genes?

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.txt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,9 +1,321 @@
-import gradio as gr
+mport gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!"
+import PIL
+import numpy as np
+
+import scipy
+from scipy.stats import gaussian_kde
+from scipy.optimize import curve_fit
+
+import pandas as pd
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.neighbors import KernelDensity
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+import copy
+
+df = pd.read_csv(
+    './gene_tpm_brain_cerebellar_hemisphere_log2minus1NEW.txt', sep='\t')
+gene_table = df.set_index('Description').drop(
+    columns=['id', 'Name']).T.reset_index(drop=True)
+
+# ===============================================================================================
+# ===============================================================================================
+# ===============================================================================================
+
+
+def plot_hist_gauss(col, ax=None, orientation='vertical', label=''):
+  show = True if ax is None else False
+
+  ax = col.plot.hist(orientation=orientation, density=True,
+                     alpha=0.2, ax=ax, subplots=False)
+
+  hist, bin_edges = np.histogram(col, density=True)
+  bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+  def gauss(x, A, mu, sigma):
+    return A * np.exp(-(x - mu)**2 / (2. * sigma**2))
+
+  p0 = [1, 5, 1]
+  popt, pcov = curve_fit(gauss, bin_centers, hist, p0=p0)  # hist
+  A, mu, sigma = popt
+
+  granularity = 100
+  x = np.linspace(col.min(), col.max(), granularity)
+  if orientation == 'horizontal':
+    ax.plot(gauss(x, *popt), x, c='C0', label='Fitted data')
+    ax.hlines(mu, *ax.get_xlim(), colors='C3', label='Fitted mean')
+    ax.set_ylabel(label)
+  else:
+    ax.plot(x, gauss(x, *popt), c='C0', label='Fitted data')
+    ax.vlines(mu, *ax.get_ylim(), colors='C3', label='Fitted mean')
+    ax.set_xlabel(label)
+
+  if show:
+    plt.show()
+
+  return popt
+
+
+def plot_gene(gene, ax=None, orientation='vertical'):
+  plot_hist_gauss(gene_table[gene], ax=ax,
+                  orientation=orientation, label=gene)
+
+# ===============================================================================================
+# ===============================================================================================
+# ===============================================================================================
+
+
+def plot_genes(x_gene=None, y_gene=None, ax=None, mode='raw', gene_table=gene_table):
+  """
+  Produces a scatterplot of the TPM (Transcriptions Per Million) of two genes,
+  and fits data to bivariate Gaussian which is also plotted.
+
+  Parameters
+  ----------
+  x_gene : str
+    The common name of the gene to be plotted along the x-axis.
+  y_gene : str
+    The common name of the gene to be plotted along the y-axis.
+  ax : matplotlib axes object, default None
+    An axes of the current figure.
+  mode : str, default 'raw'
+    The mode of plotting:
+
+    - 'raw' : plot data as is
+    - 'norm' : normalize and recenter before plotting
+
+  gene_table : pandas DataFrame, default global gene_table
+    A table containing the two genes to be plotted as columns
+
+  Returns
+  -------
+  plotted_data : pandas DataFrame
+    The two columns of data that were actually plotted
+  A : float
+    Amplitude of optimal bivariate Gaussian
+  x0 : float
+    x mean of optimal bivariate Gaussian
+  y0 : float
+    y mean of optimal bivariate Gaussian
+  sigma_x : float
+    Standard deviation along x axis of optimal bivariate Gaussian
+  sigma_y : float
+    Standard deviation along y axis of optimal bivariate Gaussian
+  rho : float
+    Pearson correlation coefficient of optimal bivariate Gaussian
+  z_offset : float
+    Additive offset of optimal bivariate Gaussian
+  """
+
+  show = True if ax is None else False
+  if ax is None:
+    ax = plt.axes()
+  ax.set_aspect('equal', adjustable='box')
+  if x_gene is not None and y_gene is not None:
+    two_cols = gene_table.loc[:, [x_gene, y_gene]]
+  else:  # testing
+    print('WARNING: plot_genes requires two gene names as input. '
+          'You have omitted at least one, so random test data will '
+          'be plotted instead.')
