#!/usr/bin/env python
# coding: utf-8
#
# EPY: stripped_notebook: {"metadata": {"hide_input": false, "kernelspec": {"display_name": "starfish", "language": "python", "name": "starfish"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5"}, "toc": {"nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false}}, "nbformat": 4, "nbformat_minor": 2}

# EPY: START markdown
### Reproduce In-situ Sequencing results with Starfish
#
#In Situ Sequencing (ISS) is an image based transcriptomics technique that can spatially resolve hundreds RNA species and their expression levels in-situ. The protocol and data analysis are described in this [publication](https://www.ncbi.nlm.nih.gov/pubmed/23852452). This notebook walks through how to use Starfish to process the raw images from an ISS experiment into a spatially resolved cell by gene expression matrix. We verify that Starfish can accurately reproduce the results from the authors' original [pipeline](https://cellprofiler.org/previous_examples/#sequencing-rna-molecules-in-situ-combining-cellprofiler-with-imagej-plugins)
#
#Please see [documentation](https://spacetx-starfish.readthedocs.io/en/stable/) for detailed descriptions of all the data structures and methods used here.
# EPY: END markdown

# EPY: START code
# EPY: ESCAPE %matplotlib inline

import numpy as np
import os
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import pprint

from starfish import data, FieldOfView
from starfish.types import Axes, Features, FunctionSource
from starfish.util.plot import imshow_plane
# EPY: END code

# EPY: START code
matplotlib.rcParams["figure.dpi"] = 150
# EPY: END code

# EPY: START markdown
### Load Data into Starfish from the Cloud
#
#The primary data from one field of view correspond to 16 images from 4 hybridzation rounds (r) 4 color channels (c) one z plane (z). Each image is 1044 x 1390 (y, x). These data arise from human breast tissue. O(10) transcripts are barcoded for subsequent spatial resolution. Average pixel intensity values for one 'spot' in the image, across all rounds and channels, can be decoded into the nearest barcode, thus resolving each pixel into a particular gene.
# EPY: END markdown

# EPY: START code
use_test_data = os.getenv("USE_TEST_DATA") is not None

# An experiment contains a codebook, primary images, and auxiliary images
experiment = data.ISS(use_test_data=use_test_data)
pp = pprint.PrettyPrinter(indent=2)
pp.pprint(experiment._src_doc)
# EPY: END code

# EPY: START code
fov = experiment.fov()

# note the structure of the 5D tensor containing the raw imaging data
imgs = fov.get_image(FieldOfView.PRIMARY_IMAGES)
print(imgs)
# EPY: END code

# EPY: START markdown
### Visualize Codebook
#
# The ISS codebook maps each barcode to a gene. This protocol asserts that genes are encoded with
# a length 4 quatenary barcode that can be read out from the images. Each round encodes a position in the codeword.
# The maximum signal in each color channel (columns in the above image) corresponds to a letter in the codeword.
# The channels, in order, correspond to the letters: 'T', 'G', 'C', 'A'.
# EPY: END markdown

# EPY: START code
experiment.codebook
# EPY: END code

# EPY: START markdown
### Visualize raw data
#
#A nice way to page through all this data is to use the display command. We have commented this out for now, because it will not render in Github. Instead, we simply show an image from the first round and color channel.
# EPY: END markdown

# EPY: START code
# # Display all the data in an interactive pop-up window. Uncomment to have this version work.
# %gui qt5
# display(imgs)

# Display a single plane of data
sel={Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 0}
single_plane = imgs.sel(sel)
imshow_plane(single_plane, title="Round: 0, Channel: 0")
# EPY: END code

# EPY: START markdown
#'dots' is a general stain for all possible transcripts. This image should correspond to the maximum projcection of all color channels within a single imaging round. This auxiliary image is useful for registering images from multiple imaging rounds to this reference image. We'll see an example of this further on in the notebook
# EPY: END markdown

# EPY: START code
from starfish.image import Filter

dots = fov.get_image("dots")
dots_single_plane = dots.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")
imshow_plane(dots_single_plane, title="Anchor channel, all RNA molecules")
# EPY: END code

# EPY: START markdown
#Below is a DAPI image, which specifically marks nuclei. This is useful cell segmentation later on in the processing.
# EPY: END markdown

# EPY: START code
nuclei = fov.get_image("nuclei")
nuclei_single_plane = nuclei.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")
imshow_plane(nuclei_single_plane, title="Nuclei (DAPI) channel")
# EPY: END code

