13.02.2024: README update

wisstock · Feb 13, 2024 · 302d4cb · 302d4cb
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diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 domb-napari
 ===========
 
-[![Stand With Ukraine](https://raw.githubusercontent.com/vshymanskyy/StandWithUkraine/main/banner2-direct.svg)](https://vshymanskyy.github.io/StandWithUkraine/)
+[![Stand With Ukraine](https://raw.githubusercontent.com/vshymanskyy/StandWithUkraine/main/banner-direct.svg)](https://vshymanskyy.github.io/StandWithUkraine/)
 
 [![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/domb-napari)](https://napari-hub.org/plugins/domb-napari)
 ![PyPI - Version](https://img.shields.io/pypi/v/domb-napari)
@@ -10,21 +10,25 @@ domb-napari
 
 __napari Toolkit of Department of Molecular Biophysics <br /> Bogomoletz Institute of Physiology of NAS of Ukraine, Kyiv,  Ukraine__
 
-napari plugin offers widgets to analyze fluorescence-labeled proteins redistribution in widefield epifluorescence time-lapse acquisitions. Useful for studying calcium-dependent translocation of neuronal calcium sensors, synaptic receptors traffic during long-term plasticity induction, membrane protein tracking, etc.
+napari plugin for analyzing fluorescence-labeled proteins redistribution. Offers widgets designed for analyzing the redistribution of fluorescence-labeled proteins in widefield epifluorescence time-lapse acquisitions. Particularly useful for studying various phenomena such as calcium-dependent translocation of neuronal calcium sensors, synaptic receptor traffic during long-term plasticity induction, and membrane protein tracking.
 
 ![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/translocation.gif)
 __Hippocalcin (neuronal calcium sensor) redistributes in dendritic branches upon NMDA application__
 
-## Widgets
-### Image Preprocessing
+# Detection of fluorescence redistributions
+A set of widgets designed for detecting fluorescence intensity redistribution through the analysis of differential image series (red-green detection).
+
+Inspired by [Dovgan et al., 2010](https://pubmed.ncbi.nlm.nih.gov/20704590/) and [Osypenko et al., 2019](https://www.sciencedirect.com/science/article/pii/S0969996119301974?via%3Dihub).
+
+## Image preprocessing
 Provides functions for preprocessing multi-channel fluorescence acquisitions:
 - If the input image has 4 dimensions (time, channel, x-axis, y-axis), channels will be split into individual 3 dimensions images (time, x-axis, y-axis) with the `_ch%index%` suffix.
 - If the `gaussian blur` option is selected, the image will be blurred with a Gaussian filter using sigma=`gaussian sigma`.
 - If the `photobleaching correction` option is selected, the image will undergo correction with exponential (method `exp`) or bi-exponential (method `bi_exp`) fitting.
 
 ![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/pic_0.png)
 
-### Red-Green Series
+## Red-green series
 Primary method for detecting fluorescent-labeled targets redistribution in time. Returns a series of differential images representing the intensity difference between the current frame and the previous one as new image with the `_red-green` suffix.
 
 Parameters:
@@ -36,7 +40,7 @@ Parameters:
 
 ![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/pic_1.png)
 
-### Up Masking
+## Up masking
 Generates labels for insertion sites (regions with increasing intensity) based on `-red-green` images. Returns labels layer with `_up-labels` suffix.
 
 Parameters:
@@ -47,7 +51,11 @@ Parameters:
 
 ![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/pic_2.png)
 
-### Individual Labels Profile
+## Intensity masking
+Extension of __Up Masking__ widget. Detects regions with increasing (`masking mode` - `up`) or decreasing (`masking mode` - `down`) intensity in `-red-green` images. Returns a labels layer with either `_up-labels` or `_down-labels` suffix, depending on the mode.
+
+
+## Individual labels profiles
 Builds a plot with mean intensity profiles for each ROI in `labels` using absolute intensity (if `raw intensity` is selected) or relative intensities (ΔF/F0).
 
 The `time scale` sets the number of seconds between frames for x-axis scaling.
@@ -63,13 +71,14 @@ Additionally, you can save ROI intensity profiles as .csv using the `save data f
 - __id__ - unique image ID, the name of the input `napari.Image` object.
 - __roi__ - ROI number, consecutively numbered starting from 1.
 - __int__ - ROI mean intensity, raw or ΔF/F0 according to the `raw intensity` option.
+- __index__ - frame index
 - __time__ - frame time point according to the `time scale`.
 
 _Note: The data frame will contain information for all ROIs; filtering options pertain to plotting only._
 
 ![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/pic_3.png)
 
-### Labels Profile
+## Labels profile
 Builds a plot with the averaged intensity of all ROIs in `labels`. Can take two images (`img 0` and `img 1`) as input if `two profiles` are selected.
 
 The `time scale` and `ΔF win` are the same as in the __Individual Labels Profiles__.
@@ -80,4 +89,14 @@ The `stat method` provides methods for calculating intensity errors:
 - `iqr` - interquartile range.
 - `ci` - 95% confidence interval for t-distribution.
 