+    x_gene, y_gene = 'x', 'y'
+    test_dist = np.random.default_rng().multivariate_normal(
+        mean=[100, 200], cov=[[1, 0.9], [0.9, np.sqrt(3)]], size=(1000))
+    two_cols = pd.DataFrame(data=test_dist, columns=[x_gene, y_gene])
+
+  # Mean and density ---------------------------------------------------------
+
+  mean = two_cols.mean()
+
+  data_for_kde = two_cols.values.T
+  density_estimator = gaussian_kde(data_for_kde)
+  z = density_estimator(data_for_kde)
+
+  # Fit to 2D Gaussian =======================================================
+
+  def bivariate_Gaussian(xy, A, x0, y0, sigma_x, sigma_y, rho, z_offset):
+    x, y = xy
+
+    # A should really be divided by (2*np.pi*sigma_x*sigma_y*np.sqrt(1-rho**2))
+    a = 1 / (2 * (1 - rho**2) * sigma_x**2)
+    b = - rho / ((1 - rho**2) * sigma_x * sigma_y)
+    c = 1 / (2 * (1 - rho**2) * sigma_y**2)
+    g = z_offset + A * \
+        np.exp(-(a * (x - x0)**2 + b * (x - x0) * (y - y0) + c * (y - y0)**2))
+
+    return g.ravel()
+
+  gran = 400  # granularity
+  x = np.linspace(two_cols[x_gene].min(), two_cols[x_gene].max(), gran)
+  y = np.linspace(two_cols[y_gene].min(), two_cols[y_gene].max(), gran)
+  pts = np.transpose(np.dstack(np.meshgrid(x, y)),
+                     axes=[2, 0, 1]).reshape(2, -1)
+
+  p0 = (1, mean[0], mean[1], 1, 1, 0, 0)
+  popt, pcov = curve_fit(bivariate_Gaussian, pts,
+                         density_estimator(pts), p0=p0)
+  A, x0, y0, sigma_x, sigma_y, rho, z_offset = popt
+
+  cov = np.array(
+      [[sigma_x**2, rho * sigma_x * sigma_y],
+       [rho * sigma_x * sigma_y, sigma_y**2]])
+  eigenvalues, eigenvectors = np.linalg.eig(cov)
+  # eigvals are variances along ellipse axes, eigvects are direction of axes
+  scaled_eigvects = np.sqrt(eigenvalues) * eigenvectors
+
+  # Plots ====================================================================
+
+  plotted_data = gene_table
+
+  if mode == 'raw':
+    # --- Plot Data ---
+    two_cols.plot.scatter(x=x_gene, y=y_gene, c=z,
+                          s=2, ylabel=y_gene, ax=ax)
+
+    # --- Plot Fitted Gaussian ---
+    pts = pts.reshape(2, gran, gran)
+    data_fitted = bivariate_Gaussian(pts, *popt).reshape(gran, gran)
+
+    # contour
+    ax.contour(pts[0], pts[1], data_fitted, 8,
+               cmap='viridis', zorder=0, alpha=.5)
+
+    # center
+    ax.plot(x0, y0, 'rx')
+
+    # gene axes
+    ax.quiver([x0, x0], [y0, y0], [1, 0], [0, 1], angles='xy', scale_units='xy',
+              width=0.005, scale=1, color=['magenta', 'violet'], alpha=0.35)
+
+    # ellipse axes
+    ax.quiver([x0, x0], [y0, y0], *scaled_eigvects, angles='xy', scale_units='xy',
+              width=0.005, scale=1, color=['red', 'firebrick'], alpha=0.35)
+
+    plotted_data = two_cols
+
+  # --------------------------------------------------------------------------
+
+  elif mode == 'norm':
+    inv_cov = np.linalg.inv(scaled_eigvects)
+    recentered_data = two_cols.values - [x0, y0]
+    normed_data = recentered_data @ inv_cov.T
+    normed_two_cols = pd.DataFrame(
+        data=normed_data, columns=[x_gene, y_gene])
+
+    # --- Plot Data ---
+    normed_two_cols.plot.scatter(x=x_gene, y=y_gene, c=z, s=2, ax=ax,
+                                 xlabel='minor axis',
+                                 ylabel='major axis')
