csObject
Short Description
The csObject
function creates a CSPOT object using the anndata
framework by inputting csScore and a pre-calculated single-cell spatial table.
This centralizes all information into one file, streamlining the data analysis
process and reducing the risk of losing data.
Function¶
csObject(spatialTablePath, csScorePath, CellId='CellID', uniqueCellId=True, split='X_centroid', removeDNA=True, remove_string_from_name=None, log=True, dropMarkers=None, verbose=True, projectDir=None)
¶
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spatialTablePath |
list
|
Provide a list of paths to the single-cell spatial feature tables, ensuring each image has a unique path specified. |
required |
csScorePath |
list
|
Supply a list of paths to the DL score tables created using generateCSScore, ensuring they correspond to the image paths specified. |
required |
CellId |
str
|
Specify the column name that holds the cell ID (a unique name given to each cell). |
'CellID'
|
uniqueCellId |
bool
|
The function generates a unique name for each cell by combining the CellId and imageid. If you don't want this, pass False. In such case the function will default to using just the CellId. However, make sure CellId is unique especially when loading multiple images together. |
True
|
split |
string
|
The spatial feature table generally includes single cell expression data and meta data such as X, Y coordinates, and cell shape size. The CSPOT object separates them. Ensure that the expression data columns come first, followed by meta data columns. Provide the column name that marks the split, i.e the column name immediately following the expression data. |
'X_centroid'
|
removeDNA |
bool
|
Exclude DNA channels from the final output. The function searches for
column names containing the string |
True
|
remove_string_from_name |
string
|
Cleans up channel names by removing user specified string from all marker names. |
None
|
log |
bool
|
Apply log1p transformation to log the data. |
True
|
dropMarkers |
list
|
Specify a list of markers to be removed from the analysis, for example: ["background_channel", "CD20"]. |
None
|
verbose |
bool
|
If True, print detailed information about the process to the console. |
True
|
projectDir |
string
|
Provide the path to the output directory. The result will be located at
|
None
|
Returns:
Name | Type | Description |
---|---|---|
csObject |
anndata
|
If projectDir is provided the CSPOT Object will be saved as a
|
Example
# set the working directory & set paths to the example data
projectDir = '/Users/aj/Documents/cspotExampleData'
# Path to all the files that are necessary files for running csObject function
segmentationPath = projectDir + '/segmentation/exampleSegmentationMask.tif'
csScorePath = projectDir + '/CSPOT/csScore/exampleImage_cspotPredict.ome.csv'
# please note that there are a number of defaults in the below function that assumes certain structure within the spatialTable.
# Please confirm it is similar with user data or modifiy the parameters accordingly
# check out the documentation for further details
adata = cs.csObject (spatialTablePath=spatialTablePath,
csScorePath=csScorePath,
CellId='CellID',
uniqueCellId=True,
split='X_centroid',
removeDNA=True,
remove_string_from_name=None,
log=True,
dropMarkers=None,
projectDir=projectDir)
# Same function if the user wants to run it via Command Line Interface
python csObject.py --spatialTablePath /Users/aj/Documents/cspotExampleData/quantification/exampleSpatialTable.csv --csScorePath /Users/aj/Documents/cspotExampleData/CSPOT/csScore/exampleImage_cspotPredict.ome.csv --projectDir /Users/aj/Documents/cspotExampleData
Source code in cspot/csObject.py
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