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Models:
Marco Lenoir
MarcoTheBlack/
spatial-alignment
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[54ded2]: / experiments / expression / slideseq / plot_slideseq_alignment.py

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import torch

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd



import seaborn as sns

import sys

from os.path import join as pjoin

import scanpy as sc

import anndata

import matplotlib.patches as patches



import matplotlib



font = {"size": 30}

matplotlib.rc("font", **font)

matplotlib.rcParams["text.usetex"] = True

matplotlib.rcParams["xtick.labelsize"] = 10

matplotlib.rcParams["ytick.labelsize"] = 10

landmark_markersize = 200

pointsize = 3



aligned_coords = pd.read_csv("./out/aligned_coords_slideseq.csv", index_col=0).values

view_idx = pd.read_csv("./out/view_idx_slideseq.csv", index_col=0).values

X = pd.read_csv("./out/X_slideseq.csv", index_col=0).values

Y = pd.read_csv("./out/Y_slideseq.csv", index_col=0).values

data = sc.read_h5ad("./out/data_slideseq.h5")



plt.style.use("dark_background")



# Locations of horn tips

view1_landmark_locs_prealignment = np.array(

    [

        [2.63, -0.28],

        [-0.81, 2.22],

        [4.67, -0.84],

    ]

)

view2_landmark_locs_prealignment = np.array(

    [

        [1.89, 0.24],

        [-0.97, 3.22],

        [5.18, 0.12],

    ]

)



view_idx = []

for vv in range(2):

    view_idx.append(np.where(data.obs.batch.values == str(vv))[0])



close_idx_1 = np.argmin(

    ((X[view_idx[0]] - view1_landmark_locs_prealignment.reshape(3, -1, 2)) ** 2).sum(

        -1

    ),

    axis=1,

)

close_idx_2 = np.argmin(

    ((X[view_idx[1]] - view2_landmark_locs_prealignment.reshape(3, -1, 2)) ** 2).sum(

        -1

    ),

    axis=1,

)



# for vv in range(len(data.obs.batch.unique())):

#     plt.scatter(

#         X[view_idx[vv], 0], X[view_idx[vv], 1], s=1, label="View {}".format(vv + 1)

#     )

# plt.show()

# import ipdb



# ipdb.set_trace()





n_genes = 3

# plt.figure(figsize=(n_genes * 5 + 5, 10), gridspec_kw={'width_ratios': [2., 1, 1, 1]})

fig, ax = plt.subplots(

    2,

    n_genes + 1,

    figsize=(n_genes * 5 + 3, 8),

    gridspec_kw={"width_ratios": [1.1, 1, 1, 1]},

)



# import ipdb



# ipdb.set_trace()



# plt.subplot(2, n_genes + 1, 1)

colors = ["magenta", "cyan"]

plt.sca(ax[0, 0])

for vv in range(len(data.obs.batch.unique())):

    plt.scatter(

        X[view_idx[vv], 0],

        X[view_idx[vv], 1],

        s=pointsize,

        label="Slice {}".format(vv + 1),

        color=colors[vv],

        alpha=0.5,

    )





# for ll in range(len(view1_landmark_locs_prealignment)):

#     plt.scatter(

#         X[view_idx[0]][close_idx_1[ll], 0],

#         X[view_idx[0]][close_idx_1[ll], 1],

#         color="red",

#         marker="*",

#         s=landmark_markersize

#     )



#     plt.scatter(

#         X[view_idx[1]][close_idx_2[ll], 0],

#         X[view_idx[1]][close_idx_2[ll], 1],

#         color="green",

#         marker="*",

#         s=landmark_markersize

#     )





# plt.show()

lgnd = plt.legend(loc="center right", bbox_to_anchor=(-0.05, 0.5))

for handle in lgnd.legendHandles:

    handle.set_sizes([60])

plt.tight_layout()

plt.axis("off")

plt.gca().invert_yaxis()



