Migrated from GitHub

.gitattributes

@@ -57,3 +57,29 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_suite/imageware.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d-core3.93.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d_plugins3.93.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/3D_Viewer-4.0.1.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/VIB-lib-2.1.1.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/gluegen-rt-2.3.2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/j3dcore-1.6.0-scijava-2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/j3dutils-1.6.0-scijava-2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-linux-amd64.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-macosx-universal.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-windows-amd64.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/vecmath-1.6.0-scijava-2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/utils/3D_Convex_Hull.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/utils/imagescience.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/utils/mpicbg_-1.4.1.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/cell_zone_detection/TissueJ4Merfish_v14.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/cell_zone_detection/merFISH_01_025_08-SEG.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/cell_zone_detection/merFISH_01_025_08-mask.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/demo_results/aligned_images_fov0_z18_01_SEG_demo_only.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_01_SEG.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_02_SEG.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_01.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_02.tif filter=lfs diff=lfs merge=lfs -text

data/Correlation/correlation.py

@@ -0,0 +1,204 @@
+
+
+
+
+# Import
+import cProfile
+from distutils import core
+from pathlib import Path
+import numpy as np
+import scipy as sp
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import seaborn as sns
+import os
+import pickle
+import scipy.stats as stats
+import argparse
+from adjustText import adjust_text
+import matplotlib.backends.backend_pdf
+
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-b', type = str, help = 'Path to barcodes')
+    parser.add_argument('-xp', type = str, help = 'Experiment ID')
+    parser.add_argument('-c', type = str, default = "D:\BCCAncer\FILES\codebook_0_C1E1_van.csv", help = 'Path to codebook')
+    parser.add_argument('-a', type = str, default = "D:\BCCAncer\FILES\XP2474_4T1_C1E1_new_bulk.csv", help = 'Path to bulk file (abundance file)')
+    parser.add_argument('-o', type = str, default = "D:\BCCAncer\EXP\XP7174\Correlations\BulkCorr", help = 'Path to save processed files')
+    parser.add_argument('-is_cambridge', type = bool, default = False, help = 'Are the results for Cambridge datasets?')
+    parser.add_argument('-drop_blanks', type = bool, default = False, help = 'Remove blanks before correlation')
+    parser.add_argument('-log', type =bool, default = True, help = 'Do we want to take log while correlation of bulk?')
+    parser.add_argument("-d", nargs="+", type = float, default=0.65, help = 'Max mean distance threshold for an area of detected barcode')
+    parser.add_argument("-removeZ", nargs="+", type=int, default=None, help="Z slices to remove from evaluation")
+    #parser.add_argument('-gene_list', type = str, action='append', required=True, help = 'List of genes which we want to exclude from analysis')
+
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.o):
+        os.makedirs(args.o)
+
+    info_np = np.zeros(shape = (len(args.d), 7))
+
+    # read sheet and remove undesired z slices
+    df = read_sheet(args.b)
+    if args.removeZ is not None:
+        removeZ_set = set(args.removeZ)
+        df = df[~df['z'].isin(removeZ_set)]
+    
+    
+    
+    pdf = matplotlib.backends.backend_pdf.PdfPages(f'{args.o}/{args.xp}_correlation.pdf')
+    correlation = Correlation(args.c, df, args.a, args.o, args.xp, args.is_cambridge, pdf)
+    args.d.sort(reverse = True)
+
+    
+    for idx, dist_threshold in enumerate(args.d):
+        filter_df = correlation.filter_distance(dist_threshold)
+        gp_df = correlation.groupby(filter_df)
+        gp_df = correlation.merge_df_cb(gp_df)
+
+        tot_counts = gp_df.counts.sum()
+        blank_counts = gp_df.loc[gp_df['gene_symbol'].isin(['Blank_01', 'Blank_02', 'Blank_03', 'Blank_04', 'Blank_05', 'Blank_06'])].counts.sum()
+
+        info_np[idx][0] = dist_threshold
+        info_np[idx][3] = int(tot_counts)
+        info_np[idx][4] = int(blank_counts)
+        info_np[idx][5] = int(len(gp_df))
+
+        gp_df = correlation.df_bulk(gp_df)
+
+        if args.drop_blanks:
+            gp_df = correlation.remove_blanks(gp_df) 
+
+        """
+            if args.gene_list:
+            print('removing')
