diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..3974579
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,7 @@
+.git
+.cache
+.DS_Store
+__pycache__
+.Rhistory
+data
+metadata.tsv
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..bb5f857
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+.Rhistory
+.cache
+.DS_Store
+__pycache__
+data
+metadata.tsv
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..94a9ed0
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
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+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+
+ Copyright (C)
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff --git a/PARPAL.py b/PARPAL.py
new file mode 100644
index 0000000..1789806
--- /dev/null
+++ b/PARPAL.py
@@ -0,0 +1,607 @@
+import os
+import sys
+from shiny import ui, render, reactive, App
+import shinyswatch
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.io import imread
+from matplotlib import colors
+import asyncio
+
+## root of data
+DATAROOT=os.getenv("PARPAL_DATA_LOCATION", "/data")
+
+## Load data and compute static values
+meta=pd.read_table(f"{DATAROOT}/metadata.tsv")## CHANGE HERE ##
+
+## add DATAROOT env var to gfp path.
+meta["gfp path"]= f"{DATAROOT}/" + meta['gfp path']
+
+## Select genes of interes to choose from
+genesofi=sorted(meta["pairs display"].unique().tolist())
+
+## Select my picture colors
+mycmap=colors.LinearSegmentedColormap.from_list("custom", colors=["#000000",'#83f52c'], N=50)
+
+
+################################################################################
+##################### ASSUMPTIONS FOR THIS TO WORK #############################
+
+# 0 - Metadata must be in /data folder.
+# 1 - There must always be at 3 replicates, no more no less
+# 2 - There are only 2 Tpairs for each gene pair
+# 3 - Only one gene-pair at the time
+# 4 - Assuming "label" is always the same in the same format.
+
+##################### ASSUMPTIONS FOR THIS TO WORK #############################
+################################################################################
+
+
+# app_ui #######################################################################
+app_ui = ui.page_sidebar(
+ ## Beginnning of Sidebar
+ ui.sidebar(
+ ui.input_selectize(id = "genepairs", label = "Search for Paralogs:"
+ , choices= genesofi
+ , multiple = False
+ , selected = None
+ , remove_button = True
+ ).add_style("font-family:Helvetica Neue;"),
+ # ui.h1("Test"),
+ ui.input_action_button("submit", "Submit", class_="btn-success").add_style("background:#7393B3; border:white; font-family:Helvetica Neue;"),
+ ui.output_ui("compute"),
+ ui.output_text_verbatim("C3G", placeholder = False).add_style("text-align:center; background:#F8F8F8; border:white; font-size:9pt; font-family:Helvetica Neue;"),
+ ui.output_image("C3Gimage", inline = True).add_style("text-align:center; padding:10px;"),
+ shinyswatch.theme.litera(),
+ width=400,
+ padding=40, ## Padding btw things inside
+ ),
+ ## Beginning of the main page
+ # ui.h1("Test"),
+ ui.output_image("EKimage", inline = True).add_style("text-align:center; padding-right:100px; padding-left:100px;"),
+ ui.h3("Introduction").add_style("text-align:center; padding:40px; font-family:Helvetica Neue; font-size:22pt;"),
+ ui.div(),
