FAQ#

Current scope of the package#

Please raise an issue to request support for additonal data formats.

Images (Tested)#

Type		Function	Supported channels	Wrapped library
.czi	Fluorescence Microscopy Single-Channel	`read_czi`	Grayscale	pylibczirw
.czi	Fluorescence Microscopy Multi-channel	`read_czi`	Grayscale	pylibczirw
.czi	Whole Slide Image	`read_czi`	RGB(A)	pylibczirw
.mrxs	Whole Slide Images	`read_openslide`	RGB(A)	openslide
.tiff	-	`read_custom`	(multichannel) grayscale	dask.array.image/skimage.io.imread

Images (supported, in principle)#

Type		Function	Supported channels	Wrapped library
.svs	Whole Slide Images (Aperio)	`read_openslide`	RGB(A)	openslide
.dcm	Whole Slide Images (DICOM)	`read_openslide`	RGB(A)	openslide
.ndpi	Hamamatsu	`read_openslide`	RGB(A)	openslide
.svs	Whole Slide Images	`read_openslide`	RGB(A)	openslide
.jpeg	-	`read_custom`	RGB(A), grayscale	dask.array.image/skimage.io.imread
.png	-	`read_custom`	RGB(A), grayscale	dask.array.image/skimage.io.imread
.tiff	-	`read_custom`	RGB(A)	dask.array.image/skimage.io.imread

Shapes#

Type		Function	Wrapped library
.xml	LMD	`dvpio.read.shapes.read_lmd`	py-lmd

Omics#

Type		Function	Wrapped library
`pandas.DataFrame`	Any type, preprocessed into correct format	`dvpio.read.omics.parse_df`	-
.tsv	alphaDIA	`dvpio.read.shapes.read_precursor_table` (alphadia)	alphabase
.tsv	DIANN	`dvpio.read.shapes.read_precursor_table` (diann)	alphabase
.tsv	DIANN	`dvpio.read.shapes.read_precursor_table` (diann)	alphabase
.tsv	alphapept	`dvpio.read.shapes.read_precursor_table` (alphapept)	alphabase
.tsv	MSFragger	`dvpio.read.shapes.read_precursor_table` (msfragger)	alphabase
.tsv	DIANN	`dvpio.read.shapes.read_precursor_table` (msfragger)	alphabase
.tsv	spectronaut	`dvpio.read.shapes.read_precursor_table` (spectronaut)	alphabase
.parquet	alphaDIA	`dvpio.read.shapes.read_precursor_table` (alphadia)	alphabase
.parquet	DIANN	`dvpio.read.shapes.read_precursor_table` (diann)	alphabase

How to…#

… open spatialdata in Napari?#

This requires you to have napari_spatialdata installed in the respective environment In a jupyter notebook, you can use the following snippet:

import spatialdata
from napari_spatialdata import Interactive

sdata = spatialdata.read_zarr("/path/to/sdata.zarr")
session = Interactive(sdata)
session.run()

You can also import it directly from the napari viewer. Open the napari viewer, e.g. from the commandline.

> conda activate <my_env>
> napari

In napari, go to File > Open Directory (or use the shortcut Cmd+Shift+O) and go to the storage location of your spatialdata object. Select the napari spatialdata reader in the pop up menu.

Known Issues#

Please raise an issue to report bugs.

Import of `napari-spatialdata` fails#

Importing napari_spatialdata might initially fail due to missing non-python dependencies. If you get the following error:

import napari_spatialdata
> qtpy.PythonQtError: No Qt bindings could be found

Try to install the pyqt5-tools binaries in your environment

pip install pyqt5-tools

Rendering cell segmentation results takes very long in Napari#

This is a known issue in Napari. Very recently, Grzegorz Bokota and colleagues implemented an experimental C++ version of the rendering algorithm for shapes that greatly improves the performance (~10x). See their blogpost. To use the feature install napari with optional dependencies:

pip install "napari[optional,pyqt6]>=0.5.6rc0"

And tick the box in the Napari GUI under Napari > Preferences > Experimental > Use C++ code to speed up creation and updates of Shapes Layers

I can’t overlay multiple channels for my fluorescence image in the Napari viewer#

On the left, select the image layer you are interested in, right click, and select Split Stack. Now, the individual channels are shown as distinct layers.

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