+            gp_df = correlation.remove_specific_genes(gp_df)
+        """
+
+        info_np[idx][-1] = len(gp_df)
+
+        gp_df['log_counts'] = np.log2(gp_df['counts']+1)
+        gp_df['log_tpm'] = np.log2(gp_df['bulk_exp']+0.0001)
+
+        gp_df.to_csv(f'{args.o}/{args.xp}_{dist_threshold}.csv')
+
+
+        info_np[idx][1], info_np[idx][2] = correlation.log_correlation(gp_df, dist_threshold)
+
+    pdf.close()
+
+    info_df = pd.DataFrame(info_np, columns = ['Distance Threshold',\
+                                        'Pearson correlation',\
+                                        'Spearman correlation',\
+                                        '# detected barcodes (including control barcodes)',\
+                                        '# detected control (blanks) barcodes',\
+                                        '# genes at dist threshold in correlation',\
+                                        '# barcodes counted towards correlaton estimation'])
+    info_df.to_csv(f'{args.o}/info_{args.xp}.csv')
+ 
+def read_sheet(file):
+    
+    ext = file.split(".")[-1]
+    if ext == "csv":
+        df = pd.read_csv(file)
+    elif ext == "tsv":
+        df = pd.read_csv(file, '\t')
+    elif ext in {"xls", "xlsx", "xlsm", "xlsb"}:
+        df = pd.read_excel(file)
+    else:
+        raise ValueError("Unexpected file extension")
+    return df    
+
+class Correlation:
+    def __init__(self, codebook, barcodes, bulk, output_dir, name, is_cambridge, pdf_object):
+        self.codebook = read_sheet(codebook)
+        self.barcodes = barcodes
+        self.bulk = read_sheet(bulk)
+        self.output_dir = output_dir
+        self.name = name
+        self.is_cambridge = is_cambridge
+        self.pdf_object = pdf_object
+        #self.remove_genes_list = remove_genes_list
+
+    def filter_distance(self, distance_threshold):
+        return self.barcodes.loc[self.barcodes['mean_distance']<distance_threshold]
+
+    def groupby(self, df):
+        return (df.groupby('barcode_id').size().reset_index(name='counts'))
+
+    def modify_codebook(self):
+        self.codebook['barcode_id'] = self.codebook.index
+        self.codebook.rename(columns={"name": "gene_symbol"}, inplace = True)
+
+    def merge_df_cb(self,df):
+        self.modify_codebook()
+        return pd.merge(self.codebook, df, how='inner', on='barcode_id')
+
+    def df_bulk(self, df):
+        return pd.merge(self.bulk, df, how='inner', on='gene_symbol')
+
+    def remove_blanks(self,df):
+        return df[df['bulk_exp'] != 0]
+
+    """
+    def remove_specific_genes(self,df):
+        c = 0
+        print(self.remove_genes_list)
+        for gene in self.remove_genes_list:
+            print(c, gene)
+            df = df.loc[df['gene_symbol']!=gene]
+            #cccprint(df.loc[df['gene_symbol']==gene])
+            c+=1
+        return df
+    """
+    
+
+    def log_correlation(self, df, dist_threshold):
+            f1, ax = plt.subplots(figsize=(9, 9))
+            sns.set_palette("deep")
+            sns.scatterplot(x="log_tpm",y="log_counts",data=df,ax=ax)
+                           
+            if self.is_cambridge == True:
+                count_type = "C_counts"
+            else: count_type = "V_counts"
+                    
+                
+            ax.set_title("TPM Correlation for " + self.name, fontsize = 15)
+            ax.set_xlabel("log2(TPM+1e-4), V_bulk", fontsize = 20)
+            ax.set_ylabel("log2(# detected counts+1), "+ count_type, fontsize = 20)
+            
+            
+            def plotlabel(xvar, yvar, label):
+                ax.text(xvar+0.02, yvar, label)
+      
+                
+            pearson, _ = stats.pearsonr(df["log_tpm"],df["log_counts"])
+            spearman, _ = stats.spearmanr(df["log_tpm"],df["log_counts"])
+            #kendalltau, _ = stats.kendalltau(df["log_tpm"],df["log_counts"])
+               
+            ax.text(.01, .95, 'Pearson = {:.2f}\nSpearman = {:.2f}'.format(pearson,spearman),transform=ax.transAxes)
+            #df.apply(lambda x: plotlabel(x['log_tpm'],  x['log_counts'], x['gene_symbol']), axis=1)
+            #plt.axvline(x = 0, color = 'r', label = 'axvline - full height')
+            
+            self.pdf_object.savefig(f1)
+
+            texts = []
+            for xs,ys,label in zip(df['log_tpm'],df['log_counts'],df['gene_symbol']):
+                    texts.append(ax.text(xs,ys,label))
+            adjust_text(texts, force_points=0.2, force_text=0.2,expand_points=(1, 1), expand_text=(1, 1), arrowprops=dict(arrowstyle="-", color='black', lw=0.5))
+            plt.tight_layout()
+            
+            #plt.savefig(f'{self.output_dir}/{self.name}_{dist_threshold}.pdf')
+            self.pdf_object.savefig(f1)
+
+            return pearson, spearman
+
+
+if __name__ == '__main__':
+    #cProfile.run('main()')
+    main()