+ ui.output_text_verbatim("message", placeholder = True).add_style("text-align:center; padding:10px; background:white; font-size:14pt; border:white; font-family:Helvetica Neue;"),
+ ui.page_navbar(
+ ui.nav_panel("Scores",
+ ui.output_table("paralogabund").add_style("text-align:center; padding-top:50px; padding-right:300px; padding-left:300px; font-size:11pt; font-family:Helvetica Neue;"),
+ ui.output_table("paralogredis").add_style("text-align:center; padding-top:30px; padding-bottom:50px; padding-right:300px; padding-left:300px; font-size:10pt; font-family:Helvetica Neue;"),
+ ),
+ ui.nav_panel("Replicate 1",
+ ui.output_plot("rep1", width = "1000px", height = "1000px").add_style("text-align:center; padding-top:50px;"),
+ ),
+ ui.nav_panel("Replicate 2",
+ ui.output_plot("rep2", width = "1000px", height = "1000px").add_style("text-align:center; padding-top:50px;"),
+ ),
+ ui.nav_panel("Replicate 3",
+ ui.output_plot("rep3", width = "1000px", height = "1000px").add_style("text-align:center; padding-top:50px;"),
+ ),
+ id="tab",
+ ),
+ ui.h3("References").add_style("text-align:center; padding:40px; font-family:Helvetica Neue;font-size:22pt;"),
+ ui.output_text_verbatim("acknowledge").add_style("text-align:center; padding:10px; background:white; font-size:14pt; border:white; font-family:Helvetica Neue;"),
+ # shinyswatch.theme.lux(),## Theme
+ # shinyswatch.theme.minty(),
+ shinyswatch.theme.litera(),
+ title="PARPAL",## Web title
+)
+
+# Server ######################################################################
+def server(input, output, session):
+
+ ## Add Image for side bar
+ @render.image
+ def C3Gimage():
+ img: ImgData = {"src": f"{DATAROOT}/images/c3g.jpg", "width": "50%"}
+ return img
+
+ ## Set code block for sidebar
+ @render.text
+ def C3G():
+ return f"\n\n\n\n\n\n\n\n\n\n\n\n" + \
+ f"Website developed by: \n" + \
+ f"Gerardo Zapata, Rohan Dandage, \n" + \
+ f"Vanessa Pereira and Elena Kuzmin \n\n" + \
+ f"In Collaboraton with: \n" + \
+ f"Canadian Centre for Computational Genomics (C3G) \n" + \
+ f"https://computationalgenomics.ca/team_profiles/#GZ"
+
+ ## Make little loading feature - form submit in sidebar
+ @output
+ @render.ui
+ @reactive.event(input.submit)
+ async def compute():
+ with ui.Progress(min=1, max=95) as p:
+ p.set(message="Calculation in progress", detail="This may take a while...")
+
+ for i in range(1, 35):
+ p.set(i, message="Computing")
+ await asyncio.sleep(0.05)
+
+ return
+
+ ## Add Image for main page
+ @render.image
+ def EKimage():
+ img: ImgData = {"src": f"{DATAROOT}/images/20240417_PARPAL_logo.png", "width": "80%"}
+ return img
+
+ ## remake metadata table --> for gene pair
+ @reactive.calc
+ def meta2():
+ meta2=meta[meta["pairs display"] == input.genepairs()].sort_values(by='label', ascending=False).reset_index(drop = True)
+ return meta2
+
+ ## make variable for the two genes
+ @reactive.calc
+ def gene1():
+ meta_of_step=meta2()
+ genes_of_step=meta_of_step["pairs"][1].split("-")
+ gene1=genes_of_step[0]
+ return gene1
+
+ @reactive.calc
+ def gene2():
+ meta_of_step=meta2()
+ genes_of_step=meta_of_step["pairs"][1].split("-")
+ gene2=genes_of_step[1]
+ return gene2
+
+ ## Set code block for text example
+ @render.text
+ def message():
+ return f"PARPAL is a web database for single-cell imaging \n" + \
+ f"of protein dynamics of paralogs revealing mechanisms of gene retention \n\n" + \
+ f"Database statistics: \n" + \
