Hello. My name is Marie Gérus-Durand, and I'm working for C erba Research Montpellier. Today, I will show you how we set up automatization of immunohistochemistry data analysis for protocol validation in Montpellier using JMP and its tools like dashboard and add-in functions.
First, some words about Cerba Research, it's a worldwide company with capabilities in all the continents. Here I highlighted in yellow the department I'm working for. It's a Histopathology EHC Department. As you can see, I'm based in Montpellier in France. W e have also other labs in US, in New York, and in Taiwan in Taipei.
First of all, what is immunohisto chemistry? The aim of the technique is to detect targets protein mainly on a tissue sample. Here you have a slice of a tissue, for example, when you do a biopsy. We will look at the targets of interest using antibodies, which will detect the target. This antibody is recognized by another one which is combined with chromal fluoroform or active components that allow the detection of the target. Here you see, for example, these three components are highlighted, meaning that antibodies bind it and we can detect it. After the experiment, we can can look at the slides under microscope or using a scanner, which allow visualization and analysis of the results.
On the next slide, I just zoom in so you can see better what it looks like. Here it's a skin sample and you have a cell nuclear in blue and the target of interest in red. One of the challenges within immunohisto chemistry and histopathology is that you have many possible protocols, colorations. Here on the left, it's two different histological colorations. It doesn't involve antibodies like I show you, but it just reactive with the different components of the tissues. You see here for the MOVAT, we have five colors. For the HE, we have only one color, but the intensity depends on the type of structure you are looking at in the tissue.
On the right, you have two immunohistochemistry protocol. One simplex, we called it, because we detect only one target, and it's a chromogenic here. It's in brown. On the top right, you have a multiplex. H ere we detect many components. Here it's a fourplex, four targets on the same slide, and each is revealed by different flow of work. You have different color for each of the targets.
Among of these coloration detection possibilities, then you have a multitude of possible analysis method. The slides can be analyzed by a pathologist, which will give us qualitative or semi- quantitative data, or by image analysis, which will give us quantitative data. Another layer of that is that you can have reportable parameters which are single. For example, if you have a simlplex, you detect the targets, only one target, and you assess only one parameter like percentage of positive cells, for example, or you can have many. For one target, you can have the percentage of positive cells and a specific histology score. Or if you have a multiplex, then you can multiply this for all targets in the multiplex. Each report level parameter is target- dependent. You can imagine that we have a lot of combination that we can have to access during our validations.
In Cerba Research M ontpellier, in 2022, we have a small part, like 20 % of our project related to animals. We are studying animal samples. The other projects were on human samples. Among this, most of them are four clinical trials of the project. That's a very [inaudible 00:04:56] . W e have some others that are outside clinical trials, a quarter of them, and a small portion, 3 % that are CAP compliant. CAP is a specific regulation for US. It is to know that before being used in a clinical trial, we should demonstrate that our protocol that we developed in Montpellier show consistency in results for section of the same sample. If we analyze the sample at different type points, for example, the samples of patients involved in the study, the first year should be the same than in the five years after.
On the different automatons, we have at our different sites, and when the samples are analyzed by different operators or pathologists. This applies a rigorous validation according to the health agency, and this validation is mainly based on statistical criterion.
The implication for the company is that we need to increase the team members to support the increasing number of projects we have each year, and we need a normal genus statistical analysis pipeline to be sure that we will give all our clients the same type of results. Obviously, we need statistical analysis tool, and it's when we choose JMP to support our validation of the protocol.
Today, I will show you only a part of what we are doing because I don't have time to show you everything. I chose a quite simple example. It's a kind of experiment we do to validate the precision of the protocol, meaning that we check the intra-run , which is called repeatability precision over three slides. Here are the three ones highlighted in purple in the same cycle. The three slides I run at the same time, they come from the same sample, and we just check that we have the same data. The inter-run, reproducibility, test over two slides highlighted in blue in each cycle. In total, we have six slides over three cycles.
For reportable parameters, I will use an image analysis dataset, which is quantitative data and usually easier to analyze. We will have two reportable parameters for one target.
How do you start? First, we need to import the data. I don't know if you are familiar with that. But in our case, we have data either from Word documents, so we use the Word Import Tool available on JMP community website . I put the link here so you can go and find it again. Or we import data from Excel either directly by opening the file in JMP or by using the JMP tool in Excel.
For this presentation, just to be faster, I create some script to help me to focus on the dashboard creation after that, which will take more time. But this is just some script and just to be faster, but I will not develop into it then. Here I will open a data table from Project X here. As you can see, it's a quite simple table. I have four columns with the validation, the slide ID, which are internal slide numbers, and the dataset for my two reportable parameters. This data continues because they come from image analysis.