data/Snakefile

@@ -0,0 +1,261 @@
+from pathlib import Path
+import re
+import os
+import makereports as reports
+from utils import fileIO,imgproc
+
+configfile: "config.json"
+
+
+def download_azure():
+    
+    if config["isRemote"]=="N" or os.path.exists(os.path.join(config['results_path'],'data')):
+        return None
+    
+    shell(config["azure_command"].format(config["raw_data_path"],os.path.join(config['results_path'],'data')))
+    
+    return None
+
+def check_params():
+
+    # Check to see if there are any params that need to be found
+    b_findz = len(config['z'])==0
+    b_findfov = len(config['fov'])==0
+    b_findir = len(config['ir'])==0
+    b_findwv = len(config['channel'])==0
+
+
+    # get all the images
+    if config["isRemote"] == "Y":
+        files = Path(os.path.join(config['results_path'],'data')).rglob('*.TIFF')
+    else:
+        files = Path(config['raw_data_path']).rglob('*.TIFF')
+    files = list(map(str,files))
+    # Find all channel, ir, fov, and z from the file names
+    re_filter = r"(.*)(\d{3})(?=nm).*(\B\d{2})\D(\B\d{3})\D(\B\d{2}\b)"
+    param_filter = re.compile(re_filter)
+    results = list(map(param_filter.search,files))
+    
+    results =list(filter(lambda x: isinstance(x,re.Match), results))
+    # Get the list of parameters if they are needed
+    #   Group 0 is the full match, so each capture group is 1 indexed
+    if b_findz:
+        zs = list(map(lambda x: x.group(5),results))
+    else:
+        zs = config['z']
+
+    if b_findir:
+        irs = list(map(lambda x: x.group(3),results))
+    else:
+        irs = config['ir'] 
+
+    if b_findfov:
+        fovs = list(map(lambda x: x.group(4),results))
+    else:
+        fovs = config['fov']
+
+    if b_findwv:
+        wvs = list(map(lambda x: x.group(2),results))
+    else:
+        wvs = config['channel']
+
+    if isinstance(results,list) and len(results)>0:
+        full_raw_path = results[0].group(1)
+    else:
+        full_raw_path=''
+
+
+    return sorted(list(set(zs)),key=int),sorted(list(set(fovs)),key=int),sorted(list(set(irs)),key=int),sorted(list(set(wvs)),key=int),full_raw_path
+
+
+download_azure()
+zs,fovs,irs,wvs,full_raw_path = check_params()
+
+
+default_message= "rule {rule}, {wildcards}, threads: {threads}"
+
+rule all_done:
+    input:
+        os.path.join(config['results_path'],'brightness_report.t'),
+        os.path.join(config['results_path'],'focus_report.t'),
+        os.path.join(config['results_path'],'deconvolved_brightness_report.t'),
+        os.path.join(config['results_path'],'masked_brightness_report.t'),
+        os.path.join(config['results_path'],'decodability_report.t')
+    output:
+        os.path.join(config['results_path'],'all_done.t')
+    run:
+        if config["isRemote"]=="Y":
+            shell("rm -rf \"{}\"".format(os.path.join(config['results_path'],'data')))
+        if config["delete_stack"]=="Y":
+            shell("rm -f \"{}\"".format(os.path.join(config['results_path'],'img*')))
+        shell("touch \"{output[0]}\"")
+
+rule all:
+    threads:1
+    input:
+        os.path.join(config['results_path'],'all_done.t')
+
+
+def isRemote(wildcards):
+    
+    if config["isRemote"]=="N":
+        return config["raw_data_path"]
+    else:
+        return directory(os.path.join(config['results_path'],'data'))
+
+rule create_image_stack:
+    threads:1
+    message: default_message
+    input:
+        isRemote
+    output:
+        out_file = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    run:
+        fileIO.create_image_stack(os.path.join(full_raw_path,config['raw_image_format']),
+                                    wildcards.fov,wildcards.z,irs,wvs,output.out_file,output.coord_file)
+
+
+rule deconvolve_images:
+    threads:1
+    message: default_message
+    input:
+        in_file = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+    output:
+        out_file = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy')
+    run:
+        imgproc._deconvolute(input.in_file,output.out_file)
+
+
+
+
+rule deconvolved_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    output:
+        out=os.path.join(config['results_path'],'deconvolved_brightness_report_{fov}_{z}.pdf')
+    run:
+        reports.generate_brightness_reports(input.img_stack,input.coord_file,output.out,wildcards.fov,wildcards.z)
+
+
+rule compile_deconvolved_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'deconvolved_brightness_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'deconvolved_brightness_report.t')
+    shell:
+        "touch \"{output}\""
+
+
+rule create_mask_images:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy'),
+    output:
+        out_mask=os.path.join(config['results_path'],'masked','mask_{fov}_{z}.npy')
+    run:
+        imgproc.maskImages(input.img_stack,output.out_mask)
+
+
+rule masked_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+        masks = os.path.join(config['results_path'],'masked','mask_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    output:
+        out=os.path.join(config['results_path'],'masked_brightness_report_{fov}_{z}.pdf')
+    run:
+        reports.generate_masked_brightness_reports(input.img_stack,input.coord_file,output.out,wildcards.fov,wildcards.z,input.masks)
+
+rule compile_masked_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'masked_brightness_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'masked_brightness_report.t')
+    shell:
+        "touch \"{output}\""
+
+rule brightness_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    output:
+        out=os.path.join(config['results_path'],'brightness_report_{fov}_{z}.pdf')
+    run:
+        reports.generate_brightness_reports(input.img_stack,input.coord_file,output.out,wildcards.fov,wildcards.z)
+
+rule compile_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'brightness_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'brightness_report.t')
+    shell:
+        "touch \"{output}\""
+
+
+
+
+rule focus_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = expand(os.path.join(config['results_path'],'imgstack_{{fov}}_{z}.npy'),z=zs),
+        coord_file = expand(os.path.join(config['results_path'],'coord_{{fov}}_{z}.json'),z=zs)
+    output:
+        out=os.path.join(config['results_path'],'focus_report_{fov}.pdf'),
+        out_csvs = os.path.join(config['results_path'],'focus_report_{fov}.csv')
+    run:
+        reports.generate_focus_reports(input.img_stack,input.coord_file,output.out,output.out_csvs,wildcards.fov)
+
+rule compile_focus_report:
+    threads:1
+    message: default_message
+    input:
+        files = expand(os.path.join(config['results_path'],'focus_report_{fov}.pdf'),fov=fovs),
+        csvs = expand(os.path.join(config['results_path'],'focus_report_{fov}.csv'),fov=fovs)
+    output:
+        combined = os.path.join(config['results_path'],'focus_report_all_fov.pdf'),
+        out = os.path.join(config['results_path'],'focus_report.t')
+    run:
+        reports.compile_focus_report(input.csvs,output.combined,irs,wvs)
+        shell("touch \"{output.out}\"")
+
+
+rule decodability_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json'),
+        codebook_file = config["codebook_file"],
+        data_organization_file = config["data_org_file"]
+    output:
+        out=os.path.join(config['results_path'],'decodability_report_{fov}_{z}.pdf'),
+        out_stats = os.path.join(config['results_path'],'decodability_stats_{fov}_{z}.txt')
+    run:
+        reports.generate_decodability_reports(input.img_stack,input.coord_file,output.out,input.codebook_file,input.data_organization_file,wildcards.fov,wildcards.z,output.out_stats)
+
+
+rule compile_decodability_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'decodability_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'decodability_report.t')
+    shell:
+        "touch \"{output}\""