# EPY: START markdown
### Filter raw data before decoding into spatially resolved gene expression
#
#A White-Tophat filter can be used to enhance spots while minimizing background autoflourescence. The ```masking_radius``` parameter specifies the expected radius, in pixels, of each spot.
# EPY: END markdown

# EPY: START code
# filter raw data
masking_radius = 15
filt = Filter.WhiteTophat(masking_radius, is_volume=False)

filtered_imgs = filt.run(imgs, verbose=True, in_place=False)
filt.run(dots, verbose=True, in_place=True)
filt.run(nuclei, verbose=True, in_place=True)
# EPY: END code

# EPY: START code
single_plane_filtered = filtered_imgs.sel(sel)

f, (ax1, ax2) = plt.subplots(ncols=2)
vmin, vmax = np.percentile(single_plane.xarray.values.data, [5, 99])
imshow_plane(
    single_plane, ax=ax1, vmin=vmin, vmax=vmax,
    title="Original data\nRound: 0, Channel: 0"
)
vmin, vmax = np.percentile(single_plane_filtered.xarray.values.data, [5, 99])
imshow_plane(
    single_plane_filtered, ax=ax2, vmin=vmin, vmax=vmax,
    title="Filtered data\nRound: 0, Channel: 0"
)
# EPY: END code

# EPY: START markdown
### Register data
# EPY: END markdown

# EPY: START markdown
#Images may have shifted between imaging rounds. This needs to be corrected for before decoding, since this shift in the images will corrupt the barcodes, thus hindering decoding accuracy. A simple procedure can correct for this shift. For each imaging round, the max projection across color channels should look like the dots stain. Below, we simply shift all images in each round to match the dots stain by learning the shift that maximizes the cross-correlation between the images and the dots stain.
# EPY: END markdown

# EPY: START code
from starfish.image import ApplyTransform, LearnTransform

learn_translation = LearnTransform.Translation(reference_stack=dots, axes=Axes.ROUND, upsampling=1000)
transforms_list = learn_translation.run(imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max"))
warp = ApplyTransform.Warp()
registered_imgs = warp.run(filtered_imgs, transforms_list=transforms_list, in_place=False, verbose=True)
# EPY: END code

# EPY: START markdown
### Decode the processed data into spatially resolved gene expression profiles
#
#To decode, first we find spots, and record, for reach spot, the average pixel intensities across rounds and channels. This spot detection can be achieved by the ```BlobDetector``` algorithm
# EPY: END markdown

# EPY: START code
import warnings
from starfish.spots import FindSpots, DecodeSpots

bd = FindSpots.BlobDetector(
    min_sigma=1,
    max_sigma=10,
    num_sigma=30,
    threshold=0.01,
    measurement_type='mean',
)

dots_max = dots.reduce((Axes.ROUND, Axes.ZPLANE), func=FunctionSource.np("max"))
spots = bd.run(image_stack=registered_imgs, reference_image=dots_max)

decoder = DecodeSpots.PerRoundMaxChannel(codebook=experiment.codebook)
decoded = decoder.run(spots=spots)

# Besides house keeping genes, VIM and HER2 should be most highly expessed, which is consistent here.
genes, counts = np.unique(decoded.loc[decoded[Features.PASSES_THRESHOLDS]][Features.TARGET], return_counts=True)
table = pd.Series(counts, index=genes).sort_values(ascending=False)
table
# EPY: END code

# EPY: START markdown
### Segment Cells and create Cell by Gene Expression Matrix
#
#After calling spots and decoding their gene information, cells must be segmented to assign genes to cells. This paper used a seeded watershed approach to segment the cells, which we also use here.
# EPY: END markdown

# EPY: START code
from starfish.morphology import Binarize, Filter, Merge, Segment
from starfish.types import Levels

dapi_thresh = .18  # binary mask for cell (nuclear) locations
stain_thresh = .22  # binary mask for overall cells // binarization of stain
min_dist = 57
min_allowed_size = 10
max_allowed_size = 10000

mp = registered_imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max")
stain = mp.reduce(
    {Axes.ROUND},
    func="mean",
    level_method=Levels.SCALE_BY_IMAGE)

nuclei_mp_scaled = nuclei.reduce(
    {Axes.ROUND, Axes.CH, Axes.ZPLANE},
    func="max",
    level_method=Levels.SCALE_BY_IMAGE)

