Start numeric headers with bold, small typo fixes

internal_use/docs/source/AdvancedSegmentation/BBBC022_AnalysisExercise.md

@@ -74,7 +74,7 @@ a similar introductory exercise.
 
 ## **Input images and configure metadata**
 
-### 1. **Load images and metadata**
+### **1. Load images and metadata**
 
 - Start CellProfiler by double-clicking the desktop icon <img src="./TutorialImages/CellProfilerLogo.png" alt="CellProfiler icon" width="35"/>
 
@@ -86,7 +86,7 @@ a similar introductory exercise.
   images (with a file extension of ‘.npy’) are included in this data
   set.
 
-### 2. **Import metadata from the CSV**
+### **2. Import metadata from the CSV**
 
 So that we can explore what cells treated with different drugs look like later in the exercise, we must add this information into CellProfiler from the CSV. Provided with this exercise is a CSV called ‘20585_AE.csv’ detailing drug treatment
 info for each image.
@@ -113,7 +113,7 @@ info for each image.
     - Image_Metadata_PlateID (from the spreadsheet) is matched to Plate (extracted from the folder name by the second extraction step)
     - Image_Metadata_CPD_WELL_POSITION (from the spreadsheet) is matched to Well (extracted from the file name by the first extraction step)
 
-### 3. **Examine the channel mappings in NamesAndTypes (optional)**
+### **3. Examine the channel mappings in NamesAndTypes (optional)**
 
 The channel mapping here is a bit more complicated than anything we've worked with before- we have a single set of illumination correction images that map to each and every well and site.  We can use the metadata we extracted in the last module to make that association possible.
 
@@ -154,7 +154,7 @@ The channel mapping here is a bit more complicated than anything we've worked wi
 ```
 ## **Illumination correction**
 
-### 4. **Examine the output of the CorrectIlluminationApply module (optional)**
+### **4. Examine the output of the CorrectIlluminationApply module (optional)**
 
 Since microscope objectives don't typically have a completely uniform illumination
 pattern, applying an illumination correction function can help make segmentation
@@ -183,9 +183,9 @@ to the top of the field of view to see the greatest effect.
 *Figure 4: Application of the illumination correction functions.*
 ```
 
-## **Segmenet Nuclei, Cells and Cytoplasm**
+## **Segment Nuclei, Cells and Cytoplasm**
 
-### 5. **IdentifyPrimaryObjects- Nuclei**
+### **5. IdentifyPrimaryObjects- Nuclei**
 
 Next we'll take a first pass at identifying nuclei and cells in our initial image.
 
@@ -202,7 +202,7 @@ Next we'll take a first pass at identifying nuclei and cells in our initial imag
   the parameters for robustness later, however, the identification
   should be good but doesn’t need to be perfect before you move on.
 
-### 6. **IdentifySecondaryObjects- Cells**
+### **6. IdentifySecondaryObjects- Cells**
 
 - **After** the IdentifyPrimaryObjects module but **before** the
   EnhanceOrSuppressFeatures module, add an IdentifySecondaryObjects
@@ -215,7 +215,7 @@ Next we'll take a first pass at identifying nuclei and cells in our initial imag
   you feel you’re ready to test them on another image; they need not be
   perfect before you move on.
 
-### 7. **Test the robustness of your segmentation parameters across multiple compounds**
+### **7. Test the robustness of your segmentation parameters across multiple compounds**
 
 It's (relatively!) easy to come up with a good set of segmentation parameters for a single image or a set of similar images; this data set however contains images from cells treated with many different classes of drugs, many of which have very different phenotypes. It's valuable to learn how to create a set of parameters that can segment cells that display a variety of morphologies since you may come across a similar problem in your own experiments!
 