data/config.json

@@ -0,0 +1,14 @@
+{
+    "raw_data_path":"/Volumes/MERFISH_COLD/XP2059",
+    "results_path":"/Volumes/MERFISH_COLD/XP2059/1/1/2059_merFISH_report_results",
+    "raw_image_format": "{wv}nm, Raw/merFISH_{ir}_{fov}_{z}.TIFF",
+    "channel": [],
+    "fov": ["001"],
+    "ir": [],
+    "z": ["01","02","03"],
+    "isRemote":"N",
+    "azure_command":"azcopy cp \"{}\" \"{}\" --recursive",
+    "delete_stack":"N",
+    "codebook_file": "/Volumes/MERFISH_COLD/Codebooks/C1E1_codebook_shahid.csv",
+    "data_org_file":"/Volumes/MERFISH_COLD/data_organization/data_organization.csv"
+}

data/debug_reports.py

@@ -0,0 +1,176 @@
+import json
+import os
+import numpy as np
+from utils import fileIO,imgproc
+import reports
+import makereports
+from pathlib import Path
+import time
+import re
+
+coord_file = "config.json"
+a_file = open(coord_file, "r")
+config = json.load(a_file)
+
+
+fov = '001'
+z='02'
+
+def download_azure():
+    
+    if config["isRemote"]=="N" or os.path.exists(os.path.join(config['results_path'],'data')):
+        return None
+    
+    os.system(config["azure_command"].format(config["raw_data_path"],os.path.join(config['results_path'],'data')))
+    
+    return None
+
+def check_params():
+
+    # Check to see if there are any params that need to be found
+    b_findz = len(config['z'])==0
+    b_findfov = len(config['fov'])==0
+    b_findir = len(config['ir'])==0
+    b_findwv = len(config['channel'])==0
+
+
+    # get all the images
+    if config["isRemote"] == "Y":
+        files = Path(os.path.join(config['results_path'],'data')).rglob('*.TIFF')
+    else:
+        files = Path(config['raw_data_path']).rglob('*.TIFF')
+    files = list(map(str,files))
+    # Find all channel, ir, fov, and z from the file names
+    re_filter = r"(.*)(\d{3})(?=nm).*(\B\d{2})\D(\B\d{3})\D(\B\d{2}\b)"
+    param_filter = re.compile(re_filter)
+    results = list(map(param_filter.search,files))
+    
+    results =list(filter(lambda x: isinstance(x,re.Match), results))
+    # Get the list of parameters if they are needed
+    #   Group 0 is the full match, so each capture group is 1 indexed
+    if b_findz:
+        zs = list(map(lambda x: x.group(5),results))
+    else:
+        zs = config['z']
+
+    if b_findir:
+        irs = list(map(lambda x: x.group(3),results))
+    else:
+        irs = config['ir'] 
+
+    if b_findfov:
+        fovs = list(map(lambda x: x.group(4),results))
+    else:
+        fovs = config['fov']
+
+    if b_findwv:
+        wvs = list(map(lambda x: x.group(2),results))
+    else:
+        wvs = config['channel']
+
+    if isinstance(results,list) and len(results)>0:
+        full_raw_path = results[0].group(1)
+    else:
+        full_raw_path=''
+
+
+    return sorted(list(set(zs)),key=int),sorted(list(set(fovs)),key=int),sorted(list(set(irs)),key=int),sorted(list(set(wvs)),key=int),full_raw_path
+
+
+def check_dirs(files):
+    """Checks to see if the directories for the files in the list exist. If they dont, then make those directories
+
+    Args:
+        files (list[str]): the list of files whose directory paths to create. Needs to be the full, not relative paths
+        
+    """
+    if not type(files) == list:
+        d = os.path.dirname(files)
+        if not os.path.isdir(d):
+            # print(files+'files' + d +'Does not exist')
+            os.makedirs(d)
+    else:
+        for f in files:
+
+            d = os.path.dirname(f)
+            if d != "" and not os.path.isdir(d):
+                # print(f+'files' + d +'Does not exist')
+                os.makedirs(d)
+
+download_azure()
+zs,fovs,irs,wvs,full_raw_path = check_params()
+
+def create_image_stack():
+
+    out_file = os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')
+    coord_file = os.path.join(config['results_path'],f'coord_{fov}_{z}.json')
+    check_dirs(out_file)
+
+    fileIO.create_image_stack(os.path.join(full_raw_path,config['raw_image_format']),fov,z,irs,wvs,out_file,coord_file)
+
+def create_deconvolved_images():
+    in_file = os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')
+
+    out_file = os.path.join(config['results_path'],'deconvolved',f'deconvolved_{fov}_{z}.npy')
+    check_dirs(out_file)
