Sensospot Data Parser

Parsing the numerical output from SensoSpot microarray analysis.

The SensoSpot microarray analyzer is an automated fluorescence microscope with an image analysis software for detecting and measuring microarrays. The original name of the product was "FLAIR" by the company Sensovation, that was later acquired by Miltenyi.

There is no affiliation on my side regarding Sensovation or Miltenyi, I just use the product and needed a way to make the data available for further analysis.

Example:

    import sensospot_parser

    # read the raw data of a folder
    raw_data = sensospot_parser.parse_folder(<path to results directory>)

    sorted(raw_data.columns) == [
        'Analysis.Datetime', 'Analysis.Image', 'Analysis.Name', 
        'Bkg.Area', 'Bkg.Mean', 'Bkg.Median', 'Bkg.StdDev', 'Bkg.Sum',
        'Exposure.Id',
        'Parameters.Channel', 'Parameters.Time',
        'Pos.Id', 'Pos.Nom.X', 'Pos.Nom.Y', 'Pos.X', 'Pos.Y',
        'Spot.Area', 'Spot.Diameter', 'Spot.Found', 'Spot.Mean', 'Spot.Median',
        'Spot.Saturation', 'Spot.StdDev', 'Spot.Sum',
        'Well.Column', 'Well.Name', 'Well.Row'
    ]

Constants

There is a columns module available, providing constans that define the column names.

    import sensospot_parser

    sensospot_parser.columns.ANALYSIS_NAME == "Analysis.Name"

Avaliable public functions:

All public functions return a pandas DataFrame object.

Be aware that some columns might contain no values. This is depending on the parsing method (xml or csv) and if a parameters file could be found or not.

• parse_folder(path_to_folder) Tries the parse_xml_folder() function first and if an error occurs, it falls back to the parse_csv_folder()
• parse_xml_folder(path_to_folder) Searches the folder for a parsable Sensospot XML result file and parses it into a pandas data frame. It will add additional meta data from parameters folder, if it is present.
• parse_csv_folder(path_to_folder) Searches the folder for parsable Sensospot .csv files, parses them into one big pandas data frame and will add additional meta data from parameters folder, if it is present.

CLI

For the (propably) most important function, there is a cli command

Usage: sensospot_parse [OPTIONS] SOURCES

Arguments:
  SOURCES:             One or more folders with Sensospot measurements

Options:
  -o, --output FILE   Output file path, defaults to 'collected_data.csv'
  -q, --quiet         Ignore sanity check for csv file parsing
  --help              Show this message and exit.

Development

To install the development version of Sensovation Data Parser:

git clone https://git.cpi.imtek.uni-freiburg.de/holgi/sensospot_data.git

# create a virtual environment and install all required dev dependencies
cd sensospot_data
make devenv

To run the tests, use make tests (failing on first error) or make coverage for a complete report.

To generate the documentation pages use make docs or make serve-docs for starting a webserver with the generated documentation