@@ -259,7 +259,7 @@ It's (relatively!) easy to come up with a good set of segmentation parameters fo
     \-  In IdentifyPrimaryObjects, adjusting the declumping settings (make sure to turn 'Use advanced settings?' on) will probably be necessary for a robust segmentation
     \-  In IdentifySecondaryObjects, you will want to test the effects of using the various methods for identifying secondary objects (Propagation, Watershed-Image, Distance-N, etc) and, if using Propagation, the regularization factor.
 
-### 8. **IdentifyTertiaryObjects- Cytoplasm**
+### **8. IdentifyTertiaryObjects- Cytoplasm**
 
 - **After** the IdentifySecondaryObjects module but **before** the
   EnhanceOrSuppressFeatures module, add an IdentifyTertiaryObjects
@@ -269,7 +269,7 @@ It's (relatively!) easy to come up with a good set of segmentation parameters fo
 
 ## **Segment Nucleoli inside the Nuclei**
 
-### 9. **Examine the steps used to segment the Nucleoli**
+### **9. Examine the steps used to segment the Nucleoli**
 
 - The next 3 modules have to do with the creation of the Nucleoli
   objects. Look at the output from each to see how the image is
@@ -308,7 +308,7 @@ It's (relatively!) easy to come up with a good set of segmentation parameters fo
 
 ## **Segment the Mitochondria inside the Cytoplasm**
 
-### 10. **Mask the Mito image by the Cytoplasm object**
+### **10. Mask the Mito image by the Cytoplasm object**
 
 Now that you’ve seen an example of how to segment an organelle, you
 will do so for Mitochondria in the following steps.
@@ -335,7 +335,7 @@ will do so for Mitochondria in the following steps.
 ```
 
 
-### 12. **IdentifyPrimaryObjects- Mitochondria**
+### **11. IdentifyPrimaryObjects- Mitochondria**
 
 - **After** your MaskImage module but **before** the RelateObjects
   modules, add an IdentifyPrimary Objects module to identify
@@ -350,7 +350,7 @@ will do so for Mitochondria in the following steps.
 
 ## **Perform Measurements**
 
-### 13. **Add measurement modules to your pipeline**
+### **12. Add measurement modules to your pipeline**
 
 - **After** your segmentation of the mitochondria but **before** the
   RelateObjects modules, add as many object measurement modules as you
@@ -426,7 +426,7 @@ only be able to examine one object at a time in CellProfiler Analyst.*
 
 ## **Relate Nucleoli and Mitochondria to their respective nuclei/cells**
 
-### 14. Examine the settings of RelateObjects
+### **13. Examine the settings of RelateObjects**
 
 - **After** your Measurement and **before** your Export modules you
   should find two RelateObjects modules. One relates Nucleoli to
@@ -439,7 +439,7 @@ only be able to examine one object at a time in CellProfiler Analyst.*
 
 ## **Perform the analysis on ALL the images**
 
-### 15. **Run the pipeline (optional)**
+### **14. Run the pipeline (optional)**
 
 - If you have time and/or if you’d like to play with the data in
   CellProfiler Analyst later, exit test mode, close the eyes next to

internal_use/docs/source/BeginnerSegmentation/CPbeginner_Segmentation.md

@@ -58,7 +58,7 @@ Read through the steps below and follow instructions where stated. Steps where y
 :width: 700
 :align: center
 
- Figure 2: **Main CellProfiler window**. To load images, drag and drop images into the right area. To load a pipeline (.ccpipe or .ccproj files), drag and drop the pipeline file into the left area.
+ Figure 2: **Main CellProfiler window**. To load images, drag and drop images into the right area. To load a pipeline (.cppipe or .cpproj files), drag and drop the pipeline file into the left area.
 ```
 
 - Drag and drop the `‘segmentation_start.cppipe’` file into the `‘Analysis modules’` pane on the left.
@@ -94,9 +94,9 @@ Read through the steps below and follow instructions where stated. Steps where y
   > **TIP** you can manually adjust brightness and contrast in the image display by right-clicking on it and going to `'Adjust Contrast'` 
 