binarized_nuclei = Binarize.ThresholdBinarize(dapi_thresh).run(nuclei_mp_scaled)
labeled_masks = Filter.MinDistanceLabel(min_dist, 1).run(binarized_nuclei)
watershed_markers = Filter.AreaFilter(min_area=min_allowed_size, max_area=max_allowed_size).run(labeled_masks)
thresholded_stain = Binarize.ThresholdBinarize(stain_thresh).run(stain)
markers_and_stain = Merge.SimpleMerge().run([thresholded_stain, watershed_markers])
watershed_mask = Filter.Reduce(
    "logical_or",
    lambda shape: np.zeros(shape=shape, dtype=np.bool)
).run(markers_and_stain)

segmenter = Segment.WatershedSegment(connectivity=np.ones((1, 3, 3), dtype=np.bool))
masks = segmenter.run(
    stain,
    watershed_markers,
    watershed_mask,
)

import matplotlib.pyplot as plt
from showit import image

plt.figure(figsize=(10, 10))

plt.subplot(321)
nuclei_numpy = nuclei_mp_scaled._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
image(nuclei_numpy, ax=plt.gca(), size=20, bar=True)
plt.title('Nuclei')

plt.subplot(322)
image(
    stain._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE),
    ax=plt.gca(), size=20, bar=True)
plt.title('Stain')

plt.subplot(323)
image(
    binarized_nuclei.uncropped_mask(0).squeeze(Axes.ZPLANE.value).values,
    bar=False,
    ax=plt.gca(),
)
plt.title('Nuclei Thresholded')

plt.subplot(324)
image(
    watershed_mask.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values,
    bar=False,
    ax=plt.gca(),
)
plt.title('Watershed Mask')

plt.subplot(325)
image(
    watershed_markers.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values,
    size=20,
    cmap=plt.cm.nipy_spectral,
    ax=plt.gca(),
)
plt.title('Found: {} cells'.format(len(watershed_markers)))

plt.subplot(326)
image(
    masks.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values,
    size=20,
    cmap=plt.cm.nipy_spectral,
    ax=plt.gca(),
)
plt.title('Segmented Cells')
plt
# EPY: END code

# EPY: START markdown
#Now that cells have been segmented, we can assign spots to cells in order to create a cell x gene count matrix
# EPY: END markdown

# EPY: START code
from starfish.spots import AssignTargets
from starfish import ExpressionMatrix

al = AssignTargets.Label()
labeled = al.run(masks, decoded)
cg = labeled.to_expression_matrix()
cg
# EPY: END code

# EPY: START markdown
###  Compare to results from paper
# EPY: END markdown

# EPY: START markdown
#This FOV was selected to make sure that we can visualize the tumor/stroma boundary, below this is described by pseudo-coloring HER2 (tumor) and vimentin (VIM, stroma). This distribution matches the one described in the original paper.
# EPY: END markdown

# EPY: START code
from skimage.color import rgb2gray

GENE1 = 'HER2'
GENE2 = 'VIM'

rgb = np.zeros(registered_imgs.tile_shape + (3,))
nuclei_numpy = nuclei.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
rgb[:,:,0] = nuclei_numpy
dots_numpy = dots.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
rgb[:,:,1] = dots_numpy
do = rgb2gray(rgb)
do = do/(do.max())

plt.imshow(do,cmap='gray')
plt.axis('off');

with warnings.catch_warnings():
    warnings.simplefilter('ignore', FutureWarning)
    is_gene1 = decoded.where(decoded[Features.AXIS][Features.TARGET] == GENE1, drop=True)
    is_gene2 = decoded.where(decoded[Features.AXIS][Features.TARGET] == GENE2, drop=True)

plt.plot(is_gene1.x, is_gene1.y, 'or', markersize=3)
plt.plot(is_gene2.x, is_gene2.y, 'ob', markersize=3)
plt.title(f'Red: {GENE1}, Blue: {GENE2}');
# EPY: END code

#!/usr/bin/env python
# coding: utf-8
#
# EPY: stripped_notebook: {"metadata": {"hide_input": false, "kernelspec": {"display_name": "starfish", "language": "python", "name": "starfish"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5"}, "toc": {"nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false}}, "nbformat": 4, "nbformat_minor": 2}

