revision

README.md

@@ -3,7 +3,7 @@
 
 SCeptre is a python package that extends the functionalities of Scanpy to analyze multiplexed single-cell proteomics data.
 
-### Installation from Github
+## Installation from Github
 
 Tested on Ubuntu 20.04.1 LTS.  
 It's recommended to work in a dedicated conda environment. E.g:
@@ -19,7 +19,8 @@ Clone the repository and `cd` into its root directory. Then:
 pip install .
 ```
 
-### Usage
+## Usage
+### Replication of Schoof et al.
 
 Usage is exemplified in the notebooks for the analysis from the mansucript "Quantitative Single-Cell Proteomics as a Tool to Characterize Cellular Hierarchies".
 
@@ -31,7 +32,11 @@ conda activate sceptre
 pip install Schoof_et_al/code/sceptre-0.1-py3-none-any.whl
 ```
 
-Find the notebooks in the subdirectory `Schoof_et_al/code` and the required data in `Schoof_et_al/data`.
+The required data can be downloaded from
+http://proteomecentral.proteomexchange.org
+using the dataset identifier PXD020586
+
+Find the notebooks in the subdirectory `Schoof_et_al/code` and the place the required data in `Schoof_et_al/data`.
 
 The following notebooks process the different datasets:
 
@@ -42,3 +47,72 @@ Notebook | Description
 bulk.ipynb | SCeptre analysis of the 'bulk' dataset.
 integrated.ipynb | SCeptre analysis of the 'integrated' dataset.
 
+### Functions and example worklow
+Each function has its own docstring explaining the function in depth. A typical workflow makes usage of the following steps:
+
+#### Create meta data
+To create the meta data for each cell, as done in Schoof et al., from a collection of tables describing the experimental design
+and layouts of the 384-well plates, the following function is used. For details on the required tables, have a look at `Schoof_et_al/data/500ms`.
+```
+import sceptre as spt
+spt.create_meta_data(input_dir="../data/500ms/", output_dir=res_dir)
+```
+Alternatively, the meta data table can be created by the user. It requires the columns `File ID` and `Channel` to map the meta data to each cell.
+
+#### Load dataset
+To load the dataset into python, to following function is used. To this end, only output tables from Proteome Discoverer are supported.
+```
+dataset = spt.load_dataset(proteins = "../data/500ms/500ms_Proteins.txt",
+                           psms = "../data/500ms/500ms_PSMs.txt",
+                           msms = "../data/500ms/500ms_MSMSSpectrumInfo.txt",
+                           files = "../data/500ms/500ms_InputFiles.txt",
+                           meta = res_dir + "meta.txt")
+```
+
+#### LC-MS QC
+The dataset object can be used to quality control each LC-MS run with the follwing functions.
+```
+spt.plot_psms_msms(dataset)
+spt.plot_avg_sn(dataset)
+spt.plot_set_overview(dataset)
+
+s_c_channels = ['127N', '128N', '128C', '129N', '129C', '130N', '130C',
+                '131N','131C', '132N', '132C', '133N', '133C', '134N']
+spt.print_ms_stats(dataset, s_c_channels=s_c_channels)
+
+spt.plot_interference(dataset)
+```
+
+#### Load dataset into Scanpy
+Subsequently the dataset object is used to create a scanpy adata object.
+```
+adata = spt.dataset_to_scanpy(dataset)
+```
+
+#### Filtering and normalization
+Non-single cell channels have to be removed.
+```
+adata = adata[adata.obs['Channel'] != '126'].copy()
+adata = adata[adata.obs['Channel'] != '127C'].copy()
+```
+Then the dataset can be normalized.
+```
+spt.normalize(adata)
+```
+
+#### Cell QC
+The follwing functions are used to filter out outlier cells.
+```
+spt.calculate_cell_filter(adata)
+spt.apply_cell_filter(adata)
+```
+
+#### Batch QC
+To detect potential systematic bias introduced by the sample preparation or measurement the following functions can be used.
+```
+spt.plot_batch_qc(adata)
+spt.plot_plate_qc(adata)
+```
+
+#### Scanpy analysis
+The adata object can now be used to perform a scanpy analysis. 

Schoof_et_al/code/300ms.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [