Mercurial > repos > galaxyp > cardinal_classification
diff classification.xml @ 3:585ef27873c9 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 2c4a1a862900b4efbc30824cbcb798f835b168b2
| author | galaxyp |
|---|---|
| date | Thu, 28 Feb 2019 09:24:53 -0500 |
| parents | cbc7e53518ce |
| children | 47fc5b518ffc |
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--- a/classification.xml Fri Feb 15 10:18:51 2019 -0500 +++ b/classification.xml Thu Feb 28 09:24:53 2019 -0500 @@ -33,6 +33,10 @@ ## to make sure that processed files work as well: iData(msidata) = iData(msidata)[] +## remove duplicated coordinates +print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed")) +msidata <- msidata[,!duplicated(coord(msidata))] + @DATA_PROPERTIES_INRAM@ @@ -231,10 +235,13 @@ ### m/z and pixel information output pls_classes = data.frame(msidata.pls\$classes[[1]]) - pixel_names = gsub(", y = ", "_", names(pixels(msidata))) - pixel_names = gsub(" = ", "y_", pixel_names) - x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2] - y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3] + ## pixel names and coordinates + ## to remove potential sample names and z dimension, split at comma and take only x and y + x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1)) + y_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 2)) + x_coordinates = gsub("x = ","",x_coords) + y_coordinates = gsub(" y = ","",y_coords) + pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates) ## remove msidata to clean up RAM space rm(msidata) @@ -373,12 +380,15 @@ maximumy = max(coord(msidata)[,2]) print(image(msidata, mz = topLabels(msidata.opls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap")) - ## m/z and pixel information output opls_classes = data.frame(msidata.opls\$classes[[1]]) - pixel_names = gsub(", y = ", "_", names(pixels(msidata))) - pixel_names = gsub(" = ", "y_", pixel_names) - x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2] - y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3] + ## pixel names and coordinates + ## to remove potential sample names and z dimension, split at comma and take only x and y + x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1)) + y_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 2)) + x_coordinates = gsub("x = ","",x_coords) + y_coordinates = gsub(" y = ","",y_coords) + pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates) + opls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, opls_classes) colnames(opls_classes2) = c("pixel names", "x", "y","predicted condition") @@ -520,10 +530,15 @@ ## m/z and pixel information output ssc_classes = data.frame(msidata.ssc\$classes[[1]]) - pixel_names = gsub(", y = ", "_", names(pixels(msidata))) - pixel_names = gsub(" = ", "y_", pixel_names) - x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2] - y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3] + + ## pixel names and coordinates + ## to remove potential sample names and z dimension, split at comma and take only x and y + x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1)) + y_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 2)) + x_coordinates = gsub("x = ","",x_coords) + y_coordinates = gsub(" y = ","",y_coords) + pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates) + ## remove msidata to clean up RAM space rm(msidata)