Python ver docs

docs/installation.rst

@@ -2,7 +2,7 @@ Installation
 ============
 
 
-scArches requires Python 3.7 or 3.8. We recommend to use Miniconda.
+scArches requires Python 3.9 or above. We recommend to use Miniconda.
 
 PyPI
 --------

scarches/models/trvae/_utils.py

@@ -1,6 +1,10 @@
 import torch
 import numpy as np
 import logging
+import seaborn as sns
+import pandas as pd
+from matplotlib import pyplot
+from scipy import stats
 
 logger = logging.getLogger(__name__)
 
@@ -22,3 +26,238 @@ def partition(data, partitions, num_partitions):
         indices = torch.nonzero((partitions == i), as_tuple=False).squeeze(1)
         res += [data[indices]]
     return res
+
+
+def reg_mean_plot(
+    adata,
+    condition_key,
+    target_condition,
+    labels,
+    path_to_save="./reg_mean.pdf",
+    save=False,
+    gene_list=None,
+    show=False,
+    top_100_genes=None,
+    verbose=False,
+    legend=True,
+    title=None,
+    x_coeff=0.30,
+    y_coeff=0.8,
+    fontsize=14,
+    **kwargs,
+):
+    """
+    Plots mean matching figure for a set of specific genes.
+
+    Parameters
+    ----------
+    adata: `~anndata.AnnData`
+        AnnData object with equivalent structure to initial AnnData. If `None`, defaults to the
+        AnnData object used to initialize the model. Must have been setup with `batch_key` and `labels_key`,
+        corresponding to batch and cell type metadata, respectively.
+    axis_keys: dict
+        Dictionary of `adata.obs` keys that are used by the axes of the plot. Has to be in the following form:
+            `{"x": "Key for x-axis", "y": "Key for y-axis"}`.
+    labels: dict
+        Dictionary of axes labels of the form `{"x": "x-axis-name", "y": "y-axis name"}`.
+    path_to_save: basestring
+        path to save the plot.
+    save: boolean
+        Specify if the plot should be saved or not.
+    gene_list: list
+        list of gene names to be plotted.
+    show: bool
+        if `True`: will show to the plot after saving it.
+
+    """
+
+    sns.set_theme()
+    sns.set_theme(color_codes=True)
+
+    axis_keys = {"x":"other", "y":target_condition}
+
+    diff_genes = top_100_genes
+    target_cd = adata[adata.obs[condition_key] == target_condition]
+    other_cd = adata[adata.obs[condition_key] != target_condition]
+    if diff_genes is not None:
+        if hasattr(diff_genes, "tolist"):
+            diff_genes = diff_genes.tolist()
+        adata_diff = adata[:, diff_genes]
+        target_diff = adata_diff[adata_diff.obs[condition_key] == target_condition]
+        other_diff = adata_diff[adata_diff.obs[condition_key] != target_condition]
+        x_diff = np.asarray(np.mean(target_diff.X, axis=0)).ravel()
+        y_diff = np.asarray(np.mean(other_diff.X, axis=0)).ravel()
+        m, b, r_value_diff, p_value_diff, std_err_diff = stats.linregress(
+            x_diff, y_diff
+        )
+        if verbose:
+            print("top_100 DEGs mean: ", r_value_diff**2)
+    x = np.asarray(np.mean(other_cd.X, axis=0)).ravel()
+    y = np.asarray(np.mean(target_cd.X, axis=0)).ravel()
+    m, b, r_value, p_value, std_err = stats.linregress(x, y)
+    if verbose: 
+        print("All genes mean: ", r_value**2)
+    df = pd.DataFrame({axis_keys["x"]: x, axis_keys["y"]: y})
+    ax = sns.regplot(x=axis_keys["x"], y=axis_keys["y"], data=df)
+    ax.tick_params(labelsize=fontsize)
+    if "range" in kwargs:
+        start, stop, step = kwargs.get("range")
+        ax.set_xticks(np.arange(start, stop, step))
+        ax.set_yticks(np.arange(start, stop, step))
+    ax.set_xlabel(labels["x"], fontsize=fontsize)
+    ax.set_ylabel(labels["y"], fontsize=fontsize)
+    if gene_list is not None:
+        texts = []
+        for i in gene_list:
+            j = adata.var_names.tolist().index(i)
+            x_bar = x[j]
+            y_bar = y[j]
+            texts.append(pyplot.text(x_bar, y_bar, i, fontsize=11, color="black"))
+            pyplot.plot(x_bar, y_bar, "o", color="red", markersize=5)
+
+    if legend:
+        pyplot.legend(loc="center left", bbox_to_anchor=(1, 0.5))
+    if title is None:
+        pyplot.title("", fontsize=fontsize)
+    else:
+        pyplot.title(title, fontsize=fontsize)
+    ax.text(
+        max(x) - max(x) * x_coeff,
+        max(y) - y_coeff * max(y),
+        r"$\mathrm{R^2_{\mathrm{\mathsf{all\ genes}}}}$= " + f"{r_value ** 2:.2f}",
+        fontsize=kwargs.get("textsize", fontsize),
+    )
+    if diff_genes is not None:
+        ax.text(
+            max(x) - max(x) * x_coeff,
+            max(y) - (y_coeff + 0.15) * max(y),
+            r"$\mathrm{R^2_{\mathrm{\mathsf{top\ 100\ DEGs}}}}$= "
+            + f"{r_value_diff ** 2:.2f}",