+    imgproc._deconvolute(in_file,out_file)
+
+def create_brightness_report():
+    
+    img_stack = os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')
+    coord_file = os.path.join(config['results_path'],f'coord_{fov}_{z}.json')
+    
+    out=os.path.join(config['results_path'],f'brightness_report_{fov}_{z}.pdf')
+    check_dirs(out)
+    
+    makereports.brightness_worker(img_stack,coord_file,out,fov,z)
+
+def create_focus_report():
+    
+    img_stack = [os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')]
+    coord_file = [os.path.join(config['results_path'],f'coord_{fov}_{z}.json')]
+    
+    out=os.path.join(config['results_path'],f'focus_report_{fov}.pdf')
+    out_csv = os.path.join(config['results_path'],f'focus_report_{fov}.csv')
+    check_dirs(out)
+    makereports.focus_worker(img_stack,coord_file,out,out_csv,fov)
+
+
+def compile_focus_reports():
+
+    in_files = [os.path.join(config['results_path'],f'focus_report_{fov}.csv')]
+    output = 'full_report_debug.csv'
+    
+    makereports.compile_focus_report(in_files,output=output,irs=irs,wvs=wvs)
+
+
+if __name__=='__main__':
+    start_time = time.time()
+
+    sub_start_time = time.time()
+    create_image_stack()
+    sub_end_time = time.time()
+    print(f'Image Stack: {sub_end_time-sub_start_time}')
+    
+    sub_start_time = time.time()
+    create_deconvolved_images()
+    sub_end_time = time.time()
+    print(f'Deconvolved Stack: {sub_end_time-sub_start_time}')
+
+    sub_start_time = time.time()
+    create_brightness_report()
+    sub_end_time = time.time()
+    print(f'Brightness Report: {sub_end_time-sub_start_time}')
+
+    sub_start_time = time.time()
+    create_focus_report()
+    sub_end_time = time.time()
+    print(f'Focus Report: {sub_end_time-sub_start_time}')
+
+    sub_start_time = time.time()
+    compile_focus_reports()
+    sub_end_time = time.time()
+    print(f'Compiled Focus Report: {sub_end_time-sub_start_time}')    
+
+    end_time = time.time()
+    print(f'Total Time: {end_time-start_time}')

data/environment.yml

@@ -0,0 +1,11 @@
+name: experiment_report
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - python=3.7
+  - snakemake
+  - matplotlib
+  - scikit-image
+  - numpy
+  - scikit-learn

data/imagej_macro/ImageJ_plugins/3D_suite/imageware.jar

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+size 103743

data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d-core3.93.jar

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+size 814496

data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d_plugins3.93.jar

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+size 252590

data/imagej_macro/ImageJ_plugins/3D_viewer/.directory

@@ -0,0 +1,4 @@
+[Dolphin]
+PreviewsShown=true
+Timestamp=2016,7,1,12,4,53
+Version=3

data/imagej_macro/ImageJ_plugins/3D_viewer/3D_Viewer-4.0.1.jar

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data/imagej_macro/ImageJ_plugins/3D_viewer/VIB-lib-2.1.1.jar

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+size 635940

data/imagej_macro/ImageJ_plugins/3D_viewer/gluegen-rt-2.3.2.jar

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+size 345605

data/imagej_macro/ImageJ_plugins/3D_viewer/j3dcore-1.6.0-scijava-2.jar

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+size 1944932

data/imagej_macro/ImageJ_plugins/3D_viewer/j3dutils-1.6.0-scijava-2.jar

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+size 1047514

data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-linux-amd64.jar

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+size 224010

data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-macosx-universal.jar

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+size 443876

data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-windows-amd64.jar

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+size 240721

data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2.jar

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+size 3414448

data/imagej_macro/ImageJ_plugins/3D_viewer/vecmath-1.6.0-scijava-2.jar

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+version https://git-lfs.github.com/spec/v1
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+size 164203

