Parsing the numerical output from Sensovation SensoSpot image analysis.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
Holger Frey 873091504c renamed the "parser" module to "csv_parser" 2 years ago
docs added mkdocs for documentation 2 years ago
example_data Added image name and recording datetime to the results 2 years ago
src/sensospot_parser renamed the "parser" module to "csv_parser" 2 years ago
tests renamed the "parser" module to "csv_parser" 2 years ago
.gitignore added mkdocs for documentation 2 years ago
.pre-commit-config.yaml finally separate flake8 config file removed 2 years ago
CHANGES.md added type hints and more docs to parser 3 years ago
CONTRIBUTING.md import of project template 5 years ago
LICENSE import of project template 5 years ago
Makefile added mkdocs for documentation 2 years ago
README.md cleaned up the cli interface 2 years ago
mkdocs.yml added mkdocs for documentation 2 years ago
pyproject.toml cleaned up the cli interface 2 years ago
tox.ini modernized project layout and infrastructure 2 years ago

README.md

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:

  • parse_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.
  • parse_file(path_to_csv_file) Parses a Sensospot csv file into a pandas data frame and will add some additional meta data from the file name. Is internally also used by parse_folder()

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
  --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