RASPRoutines module

This class contains functions that collect analysis routines for RASP. jsb92, 2024/02/08

class RASPRoutines.RASP_Routines(defaultfolder=None, defaultarea=True, defaultd=True, defaultrad=True, defaultflat=True, defaultdfocus=True, defaultintfocus=True, defaultcellparams=True, defaultcameraparams=True)

Bases: object

analyse_images(folder, imtype='.tif', thres=0.05, large_thres=450.0, gsigma=1.4, rwave=2.0, oligomer_string='C1', cell_string='C0', if_filter=True, im_start=1, cell_analysis=True, one_savefile=False, disp=True)

analyses data from images in a specified folder, saves spots, locations, intensities and backgrounds in a folder created next to the folder analysed with _analysis string attached also writes a folder with _analysisparameters and saves analysis parameters used for particular experiment

Parameters:

  • folder (string) – Folder containing images

  • imtype (string) – Type of images being analysed, default tif

  • thres (float) – fraction of bright pixels accepted

  • large_thres (float) – large object intensity threshold

  • gisgma (float) – gaussian blurring parameter (default 1.4)

  • rwave (float) – Ricker wavelent sigma (default 2.)

  • oligomer_string (string) – string for oligomer-containing data (default C1)

  • string (cell) – string for cell-containing data (default C0)

  • if_filter (boolean) – Filter images for focus (default True)

  • im_start (integer) – Images to start from (default 1)

  • cell_analysis (boolean) – Parameter where script also analyses cell images and computes colocalisation likelihood ratios.

  • one_savefile (boolean) – Parameter that, if true, doesn’t save a file per image but amalgamates them into one file

  • disp (boolean) – If true, prints when analysed an image stack.

analyse_round_images(folder, imtype='.tif', thres=0.05, large_thres=450.0, gsigma=1.4, rwave=2.0, oligomer_string='C1', cell_string='C0', if_filter=True, im_start=1, cell_analysis=False, one_savefile=True)

analyses data in a folder specified, folder has either “Round” in the title or multiple rounds below; structure as in Lee Lab Cambridge Experiment saves spots, locations, intensities and backgrounds in a folder created next to the folder analysed with _analysis string attached also writes a folder with _analysisparameters and saves analysis parameters used for particular experiment

Parameters:

  • folder (string) – Folder containing images

  • imtype (string) – Type of images being analysed, default tif

  • gisgma (float) – gaussian blurring parameter (default 1.4)

  • rwave (float) – Ricker wavelent sigma (default 2.)

  • oligomer_string (string) – string for oligomer-containing data (default C1)

  • string (cell) – string for cell-containing data (default C0)

  • if_filter (boolean) – Filter images for focus (default True)

  • im_start (integer) – Images to start from (default 1)

  • one_savefile (boolean) – Parameter that, if true, doesn’t save a file per image but amalgamates them into one file

  • cell_analysis (boolean) – Parameter where script also analyses cell images and computes colocalisation likelihood ratios.

analyse_round_subfolder(folder, k1, k2, rdl, imtype='.tif', thres=0.05, large_thres=450.0, gsigma=1.4, rwave=2.0, oligomer_string='C1', cell_string='C0', if_filter=True, im_start=1, cell_analysis=False, one_savefile=True, disp=True)

analyses data in a folder specified, folder has either “Round” in the title or multiple rounds below; structure as in Lee Lab Cambridge Experiment saves spots, locations, intensities and backgrounds in a folder created next to the folder analysed with _analysis string attached also writes a folder with _analysisparameters and saves analysis parameters used for particular experiment

Parameters:

  • folder (string) – Folder containing images

  • k1 (matrix) – convolution kernel 1

  • k2 (matrix) – convolution kernel 2

  • rdl (vector) – radiality filter

  • thres (float) – fraction of bright pixels accepted

  • large_thres (float) – large object intensity threshold

  • imtype (string) – Type of images being analysed, default tif

  • gisgma (float) – gaussian blurring parameter (default 1.4)

  • rwave (float) – Ricker wavelent sigma (default 2.)

  • oligomer_string (string) – string for oligomer-containing data (default C1)

  • string (cell) – string for cell-containing data (default C0)

  • if_filter (boolean) – Filter images for focus (default True)

  • im_start (integer) – Images to start from (default 1)

  • one_savefile (boolean) – Parameter that, if true, doesn’t save a file per image but amalgamates them into one file

  • cell_analysis (boolean) – Parameter where script also analyses cell images and computes colocalisation likelihood ratios.