 -------------------------------------------------------------------------------------------------------------------------
-###  3. [OPTIONAL STEP] Set up the input modules
+###  **3. [OPTIONAL STEP] Set up the input modules**
 
-  > *We suggest you skip this step for now, it will not affect the rest of the pipeline, as these modules have been properly set up in the starting pipeline (`segmentation_start.cpipe`).*
+  > *We suggest you skip this step for now, it will not affect the rest of the pipeline, as these modules have been properly set up in the starting pipeline (`segmentation_start.cppipe`).*
 
   > *At the end of this tutorial you will find instructions on how to set up the input modules*
 

internal_use/docs/source/QualityControl/BBBC022_QualityControlExercise.md

@@ -46,7 +46,7 @@ tools in CellProfiler and CellProfiler Analyst, preferably after
 completing the Translocation tutorial or a similar introductory
 exercise.
 
-## 1. **Start the provided QC pipeline on the BBBC022 dataset**
+## **1. Start the provided QC pipeline on the BBBC022 dataset**
 
 In order to do quality control, we need to first measure the images in many ways.
 This will allow us to do machine learning to use the measurements to identify the
@@ -73,7 +73,7 @@ good images from the bad.
   three files should be created- a .db database file, a .properties
   text file, and a .workspace text file.
 
-## 2. **Examine the QC pipeline (~15 minutes)**
+## **2. Examine the QC pipeline (~15 minutes)**
 
 - While the pipeline is running, take some time to look over the
   pipeline and make sure you understand the various parts. You will
@@ -150,7 +150,7 @@ good images from the bad.
     table, etc). As we are not identifying any objects, we don’t
     need to worry about these.
 
-## 3. **Open the CellProfiler Analyst workspace and determine reasonable parameter cutoffs (~20 minutes)**
+## **3. Open the CellProfiler Analyst workspace and determine reasonable parameter cutoffs (~20 minutes)**
 
 In the first step of the quality control pipeline, we'll look at graphs of how
 various measurements are distributed in the population.  This allows us to get
@@ -211,7 +211,7 @@ in later steps.
   different/smaller subset or delete it altogether by using the Gate
   Inspector (‘gate’-> ‘MANAGE GATES’).
 
-## 4. **Optional — use the PlateViewer tool to check for other features to gate on (~10 minutes)**
+## **4. Optional — use the PlateViewer tool to check for other features to gate on (~10 minutes)**
 
 If you want to see if you can find additional features that might distinguish good
 images from bad images, feel free to explore the feature set more thoroughly.
@@ -239,7 +239,7 @@ images from bad images, feel free to explore the feature set more thoroughly.
   ImageNumber on the X-axis as in the Workspace plots, but feel free to play around).
   If you find a gate that seems logical to make, proceed as in Step 3.
 
-## 5. **Create filters based on the cutoffs you’ve determined (~10 minutes)**
+## **5. Create filters based on the cutoffs you’ve determined (~10 minutes)**
 
 Now that we've created gates around our poor quality images, we need to convert
 them into filters so that we can access them in the Classifier tool.  This
@@ -317,7 +317,7 @@ presented in case you cannot or will not edit the properties file.
 *Figure 3: Creating filters inside CPA*
 ```
 
-## 6. **Create classifier rules to distinguish good from bad images (~30 minutes)**
+## **6. Create classifier rules to distinguish good from bad images (~30 minutes)**
 
 Creating gates based on the 12 measurements we graphed has helped us identify
 some low quality images so far, but we are not utilizing the rest of our
@@ -359,7 +359,7 @@ low quality images.
 - Save your training set for future reference if desired, then close
   CellProfiler Analyst.
 
-## 7. **Add quality control steps to an analysis pipeline (~15 minutes)**
+## **7. Add quality control steps to an analysis pipeline (~15 minutes)**
 
 If you have time, you can add the list of rules you identified in your machine
 learning classifier to the CellProfiler pipeline that corresponds to this data