+ f"Proteins screened = 164 \n" + \
+ f"Paralog pairs screened = 82 \n" + \
+ f"Total micrographs = ~3.5K \n" + \
+ f"Total cells = ~460K \n\n" + \
+ f"For details on the PARPAL project, data and website please contact Elena Kuzmin: \n" + \
+ f"Email: elena.kuzmin@concordia.ca \n" + \
+ f"Website: https://kuzmin-lab.github.io/ \n\n"
+
+ ## abundance table
+ @render.table
+ @reactive.event(input.submit)
+ def paralogabund():
+ meta_of_step=meta2()
+
+ ## Set table options for 3 decimal points
+ pd.options.display.float_format = "{:,.3f}".format
+
+ abund=pd.read_csv(f"{DATAROOT}/scores/by_label_abundance.tsv", sep='\t', float_precision='round_trip')
+ abund=abund[abund['label'] == meta_of_step["label"][0]]
+ abund=abund[['Paralog pair','protein abundance mean']]
+
+ return abund
+
+ ## redistribution table
+ @render.table
+ @reactive.event(input.submit)
+ def paralogredis():
+ gene_of_step1=gene1()
+ gene_of_step2=gene2()
+
+ ## Set table options for 3 decimal points
+ pd.options.display.float_format = "{:,.3f}".format
+
+ redis=pd.read_csv(f"{DATAROOT}/scores/by_gene_redistribution.tsv", sep='\t', float_precision='round_trip')
+ redis=redis[(redis['gene symbol'] == gene_of_step1) | (redis['gene symbol'] == gene_of_step2)]
+
+ return redis
+
+ ## Set variable for max intensity value per replicate
+ @reactive.calc
+ @reactive.event(input.submit)
+ def maxs1():
+ maxs=[0]
+ return maxs
+
+ ## Set variable for max intensity value per replicate
+ @reactive.calc
+ @reactive.event(input.submit)
+ def maxs2():
+ maxs=[0]
+ return maxs
+
+ ## Set variable for max intensity value per replicate
+ @reactive.calc
+ @reactive.event(input.submit)
+ def maxs3():
+ maxs=[0]
+ return maxs
+
+
+ ## Set text for replicate 1 ## CHANGE HERE ##
+ @render.text
+ @reactive.event(input.submit)
+ def rep1t(): ## CHANGE HERE ##
+ return f"This is the Beginning of Replicate 1"## CHANGE HERE ##
+
+ ## Set plot for replicate 1 ## CHANGE HERE ##
+ @render.plot
+ @reactive.event(input.submit)
+ def rep1():## CHANGE HERE ##
+ meta_of_step=meta2()
+
+ if len(meta_of_step[meta_of_step["replicate"] == "replicate1"]) > 0:## CHANGE HERE ##
+
+ ## set meta for replicate 1
+ meta_of_step=meta_of_step[meta_of_step["replicate"] == "replicate1"].sort_values(by='label', ascending=False).reset_index(drop = True)## CHANGE HERE ##
+
+ ## set gfp path for range
+ gfp_path=meta_of_step["gfp path"]
+
+ ## Get range of both labels
+ ima_range=range(meta_of_step.index.min()
+ , meta_of_step.index.max() + 1
+ )
+
+ ## Get maxs limits
+ maxlist=maxs1()
+ for i in ima_range:
+ im1=imread(fname = gfp_path[i])
+ maxi=im1.max()
+ maxlist.append(maxi)
+ maxlistT=maxlist[1:]
+
+ ## T n figures
+ n_figs=max(ima_range)+1
+
+ ## Set number of rows based on # of labels
+ n_rows=len(meta_of_step["label"].unique())
+
+ if n_rows > 2 :
+
+ ## Set fig --> for entire set of Axs
+ fig = plt.figure(figsize=(10,10), layout = 'tight')
+
+ ## Set number of rows based on # of labels
+ subplots = fig.subfigures(n_rows, 1)
+
+ #### Set Loop per number of labels
+ for j in range(0, n_rows):
+
+ ## n figures per row
+ nfigsr=round(n_figs/n_rows)
+
+ ## range of nfigsr --> n *** when there are exactly 3 rows
+ if len(meta_of_step[meta_of_step["Tpairs"] == meta_of_step["Tpairs"].unique()[0]]) > 4 and j < 2 :
+ range3=range(0,nfigsr*2+1)