# EPY: START markdown
### Reproduce In-situ Sequencing results with Starfish
#
#In Situ Sequencing (ISS) is an image based transcriptomics technique that can spatially resolve hundreds RNA species and their expression levels in-situ. The protocol and data analysis are described in this [publication](https://www.ncbi.nlm.nih.gov/pubmed/23852452). This notebook walks through how to use Starfish to process the raw images from an ISS experiment into a spatially resolved cell by gene expression matrix. We verify that Starfish can accurately reproduce the results from the authors' original [pipeline](https://cellprofiler.org/previous_examples/#sequencing-rna-molecules-in-situ-combining-cellprofiler-with-imagej-plugins)
#
#Please see [documentation](https://spacetx-starfish.readthedocs.io/en/stable/) for detailed descriptions of all the data structures and methods used here.
# EPY: END markdown

# EPY: START code
# EPY: ESCAPE %matplotlib inline

import numpy as np
import os
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import pprint

from starfish import data, FieldOfView
from starfish.types import Axes, Features, FunctionSource
from starfish.util.plot import imshow_plane
# EPY: END code

# EPY: START code
matplotlib.rcParams["figure.dpi"] = 150
# EPY: END code

# EPY: START markdown
### Load Data into Starfish from the Cloud
#
#The primary data from one field of view correspond to 16 images from 4 hybridzation rounds (r) 4 color channels (c) one z plane (z). Each image is 1044 x 1390 (y, x). These data arise from human breast tissue. O(10) transcripts are barcoded for subsequent spatial resolution. Average pixel intensity values for one 'spot' in the image, across all rounds and channels, can be decoded into the nearest barcode, thus resolving each pixel into a particular gene.
# EPY: END markdown

# EPY: START code
use_test_data = os.getenv("USE_TEST_DATA") is not None

# An experiment contains a codebook, primary images, and auxiliary images
experiment = data.ISS(use_test_data=use_test_data)
pp = pprint.PrettyPrinter(indent=2)
pp.pprint(experiment._src_doc)
# EPY: END code

# EPY: START code
fov = experiment.fov()

# note the structure of the 5D tensor containing the raw imaging data
imgs = fov.get_image(FieldOfView.PRIMARY_IMAGES)
print(imgs)
# EPY: END code

# EPY: START markdown
### Visualize Codebook
#
# The ISS codebook maps each barcode to a gene. This protocol asserts that genes are encoded with
# a length 4 quatenary barcode that can be read out from the images. Each round encodes a position in the codeword.
# The maximum signal in each color channel (columns in the above image) corresponds to a letter in the codeword.
# The channels, in order, correspond to the letters: 'T', 'G', 'C', 'A'.
# EPY: END markdown

# EPY: START code
experiment.codebook
# EPY: END code

# EPY: START markdown
### Visualize raw data
#
#A nice way to page through all this data is to use the display command. We have commented this out for now, because it will not render in Github. Instead, we simply show an image from the first round and color channel.
# EPY: END markdown

# EPY: START code
# # Display all the data in an interactive pop-up window. Uncomment to have this version work.
# %gui qt5
# display(imgs)

# Display a single plane of data
sel={Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 0}
single_plane = imgs.sel(sel)
imshow_plane(single_plane, title="Round: 0, Channel: 0")
# EPY: END code

# EPY: START markdown
#'dots' is a general stain for all possible transcripts. This image should correspond to the maximum projcection of all color channels within a single imaging round. This auxiliary image is useful for registering images from multiple imaging rounds to this reference image. We'll see an example of this further on in the notebook
# EPY: END markdown

# EPY: START code
from starfish.image import Filter

dots = fov.get_image("dots")
dots_single_plane = dots.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")
imshow_plane(dots_single_plane, title="Anchor channel, all RNA molecules")
# EPY: END code

# EPY: START markdown
#Below is a DAPI image, which specifically marks nuclei. This is useful cell segmentation later on in the processing.
# EPY: END markdown

# EPY: START code
nuclei = fov.get_image("nuclei")
nuclei_single_plane = nuclei.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")
imshow_plane(nuclei_single_plane, title="Nuclei (DAPI) channel")
# EPY: END code

# EPY: START markdown
### Filter raw data before decoding into spatially resolved gene expression
#
#A White-Tophat filter can be used to enhance spots while minimizing background autoflourescence. The ```masking_radius``` parameter specifies the expected radius, in pixels, of each spot.
# EPY: END markdown

# EPY: START code
# filter raw data
masking_radius = 15
filt = Filter.WhiteTophat(masking_radius, is_volume=False)

filtered_imgs = filt.run(imgs, verbose=True, in_place=False)
filt.run(dots, verbose=True, in_place=True)
filt.run(nuclei, verbose=True, in_place=True)
# EPY: END code