-![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/pic_4.png)
+![](https://raw.githubusercontent.com/wisstock/domb-napari/master/images/pic_4.png)
+
+# Traffic monitoring with pH-sensitive tag
+A collection of widgets designed for the analysis of image series containing the pH-sensitive fluorescence protein Superecliptic pHluorin (SEP).
+
+Insipred by [Fujii et al., 2017](https://pubmed.ncbi.nlm.nih.gov/28474392/), [Gao et al., 2018](https://www.beilstein-journals.org/bjnano/articles/9/79) and [Sposini et al., 2020](https://www.nature.com/articles/s41596-020-0371-z).
+
+## SEP image preprocessing
+Processes image series obtained through repetitive pH exchange methods (such as U-tube or ppH approaches). Frames with odd indexes, including index 0, are interpreted as images acquired at pH 7.0, representing total fluorescence intensity (saved with the suffix `_total`). Even frames are interpreted as images obtained at acidic pH (5.5-6.0), representing intracellular fluorescence only (saved with the suffix `_intra`).
+
+If `calc surface img` is selected, an additional total fluorescence image with subtracted intracellular intensity will be saved as the cell surface fluorescence fraction (suffix `_surface`).
diff --git a/setup.cfg b/setup.cfg
@@ -5,7 +5,7 @@ version = 2024.02.13
 author = Borys Olifirov
 author_email = [email protected]
 license = MIT
-description = napari plugin offers widgets to analyze fluorescence-labeled proteins redistribution in widefield epifluorescence time-lapse acquisitions
+description = napari plugin for analyzing fluorescence-labeled proteins redistribution
 long_description = file: README.md
 long_description_content_type = text/markdown
 

diff --git a/src/domb_napari/_widget.py b/src/domb_napari/_widget.py
@@ -202,11 +202,11 @@ def mask_calc(viewer: Viewer, img:Image, detection_frame_index:int=2,
         if masking_mode == 'up':
             mask = detection_img >= np.max(np.abs(detection_img)) * up_threshold
             labels_name = img.name + '_up-labels'
-            mask_name = img.name + '_up-mask'
+            # mask_name = img.name + '_up-mask'
         elif masking_mode == 'down':        
             mask = detection_img <= np.max(np.abs(detection_img)) * down_threshold
             labels_name = img.name + '_down-labels'
-            mask_name = img.name + '_down-mask'
+            # mask_name = img.name + '_down-mask'
 
         mask = morphology.opening(mask, footprint=morphology.disk(3))
         mask = ndi.binary_fill_holes(mask)
@@ -271,12 +271,13 @@ def labels_profile_line(viewer: Viewer, img:Image, labels:Labels,
 
         if save_data_frame:
             import pandas as pd
-            output_df = pd.DataFrame(columns=['id','roi','int', 'time'])
+            output_df = pd.DataFrame(columns=['id','roi','int', 'index', 'time'])
             for num_ROI in range(profile_to_plot.shape[0]):
                 profile_ROI = profile_to_plot[num_ROI]
                 df_ROI = pd.DataFrame({'id':np.full(profile_ROI.shape[0], img.name),
                                        'roi':np.full(profile_ROI.shape[0], num_ROI+1),
                                        'int':profile_ROI,
+                                       'index': np.linspace(0, input_img.shape[0], num=input_img.shape[0], dtype=int),
                                        'time':time_line})
                 output_df = pd.concat([output_df.astype(df_ROI.dtypes),
                                        df_ROI.astype(output_df.dtypes)],

diff --git a/src/domb_napari/napari.yaml b/src/domb_napari/napari.yaml
@@ -2,38 +2,38 @@ name: domb-napari
 contributions:
   commands:
     - id: domb-napari.split_channels_widget
-      title: Multichannel Image Preprocessing
+      title: Multichannel image preprocessing
       python_name: domb_napari._widget:split_channels
     - id: domb-napari.split_sep_widget
-      title: SEP Image Preprocessing
+      title: SEP image preprocessing
       python_name: domb_napari._widget:split_sep
     - id: domb-napari.der_series_widget
-      title: Red-Green Series
+      title: Red-green series
       python_name: domb_napari._widget:der_series
     - id: domb-napari.up_mask_calc_widget
-      title: Up Masking
+      title: Up masking
       python_name: domb_napari._widget:up_mask_calc
     - id: domb-napari.mask_calc_widget
-      title: Intensity Masking
+      title: Intensity masking
       python_name: domb_napari._widget:mask_calc
     - id: domb-napari.labels_profiles_set_widget
-      title: Individual Labels Profiles
+      title: Individual labels profiles
       python_name: domb_napari._widget:labels_profile_line
     - id: domb-napari.labels_profile_widget
-      title: Labels Profile
+      title: Labels profile
       python_name: domb_napari._widget:labels_profile_stat
   widgets:
     - command: domb-napari.split_channels_widget
-      display_name: Image Preprocessing
+      display_name: Image preprocessing
     - command: domb-napari.der_series_widget
-      display_name: Red-Green Series
+      display_name: Red-green series
     - command: domb-napari.split_sep_widget
-      display_name: SEP Preprocessing
+      display_name: SEP preprocessing
     - command: domb-napari.up_mask_calc_widget
-      display_name: Up Mask
+      display_name: Up masking
     - command: domb-napari.mask_calc_widget
-      display_name: Intensity Masking
+      display_name: Intensity masking
     - command: domb-napari.labels_profiles_set_widget
-      display_name: Individual Labels Profile
+      display_name: Individual labels profiles
     - command: domb-napari.labels_profile_widget
-      display_name: Labels Profile
+      display_name: Labels profile