Update README.md

README.md

@@ -20,36 +20,37 @@
 
 ## Description #
 
-This 3D-SeqFISH analysis toolbox is a integrated 3d_SeqFISH analysis method, aiming to provide the general solution for 3D registration and decoding analysis for 3D spatial transcriptome. This toolbox handles large-scale, multi-round, high-resolution image data acquired using EASI-FISH (Expansion-Assisted Iterative Fluorescence *In Situ* Hybridization). It takes advantage of the [n5](https://github.com/saalfeldlab/n5) filesystem to allow for rapid and parallel data reading and writing. It performs automated image stitching, distributed and highly accurate multi-round image and FISH spot registration (nm-level accuracy), 3D cell segmentation, and distributed spot detection. We also envision this workflow being adapted for analysis of other image-based spatial transcriptomic data.  <br/>
+This 3D-SeqFISH analysis toolbox is a integrated SeqFISH analysis method for 3D FISH images, aiming to provide the general solution for 3D spatial transcriptome, including automated image stitching, 3D cell segmentation, distributed spot detection, and distributed and highly accurate multi-round 3D FISH spot registration (nm-level accuracy), and decoding analysis. We also envision this workflow being adapted for analysis of other image-based spatial transcriptomic data.  <br/>
 ![](/Diagrams/3DseqFISH_diagram_v1.png)
 
 ## Installation #
-Preprocessing for point clouds is largely based on EASIFISH, MULTIFISH, bigstream, napari, and stardist, etc. <br/> 
+
+`Point cloud registration and decoding scripts are inspired by SeqFISH and 3D-SeqFISH` can be installed with `pip`:
+```
+   pip install 3D-SeqFISH
+```
+
+Preprocessing for point cloud registration is largely based on EASIFISH, MULTIFISH, bigstream, napari, and stardist, etc. <br/> 
 
 `bigstream`  can be installed with  `pip`:
 ```
    pip install bigstream
 ```
 `napari`  can be installed with  `pip`:
 ```
-   pip install napari
+   python -m pip install "napari[all]"
 ```
 `Starfinity`  can be installed with  `pip`:
 ```
    pip install git+https://github.com/mpicbg-csbd/stardist@refinement
 ```
 
-`Point cloud registration and decoding scripts are inspired by SeqFISH and 3D-SeqFISH` can be installed with `pip`:
-```
-   pip install 3D-SeqFISH
-```
-
 ## Modules #
 
 ### Preprocessing with EASIFISH Pipeline #
 See [here](https://github.com/multiFISH/EASI-FISH) for active EASI-FISH pipeline updates, and [here](https://github.com/JaneliaSciComp/multifish) for active MULTIFISH pipeline updates. <br/>
 For imaging large volumes, multiple sub-volumes (tiles) are sequentially acquired, followed by computational stitching into a single large image([Gao et al., 2019](https://science.sciencemag.org/content/363/6424/eaau8302.long)).The stitching module can be executed with the EASI-FISH pipeline (see above). <br/> 
-For coarsely register images, We used [BigStream](https://github.com/GFleishman/bigstream) for robust and fully automated non-rigid registration of multi-round FISH data. <br/> 
+For coarsely registering images, we used [BigStream](https://github.com/GFleishman/bigstream) for robust and fully automated non-rigid registration of multi-round FISH data. <br/> 
 For 3D segmentation, [Starfinity](https://github.com/mpicbg-csbd/stardist/tree/refinement) is a deep learning-based automatic 3D segmentation software. Starfinity is an extension of [Stardist](https://github.com/mpicbg-csbd/stardist), an earlier cell detection approach (Schmidt et al., 2018; Weigert et al., 2020) and is based on the dense prediction of cell border distances and their subsequent aggregation into pixel affinities. A starfinity [model](https://doi.org/10.25378/janelia.13624268) was trained to predict cell body shapes from DAPI-stained RNA images and is provided for testing. Starfinity can be executed as part of the EASI-FISH pipeline. <br/>
 For distributed spot detection, we used [RS-FISH](https://github.com/PreibischLab/RS-FISH) or hAirlocalize [Airlocalize](https://github.com/timotheelionnet/AIRLOCALIZE)([Lionnet et al., 2011](https://www.nature.com/articles/nmeth.1551)) to allow rapid spot detection on full-resolution large image datasets. hAirlocalize can be executed independently or as part of the EASI-FISH pipeline (see above). For independent execution, we recommend working with the n5 filesystem due to large file size.
 ![](/Diagrams/Pipeline.gif) 
@@ -76,8 +77,9 @@ For increase the detection efficency, the nearest 3 spots for a specific bit wil
 We build a self-contained, highly flexible, and platform agnostic computational [pipeline](https://github.com/JaneliaSciComp/multifish), which supports turnkey 3D-SeqFISH analysis on local machines and the High performance compute cluster (such as Slurm or LSF). The pipeline is freely available, open source, and modular. It can rapidly process large datasets greater than 10 TB in size with minimal manual intervention. The pipeline can be used to analyze EASI-FISH dataset end-to-end. It takes `czi` image files acquired from Zeiss Z.1 lightsheet microscope as input and outputs 1) processed image data at different scales and 2) transcript counts that can be readily used for cell type identification. The pipeline also provides options to run individual analysis modules, such as image stitching or registration. 
 
 ### Example data #
+This toolbox handles large-scale, multi-round, high-resolution image data acquired using EASI-FISH (Expansion-Assisted Iterative Fluorescence *In Situ* Hybridization). It takes advantage of the [n5](https://github.com/saalfeldlab/n5) filesystem to allow for rapid and parallel data reading and writing.  <br/>
+Example images [example datasets](https://doi.org/10.25378/janelia.c.5276708.v1) are provided for software testing. For instructions on performing a demo run with the example data using the end-to-end EASIFISH pipeline, see [here](https://github.com/JaneliaSciComp/multifish).  <br/>
 We provide the related images and point clouds of a example Cell 5# for testing the 3D registration. Stay tuned to the description later. <br/>
-Example images [example datasets](https://doi.org/10.25378/janelia.c.5276708.v1) are provided for software testing. For instructions on performing a demo run with the example data using the end-to-end EASIFISH pipeline, see [here](https://github.com/JaneliaSciComp/multifish). 
 
 ### Jupyter notebooks #
 ROI_ransac_napari_fixmovROI.ipynb now is the only python script for processing the 3d registration of FISH point clouds. Stay tuned for individual components.