4. smFISH Spot Detection

from merfishing import Merfish

4.1. Entry Point

merfish = Merfish('dummy_experiment/output/region_0/')

MERFISH Experiment Region
Region data located at /gale/netapp/cemba3c/merfish/example/dummy_experiment/output/region_0
Experiment dir located at /gale/netapp/cemba3c/merfish/example/dummy_experiment
/gale/netapp/cemba3c/merfish/example/dummy_experiment/output/region_0 does not contain cellpose results or the results are incomplete, using watershed results from vizgen pipeline instead.

4.2. Call Spots

merfish.smfish_genes

['Snap25', 'Mbp']

merfish.call_spots(
    image_names='all',
    cpu=15,
    padding=50,
    detect_dense=True,
    projection='max',
    verbose=False,
    redo=False,
)

4.3. Plot

fig = merfish.plot_fov(
    0,
    n_cols=3,
    image_names=['PolyT+Snap25+DAPI', 'PolyT', 'DAPI'] + merfish.smfish_genes,
    genes=merfish.smfish_genes,
    dpi=150
)

4.4. Transcripts

transcripts = merfish.get_transcripts(fov=0)

transcripts[transcripts['gene'].isin(merfish.smfish_genes)]

	barcode_id	global_x	global_y	global_z	x	y	fov	gene	transcript_id
0	550	285.535095	2393.415283	0	117.057625	-253.842117	0	Snap25	ENSMUST00000028727
1	550	286.183105	2393.415283	0	117.705612	-253.842117	0	Snap25	ENSMUST00000028727
2	550	295.794952	2393.415283	0	127.317474	-253.842117	0	Snap25	ENSMUST00000028727
3	550	303.894836	2393.415283	0	135.417358	-253.842117	0	Snap25	ENSMUST00000028727
4	550	276.679230	2393.631348	0	108.201759	-253.626114	0	Snap25	ENSMUST00000028727
...	...	...	...	...	...	...	...	...	...
1552	551	296.550934	2531.545166	5	128.073456	-115.712135	0	Mbp	ENSMUST00000133193
1553	551	288.775055	2531.977295	5	120.297577	-115.280144	0	Mbp	ENSMUST00000133193
1554	551	277.327240	2532.301270	5	108.849747	-114.956146	0	Mbp	ENSMUST00000133193
1555	551	273.547272	2532.409180	5	105.069801	-114.848145	0	Mbp	ENSMUST00000133193
1556	551	297.630920	2532.301270	5	129.153442	-114.956146	0	Mbp	ENSMUST00000133193

15264 rows × 9 columns

4.5. Details - BigFish Package

Spot detection is done by the bigfish package with its automatic pipeline and default parameters. Each z-plane is run separately. Please read the bigfish package for more details and fine tune the parameter if needed. You can pass the key parameters to merfish.call_spots function.

• Github: https://github.com/fish-quant/big-fish

• Documentation: https://big-fish.readthedocs.io/en/stable/index.html

• Jupyter Notebook Example: https://github.com/fish-quant/big-fish-examples/tree/master/notebooks

from bigfish.stack import maximum_projection
from bigfish.plot import plot_detection, plot_images
from bigfish.detection import detect_spots

image = merfish.get_image_fov('Snap25', 2)
plot_images(maximum_projection(image), contrast=True, framesize=(3, 3))

spots, threshold = detect_spots(
    images=image[3],
    return_threshold=True,
    voxel_size=(103, 103),  # in nanometer (one value per dimension zyx)
    spot_radius=(150, 150))  # in nanometer (one value per dimension zyx)
print("detected spots")
print("\r shape: {0}".format(spots.shape))
print("\r dtype: {0}".format(spots.dtype))
print("\r threshold: {0}".format(threshold))

detected spots

 shape: (3100, 2)

 dtype: int64

 threshold: 226.0

plot_detection(image[3], spots, contrast=True, framesize=(6, 6), radius=1)