From 308fd0f4633d6ba3b393a040bb16da92b0e56184 Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 05:43:35 -0600 Subject: [PATCH] Move singlecells note to CellProfiler support section Also delete table of contents, which was in a strange place --- README.md | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 8229344d..60981248 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,9 @@ CellProfiler-generated image-based profiles typically consist of two main compon - **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 captured from microscopy images. Thse feature names are prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer 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/). @@ -157,23 +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) - - [Data structure](#data-structure) - - [API](#api) - - [Usage](#usage) - - [Handling Non-CellProfiler Morphological Features in Pycytominer](#handling-non-cellprofiler-morphological-features-in-pycytominer) - - [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.