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Models:
Marco Lenoir
MarcoTheBlack/
spatial-alignment
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[5c09f6]: / experiments / expression / slideseq / plot_prediction_results.py

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import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

import numpy as np

from scipy.stats import pearsonr



import matplotlib



font = {"size": 30}

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

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





# error_df = pd.read_csv("./out/prediction_comparison_visium.csv", index_col=0)

# error_df = pd.read_csv("./out/twod_prediction_visium.csv", index_col=0)



errors_union = pd.read_csv("./out/prediction_errors_union.csv", index_col=0)

errors_separate = pd.read_csv("./out/prediction_errors_separate.csv", index_col=0)

errors_gpsa = pd.read_csv("./out/prediction_errors_gpsa.csv", index_col=0)



errors_union_melted = pd.melt(errors_union)

errors_union_melted["method"] = "Union"



errors_separate_melted = pd.melt(errors_separate)

errors_separate_melted["method"] = "Separate"



errors_gpsa_melted = pd.melt(errors_gpsa)

errors_gpsa_melted["method"] = "GPSA"



results_df = pd.concat(

    [errors_union_melted, errors_separate_melted, errors_gpsa_melted], axis=0

)





results_df_means = results_df.groupby(["variable", "method"], as_index=False).mean()

results_df_stddevs = results_df.groupby(["variable", "method"], as_index=False).std()



results_df_gpsa = pd.merge(

    results_df_means[results_df_means.method == "GPSA"],

    results_df_stddevs[results_df_stddevs.method == "GPSA"],

    on=["variable", "method"],

    suffixes=["_mean", "_stddev"],

)

results_df_union = pd.merge(

    results_df_means[results_df_means.method == "Union"],

    results_df_stddevs[results_df_stddevs.method == "Union"],

    on=["variable", "method"],

    suffixes=["_mean", "_stddev"],

)

assert np.array_equal(results_df_gpsa.variable.values, results_df_union.variable.values)





plt.figure(figsize=(14, 7))



plt.subplot(121)



# results_df_trialwise_mean = results_df.groupby(["method", "variable"], as_index=False).mean()

# results_df_trialwise_mean = results_df_trialwise_mean[results_df_trialwise_mean.method != "Separate"]

results_df_trialwise_mean = pd.DataFrame(

    pd.concat([errors_union.mean(1), errors_gpsa.mean(1)]), columns=["value"]

)

results_df_trialwise_mean["method"] = np.concatenate(

    [["Union"] * len(errors_union), ["GPSA"] * len(errors_gpsa)]

)

g = sns.boxplot(data=results_df_trialwise_mean, x="method", y="value", color="gray")

plt.xlabel("")

plt.ylabel(r"Pearson $\rho$")

plt.suptitle("Slide-seqV2 prediction")





plt.subplot(122)

plt.errorbar(

    x=results_df_union.value_mean.values,

    y=results_df_gpsa.value_mean.values,

    xerr=results_df_union.value_stddev.values,

    yerr=results_df_gpsa.value_stddev.values,

    fmt="o",

    ecolor="black",

    color="black",

)



plt.xlabel(r"Pearson $\rho$, Union")

plt.ylabel(r"Pearson $\rho$, GPSA")



ax = plt.gca()



lims = [

    np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes

    np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes

]



# now plot both limits against eachother

ax.plot(lims, lims, "k-", alpha=0.75, zorder=0, color="gray")

ax.set_aspect("equal")

ax.set_xlim(lims)

ax.set_ylim(lims)

plt.tight_layout()



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

plt.show()





preds = pd.read_csv("./out/slideseq_preds_gpsa.csv", index_col=0)

truth = pd.read_csv("./out/slideseq_truth_gpsa.csv", index_col=0)



pearson_corrs = np.zeros(preds.shape[1])

for jj in range(preds.shape[1]):

    pearson_corrs[jj] = pearsonr(truth.iloc[:, jj].values, preds.iloc[:, jj].values)[0]



sorted_idx = np.argsort(pearson_corrs)

n_genes_to_plot = 3



plt.figure(figsize=(n_genes_to_plot * 7, 14))



gene_names = pd.read_csv("./out/slideseq_pred_gene_names.csv").iloc[:, 0].values



# import ipdb



# ipdb.set_trace()



for ii, gene_idx in enumerate(sorted_idx[-n_genes_to_plot:]):

    plt.subplot(2, n_genes_to_plot, ii + 1)

    plt.scatter(

        truth.iloc[:, gene_idx].values, preds.iloc[:, gene_idx].values, c="gray"

    )

    plt.xlabel("True expression")

    plt.ylabel("Predicted expression")

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



for ii, gene_idx in enumerate(sorted_idx[:n_genes_to_plot]):

    plt.subplot(2, n_genes_to_plot, ii + 4)

    plt.scatter(

        truth.iloc[:, gene_idx].values, preds.iloc[:, gene_idx].values, c="gray"

    )

    plt.xlabel("True expression")

    plt.ylabel("Predicted expression")

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



plt.tight_layout()

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

plt.show()



# for jj in range(preds.shape[1]):

#     # import ipdb; ipdb.set_trace()

#     print(round(pearsonr(truth.iloc[:, jj].values, preds.iloc[:, jj].values)[0], 3))

#     nonzero_idx = np.where(truth.iloc[:, jj].values != np.min(truth.iloc[:, jj].values))[0]

#     print(round(pearsonr(truth.iloc[:, jj].values[nonzero_idx], preds.iloc[:, jj].values[nonzero_idx])[0], 3))

#     print()

#     plt.scatter(truth.iloc[:, jj].values, preds.iloc[:, jj].values, c="gray")

#     plt.xlabel("True expression")

#     plt.ylabel("Predicted expression")

#     plt.show()

#     # import ipdb; ipdb.set_trace()





import ipdb



ipdb.set_trace()