Improve Documentation for Non-CellProfiler Datasets in Pycytominer

README.md

@@ -66,6 +66,29 @@ Pycytominer is primarily built on top of [pandas](https://pandas.pydata.org/docs
 
 Pycytominer currently supports [parquet](https://parquet.apache.org/) and compressed text file (e.g. `.csv.gz`) i/o.
 
+### CellProfiler support
+
+Currently, Pycytominer fully supports data generated by [CellProfiler]('https://cellprofiler.org/'), adhering defaults to its specific data structure and naming conventions.
+
+CellProfiler-generated image-based profiles typically consist of two main components:
+
+- **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information.
+- **Morphology features:** These are the quantified morphological features prefixed with the default compartments (`Cells_`, `Cytoplasm_`, and `Nuclei_`). Pycytominer also supports non-default compartment names.
+
+Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler.
+Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores).
+
+### Handling inputs from other image analysis tools (other than CellProfiler)
+
+Pycytominer also supports processing of raw morphological features from image analysis tools beyond [CellProfiler](https://cellprofiler.org/).
+These tools include In Carta, Harmony, and others.
+Using Pycytominer with these tools requires minor modifications to function arguments, and we encourage these users to pay particularly close attention to individual function documentation.
+
+For example, to resolve potential feature issues in the `normalize()` function, you must manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize).
+This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select).
+
+If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases.
+
 ## API
 
 Pycytominer has five major processing functions:
@@ -97,6 +120,8 @@ Each processing function has unique arguments, see our [documentation](https://p
 
 The default way to use pycytominer is within python scripts, and using pycytominer is simple and fun.
 
+The example below demonstrates how to perform normalization with a dataset generated by [CellProfiler](https://cellprofiler.org/).
+
 ```python
 # Real world example
 import pandas as pd
@@ -135,21 +160,6 @@ And, more specifically than that, image-based profiling readouts from [CellProfi
 
 Therefore, we have included some custom tools in `pycytominer/cyto_utils` that provides other functionality:
 
-- [Data processing for image-based profiling](#data-processing-for-image-based-profiling)
-  - [Installation](#installation)
-  - [Frameworks](#frameworks)
-  - [API](#api)
-  - [Usage](#usage)
-    - [Pipeline orchestration](#pipeline-orchestration)
-  - [Other functionality](#other-functionality)
-    - [CellProfiler CSV collation](#cellprofiler-csv-collation)
-    - [Creating a cell locations lookup table](#creating-a-cell-locations-lookup-table)
-    - [Generating a GCT file for morpheus](#generating-a-gct-file-for-morpheus)
-  - [Citing pycytominer](#citing-pycytominer)
-
-Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler.
-Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores).
-
 ### CellProfiler CSV collation
 
 If running your images on a cluster, unless you have a MySQL or similar large database set up then you will likely end up with lots of different folders from the different cluster runs (often one per well or one per site), each one containing an `Image.csv`, `Nuclei.csv`, etc.