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"poetry.core.masonry.api" diff --git a/walkthroughs/nbconverted/single_cell_usage.py b/walkthroughs/nbconverted/single_cell_usage.py index 91d8ce4a..227cd375 100644 --- a/walkthroughs/nbconverted/single_cell_usage.py +++ b/walkthroughs/nbconverted/single_cell_usage.py @@ -78,9 +78,9 @@ # # Currently, the NF1 morphology `SQLite` dataset was generated by using [CellProfiler's](https://github.com/CellProfiler/CellProfiler) [ExportToDatabase](https://cellprofiler-manual.s3.amazonaws.com/CellProfiler-4.2.5/modules/fileprocessing.html?highlight=exporttodatabase#module-cellprofiler.modules.exporttodatabase) function, where each table represents a different compartment, such as Image, Cell, Nucleus, and Cytoplasm. # -# To achieve this, we will utilize the SingleCells class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells). +# To achieve this, we will utilize the `SingleCells` class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells). # -# However, for our purpose in this walkthrough, we will focus on using the SingleCells class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset. +# However, for our purpose in this walkthrough, we will focus on using the `SingleCells` class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset. # # ### Updating defaults # Before we proceed further, it is important to update the default parameters in the `SingleCells`class to accommodate the table name changes in our NF1 dataset. @@ -136,7 +136,7 @@ # Now that we have created our merged single-cell profile, let's move on to the next step: loading our `platemaps`. # -# Platemaps provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile. +# `Platemaps` provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile. # # In[5]: diff --git a/walkthroughs/single_cell_usage.ipynb b/walkthroughs/single_cell_usage.ipynb index 95b54d0f..440f4259 100644 --- a/walkthroughs/single_cell_usage.ipynb +++ b/walkthroughs/single_cell_usage.ipynb @@ -100,9 +100,9 @@ "\n", "Currently, the NF1 morphology `SQLite` dataset was generated by using [CellProfiler's](https://github.com/CellProfiler/CellProfiler) [ExportToDatabase](https://cellprofiler-manual.s3.amazonaws.com/CellProfiler-4.2.5/modules/fileprocessing.html?highlight=exporttodatabase#module-cellprofiler.modules.exporttodatabase) function, where each table represents a different compartment, such as Image, Cell, Nucleus, and Cytoplasm.\n", "\n", - "To achieve this, we will utilize the SingleCells class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells).\n", + "To achieve this, we will utilize the `SingleCells` class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells).\n", "\n", - "However, for our purpose in this walkthrough, we will focus on using the SingleCells class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset.\n", + "However, for our purpose in this walkthrough, we will focus on using the `SingleCells` class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset.\n", "\n", "### Updating defaults\n", "Before we proceed further, it is important to update the default parameters in the `SingleCells`class to accommodate the table name changes in our NF1 dataset.\n", @@ -187,7 +187,7 @@ "source": [ "Now that we have created our merged single-cell profile, let's move on to the next step: loading our `platemaps`. \n", "\n", - "Platemaps provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile.\n" + "`Platemaps` provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile.\n" ] }, {