+ elif len(meta_of_step[meta_of_step["Tpairs"] == meta_of_step["Tpairs"].unique()[0]]) < 5 and j == 0 :
+ range3=range(0, nfigsr+1)
+ else :
+ range3=range(nfigsr,n_figs+1)
+ # range3=range(nfigsr*j,nfigsr*(j+1)+1)
+
+ ## range *** when there are exactly 4 rows
+ range4=range(0, nfigsr*2+1) if j < 2 else range(nfigsr*2, n_figs+1)
+
+ ## set maxlistTT
+ maxlistTT=np.median(maxlistT)*0.3 if n_rows < 3 else min(maxlistT[min(range3):max(range3)]) if n_rows == 3 else min(maxlistT[min(range4):max(range4)])
+
+ ## If maxlistTT is less than then double
+ maxlistTT=maxlistTT*0.5 if maxlistTT < 300 and n_rows > 2 else maxlistTT
+
+ ## maxlistTT only for HTA1-HTA2 -- UGLY
+ maxlistTT=maxlistTT*0.55 if maxlistTT < 200 and n_rows > 2 and meta_of_step["pairs display"][0] == "HTA1-HTA2" else maxlistTT*0.2 if maxlistTT > 1000 and n_rows > 2 and meta_of_step["pairs display"][0] == "HTA1-HTA2" else maxlistTT
+
+ ## number of figs per "label" --> for the first unique label
+ n_cols=len(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]])
+
+ locals()["ax" + str(j)] = subplots[j].subplots(1, n_cols, sharex = True, sharey=True, squeeze = False) if n_cols == 1 else subplots[j].subplots(1, n_cols, sharex = True, sharey=True)
+ locals()["ax" + str(j)] = locals()["ax" + str(j)].ravel()
+
+ for i in range(0, n_cols):
+ test=imread(fname = meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['gfp path'].reset_index(drop = True)[i])
+ locals()["ax" + str(j)][i].imshow(X=test
+ , cmap = mycmap
+ , vmax = maxlistTT
+ )
+ locals()["ax" + str(j)][i].set_title(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['label display'].reset_index(drop = True)[i], size=8) ##CHANGE HERE##
+ locals()["ax" + str(j)][i].set_xticks([])
+ locals()["ax" + str(j)][i].set_yticks([])
+
+ fig.suptitle(' ', size=50)
+ return fig
+
+ else:
+ j=0
+
+ ## number of figs per "label" --> for the first unique label
+ n_cols=len(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]])##CHANGE HERE##
+
+ fig, axs = plt.subplots(1, n_cols, sharex = True, sharey=True, squeeze = False, figsize=(5, 20))
+ axs = axs.ravel()
+
+ ## Loop per axs
+ for i in range(0, n_cols):
+ test=imread(fname = meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['gfp path'].reset_index(drop = True)[i])
+ axs[i].imshow(X=test, cmap = mycmap
+ , vmax = np.median(maxlistT)*0.3
+ )
+ axs[i].set_title(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['label display'].reset_index(drop = True)[i], size=8)
+ axs[i].set_xticks([])
+ axs[i].set_yticks([])
+
+ fig.suptitle(' ', size=50)
+ return fig
+
+ else:
+
+ ## Notification "figure" for no image replicate
+ fig, ax = plt.subplots()
+ ax.axis([0, 1, 0, 1])
+ ax.tick_params(axis='x', colors='white')
+ ax.tick_params(axis='y', colors='white')
+ ax.spines['bottom'].set_color('white')
+ ax.spines['top'].set_color('white')
+ ax.spines['right'].set_color('white')
+ ax.spines['left'].set_color('white')
+ ax.set_title('No Replicate 1', size=15)## CHANGE HERE ##
+
+ return fig
+
+ ## Set plot for replicate 2 ## CHANGE HERE ##
+ @render.plot
+ @reactive.event(input.submit)
+ def rep2():## CHANGE HERE ##
+ meta_of_step=meta2()
+
+ if len(meta_of_step[meta_of_step["replicate"] == "replicate2"]) > 0:## CHANGE HERE ##
+
+ ## set meta for replicate 1
+ meta_of_step=meta_of_step[meta_of_step["replicate"] == "replicate2"].sort_values(by='label', ascending=False).reset_index(drop = True)## CHANGE HERE ##
