流程升级---原位捕获数据的无分割分析（Stereo-seq、HD）

pip install sainsc
###conda install bioconda::sainsc
from pathlib import Path

import numpy as np
import pandas as pd

from sainsc.io import read_StereoSeq

####加载数据
embryo = read_StereoSeq(stereo_seq_file)

embryo.calculate_total_mRNA()

_ = embryo.plot_genecount(im_kwargs={"vmax": 5})

embryo.counts.crop((5_000, 33_000), (9_000, None))

embryo.calculate_total_mRNA()

_ = embryo.plot_genecount(im_kwargs={"vmax": 5})

from PIL import Image

# export total mRNA as png
u8 = np.uint8
Image.fromarray(
    np.clip(embryo.total_mRNA, np.iinfo(u8).min, np.iinfo(u8).max).astype(u8)
).save("embryo_counts.png")

import matplotlib.pyplot as plt

from sainsc.datasets import fetch_embryo_mask

# avoid the DecompressionBombError
Image.MAX_IMAGE_PIXELS = None

mask_file = fetch_embryo_mask()
embryo_mask = np.array(Image.open(mask_file), dtype=bool)

_ = plt.imshow(embryo_mask.T, origin="lower")

embryo.counts.filter_mask(embryo_mask)

embryo.counts.filter_genes_by_count(1)

embryo.calculate_total_mRNA()

_ = embryo.plot_genecount(im_kwargs={"vmax": 5})

###核密度
embryo.gaussian_kernel(4, unit="um")

embryo.calculate_total_mRNA_KDE()

_ = embryo.plot_KDE()

embryo.filter_background(0.9)

_ = embryo.plot_KDE(remove_background=True)

####Cell-type assignment
from sainsc.datasets import fetch_embryo_signatures

signatures = fetch_embryo_signatures()
signatures.iloc[:, :4].head()

embryo.assign_celltype(signatures)

import colorcet as cc
import seaborn as sns

cmap = dict(
    zip(embryo.celltypes, sns.color_palette(cc.glasbey, n_colors=len(embryo.celltypes)))
)

_ = embryo.plot_celltype_map(cmap=cmap)

_ = embryo.plot_celltype_map(
    crop=((2_000, 6_000), (10_500, 15_000)),
    cmap=cmap,
    scalebar_kwargs={"location": "upper left"},
)

_ = embryo.plot_assignment_score()