# EPY: START code
single_plane_filtered = filtered_imgs.sel(sel)

f, (ax1, ax2) = plt.subplots(ncols=2)
vmin, vmax = np.percentile(single_plane.xarray.values.data, [5, 99])
imshow_plane(
    single_plane, ax=ax1, vmin=vmin, vmax=vmax,
    title="Original data\nRound: 0, Channel: 0"
)
vmin, vmax = np.percentile(single_plane_filtered.xarray.values.data, [5, 99])
imshow_plane(
    single_plane_filtered, ax=ax2, vmin=vmin, vmax=vmax,
    title="Filtered data\nRound: 0, Channel: 0"
)
# EPY: END code

# EPY: START markdown
### Register data
# EPY: END markdown

# EPY: START markdown
#Images may have shifted between imaging rounds. This needs to be corrected for before decoding, since this shift in the images will corrupt the barcodes, thus hindering decoding accuracy. A simple procedure can correct for this shift. For each imaging round, the max projection across color channels should look like the dots stain. Below, we simply shift all images in each round to match the dots stain by learning the shift that maximizes the cross-correlation between the images and the dots stain.
# EPY: END markdown

# EPY: START code
from starfish.image import ApplyTransform, LearnTransform

learn_translation = LearnTransform.Translation(reference_stack=dots, axes=Axes.ROUND, upsampling=1000)
transforms_list = learn_translation.run(imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max"))
warp = ApplyTransform.Warp()
registered_imgs = warp.run(filtered_imgs, transforms_list=transforms_list, in_place=False, verbose=True)
# EPY: END code

# EPY: START markdown
### Decode the processed data into spatially resolved gene expression profiles
#
#To decode, first we find spots, and record, for reach spot, the average pixel intensities across rounds and channels. This spot detection can be achieved by the ```BlobDetector``` algorithm
# EPY: END markdown

# EPY: START code
import warnings
from starfish.spots import FindSpots, DecodeSpots

bd = FindSpots.BlobDetector(
    min_sigma=1,
    max_sigma=10,
    num_sigma=30,
    threshold=0.01,
    measurement_type='mean',
)

dots_max = dots.reduce((Axes.ROUND, Axes.ZPLANE), func=FunctionSource.np("max"))
spots = bd.run(image_stack=registered_imgs, reference_image=dots_max)

decoder = DecodeSpots.PerRoundMaxChannel(codebook=experiment.codebook)
decoded = decoder.run(spots=spots)

# Besides house keeping genes, VIM and HER2 should be most highly expessed, which is consistent here.
genes, counts = np.unique(decoded.loc[decoded[Features.PASSES_THRESHOLDS]][Features.TARGET], return_counts=True)
table = pd.Series(counts, index=genes).sort_values(ascending=False)
table
# EPY: END code

# EPY: START markdown
### Segment Cells and create Cell by Gene Expression Matrix
#
#After calling spots and decoding their gene information, cells must be segmented to assign genes to cells. This paper used a seeded watershed approach to segment the cells, which we also use here.
# EPY: END markdown

# EPY: START code
from starfish.morphology import Binarize, Filter, Merge, Segment
from starfish.types import Levels

dapi_thresh = .18  # binary mask for cell (nuclear) locations
stain_thresh = .22  # binary mask for overall cells // binarization of stain
min_dist = 57
min_allowed_size = 10
max_allowed_size = 10000

mp = registered_imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max")
stain = mp.reduce(
    {Axes.ROUND},
    func="mean",
    level_method=Levels.SCALE_BY_IMAGE)

nuclei_mp_scaled = nuclei.reduce(
    {Axes.ROUND, Axes.CH, Axes.ZPLANE},
    func="max",
    level_method=Levels.SCALE_BY_IMAGE)

binarized_nuclei = Binarize.ThresholdBinarize(dapi_thresh).run(nuclei_mp_scaled)
labeled_masks = Filter.MinDistanceLabel(min_dist, 1).run(binarized_nuclei)
watershed_markers = Filter.AreaFilter(min_area=min_allowed_size, max_area=max_allowed_size).run(labeled_masks)
thresholded_stain = Binarize.ThresholdBinarize(stain_thresh).run(stain)
markers_and_stain = Merge.SimpleMerge().run([thresholded_stain, watershed_markers])
watershed_mask = Filter.Reduce(
    "logical_or",
    lambda shape: np.zeros(shape=shape, dtype=np.bool)
).run(markers_and_stain)