+
+ ## set gfp path for range
+ gfp_path=meta_of_step["gfp path"]
+
+ ## Get range of both labels
+ ima_range=range(meta_of_step.index.min()
+ , meta_of_step.index.max() + 1
+ )
+
+ ## Get maxs limits
+ maxlist=maxs2()
+ for i in ima_range:
+ im1=imread(fname = gfp_path[i])
+ maxi=im1.max()
+ maxlist.append(maxi)
+ maxlistT=maxlist[1:]
+
+ ## T n figures
+ n_figs=max(ima_range)+1
+
+ ## Set number of rows based on # of labels
+ n_rows=len(meta_of_step["label"].unique())
+
+ if n_rows > 2 :
+
+ ## Set fig --> for entire set of Axs
+ fig = plt.figure(figsize=(10,10), layout = 'tight')
+
+ ## Set number of rows based on # of labels
+ subplots = fig.subfigures(n_rows, 1)
+
+ #### Set Loop per number of labels
+ for j in range(0, n_rows):
+
+ ## n figures per row
+ nfigsr=round(n_figs/n_rows)
+
+ ## range of nfigsr --> n *** when there are exactly 3 rows
+ if len(meta_of_step[meta_of_step["Tpairs"] == meta_of_step["Tpairs"].unique()[0]]) > 4 and j < 2 :
+ range3=range(0,nfigsr*2+1)
+ elif len(meta_of_step[meta_of_step["Tpairs"] == meta_of_step["Tpairs"].unique()[0]]) < 5 and j == 0 :
+ range3=range(0, nfigsr+1)
+ else :
+ range3=range(nfigsr,n_figs+1)
+ # range3=range(nfigsr*j,nfigsr*(j+1)+1)
+
+ ## range *** when there are exactly 4 rows
+ range4=range(0, nfigsr*2+1) if j < 2 else range(nfigsr*2, n_figs+1)
+
+ ## set maxlistTT
+ maxlistTT=np.median(maxlistT)*0.3 if n_rows < 3 else min(maxlistT[min(range3):max(range3)]) if n_rows == 3 else min(maxlistT[min(range4):max(range4)])
+
+ ## If maxlistTT is less than then double
+ maxlistTT=maxlistTT*0.5 if maxlistTT < 300 and n_rows > 2 else maxlistTT
+
+ ## maxlistTT only for HTA1-HTA2 -- UGLY
+ maxlistTT=maxlistTT*0.55 if maxlistTT < 200 and n_rows > 2 and meta_of_step["pairs display"][0] == "HTA1-HTA2" else maxlistTT*0.2 if maxlistTT > 1000 and n_rows > 2 and meta_of_step["pairs display"][0] == "HTA1-HTA2" else maxlistTT
+
+ ## number of figs per "label" --> for the first unique label
+ n_cols=len(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]])
+
+ locals()["ax" + str(j)] = subplots[j].subplots(1, n_cols, sharex = True, sharey=True, squeeze = False)
+ locals()["ax" + str(j)] = locals()["ax" + str(j)].ravel()
+
+ for i in range(0, n_cols):
+ test=imread(fname = meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['gfp path'].reset_index(drop = True)[i])
+ locals()["ax" + str(j)][i].imshow(X=test
+ , cmap = mycmap
+ , vmax = maxlistTT
+ )
+ locals()["ax" + str(j)][i].set_title(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['label display'].reset_index(drop = True)[i], size=8) ##CHANGE HERE##
+ locals()["ax" + str(j)][i].set_xticks([])
+ locals()["ax" + str(j)][i].set_yticks([])
+
+ fig.suptitle(' ', size=50)
+ return fig
+
+ else:
+ j=0
+
+ ## number of figs per "label" --> for the first unique label
+ n_cols=len(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]])##CHANGE HERE##
+
+ fig, axs = plt.subplots(1, n_cols, sharex = True, sharey=True, squeeze = False, figsize=(5, 20))
+ axs = axs.ravel()
+
+ ## Loop per axs
+ for i in range(0, n_cols):
+ test=imread(fname = meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['gfp path'].reset_index(drop = True)[i])
+ axs[i].imshow(X=test, cmap = mycmap
+ , vmax = np.median(maxlistT)*0.3
+ )