data/imagej_macro/ImageJ_plugins/readme.txt

@@ -0,0 +1,35 @@
+Please copy all folders here to ImageJ/plugins/ folder and relaunch ImageJ platform
+
+For update, you just need to replace TissueJ4Merfish.jar in the folder spatial3dtissuej_plugin/ by the most updated plugin and relaunch ImageJ. 
+
+
+
+Other notes: 
+For 3D viewer, you can copy 3dviewer plugins folder into your plugins directory
+or install latest 3d viewer plugin from Fiij
+Note: if the program do not work, the main issue may be due lack of supporting packages from 3D viewers. In this case, you can delete your current 3d viewer version, and replaced by the folder that I provided in this github
+
+For Fiji_plugins.jar you can use the latest version in your local ImageJ
+
+When launching ImageJ, platform will notice you a plugin with multiple versions in plugins folder, and overwrite it using one version. In this case, you can go into your local ImageJ/plugins folder, and remove one old version of plugin
+
+Acknowledgement: 
+See more at: 
+https://mcib3d.frama.io/3d-suite-imagej/
+J. Ollion, J. Cochennec, F. Loll, C. Escudé, T. Boudier. (2013) TANGO: A Generic Tool for High-throughput 3D Image Analysis for Studying Nuclear Organization. Bioinformatics 2013 Jul 15;29(14):1840-1.
+
+The 3D suite would like to thank P. Andrey, J.-F. Gilles and the developers of the following plugins :
+
+    Imagescience
+    LocalThickness
+    ConvexHull3D
+    3D Object Counter
+    Droplet Counter
+
+Links
+    BoneJ
+    3D Shapes
+    LabKit
+    3D Viewer
+    MorphoLibJ
+    CLIJ

data/imagej_macro/ImageJ_plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar

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+size 841067

data/imagej_macro/ImageJ_plugins/utils/3D_Convex_Hull.jar

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+size 130741

data/imagej_macro/ImageJ_plugins/utils/imagescience.jar

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+version https://git-lfs.github.com/spec/v1
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+size 283410

data/imagej_macro/ImageJ_plugins/utils/mpicbg_-1.4.1.jar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1c962431c6f7998d3e263a32308471128dd050373603d7419954fba456ce859
+size 138726

data/imagej_macro/bleed_throught_validate/bleed_throught_macro.ijm

@@ -0,0 +1,155 @@
+//// How to use
+//// Open macro from ImageJ menu
+//// Change parameter setting here, and run entire macro
+//// For input folder browser, choose the input image folder 
+//// It takes ~20 seconds to run this macro
+
+print("\\Clear");
+
+
+// Parameters setting
+source_image="merFISH_02_007_01_wavelength_561.TIFF";  // change parameter here
+target_image="merFISH_02_007_01_wavelength_647.TIFF";  // change parameter here
+suffixe=".TIFF";
+
+
+
+
+
+
+print("----------------------------------------------------------------");
+//dir = getArgument;
+dir=getDirectory("mouse"); // open a browser, allow you to choose input directory
+//dir = "yourlocaldir/bleed_throught_validate/raw/";  
+// ex: dir="/Users/htran/Documents/storage_tmp/merfish_XP2059/bleed_throught_validate/raw/";
+if (dir=="") 
+	exit ("No argument!");
+
+print("Working dir: "+dir+"\n");
+
+results_dir=File.getParent(dir)+"/results/";
+if(!File.exists(results_dir)) 
+      File.mkdir(results_dir);
+
+            
+
+
+					
+					
+short_name_source = substring(source_image,0,lastIndexOf(source_image,suffixe));
+short_name_target = substring(target_image,0,lastIndexOf(target_image,suffixe));
+IJ.log(short_name_source);
+IJ.log(short_name_target);
+
+// Source image first
+print("Loading image: "+source_image);
+open(dir+source_image);																				
+selectWindow(source_image);
+run("Enhance Contrast", "saturated=0.35");
+if (bitDepth > 8) {run("8-bit");}
+//run("Median...", "radius=2");  // in case you see lots of noises detected as signals
+run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+
+
+////only signals with intensity values from 250 to 255 are considered as signals, from my observation of spots and noise in images. 
+////You can use other thresholds, this macro just give an estimation, not provide accurate results for publication. 
+setThreshold(250, 255, "raw"); 
+//setThreshold(250, 255);
+setOption("BlackBackground", true);
+run("Convert to Mask");
+run("Grays");
+
+selectWindow(source_image);
+bin_source=short_name_source+"_BINARY";  //can open zip file from ImageJ to have tif format
+saveAs("ZIP", results_dir+bin_source+".zip");
+print("Deconvolution done!");
+
+
+
+					
+print("Loading image: "+target_image);
+open(dir+target_image);
+selectWindow(target_image);
+run("Enhance Contrast", "saturated=0.35");
+if (bitDepth > 8) {run("8-bit");}
+
+// ATTENTION: in case you see lots of noises detected as signals, because this image contains large amount of noises --> need to use median filter here
+// If you don't see lots of noise, please comment median filter.  
+run("Median...", "radius=2"); 
+
+run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+setThreshold(250, 255, "raw");
+//setThreshold(250, 255);
+setOption("BlackBackground", true);
+run("Convert to Mask");
+run("Grays");
+
+selectWindow(target_image);
+bin_target=short_name_target+"_BINARY"; //can open zip file from ImageJ to have tif format
+saveAs("ZIP",results_dir+bin_target+".zip");
+print("Deconvolution done!");
+
+
+
+
+
+
+// histogram here
+
+//// First, extracting signals that are overlapped in source and target images
+imageCalculator("AND create", bin_source+".tif", bin_target+".tif");
+selectWindow("Result of "+bin_source+".tif");
+output_image="bleedthrought_signals"; //can open zip file from ImageJ to have tif format
+saveAs("ZIP",results_dir+output_image+".zip");
+
+
+//Counting number of spots in source image
+selectWindow(bin_source+".tif");
+nBins = 256; 
+getHistogram(values, counts, nBins);
+source_spots=counts[255]; // number of spots
+IJ.log("Source image is: "+source_image);
+IJ.log("Number of spots in source image is: "+source_spots);
+
+
+//Counting number of spots in bleedthrought image
+selectWindow(output_image+".tif");
+getHistogram(values, counts, nBins);
+bleedthrought_spots=counts[255]; // number of spots
+IJ.log("Number of spots that bleed throught other wavelength channel is: "+bleedthrought_spots);
+
+
+//Counting number of spots in target image
+selectWindow(bin_target+".tif");
+getHistogram(values, counts, nBins);
+target_spots=counts[255]; // number of spots
+IJ.log("Target image is: "+target_image);
+IJ.log("Number of spots in target image is: "+target_spots);
+
+
+pct_bleed=100*bleedthrought_spots/source_spots;
+IJ.log("Percentage of bleed throught is: "+pct_bleed);
+
+pct_bleed_target=100*bleedthrought_spots/target_spots;
+IJ.log("Percentage of bleed throught is: "+pct_bleed_target);
+
+
+// Save results into a csv file
+setResult("source_img", 0, source_image);
+setResult("target_img", 0, target_image);
+setResult("pct_bleedthrought_source", 0, pct_bleed);
+setResult("pct_bleedthrought_target", 0, pct_bleed_target);
+updateResults();
+
+selectWindow("Results");
+saveAs("Results",results_dir+"bleed_throught_report.csv");
+selectWindow("Results");
+run("Close");
+print("Save output into the folder: "+results_dir);
+
+run("Close All");
+
+print("Completed");
+print("----------------------------------------------------------------");
+
+

