procedure){ | ||
| # Center and scale the data | ||
| print(paste0("Do chosen Preprocessing: ", scaling_procedure)) | ||
| # prefilter the data | ||
| data <- prefiltering(data, omic_type) | ||
| # scaling functions | ||
| fun_scale <- function(x){ | ||
| return((x-min(x))/(max(x)-min(x))) | ||
| } | ||
| fun_pareto <- function(x){ | ||
| return((x-mean(x))/sqrt(var(x))) | ||
| } | ||
| scaling_function <- ifelse(scaling_procedure == "Scaling_0_1", fun_scale, fun_pareto) | ||
| processedData <- as.data.frame(t(apply( | ||
| X = assay(data), | ||
| MARGIN = 1, | ||
| FUN = scaling_function | ||
| ))) | ||
| assay(data) <- processedData | ||
| return(data) | ||
| } | ||
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| ln_normalisation <- function(data, omic_type, logarithm_procedure){ | ||
| # Center and scale the data | ||
| print(paste0("Do chosen Preprocessing: ", logarithm_procedure)) | ||
| logarithm <- ifelse(logarithm_procedure == "log10", log10, log) | ||
| # prefilter the data | ||
| data <- prefiltering(data, omic_type) | ||
| # log the data and always add 1 to avoid -Inf | ||
| processedData <- as.data.frame(logarithm(as.data.frame(assay(data)) + 1)) | ||
| assay(data) <- processedData | ||
| return(data) | ||
| } | ||
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| deseq_processing <- function( | ||
| data, omic_type, formula_main, formula_sub, session_token, advanced_formula = NULL | ||
| ){ | ||
| # Center and scale the data | ||
| print("Do chosen Preprocessing: vst_DESeq") | ||
| # prefilter the data | ||
| data <- prefiltering(data, omic_type) | ||
| # DESeq2 | ||
| par_tmp[[session_token]]["DESeq_advanced"] <<- FALSE | ||
| if(omic_type == "Transcriptomics"){ | ||
| design_formula <- paste("~", formula_main) | ||
| # only do this locally | ||
| colData(data)[,formula_main] <- as.factor( | ||
| colData(data)[,formula_main] | ||
| ) | ||
| if(length(formula_sub) > 0){ | ||
| design_formula <- paste( | ||
| design_formula, " + ", | ||
| paste(formula_sub, collapse = " + ") | ||
| ) | ||
| # turn each factor into a factor | ||
| for(i in formula_sub){ | ||
| colData(data)[,i] <- as.factor( | ||
| colData(data)[,i] | ||
| ) | ||
| } | ||
| par_tmp[[session_token]][["DESeq_factors"]] <<- c( | ||
| formula_main,formula_sub | ||
| ) | ||
| } | ||
| else{ | ||
| par_tmp[[session_token]][["DESeq_factors"]] <<- c(formula_main) | ||
| } | ||
| # if advanced formula is used, overwrite the other formula | ||
| if(!(advanced_formula == "") & startsWith(advanced_formula, "~")){ | ||
| print("Advanced formula used") | ||
| design_formula <- advanced_formula | ||
| par_tmp[[session_token]]["DESeq_advanced"] <<- TRUE | ||
| } | ||
| print(design_formula) | ||
| par_tmp[[session_token]]["DESeq_formula"] <<- design_formula | ||
| # on purpose local | ||
| print(colData(data)[,formula_main]) | ||
|
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| dds <- DESeq2::DESeqDataSetFromMatrix( | ||
| countData = assay(data), | ||
| colData = colData(data), | ||
| design = as.formula(design_formula) | ||
| ) | ||
|
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| de_seq_result <- DESeq2::DESeq(dds) | ||
| res_tmp[[session_token]]$DESeq_obj <<- de_seq_result | ||
| dds_vst <- vst( | ||
| object = de_seq_result, | ||
| blind = TRUE | ||
| ) | ||
| assay(data) <- as.data.frame(assay(dds_vst)) | ||
| return(data) | ||
| } | ||
| addWarning <- "<font color=\"#FF0000\"><b>DESeq makes only sense for transcriptomics data - data treated as if 'filterOnly' was selected!</b></font>" | ||
| return(data) | ||
| } |
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