# Simple Pipeline

The [Getting Set Up](https://disc4all-qupath.gitbook.io/qupath-project/fundamentals/getting-set-up) page explains what and how to download everything needed in this tutorial. 

<figure><img src="https://2829430504-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FSleK316zl0BYwa7DfK2J%2Fuploads%2FuiImtEqDO57J6iYvLivu%2Fimage.png?alt=media&#x26;token=486f7646-c2f1-4544-a57f-0489247249e4" alt=""><figcaption><p>Pipeline of the Project. As a first step, all the H-DAB stained slides need to be slide scanned and stored in a Folder. In QuPath a project will be created as a final step and Object classifier gets trained recognising detected objects as positive cells, negative cells, or no cell artifacts. With git bash a prewritten Python script gets called resulting in an outcome sheet linking the ID_Slidescanning with the ID_Sample and providing us with the positivity rate of each sample. With the merger, the donor information gets linked with the positivity rate resulting in a final Excel spreadsheet. </p></figcaption></figure>

The prewritten QuPath script that will be downloaded at the end of the tutorial needs information that's specific to your slides. Therefore we start by creating a project in QuPath and creating an object classifier, that distinguishes between immuno-positive and negative cells. To show the script where it needs to detect and classify the cells either a manual region detection or an automatic threshold can be chosen.&#x20;

Afterward, the project-specific parts get integrated into the code and we can run a batch analysis on hundreds of slides.  The results get processed by a Python script that you can locally run, resulting in a spreadsheet containing the positivity rate for each slide.&#x20;

### Step 1: [Opening Scans in QuPath](https://disc4all-qupath.gitbook.io/qupath-project/qupath-h-dab-docs/qupath-h-dab-tutorial)

### Step 2: [Train object classifier](https://disc4all-qupath.gitbook.io/qupath-project/qupath-h-dab-docs/qupath-h-dab-tutorial/object-classifier)

### Step 3: [Running QuPath script locally](https://disc4all-qupath.gitbook.io/qupath-project/qupath-h-dab-docs/qupath-script)

### Step 4: [Collect the results file](https://disc4all-qupath.gitbook.io/qupath-project/qupath-h-dab-docs/output)

### Step 5: [Move the Inventory file to the same directory](https://disc4all-qupath.gitbook.io/qupath-project/result-analysis-docs/processing-package-tutorial#step-by-step-instructions)&#x20;

### Step 6: [Call ProcessScan Class in the directory](https://disc4all-qupath.gitbook.io/qupath-project/result-analysis-docs/processing-package-tutorial)

### Step 7: [<mark style="color:blue;">Create a final spreadsheet merging everything</mark>](https://disc4all-qupath.gitbook.io/qupath-project/result-analysis-docs/final-spreadsheet-creation)