+ axs[i].set_title(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['label display'].reset_index(drop = True)[i], size=8)
+ axs[i].set_xticks([])
+ axs[i].set_yticks([])
+
+ fig.suptitle(' ', size=50)
+ return fig
+
+ else:
+
+ ## Notification "figure" for no image replicate
+ fig, ax = plt.subplots()
+ ax.axis([0, 1, 0, 1])
+ ax.tick_params(axis='x', colors='white')
+ ax.tick_params(axis='y', colors='white')
+ ax.spines['bottom'].set_color('white')
+ ax.spines['top'].set_color('white')
+ ax.spines['right'].set_color('white')
+ ax.spines['left'].set_color('white')
+ ax.set_title('No Replicate 2', size=15)## CHANGE HERE ##
+
+ return fig
+
+ ## Set plot for replicate 3 ## CHANGE HERE ##
+ @render.plot
+ @reactive.event(input.submit)
+ def rep3():## CHANGE HERE ##
+ meta_of_step=meta2()
+
+ if len(meta_of_step[meta_of_step["replicate"] == "replicate3"]) > 0:## CHANGE HERE ##
+
+ ## set meta for replicate 1
+ meta_of_step=meta_of_step[meta_of_step["replicate"] == "replicate3"].sort_values(by='label', ascending=False).reset_index(drop = True)## CHANGE HERE ##
+
+ ## set gfp path for range
+ gfp_path=meta_of_step["gfp path"]
+
+ ## Get range of both labels
+ ima_range=range(meta_of_step.index.min()
+ , meta_of_step.index.max() + 1
+ )
+
+ ## Get maxs limits
+ maxlist=maxs3()
+ for i in ima_range:
+ im1=imread(fname = gfp_path[i])
+ maxi=im1.max()
+ maxlist.append(maxi)
+ maxlistT=maxlist[1:]
+
+ ## T n figures
+ n_figs=max(ima_range)+1
+
+ ## Set number of rows based on # of labels
+ n_rows=len(meta_of_step["label"].unique())
+
+ if n_rows > 2 :
+
+ ## Set fig --> for entire set of Axs
+ fig = plt.figure(figsize=(10,10), layout = 'tight')
+
+ ## Set number of rows based on # of labels
+ subplots = fig.subfigures(n_rows, 1)
+
+ #### Set Loop per number of labels
+ for j in range(0, n_rows):
+
+ ## n figures per row
+ nfigsr=round(n_figs/n_rows)
+
+ ## range of nfigsr --> n *** when there are exactly 3 rows
+ if len(meta_of_step[meta_of_step["Tpairs"] == meta_of_step["Tpairs"].unique()[0]]) > 4 and j < 2 :
+ range3=range(0,nfigsr*2+1)
+ elif len(meta_of_step[meta_of_step["Tpairs"] == meta_of_step["Tpairs"].unique()[0]]) < 5 and j == 0 :
+ range3=range(0, nfigsr+1)
+ else :
+ range3=range(nfigsr,n_figs+1)
+ # range3=range(nfigsr*j,nfigsr*(j+1)+1)
+
+ ## range *** when there are exactly 4 rows
+ range4=range(0, nfigsr*2+1) if j < 2 else range(nfigsr*2, n_figs+1)
+
+ ## set maxlistTT
+ maxlistTT=np.median(maxlistT)*0.3 if n_rows < 3 else min(maxlistT[min(range3):max(range3)]) if n_rows == 3 else min(maxlistT[min(range4):max(range4)])
+
+ ## If maxlistTT is less than then double
+ maxlistTT=maxlistTT*0.5 if maxlistTT < 300 and n_rows > 2 else maxlistTT
+
+ ## maxlistTT only for HTA1-HTA2 -- UGLY
+ maxlistTT=maxlistTT*0.55 if maxlistTT < 200 and n_rows > 2 and meta_of_step["pairs display"][0] == "HTA1-HTA2" else maxlistTT*0.2 if maxlistTT > 1000 and n_rows > 2 and meta_of_step["pairs display"][0] == "HTA1-HTA2" else maxlistTT
+
+ ## number of figs per "label" --> for the first unique label
+ n_cols=len(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]])
+
+ locals()["ax" + str(j)] = subplots[j].subplots(1, n_cols, sharex = True, sharey=True, squeeze = False)
+ locals()["ax" + str(j)] = locals()["ax" + str(j)].ravel()
+
+ for i in range(0, n_cols):
+ test=imread(fname = meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['gfp path'].reset_index(drop = True)[i])