# plt.subplot(2, n_genes + 1, 5)

plt.sca(ax[1, 0])

for vv in range(len(data.obs.batch.unique())):

    plt.scatter(

        aligned_coords[view_idx[vv], 0],

        aligned_coords[view_idx[vv], 1],

        s=pointsize,

        color=colors[vv],

        alpha=0.5,

    )



# for ll in range(len(view1_landmark_locs_prealignment)):

#     plt.scatter(

#         aligned_coords[view_idx[0]][close_idx_1[ll], 0],

#         aligned_coords[view_idx[0]][close_idx_1[ll], 1],

#         color="red",

#         marker="*",

#         s=landmark_markersize

#     )



#     plt.scatter(

#         aligned_coords[view_idx[1]][close_idx_2[ll], 0],

#         aligned_coords[view_idx[1]][close_idx_2[ll], 1],

#         color="green",

#         marker="*",

#         s=landmark_markersize

#     )



plt.axis("off")

plt.gca().invert_yaxis()





# for gg in range(n_genes):



#     # plt.subplot(2, n_genes + 1, gg + 2)

#     plt.sca(ax[0, gg + 1])

#     for vv in range(len(data.obs.batch.unique())):

#         plt.scatter(

#             X[view_idx[vv], 0],

#             X[view_idx[vv], 1],

#             c=Y[view_idx[vv]][:, gg],

#             s=1,

#         )

#     plt.title(r"$\emph{" + data.var.gene_ids.values[gg].upper() + "}$")

#     plt.axis("off")

#     plt.gca().invert_yaxis()



#     # plt.subplot(2, n_genes + 1, gg + (n_genes + 2) + 1)

#     plt.sca(ax[1, gg + 1])

#     for vv in range(len(data.obs.batch.unique())):

#         plt.scatter(

#             aligned_coords[view_idx[vv], 0],

#             aligned_coords[view_idx[vv], 1],

#             c=Y[view_idx[vv]][:, gg],

#             s=1,

#         )

#     # plt.title(r"$\emph{" + data.var.gene_ids.values[gg] + "}$")

#     plt.axis("off")

#     plt.gca().invert_yaxis()



# # plt.savefig("./out/slideseq_alignment_per_gene.png")

# plt.show()





# import ipdb



# ipdb.set_trace()





gene_names = ["Hpca", "Atp2b1", "Camk2a"]

# gene_idx = np.where(np.isin(data.var.gene_ids.values, gene_names))[0]

gene_idx = [np.where(data.var.gene_ids.values == g)[0] for g in gene_names]

print(gene_idx)



# for gg in range(n_genes):

for ii, gg in enumerate(gene_idx):



    # plt.subplot(2, n_genes + 1, gg + 2)

    plt.sca(ax[0, ii + 1])

    for vv in range(len(data.obs.batch.unique())):

        plt.scatter(

            X[view_idx[vv], 0],

            X[view_idx[vv], 1],

            c=Y[view_idx[vv]][:, gg],

            s=pointsize,

        )

    # plt.title(r"$\emph{" + data.var.gene_ids.values[gg].upper() + "}$")

    plt.title(r"$\emph{" + gene_names[ii].upper() + "}$")

    plt.axis("off")

    plt.gca().invert_yaxis()



    # plt.subplot(2, n_genes + 1, gg + (n_genes + 2) + 1)

    plt.sca(ax[1, ii + 1])

    for vv in range(len(data.obs.batch.unique())):

        plt.scatter(

            aligned_coords[view_idx[vv], 0],

            aligned_coords[view_idx[vv], 1],

            c=Y[view_idx[vv]][:, gg],

            s=pointsize,

        )

    # plt.title(r"$\emph{" + data.var.gene_ids.values[gg] + "}$")

    plt.axis("off")

    plt.gca().invert_yaxis()



plt.savefig("./out/slideseq_alignment_per_gene.png")

plt.show()





import ipdb



ipdb.set_trace()