Once I have this, I will need to prepare my data. It's a most time consuming part of analysis data. Again, it's why I have a script. You see the five columns where I did. The sample to be able to correlate each data to the same sample, which is a part of the slide ID we have internally. I just get a formula here to help me to do that. The slide number, which are the last digits of the slide ID, and the slide order, the 1, 2, 3, 4, 5, 6, 7 for each sample. Thanks to this slide ordering, I would say, I implement the repeater. The three first slides which were staying in the same cycle for repeater, and two first slides of each cycle for reproductivity test.
Here I have all the information needed to do my analysis. I go back to my journal, and we will want to do the dashboard question. I still have some steps to do before that because I would like to have all the analysis I want to put in the dashboard. Here are the two little table you see where we are required to analyze the CV of our protocol for each sample for repeatability. I selected only the three first slides thanks to the local data filter. The same for reproducibility, where you see I have the slides from the reproducibility column.
Here are the data that I need. This data I updated them in here. You see I have much more columns now. It's easier to find the name here. I have the sample CV for repeatability, for reportable parameter 1 and 2, and then the same for reproducibility for the two parameters, and then I do the mean of both samples for each of these columns. Here, all the data I will need to implement in my dashboard. I can cross this table. I don't need them anymore.
I will now do the graphs and tables that will really fit in the dashboard. Again, what I want to show to the client is the distribution of the data for repeatability. I put as well the standard deviation and the mean. Usually, it's pretty good here and for reproducibility where I have all my six slides. Then I would put a table with a CV for each sample for repeatability and the reproducibility on the left for the first parameter and on the right for the second one. The same outline for the mean of the two samples.
These are the four part I want to show on my dashboard. I will show you how it looks like. I want to obtain something like that. This is often I did that I can show it, the two graphs and the two table. It's what I would show you how to do now. You see that all the graphs are to the same data table, sorry, and it's much easier to do the dashboard after. I saved as well all the scripts so I can redo them whenever I want. I will create a new file, new dashboard. You have many templates. I usually start from blank and just you have to put in what you want to see. Sometimes it's a bit difficult because it's small, but we always manage to find our way.
My table at the bottom, you just drop them where you want to have them. It's pretty simple. You can change the names of each part, so I will not do it just. But you can see that you can edit all the parts. You can run your script and then give you the dashboard. It's pretty similar to what I showed you before. I have my two tables at the bottom and my two graphs at the top here. I have inverted the two, so I usually prefer to start with repeat that. I will just switch them. If I put it where something was already, they just switch.
Here we are. This is the layout I want, so it's good. After, you can play to see better, more or less, of the table, et cetera. But this is just for visual [inaudible 00:15:00] process. Then, okay, now we have dashboard, but it would be more interested if we can directly go for the dashboard when you have all your data you want on the dashboard and click and you have a dashboard. For that, we just need to do an add- in. It's quite simple. Thanks to the magic triangle, I call it, the red triangle here. You click Save Script and just To Add-I n. Then it will create a script that will do the same dashboard again.
Let's go back to the data table. I will close this one. You are sure that the one that you will see is not this one. Sorry, I shouldn't have closed it before doing the dashboard. I will just use this to be faster and not to create it again. Here, if you click on the red, Save Script, To Add -In. Then this is the name you will have in the add- in list, but it's to manage your add- ins, I would say, but it's not the one that will figure out in the add-in tab in JMP. The name that you're in the add-in tab is this one. For today, I would just call it Test so I know which one is this. Save. You see here, you have all the script used by JMP to do this dashboard, and I will save it in our Project X. Here you see it take the name on the first tab Dashboard only.
I save it. Here I have this both tick Install after save, so it was already put in my add-in list. If the box was not ticked, then you have just to go to the location. You save your file and click on it and it's installed. Now I can close this dashboard .
I have created my complement already. Now, how to use it? I just went a bit faster. If you open it, it installs. As I have it already, I will not start it. It's just under it. If you go to View, Add-ins, sorry, Dashboard, and Unregister, it's not erased. I will find it again when I go to my project. Here you see I have it. If I double -click, it ask me if I want to install it. Sure, I want to install it. It's back here again. You can share it. You just have to copy the same file I clicked on and paste it in a shared folder or send it to a JMP user colleague. You can modify it. For this you have to go in Open. Again, this is the dashboard but then the black click this time, just go on the arrow here to open and Open using Add-In Builder.
Here you go back to the first time window where you have your script here, and you can either edit the script or put other functions that I don't really use, to be honest. But you have many functions. I'm sure you will find more information on JMP website about that. This, for example, will allow you to put all the preparation step in the same complement. When you run, everything is done at the same time. This is it.
In conclusion, using this dashboard and add-in functions allow us to have reports consistency because we have always the same set up of results to send to the clients. We increase the traceability. Thanks as well for the use of the scripts because we are sure that we are all doing the same. I t's a great time saver because as you say, I just have to click on one button and I have my dashboard. If you combine this with a precision timing. All the data preparation, then you have your table, you click on one button, and you have everything done. It's a great time saver.
It's all I wanted to show you today. I hope you enjoy it. If you have any question, don't hesitate to reach out to me either by email— you have the email on the first slide here— or through the JMP community. Thank you.