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EdRey05 authored Jan 15, 2024
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information for over <strong>2,500 patients</strong> and I generated tools in <strong>Jupyter/Colab notebooks</strong> and a
<strong>Streamlit app (see GIF --></strong>) to automate the creation of these plots with <strong>Python</strong>. <br><br>
At the end of the project, we were able to identify <strong>less than 10 gene pairs</strong> showing the behavior of interest. That
information was used in combination with other data from different techniques (in-silico and wet-lab) to prioritize further
studies evaluating the effect of inhibtion of those genes in cancer cell models. </p>
information was used in combination with other data from different techniques (<i>in silico</i> and <i>in vitro</i>) to prioritize
further studies evaluating the effect of inhibtion of those genes in cancer cell models.
</p>
</div>
<div class="right-column-65">
<br><br>
<video width="100%" height="auto" autoplay loop muted><source src="Images_GIFs_Videos/Preview_003.mp4" type="video/mp4"></video>
<p class="center-text">To see in full screen, right click on image and select "Open in new tab" </p>
</div>
</div>
</p>
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<strong>any measured variable</strong> to divide the dataset into <strong>2 or more groups</strong> and
re-plotting curves easily (<a href="https://github.com/EdRey05/Resources_for_Mulligan_Lab/blob/de82796fe821b96c18ab0709018c02c3b02aba92/Tutorials/Preview_Interactive_KM.gif" target="_blank">View tool</a>). </li>
<li class="justify-text">Finally, I discovered <strong>Streamlit</strong> and adapted my interactive notebook to a <strong>data app</strong>
(GIF above) that used a similar approach but has <strong>more interactivy, improved outputs and better user experience</strong>. </li>
(GIF above) that used a similar approach but has <strong>more interactivy, improved outputs and better user experience</strong>. </li>
<li class="justify-text">Although the app works well for several datasets, I noticed <strong>high variability in the formatting of clinical
trial data</strong>, and try to improve my app to generalize it more!. </li>
</ul>
</div>
</div>
<p><strong> <u>NOTE:</u> I am not planning on deploying my app to any server, it runs locally and I also set it up in Github Codespaces to share with others (see last section).</strong></p>
<p><strong> Read the instructions and watch another demo of the Streamlit app here: <a href="https://github.com/EdRey05/Streamlit_projects/tree/main/003_KM_plotter">Demo_KM_plotter</a></strong></p>
</details>

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<div class="two-columns">
<div class="left-column-35">
<p><strong> Summary </strong></p>
<p class="justify-text"> During my graduate studies, I performed numerous <strong>fluorescence microscopy experiments</strong>. These
experiments required to acquire sufficient images of individual cancer cells, process them and analyze them. Typical analysis of my
experiments would involve <strong>co-localization</strong> between signals on different channels, or <strong>object/particle detection
and counting</strong>. <br><br>
I carried out <strong>Proximity Ligation Assay (PLA)</strong> experiments to evaluate <strong>protein-protein interactions</strong>
using a cell imager (<strong>EVOS M7000 - Thermo Fisher</strong>) that has automated imaging features. I automated the acquisition of
hundreds of images, and wrote scripts in <strong>Jython</strong> (Python wrapper for Java) to automate the image pre-processing and
analysis in <strong>ImageJ/Fiji</strong>. The final output of my series of scripts was a <strong>csv file with the quantification
result for each individual cell</strong> and the <strong>cropped images with their object mask image</strong> (shows colored blobs
which are particles both detected and counted). <br><br>
To <strong>validate the quantification results</strong> before proceeding to statistical analysis, I designed a tool to <strong>consolidate
all the outputs</strong> into a summary Power Point presentation. I automated the creation of slides with a <strong>customized layout
</strong> and insertion of fluorescence + object mask <strong>image pairs with their names and quantification results</strong> using
the <strong>python-pptx</strong> library. <br><br>
I created a tool first in the form of a <strong>Google Colab notebook</strong> and then as a <strong>Streamilt data app</strong> which
