def compute_nuclei_area(gene, analysis_repo, gene_label, timepoint):
dict_nucleus_area = {"Gene": [], "value": []}
image_set = ImageSet(analysis_repo, [f"{'mrna'}/{gene}/{timepoint}/"])
[dict_nucleus_area["value"].append(image.compute_nucleus_area()) for image in image_set.get_images()]
[dict_nucleus_area["Gene"].append(gene_label) for image in image_set.get_images()]
return pd.DataFrame(dict_nucleus_area)
def compute_transcript_by_cell_area(analysis_repo, gene, timepoints):
transcript_by_cell_area = {"total_transcript": [], "cell_area": []}
for timepoint in timepoints:
image_set = ImageSet(analysis_repo, [f"{'mrna'}/{gene}/{timepoint}/"], force2D=False)
[transcript_by_cell_area["total_transcript"].append(image.compute_cytoplasmic_total_spots()) for image in image_set.get_images()]
[transcript_by_cell_area["cell_area"].append(image.compute_cell_area()) for image in image_set.get_images()]
return pd.DataFrame(transcript_by_cell_area)
for g in genes:
[gene, timepoint] = g.split("-")
image_set = ImageSet(analysis_repo, [f"{'mrna'}/{gene}/{timepoint}/"])
nuc_dist, nucs_dist, cell_masks, nucs_pos = image_set.compute_cell_mask_between_nucleus_centroids(
)
# compute histogram mod
hx, hy, _ = plt.hist(nuc_dist)
bin_max = np.where(hx == hx.max())[0]
hist_mod = hy[bin_max]
if len(hist_mod) > 1:
hist_mod = np.mean(hist_mod)
# compute density by stripe and build spline wave graph
image_counter = 0
for im in image_set.get_images():
nucleus_mask = im.get_nucleus_mask()
nucleus_centroids = im.get_multiple_nucleus_centroid()
spots = im.get_spots()
z_lines = im.get_z_lines_masks()
cell_masks_im = cell_masks[image_counter]
nuc_dist_im = nucs_dist[image_counter]
nuc_pos_im = nucs_pos[image_counter]
mask_count = 0
for i in range(len(cell_masks_im)):
mask = cell_masks_im[i]
nuc_d = nuc_dist_im[i]
nuc_pos = nuc_pos_im[i]
if hist_mod - 250 < nuc_d < hist_mod + 250:
spots_reduced = helpers.keep_cell_mask_spots(spots, mask)
spots_reduced = np.array(spots_reduced).reshape(