data/imagej_macro/bleed_throught_validate/results/bleed_throught_report.csv

@@ -0,0 +1,2 @@
+ ,source_img,target_img,pct_bleedthrought_source,pct_bleedthrought_target
+1,merFISH_02_007_01_wavelength_561.TIFF,merFISH_02_007_01_wavelength_647.TIFF,0.258,17.386

data/imagej_macro/bleed_throught_validate/results/bleedthrought_signals.zip

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+oid sha256:169ff3492e5cff7155db973c062af8d630354b5898c2609de67e47438628b843
+size 3099

data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_561_BINARY.zip

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+size 72243

data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_647_BINARY.zip

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data/imagej_macro/blur_detector/detecting_blur_image.R

@@ -0,0 +1,85 @@
+suppressPackageStartupMessages({
+  # library(tidyverse)
+  library(dplyr)
+  library(ggplot2)
+  # library(igraph)
+  # library(ggraph)
+  library(data.table)
+  library(RColorBrewer)
+})
+
+
+read_input_data <- function(input_dir, total_pixels){
+  fns <- list.files(input_dir)
+  fns <- fns[grepl('.csv',fns)]
+  print("Number of csv histogram files: ")
+  print(length(fns))
+  good_signals_intensity_thrs <- 5
+  summary_stat <- tibble::tibble()
+  for(fn in fns){
+    df <- data.table::fread(paste0(input_dir, fn))
+    nbOverExp <- df %>%
+      dplyr::filter(intensity_val=='greater_30') %>%
+      dplyr::pull(counts)
+    total_counts <- df %>%
+      dplyr::filter(intensity_val!='greater_30')%>%
+      dplyr::mutate(intensity_val=as.numeric(intensity_val)) %>%
+      dplyr::filter(intensity_val>good_signals_intensity_thrs)%>%
+      dplyr::summarise(total_counts=sum(counts)) %>%
+      dplyr::pull(total_counts)
+    
+    stat <- tibble::tibble(image_fn=fn,
+                           pct_signals=round(100*total_counts/total_pixels,2),
+                           nb_overExp=nbOverExp)
+    summary_stat <- dplyr::bind_rows(summary_stat, stat)
+    
+    
+  }
+  return(summary_stat)
+}
+
+get_stat <- function(summary_stat, datatag, save_dir){
+  # fn <- 'merFISH_02_006_05_histogram.csv'
+  # View(df)
+  outliers <- tibble::tibble()
+  dim(summary_stat)
+  summary_stat$pct_overExp <- round(100*summary_stat$nb_overExp/total_pixels,3)
+  # summary(summary_stat$pct_signals)
+  # summary(summary_stat$pct_overExp)
+  # head(summary_stat)
+  
+  ## Detecting all outliers
+  outliers_top_thrs <- 0.95
+  outliers_top_FOVs <- summary_stat %>%
+    dplyr::filter(pct_signals > quantile(pct_signals, outliers_top_thrs))
+  print('Amount of outliers with large number of signals: ')
+  print(dim(outliers_top_FOVs))
+  outliers_top_FOVs$desc <- 'high_pct_signals'
+  outliers <- dplyr::bind_rows(outliers, outliers_top_FOVs)
+  
+  outliers_bottom_thrs <- 0.05
+  outliers_bottom_FOVs <- summary_stat %>%
+    dplyr::filter(pct_signals < quantile(pct_signals, outliers_bottom_thrs))
+  print('Amount of outliers with low signals: ')
+  print(dim(outliers_bottom_FOVs))
+  outliers_bottom_FOVs$desc <- 'low_pct_signals'
+  outliers <- dplyr::bind_rows(outliers, outliers_bottom_FOVs)
+  
+  artifact_thres <- 0.3
+  outliers_artifacts <- summary_stat %>%
+    dplyr::filter(pct_overExp>=artifact_thres)
+  outliers_artifacts$desc <- 'contain_artifacts'
+  outliers <- dplyr::bind_rows(outliers, outliers_artifacts)
+  print('Amount of artifacts: ')
+  print(dim(outliers_artifacts))
+  data.table::fwrite(outliers, paste0(save_dir, datatag, '_outliers.csv'))
+  
+}
+
+
+total_pixels = 1608 * 1608
+datatag <- 'XP2509'
+input_dir <- '/Users/htran/Documents/merfish_temp_storage/testing/results/'
+summary_stat <- read_input_data(input_dir, total_pixels)
+save_dir <- input_dir
+get_stat(summary_stat, datatag, save_dir)