  • disp (boolean) – If True, outputs a message saying analysed image.

calibrate_area(folder, imtype='.tif', gsigma=1.4, rwave=2.0, large_thres=10000.0)

Calibrates area threshold. Given a folder of bead images, analyses them and saves the radiality parameter to the .json file, as well as writing it to the current class radiality and flatness values

Parameters:

  • folder (string) – Folder containing bead (bright) control tifs

  • imtype (string) – Type of images being analysed, default tif

  • gisgma (float) – gaussian blurring parameter (default 1.4)

  • rwave (float) – Ricker wavelent sigma (default 2.)

calibrate_radiality(folder, imtype='.tif', gsigma=1.4, rwave=2.0, accepted_ratio=1)

Calibrates radility parameters. Given a folder of negative controls, analyses them and saves the radiality parameter to the .json file, as well as writing it to the current class radiality and flatness values

Parameters:

  • folder (string) – Folder containing negative control tifs

  • imtype (string) – Type of images being analysed, default tif

  • gsigma (float) – gaussian blurring parameter (default 1.4)

  • rwave (float) – Ricker wavelent sigma (default 2.)

  • accepted_ratio (float) – Percentage accepted of false positives

count_spots(database, z_planes)

Counts spots per z plane

Parameters:

  • database (pandas array) – pandas array of spots

  • z_planes (np.1darray) – is range of zplanes

Returns:

n_spots

Search for files containing ‘string1’ in their names within ‘folder’, and then filter the results to include only those containing ‘string2’.

Parameters:

  • folder (str) – The directory to search for files.

  • string1 (str) – The first string to search for in the filenames.

  • string2 (str) – The second string to filter the filenames containing string1.

Returns:

file_list (list) – A sorted list of file paths matching the search criteria.

get_infocus_planes(image, kernel)

Gets z planes that area in focus from an image stack

Parameters:

  • image (array) – image as numpy array

  • kernel (array) – gaussian blur kernel

Returns:

z_planes (np.1darray) – z_plane range that is in focus

save_analysis_results(directory, file, to_save, rsid, cell_analysis=False, to_save_cell=0, cell_mask=0)

Saves analysis results to the specified directory.

Parameters:

  • directory (str) – The directory where the results will be saved.

  • file_path (str) – The file path of the original data file.

  • data_to_save (pandas.DataFrame) – The data to be saved.

  • rsid (float) – The rsid value associated with the data.

  • cell_analysis (bool) – Indicates whether cell analysis was performed (default False).

  • cell_data_to_save (pandas.DataFrame) – The cell data to be saved (default None).

  • cell_mask (numpy.ndarray) – The cell mask to be saved as a TIFF file (default None).

save_analysis_results_onesavefile(analysis_directory, file, to_save, rsid, z_planes, i, cell_analysis=False, cell_file=0, to_save_cell=0, cell_mask=0)

Saves analysis results to the specified directory.

Parameters:

  • directory (str) – The directory where the results will be saved.

  • file_path (str) – The file path of the original data file.

  • data_to_save (pandas.DataFrame) – The data to be saved.

  • rsid (float) – The rsid value associated with the data.

  • cell_analysis (bool) – Indicates whether cell analysis was performed (default False).

  • cell_data_to_save (pandas.DataFrame) – The cell data to be saved (default None).

  • cell_mask (numpy.ndarray) – The cell mask to be saved as a TIFF file (default None).

single_image_analysis(protein_file, thres=0.05, large_thres=450.0, gsigma=1.4, rwave=2.0, image_size=200, save_figure=False, cell_analysis=False, cell_file=None)

analyses data from specified image, presents spots, locations, intensities in a figure, with the option of saving this figure

Parameters:

  • file (string) – image location

  • thres (float) – fraction of bright pixels accepted

  • large_thres (float) – large object intensity threshold

  • gisgma (float) – gaussian blurring parameter (default 1.4)

  • rwave (float) – Ricker wavelent sigma (default 2.)

  • image_size (int) – Amount of image to plot—by default plots 100x100 chunk of an image to give you an idea, can scale up

  • save_figure (boolean) – save the figure as an svg, default no

  • cell_analysis (boolean) – Parameter where script also analyses cell images and computes colocalisation likelihood ratios.

  • cell_file (string) – cell image location