+            fontsize=kwargs.get("textsize", fontsize),
+        )
+    if save:
+        pyplot.savefig(f"{path_to_save}", bbox_inches="tight", dpi=100)
+    if show:
+        pyplot.show()
+    pyplot.close()
+    if diff_genes is not None:
+        return r_value**2, r_value_diff**2
+    else:
+        return r_value**2
+
+def reg_var_plot(
+    adata,
+    condition_key,
+    target_condition,
+    labels,
+    path_to_save="./reg_var.pdf",
+    save=False,
+    gene_list=None,
+    show=False,
+    top_100_genes=None,
+    verbose=False,
+    legend=True,
+    title=None,
+    x_coeff=0.30,
+    y_coeff=0.8,
+    fontsize=14,
+    **kwargs,
+):
+    """
+    Plots variance matching figure for a set of specific genes.
+
+    Parameters
+    ----------
+    adata: `~anndata.AnnData`
+        AnnData object with equivalent structure to initial AnnData. If `None`, defaults to the
+        AnnData object used to initialize the model. Must have been setup with `batch_key` and `labels_key`,
+        corresponding to batch and cell type metadata, respectively.
+    axis_keys: dict
+        Dictionary of `adata.obs` keys that are used by the axes of the plot. Has to be in the following form:
+            `{"x": "Key for x-axis", "y": "Key for y-axis"}`.
+    labels: dict
+        Dictionary of axes labels of the form `{"x": "x-axis-name", "y": "y-axis name"}`.
+    path_to_save: basestring
+        path to save the plot.
+    save: boolean
+        Specify if the plot should be saved or not.
+    gene_list: list
+        list of gene names to be plotted.
+    show: bool
+        if `True`: will show to the plot after saving it.
+
+    """
+
+    sns.set_theme()
+    sns.set_theme(color_codes=True)
+
+    axis_keys = {"x":"other", "y":target_condition}
+
+    diff_genes = top_100_genes
+    target_cd = adata[adata.obs[condition_key] == target_condition]
+    other_cd = adata[adata.obs[condition_key] != target_condition]
+    if diff_genes is not None:
+        if hasattr(diff_genes, "tolist"):
+            diff_genes = diff_genes.tolist()
+        adata_diff = adata[:, diff_genes]
+        target_diff = adata_diff[adata_diff.obs[condition_key] == target_condition]
+        other_diff = adata_diff[adata_diff.obs[condition_key] != target_condition]
+        x_diff = np.asarray(np.var(target_diff.X, axis=0)).ravel()
+        y_diff = np.asarray(np.var(other_diff.X, axis=0)).ravel()
+        m, b, r_value_diff, p_value_diff, std_err_diff = stats.linregress(
+            x_diff, y_diff
+        )
+        if verbose:
+            print("top_100 DEGs var: ", r_value_diff**2)
+    x = np.asarray(np.var(other_cd.X, axis=0)).ravel()
+    y = np.asarray(np.var(target_cd.X, axis=0)).ravel()
+    m, b, r_value, p_value, std_err = stats.linregress(x, y)
+    if verbose: 
+        print("All genes var: ", r_value**2)
+    df = pd.DataFrame({axis_keys["x"]: x, axis_keys["y"]: y})
+    ax = sns.regplot(x=axis_keys["x"], y=axis_keys["y"], data=df)
+    ax.tick_params(labelsize=fontsize)
+    if "range" in kwargs:
+        start, stop, step = kwargs.get("range")
+        ax.set_xticks(np.arange(start, stop, step))
+        ax.set_yticks(np.arange(start, stop, step))
+    ax.set_xlabel(labels["x"], fontsize=fontsize)
+    ax.set_ylabel(labels["y"], fontsize=fontsize)
+    if gene_list is not None:
+        texts = []
+        for i in gene_list:
+            j = adata.var_names.tolist().index(i)
+            x_bar = x[j]
+            y_bar = y[j]
+            texts.append(pyplot.text(x_bar, y_bar, i, fontsize=11, color="black"))
+            pyplot.plot(x_bar, y_bar, "o", color="red", markersize=5)
+
+    if legend:
+        pyplot.legend(loc="center left", bbox_to_anchor=(1, 0.5))
+    if title is None:
+        pyplot.title("", fontsize=fontsize)
+    else:
+        pyplot.title(title, fontsize=fontsize)
+    ax.text(
+        max(x) - max(x) * x_coeff,
+        max(y) - y_coeff * max(y),
+        r"$\mathrm{R^2_{\mathrm{\mathsf{all\ genes}}}}$= " + f"{r_value ** 2:.2f}",
+        fontsize=kwargs.get("textsize", fontsize),
+    )
+    if diff_genes is not None:
+        ax.text(
+            max(x) - max(x) * x_coeff,
+            max(y) - (y_coeff + 0.15) * max(y),
+            r"$\mathrm{R^2_{\mathrm{\mathsf{top\ 100\ DEGs}}}}$= "
+            + f"{r_value_diff ** 2:.2f}",
+            fontsize=kwargs.get("textsize", fontsize),
+        )
+    if save:
+        pyplot.savefig(f"{path_to_save}", bbox_inches="tight", dpi=100)
+    if show:
+        pyplot.show()
+    pyplot.close()
+    if diff_genes is not None:
+        return r_value**2, r_value_diff**2
+    else:
+        return r_value**2