data/imagej_macro/blur_detector/execute_blur_detector_dataset1.sh

@@ -0,0 +1,59 @@
+#!/bin/sh
+
+# batch macro input_filename
+# Ref: page 20: https://imagej.nih.gov/ij/docs/macro_reference_guide.pdf
+
+## Configuration MacOS
+imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+macro_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+project_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+
+series="dataset1/" 
+task="blur_detector1" 
+
+## ImageJ/ Fiji can be installed to Applications, or put into any folder in your drive. 
+# imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+# imagej_dir="/Applications/Fiji.app" ## MacOS Fiji java run folder
+# imagej_dir="/Users/htran/Downloads/Fiji.app/" ## MacOS Fiji java run folder - from my computer
+
+
+# imagej_exe_file=${imagej_dir}jars/ij-1.53q.jar  ## Fiji file location, in general file name is ij.jar, sometimes include version here
+imagej_exe_file=${imagej_dir}/Contents/Java/ij.jar ## ImageJ exe file location, in general file name is ij.jar, sometimes include version here
+
+
+macro_fn="${macro_dir}macro_blur_detector.ijm"
+
+
+
+log_file="${macro_dir}${task}.log"
+echo $log_file
+exec >> $log_file 2>&1 && tail $log_file
+
+input_dir="${project_dir}${series}"
+echo "__________________________________\n"
+echo "Input directory is: \n"
+echo $input_dir
+echo "Blur detector \n"
+## You can change memory amount, ex: 20000m to 30000m so program will run faster.
+## MacOS background mode here
+
+## If you have java environment jre installed in your computer
+java -Xmx10000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Otherwise using existing jre env from Fiji here
+# /Users/htran/Downloads/Fiji.app/java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar $imagej_dir/Contents/Java/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+# ${imagej_dir}java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Linux background mode here, using xvfb-run in case you run in server (graphical env), in local computer, java command is enough
+# xvfb-run -a java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+## In Linux local computer, java command is sufficient
+# java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+echo "Nucleus Segmentation Completed!"
+echo "__________________________________\n"
+
+
+
+
+

data/imagej_macro/blur_detector/execute_blur_detector_dataset2.sh

@@ -0,0 +1,59 @@
+#!/bin/sh
+
+# batch macro input_filename
+# Ref: page 20: https://imagej.nih.gov/ij/docs/macro_reference_guide.pdf
+
+## Configuration MacOS
+imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+macro_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+project_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+
+series="dataset2/" 
+task="blur_detector2" 
+
+## ImageJ/ Fiji can be installed to Applications, or put into any folder in your drive. 
+# imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+# imagej_dir="/Applications/Fiji.app" ## MacOS Fiji java run folder
+# imagej_dir="/Users/htran/Downloads/Fiji.app/" ## MacOS Fiji java run folder - from my computer
+
+
+# imagej_exe_file=${imagej_dir}jars/ij-1.53q.jar  ## Fiji file location, in general file name is ij.jar, sometimes include version here
+imagej_exe_file=${imagej_dir}/Contents/Java/ij.jar ## ImageJ exe file location, in general file name is ij.jar, sometimes include version here
+
+
+macro_fn="${macro_dir}macro_blur_detector.ijm"
+
+
+
+log_file="${macro_dir}${task}.log"
+echo $log_file
+exec >> $log_file 2>&1 && tail $log_file
+
+input_dir="${project_dir}${series}"
+echo "__________________________________\n"
+echo "Input directory is: \n"
+echo $input_dir
+echo "Blur detector \n"
+## You can change memory amount, ex: 20000m to 30000m so program will run faster.
+## MacOS background mode here
+
+## If you have java environment jre installed in your computer
+java -Xmx10000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Otherwise using existing jre env from Fiji here
+# /Users/htran/Downloads/Fiji.app/java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar $imagej_dir/Contents/Java/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+# ${imagej_dir}java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Linux background mode here, using xvfb-run in case you run in server (graphical env), in local computer, java command is enough
+# xvfb-run -a java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+## In Linux local computer, java command is sufficient
+# java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+echo "Nucleus Segmentation Completed!"
+echo "__________________________________\n"
+
+
+
+
+