Accuri2svg is a command line program for the conversion of Accuri
.c6 flow cytometry data
files to the standard
Usage is not exactly straightforward, however it is quite powerful. The best approach is to gradually build up the matching regular expressions to get to your target files. If your sample naming is particularly weird, you might need to do >1 run.
In it's standard configuration it will find all
.c6 files in the current directory,
match every sample name outputting
.fcs files named [filename.c6]_[row|column].fcs.
This is probably perfectly fine for most uses.
Usage: accuri2fcs.py [options]
Options: -h, --help show this help message and exit -f FILE.c6, --filename=FILE.c6 match to find filename(s) to convert supports wildcards -n NAME_REGEXP, --name-regexp=NAME_REGEXP regexp pattern match to split Sample Name (customise this together with output path pattern to file in subfolders) --op=OUTPUT_PATH_PATTERN pattern for resulting output path (based on named strings in -n regexp) --on=OUTPUT_NAME_PATTERN pattern for resulting output filename (based on named strings in -n regexp) -t TARGET, --target=TARGET target folder in current folder to copy resulting fcs files -d DIRECTION, --direction=DIRECTION process data (c)olumn wise or (r)ow wise --fill fill in data from previous column/row contents (e.g. header will apply to all subsequent in column) --fill-all fill in data from any previous cell (first header will apply to all subsequent) --discard discard samples where no name regex matches
More complicated usage
Named regular expression matching can be used to extract variables from the Sample Names.
For example, you may want to search for a particular condition and then use this for the
destination folder of the resulting
.fcs file, or to build the output file name.
Additionally, you can use the fill options together with direction, to pre-fill data not present for a given sample. This is useful if you've labelled your first sample in a row but then only labelled subsequent changes.
A few examples are listed below (indented for clarity)
~/Scripts/accuri2fcs.py -n '^(?P<treatment>\d+TT)\s+?(?P<patient>SI?R?[0-9]+)\s+?(?P<timepoint>Pre Op\d+|D\d+|M\d+|\d+)$' --op '%(patient)s' --on '%(treatment)s_%(timepoint)s_(%(id)s)_%(file)s.fcs' --discard -t '/Volumes/USB-HDD/MM/fcs' -f '*.c6' 0TT S32 D14
In the above example we get all
.c6 files in the current folder. We match possible variables
in the target sample names, extracting 3 variables 'treatment', 'patient' and 'timepoint'.
The patient identifier is used for the output path, the treatment and timepoint (together with
the row/column identifier e.g. A6 from the Accuri file and the origin filename - two
always available variables). Non-matching samples are discarded (rather than moved to a 'everything else' folder)
on the target volume.
While it looks horrendous, if you build it up bit by bit it's really very logical.
GPML2SVG is available free for any use under the New BSD license.