Modify behavior of tissue surface merging for individual file: use min instead of median

R/config.R

@@ -20,7 +20,8 @@ AUTHORIZED_STROMA_VALUES <<- c('DAPI', 'stroma', 'other')
 AUTHORIZED_TUMOR_VALUES  <<- c('CK', 'tumor')
 AUTHORIZED_MARKERS       <<- c('CAL', 'CD3', 'CD4', 'CD8', 'CD11C', 'CD15', 'CD20', 'CD56', 'CD68',
                                'CD103', 'CD163', 'CD206', 'FOXP3', 'GB', 'gH2AX', 'gH2AXN', 'IDO',
-                               'Keratin', 'KI67', 'PD1', 'PDL1', 'PERFORIN', 'WT1', 'CK', 'VISTA')
+                               'Keratin', 'KI67', 'PD1', 'PDL1', 'PERFORIN', 'SOX10', 'WT1', 'CK',
+                               'VISTA')
 IGNORED_PHENOTYPES       <<- c('DAPIp', 'MISSING')
 
 DATAREDUCE_SCRIPT      <<- file.path(dirname(dirname(sys.frame(1)$ofile)),

R/functions.R

@@ -542,34 +542,44 @@ extract_imageid <- function(input_vector, input_file='file not specified'){
 
 
 ####################################################################################################
-decompress_file <- function(input_file, dry_run=FALSE){
+decompress_file <- function(input_file, allow_overwrite=FALSE, dry_run=FALSE){
     # ********************************************************************************************
     # Decompress .zip and .tar.gz input files and return the directory containing the uncompressed
     # data.
     #
     # Input parameters:
-    #  -> input_file: input .tar.gz or .zip file to uncompress.
+    #  -> input_file: input .tar.gz or .zip file to decompress.
     # ********************************************************************************************
     root_dir = dirname(input_file)
 
-    # Input file is tarball.
+    # Determine archive type:
     if(endsWith(input_file, '.tar.gz')){
-        data_dir = file.path(root_dir, unlist(strsplit(untar(input_file, list=TRUE)[1], '/'))[1])
-        if(dry_run) return(data_dir)
-        if(dir.exists(data_dir)) unlink(data_dir, recursive=TRUE)
-        untar(input_file, list=FALSE, exdir=root_dir)
-
-    # Input file is zip archive.
+        type = 'tar'
     } else if(endsWith(input_file, '.zip')){
+        type = 'zip'
+    } else stop('Unsupported compression format. Only [.tar.gz] and [.zip] files are supported.')
+
+    # Determine output directory:
+    if(type == 'tar'){
+        data_dir = file.path(root_dir, unlist(strsplit(untar(input_file, list=TRUE)[1], '/'))[1])
+    } else if(type == 'zip'){
         data_dir = file.path(root_dir,
                              unlist(strsplit(zip::zip_list(zipfile=input_file)[1,1], '/'))[1])
-        if(dry_run) return(data_dir)
-        if(dir.exists(data_dir)) unlink(data_dir, recursive=TRUE)
-        zip::unzip(input_file, exdir=root_dir, overwrite=TRUE)
+    }
+    if(dry_run) return(data_dir)
 
-    # Unsupported compression format.
-    } else stop('Unsupported compression format.')
+    # Test whether output already exists, and if yes delete it if allowed by user.
+    if(dir.exists(data_dir)){
+        if(!allow_overwrite) stop('File decompression failed: output already exists [',data_dir,']')
+        unlink(data_dir, recursive=TRUE)
+    }
 
+    # Decompress file.
+    if(type == 'tar'){
+        untar(input_file, list=FALSE, exdir=root_dir)
+    } else if(type == 'zip'){
+        zip::unzip(input_file, exdir=root_dir, overwrite=TRUE)
+    }
 
     # Return dirname of extracted files.
     stopifnot(file.info(data_dir)$isdir)

R/individual_markers.R

@@ -229,7 +229,7 @@ merge_tissue_data_files <- function(files_to_merge){
                         file_name = f)
         stopifnot(all(colnames(input_df) == c(key_fields, non_key_fields)))
 
-        # Merge data frame for the current marker with the global dataframe 'merged_df'.
+        # Merge data frame for the current marker with the global data frame 'merged_df'.
         if(is.null(merged_df)){
             merged_df = input_df
         } else{
@@ -240,34 +240,22 @@ merge_tissue_data_files <- function(files_to_merge){
         }
     }
 