segmenter = Segment.WatershedSegment(connectivity=np.ones((1, 3, 3), dtype=np.bool))
masks = segmenter.run(
    stain,
    watershed_markers,
    watershed_mask,
)

import matplotlib.pyplot as plt
from showit import image

plt.figure(figsize=(10, 10))

plt.subplot(321)
nuclei_numpy = nuclei_mp_scaled._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
image(nuclei_numpy, ax=plt.gca(), size=20, bar=True)
plt.title('Nuclei')

plt.subplot(322)
image(
    stain._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE),
    ax=plt.gca(), size=20, bar=True)
plt.title('Stain')

plt.subplot(323)
image(
    binarized_nuclei.uncropped_mask(0).squeeze(Axes.ZPLANE.value).values,
    bar=False,
    ax=plt.gca(),
)
plt.title('Nuclei Thresholded')

plt.subplot(324)
image(
    watershed_mask.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values,
    bar=False,
    ax=plt.gca(),
)
plt.title('Watershed Mask')

plt.subplot(325)
image(
    watershed_markers.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values,
    size=20,
    cmap=plt.cm.nipy_spectral,
    ax=plt.gca(),
)
plt.title('Found: {} cells'.format(len(watershed_markers)))

plt.subplot(326)
image(
    masks.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values,
    size=20,
    cmap=plt.cm.nipy_spectral,
    ax=plt.gca(),
)
plt.title('Segmented Cells')
plt
# EPY: END code

# EPY: START markdown
#Now that cells have been segmented, we can assign spots to cells in order to create a cell x gene count matrix
# EPY: END markdown

# EPY: START code
from starfish.spots import AssignTargets
from starfish import ExpressionMatrix

al = AssignTargets.Label()
labeled = al.run(masks, decoded)
cg = labeled.to_expression_matrix()
cg
# EPY: END code

# EPY: START markdown
###  Compare to results from paper
# EPY: END markdown

# EPY: START markdown
#This FOV was selected to make sure that we can visualize the tumor/stroma boundary, below this is described by pseudo-coloring HER2 (tumor) and vimentin (VIM, stroma). This distribution matches the one described in the original paper.
# EPY: END markdown

# EPY: START code
from skimage.color import rgb2gray

GENE1 = 'HER2'
GENE2 = 'VIM'

rgb = np.zeros(registered_imgs.tile_shape + (3,))
nuclei_numpy = nuclei.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
rgb[:,:,0] = nuclei_numpy
dots_numpy = dots.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE}, func="max")._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
rgb[:,:,1] = dots_numpy
do = rgb2gray(rgb)
do = do/(do.max())

plt.imshow(do,cmap='gray')
plt.axis('off');

with warnings.catch_warnings():
    warnings.simplefilter('ignore', FutureWarning)
    is_gene1 = decoded.where(decoded[Features.AXIS][Features.TARGET] == GENE1, drop=True)
    is_gene2 = decoded.where(decoded[Features.AXIS][Features.TARGET] == GENE2, drop=True)

plt.plot(is_gene1.x, is_gene1.y, 'or', markersize=3)
plt.plot(is_gene2.x, is_gene2.y, 'ob', markersize=3)
plt.title(f'Red: {GENE1}, Blue: {GENE2}');
# EPY: END code