helped me analyze outputs for <strong>almost 10,000 images</strong>. With this tool, I was able to easily <strong>compare two quantification
<p class="justify-text"> During my graduate studies, I performed numerous <strong>fluorescence microscopy experiments</strong>, acquiring
images of individual cancer cells <i>in vitro</i>. Typical analyses involved <strong>co-localization</strong> between signals coming
from different proteins, or <strong>object/particle detection and counting</strong>. <br><br>
For <strong>Proximity Ligation Assay (PLA)</strong> experiments, which evaluate <strong>protein-protein interactions</strong>, I used
a <strong>EVOS M7000 </strong> cell imager (Thermo-Fisher) to automate the acquisition of thousands of images. I also wrote scripts in
<strong>Jython</strong> (Python wrapper for Java) to automate their pre-processing and analysis in the <strong>ImageJ/Fiji</strong>
software. The outputs are a <strong>csv file with the object count for each individual cell</strong> and each <strong>cropped cell image
with its object mask image</strong> (shows colored blobs for particles detected and counted if met the criteria). <br><br>
To <strong>validate the outputs</strong> before statistical analysis, I designed a tool to <strong>consolidate all the outputs</strong>
for each experimental condition into a summary Power Point presentation. I automated the creation of slides with a <strong>customized layout
</strong> and inserted all relevant information and images using the <strong>python-pptx</strong> library. I created first a tool in the form
of a <strong>Google Colab notebook</strong> and then converted it into a <strong>Streamilt app</strong>. <br><br>
This tool helped me visualize outputs for <strong>almost 10,000 images</strong> and easily <strong>compare two quantification
methods, test different pre-processing and object detection parameters, and fully optimize the whole workflow for each experiment</strong>.
</p>
</div>
<div class="right-column-65">
<br><br>
<img src="Images_GIFs_Videos/Preview_002.gif" alt="Streamlit Projects 002 GIF" />
<p class="center-text">To see in full screen, right click on image and select "Open in new tab" </p>
</div>
</div>
<div class="two-columns">
<div class="left-column-50">
<p class="justify-text"><strong> Problem </strong></p>
<ul>
<li class="justify-text"> A. </li>
<li class="justify-text"> B. </li>
<li class="justify-text"> C. </li>
<li class="justify-text"> D. </li>
<li class="justify-text"> E. </li>
<li class="justify-text"> F. </li>
<li class="justify-text">Manually inserting, resizing, arranging and labeling all the images is incredibly <strong>time consuming and
prone to errors</strong>. </li>
<li class="justify-text"><strong>ImageJ/Fiji is not fully compatible with Python 3 code</strong>, so I could not integrate a feasible
solution into my other Jython scripts. </li>
<li class="justify-text">Each experimental group may be <strong>quantified by both methods, one or the other</strong>. </li>
<li class="justify-text">Depending on the quantification type, the output <strong>csv may contain less/additional columns</strong>. </li>
<li class="justify-text">The real image labels are in the csv alongside their count numbers, however, the fluorescence images are in one
subdirectory and adds a "_2" to their name, whereas the object mask image is in a different subdirectory and adds
a "_1" to their name (may be one or two sets of object masks, one for each quantification method used). </li>
<li class="justify-text">Due to the large number of images quantified per experimental group (<strong>100-500</strong>) we needed an
<strong>efficient layout</strong>, balancing image visibility and number of slides (<strong>fewer slides = faster
review</strong>). </li>
<li class="justify-text">Since our research group was planning on doing several more PLA experiments, <strong>automation</strong> was
essential. </li>
</ul>
</div>
<div class="right-column-50">
<p class="justify-text"><strong> Solution </strong></p>
<ul>
<li class="justify-text"> A. </li>
<li class="justify-text"> B. </li>
<li class="justify-text"> C. </li>
<li class="justify-text"> D. </li>
<li class="justify-text"> E. </li>
<li class="justify-text"> F. </li>
<li class="justify-text">I manually tested different arrangements of images + labels in rows and columns until <strong>I set one layout
that best worked for the type and amount of data I had</strong> (see app info page). </li>
<li class="justify-text">I <strong>measured and defined each item's coordinates</strong> and dove in the documentation of python-pptx to
figure out how to make that very specific layout (see app info page). </li>