-    # Search for mismatches among values of non-key fields. If some are detected, a warning
-    # is displayed. For rows with mismatches, if any, compute the median of surface values. In
-    # this way, if one of the input files has a different values it gets excluded (provided there
-    # are at least 3 files).
-    mismatches = NULL
-    for(col_name in non_key_fields){
-        col_index = grep(col_name, colnames(merged_df))
-        difference = abs(merged_df[,col_index] - merged_df[,col_index[1]])
-        mismatches = unique(c(mismatches, which(apply(difference, MARGIN=1, sum) > 0)))
-    }
-    if(length(mismatches) > 0){
-        percentage = round(length(mismatches)/nrow(merged_df) * 100, 2)
-        raise_error(msg = c('Mismatches in tissue surface among tissue seg files were found',
-                            'Median value of tissue surface will be used for the following rows:',
-                            paste0(paste(mismatches, collapse=', '), ' [', percentage, '%]')),
-                    file = dirname(files_to_merge[1]),
-                    type='warning')
-
-        # Compute median values to reconciliate mismatches.
-        for(col_name in non_key_fields){
-            col_index = grep(col_name, colnames(merged_df))
-            merged_df[mismatches, col_index[1]] = apply(merged_df[mismatches, col_index], 1, median)
-        }
+    # Merge tissue surface values (absolute value or percentage).
+    # The merge is made by keeping the smallest surface value from the individual files. This is
+    # because, when merging cells value, we keep the intersection of all individual files, which,
+    # in terms of surface, corresponds (roughly) to the smallest surface value in the tissue
+    #surface files.
+    for(i in 1:nrow(merged_df)){
+        # Identify column with the smallest surface value.
+        col_index = grep('region_area_surface', colnames(merged_df))
+        min_index = which(merged_df[i, col_index] == min(merged_df[i, col_index]))[1]
+        # Replace tissue surface values with the minimum value across all files.
+        merged_df[i, 'region_area_surface'] = merged_df[i, col_index[min_index]]
+        merged_df[i, 'region_area_percent'] = merged_df[i, col_index[min_index] + 1]
     }
 
     # Remove duplicated columns.
-    merged_df = merged_df[,1:5]
-    return(merged_df)
+    return(merged_df[,1:5])
 }
 ####################################################################################################
 

R/postinform.R

@@ -94,7 +94,7 @@ postinform <- function(input_file_or_dir,
     # If the input is a compressed .zip or .tar.gz file, decompress it.
     if(!file.info(input_file_or_dir)$isdir){
         message("Decompressing ", input_file_or_dir, "...")
-        input_dir = decompress_file(input_file_or_dir)
+        input_dir = decompress_file(input_file_or_dir, allow_overwrite=allow_overwrite)
     }
 
     # Create output directory and log file.

README.md

@@ -62,3 +62,47 @@ This command will produce an output file named "Test_session_random_suffix.zip".
 postinform(input_file_or_dir="Test_session.zip", command='process', output_suffix="random_suffix",
          compress_output=TRUE, immucan_output=TRUE, allow_overwrite=FALSE)
 ```
+
+
+
+### Post-inForm input parameter file format.
+The list of samples, tissues, markers and marker combinations to process are passed to post-inForm 
+via a single plain text file that must be named `parameters.txt` and be located at the root of the 
+input directory.
+**Important:** for **Windows users**, the `parameters.txt` file should be encoded in `UTF-8` 
+
+The parameters input file must contain the following 5 sections. For each section, values can be 
+passed on the same line separated by a `,`, or on multiple lines (one value per line). Any line 
+starting the a `#` value is ignored (allows to add comments to file).  
+For the `marker_combinations:` section, the special value `all` can be passed to process all 
+possible combinations of markers (to avoid having to enter all combinations manually).
+```
+samples:
+tissues:
+phenotyped_markers:
+scored_markers: 
+marker_combinations: 
+```
+
+A template file can be downloaded [here](tests/parameters.txt).  
+`parameters.txt` file example:
+```
+# List of samples to process.
+samples:
+SAMPLE_1
+SAMPLE_2
+SAMPLE_3
+
+# List of tissues to process.
+tissues: stroma, tumor
+
+# List of markers.
+phenotyped_markers: CD20, CD3, CD68
+scored_markers: 
+
+# Marker combinations to test
+marker_combinations: all
+```
+
+
+

tests/parameters.txt

+# Post-inForm input parameter template.
+# List of samples to process.
+samples:
+
+
+# List of tissues to process.
+tissues: stroma, tumor
+
+# List of markers.
+phenotyped_markers:
+scored_markers: 
+
+# Marker combinations to test
+marker_combinations: all