#!/usr/bin/env python # coding: utf-8 # # EPY: stripped_notebook: {"metadata": {"hide_input": false, "kernelspec": {"display_name": "starfish", "language": "python", "name": "starfish"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5"}, "toc": {"nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false}}, "nbformat": 4, "nbformat_minor": 2} # EPY: START markdown ### Reproduce In-situ Sequencing results with Starfish # #In Situ Sequencing (ISS) is an image based transcriptomics technique that can spatially resolve hundreds RNA species and their expression levels in-situ. The protocol and data analysis are described in this [publication](https://www.ncbi.nlm.nih.gov/pubmed/23852452). This notebook walks through how to use Starfish to process the raw images from an ISS experiment into a spatially resolved cell by gene expression matrix. We verify that Starfish can accurately reproduce the results from the authors' original [pipeline](https://cellprofiler.org/previous_examples/#sequencing-rna-molecules-in-situ-combining-cellprofiler-with-imagej-plugins) # #Please see [documentation](https://spacetx-starfish.readthedocs.io/en/stable/) for detailed descriptions of all the data structures and methods used here. # EPY: END markdown # EPY: START code # EPY: ESCAPE %matplotlib inline # EPY: END code # EPY: START code # EPY: END code # EPY: START markdown ### Load Data into Starfish from the Cloud # #The primary data from one field of view correspond to 16 images from 4 hybridzation rounds (r) 4 color channels (c) one z plane (z). Each image is 1044 x 1390 (y, x). These data arise from human breast tissue. O(10) transcripts are barcoded for subsequent spatial resolution. Average pixel intensity values for one 'spot' in the image, across all rounds and channels, can be decoded into the nearest barcode, thus resolving each pixel into a particular gene. # EPY: END markdown # EPY: START code # An experiment contains a codebook, primary images, and auxiliary images # EPY: END code # EPY: START code # note the structure of the 5D tensor containing the raw imaging data # EPY: END code # EPY: START markdown ### Visualize Codebook # # The ISS codebook maps each barcode to a gene. This protocol asserts that genes are encoded with # a length 4 quatenary barcode that can be read out from the images. Each round encodes a position in the codeword. # The maximum signal in each color channel (columns in the above image) corresponds to a letter in the codeword. # The channels, in order, correspond to the letters: 'T', 'G', 'C', 'A'. # EPY: END markdown # EPY: START code # EPY: END code # EPY: START markdown ### Visualize raw data # #A nice way to page through all this data is to use the display command. We have commented this out for now, because it will not render in Github. Instead, we simply show an image from the first round and color channel. # EPY: END markdown # EPY: START code # # Display all the data in an interactive pop-up window. Uncomment to have this version work. # %gui qt5 # display(imgs) # Display a single plane of data # EPY: END code # EPY: START markdown #'dots' is a general stain for all possible transcripts. This image should correspond to the maximum projcection of all color channels within a single imaging round. This auxiliary image is useful for registering images from multiple imaging rounds to this reference image. We'll see an example of this further on in the notebook # EPY: END markdown # EPY: START code # EPY: END code # EPY: START markdown #Below is a DAPI image, which specifically marks nuclei. This is useful cell segmentation later on in the processing. # EPY: END markdown # EPY: START code # EPY: END code # EPY: START markdown ### Filter raw data before decoding into spatially resolved gene expression # #A White-Tophat filter can be used to enhance spots while minimizing background autoflourescence. The ```masking_radius``` parameter specifies the expected radius, in pixels, of each spot. # EPY: END markdown # EPY: START code # filter raw data # EPY: END code # EPY: START code # EPY: END code # EPY: START markdown ### Register data # EPY: END markdown # EPY: START markdown #Images may have shifted between imaging rounds. This needs to be corrected for before decoding, since this shift in the images will corrupt the barcodes, thus hindering decoding accuracy. A simple procedure can correct for this shift. For each imaging round, the max projection across color channels should look like the dots stain. Below, we simply shift all images in each round to match the dots stain by learning the shift that maximizes the cross-correlation between the images and the dots stain. # EPY: END markdown # EPY: START code # EPY: END code # EPY: START markdown ### Decode the processed data into spatially resolved gene expression profiles # #To decode, first we find spots, and record, for reach spot, the average pixel intensities across rounds and channels. This spot detection can be achieved by the ```BlobDetector``` algorithm # EPY: END markdown # EPY: START code # Besides house keeping genes, VIM and HER2 should be most highly expessed, which is consistent here. # EPY: END code # EPY: START markdown ### Segment Cells and create Cell by Gene Expression Matrix # #After calling spots and decoding their gene information, cells must be segmented to assign genes to cells. This paper used a seeded watershed approach to segment the cells, which we also use here. # EPY: END markdown # EPY: START code # binary mask for cell (nuclear) locations # binary mask for overall cells // binarization of stain # EPY: END code # EPY: START markdown #Now that cells have been segmented, we can assign spots to cells in order to create a cell x gene count matrix # EPY: END markdown # EPY: START code # EPY: END code # EPY: START markdown ###  Compare to results from paper # EPY: END markdown # EPY: START markdown #This FOV was selected to make sure that we can visualize the tumor/stroma boundary, below this is described by pseudo-coloring HER2 (tumor) and vimentin (VIM, stroma). This distribution matches the one described in the original paper. # EPY: END markdown # EPY: START code # EPY: END code