+ locals()["ax" + str(j)][i].imshow(X=test
+ , cmap = mycmap
+ , vmax = maxlistTT
+ )
+ locals()["ax" + str(j)][i].set_title(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['label display'].reset_index(drop = True)[i], size=8) ##CHANGE HERE##
+ locals()["ax" + str(j)][i].set_xticks([])
+ locals()["ax" + str(j)][i].set_yticks([])
+
+ fig.suptitle(' ', size=50)
+ return fig
+
+ else:
+ j=0
+
+ ## number of figs per "label" --> for the first unique label
+ n_cols=len(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]])##CHANGE HERE##
+
+ fig, axs = plt.subplots(1, n_cols, sharex = True, sharey=True, squeeze = False, figsize=(5, 20))
+ axs = axs.ravel()
+
+ ## Loop per axs
+ for i in range(0, n_cols):
+ test=imread(fname = meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['gfp path'].reset_index(drop = True)[i])
+ axs[i].imshow(X=test, cmap = mycmap
+ , vmax = np.median(maxlistT)*0.3
+ )
+ axs[i].set_title(meta_of_step[meta_of_step["label"] == meta_of_step["label"].unique()[j]]['label display'].reset_index(drop = True)[i], size=8)
+ axs[i].set_xticks([])
+ axs[i].set_yticks([])
+
+ fig.suptitle(' ', size=50)
+ return fig
+
+ else:
+
+ ## Notification "figure" for no image replicate
+ fig, ax = plt.subplots()
+ ax.axis([0, 1, 0, 1])
+ ax.tick_params(axis='x', colors='white')
+ ax.tick_params(axis='y', colors='white')
+ ax.spines['bottom'].set_color('white')
+ ax.spines['top'].set_color('white')
+ ax.spines['right'].set_color('white')
+ ax.spines['left'].set_color('white')
+ ax.set_title('No Replicate 3', size=15)## CHANGE HERE ##
+
+ return fig
+
+ ## Acknowledgement text
+ @render.text
+ def acknowledge():## CHANGE HERE ##
+ return f"All the images for each paralog pair, per replicate, have been thresholded separately for visualization only. \n\n" + \
+ f"Supplementary data files are available from here: \n\n" + \
+ f"Rohan Dandage, Mikhail Papkov, Brittany M. Greco, Dmytro Fishman, Helena Friesen, Kyle Wang, \n" + \
+ f"Erin Styles, Oren Kraus, Benjamin Grys, Charles Boone, Brenda Andrews,Leopold Parts, Elena Kuzmin" + \
+ f" \n'Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention.'" + \
+ f" \nbioRxiv (2023): 2023-11. doi: https://doi.org/10.1101/2023.11.23.568466"
+
+
+# Close app ####################################################################
+app = App(app_ui, server)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a5f8923
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,48 @@
+anyio==4.3.0
+appdirs==1.4.4
+asgiref==3.7.2
+click==8.1.7
+contourpy==1.2.0
+cycler==0.12.1
+fonttools==4.49.0
+h11==0.14.0
+htmltools==0.5.1
+idna==3.6
+imageio==2.34.0
+Jinja2==3.1.3
+kiwisolver==1.4.5
+lazy_loader==0.3
+linkify-it-py==2.0.3
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.3
+mdit-py-plugins==0.4.0
+mdurl==0.1.2
+networkx==3.2.1
+numpy==1.26.4
+packaging==24.0
+pandas==2.2.1
+pillow==10.2.0
+prompt-toolkit==3.0.36
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-multipart==0.0.9
+pytz==2024.1
+questionary==2.0.1
+scikit-image==0.22.0
+scipy==1.12.0
+setuptools==69.2.0
+shiny==0.7.0
+shinyswatch==0.4.2
+six==1.16.0
+sniffio==1.3.1
+starlette==0.34.0
+tifffile==2024.2.12
+typing_extensions==4.10.0
+tzdata==2024.1
+uc-micro-py==1.0.3
+uvicorn==0.28.0
+watchfiles==0.21.0
+wcwidth==0.2.13
+websockets==12.0
+wheel==0.43.0