<li class="justify-text">I generated the neccesary code to scan through a zip file in search for csv files, then read the content and go back
to the root directory for that experimental group to find the pairs of images to insert. </li>
<li class="justify-text">A big iterable is generated with names, counts, and image locations which are analyzed to separate in groups of
up to 20 for a single slide (see app info). </li>
<li class="justify-text">I implemented this approach first in a <strong>Google Colab notebook</strong> (<a href="https://user-images.githubusercontent.com/62916582/204415085-cc39bb7c-904e-487c-a16d-0d894c1e3249.gif" target="_blank">View tool</a>) and then created a <strong>Streamlit app</strong> (GIF above). The app has the <strong>same functionality
</strong> but <strong>better user experience</strong>, especially to read additional info on the input/output and the
design of the slides. </li>
<li class="justify-text">The app allows <strong>quick and easy automation</strong>, as the user only needs to upload a <strong>zip file with as
many experimental group folders as desired</strong> (with the outputs of my quantification script), and indicate the
quantification method in the app. </li>
</ul>
</div>
</div>
<p><strong> <u>NOTE:</u> I am not planning on deploying my app to any server, it runs locally and I also set it up in Github Codespaces to share with others (see last section).</strong></p>
<p><strong> Read the instructions and watch another demo of the Streamlit app here: <a href="https://github.com/EdRey05/Streamlit_projects/tree/main/002_Automated_PPTX_PLA">Demo_PPTX_PLA</a></strong></p>
</details>

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</p>
</div>
<div class="right-column-65">
<br><br>
<img src="Images_GIFs_Videos/Preview_001.gif" alt="Streamlit Projects 001 GIF" />
<p class="center-text">To see in full screen, right click on image and select "Open in new tab" </p>
</div>
</div>
<div class="two-columns">
<div class="left-column-50">
<p class="justify-text"><strong> Problem </strong></p>
<ul>
<li class="justify-text"> A. </li>
<li class="justify-text"> B. </li>
<li class="justify-text"> C. </li>
<li class="justify-text"> D. </li>
<li class="justify-text"> E. </li>
<li class="justify-text"> F. </li>
<li class="justify-text">A. </li>
<li class="justify-text">B. </li>
<li class="justify-text">C. </li>
<li class="justify-text">D. </li>
<li class="justify-text">E. </li>
<li class="justify-text">F. </li>
</ul>
</div>
<div class="right-column-50">
<p class="justify-text"><strong> Solution </strong></p>
<ul>
<li class="justify-text"> A. </li>
<li class="justify-text"> B. </li>
<li class="justify-text"> C. </li>
<li class="justify-text"> D. </li>
<li class="justify-text"> E. </li>
<li class="justify-text"> F. </li>
<li class="justify-text">A. </li>
<li class="justify-text">B. </li>
<li class="justify-text">C. </li>
<li class="justify-text">D. </li>
<li class="justify-text">E. </li>
<li class="justify-text">F. </li>
</ul>
</div>
</div>
<p><strong> <u>NOTE:</u> I am not planning on deploying my app to any server, it runs locally and I also set it up in Github Codespaces to share with others (see last section).</strong></p>
<p><strong> Read the instructions and watch another demo of the Streamlit app here: <a href="https://github.com/EdRey05/Streamlit_projects/tree/main/001_RNA_expression_DepMap">Demo_RNA_DepMap</a></strong></p>
</details>

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<details><summary markdown="span"> Expand this to read more...</summary>
<div class="two-columns">
<div class="left-column-35">
<br>
<br><br>
<p class="justify-text"> If you have a Github account, you can create a <strong>Github Codespace</strong> with all the requirements to
run my apps. You only have to log into you account, click on the button below, create your Codespace (<strong>we all have 60h of
free usage per month!</strong>), and follow the instructions in this video-->.<br>
*Note that due to size limits, I did everything quickly but added notes so pause, read and see where I clicked! <br><br></p>
free usage per month!</strong>), and follow the instructions in this video→. <br><br>
***Note that due to size limits, I did everything quickly but added notes so pause, read and see where I clicked! <br><br></p>
<div class="center-text">
<a href="https://codespaces.new/EdRey05/Streamlit_projects?quickstart=1" target="_blank">
<img src="https://github.com/codespaces/badge.svg" alt="Open in GitHub Codespaces">
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