+
+    # --- Plot Fitted Gaussian ---
+    x = np.linspace(normed_two_cols[x_gene].min(),
+                    normed_two_cols[x_gene].max(), gran)
+    y = np.linspace(normed_two_cols[y_gene].min(),
+                    normed_two_cols[y_gene].max(), gran)
+    pts = np.transpose(np.dstack(np.meshgrid(x, y)), axes=[2, 0, 1])
+
+    pts = pts.reshape(2, gran, gran)
+    data_fitted = bivariate_Gaussian(pts, A, 0, 0, 1, 1, 0, z_offset)
+    data_fitted = data_fitted.reshape(gran, gran)
+
+    # contour
+    ax.contour(pts[0], pts[1], data_fitted, 8,
+               cmap='viridis', zorder=0, alpha=.5)
+
+    # center
+    ax.plot(0, 0, 'rx')
+
+    # gene axes
+    ax.quiver([0, 0], [0, 0], *inv_cov, angles='xy', scale_units='xy',
+              width=0.005, scale=1, color=['magenta', 'violet'], alpha=0.35)
+
+    # ellipse axes
+    ax.quiver([0, 0], [0, 0], [1, 0], [0, 1], angles='xy', scale_units='xy',
+              width=0.005, scale=1, color=['red', 'firebrick'], alpha=0.35)
+
+    plotted_data = normed_two_cols
+
+  # ==========================================================================
+
+  if show:
+    plt.show()
+
+  return (plotted_data,
+          A, x0, y0, sigma_x, sigma_y, rho, z_offset)  # optimal gaussian params
+
+# ===============================================================================================
+# ===============================================================================================
+# ===============================================================================================
+
+
+def plot_scatter_hist(x_gene, y_gene, mode='raw'):
+  fig = plt.figure(layout='constrained')
+  ax = fig.add_gridspec(top=0.75, right=0.75).subplots()
+  # ax.set_aspect('equal', adjustable='box') # ax.set(aspect=1)
+  ax_histx = ax.inset_axes([0, 1.05, 1, 0.25], sharex=ax)
+  ax_histy = ax.inset_axes([1.05, 0, 0.25, 1], sharey=ax)
+
+  ax_histx.tick_params(axis="x", labelbottom=False)
+  ax_histy.tick_params(axis="y", labelleft=False)
+
+  plot_result = plot_genes(x_gene, y_gene, ax=ax, mode=mode)
+  plotted_data = plot_result[0]
+  x_A, x_mu, x_sigma = plot_hist_gauss(plotted_data[x_gene], ax=ax_histx)
+  y_A, y_mu, y_sigma = plot_hist_gauss(plotted_data[y_gene], ax=ax_histy,
+                                       orientation='horizontal')
+  ax_histx.set_ylabel('Freq')
+  ax_histy.set_xlabel('Freq')
+
+  ax.vlines(x_mu, *ax.get_ylim(),
+            label=f'{x_gene} mean', colors='C3', zorder=0)
+  ax.hlines(y_mu, *ax.get_xlim(),
+            label=f'{y_gene} mean', colors='C3', zorder=0)
+
+  # ax.fill_between([plotted_data[x_gene].min(), plotted_data[x_gene].max()],
+  #                 *ax.get_ylim(), color='C0', alpha=0.01, lw=0)
+  # ax.fill_betweenx([plotted_data[y_gene].min(), plotted_data[y_gene].max()],
+  #                 *ax.get_xlim(), color='C0', alpha=0.01, lw=0)
+
+
+def create_correct_gene_plot(genes, mode):
+  if len(genes) == 0:
+    raise gr.Error("Please select at least one gene to plot.")
+  elif len(genes) == 1:
+    plot_gene(gene)
+  elif len(genes) == 2:
+    mode = 'norm' if mode else None
+    plot_scatter_hist(genes, mode)
+  else:
+    raise gr.Error("Cannot plot more than two genes at a time.")
+
+  fig = plt.gcf()
+
+  return PIL.Image.frombytes(
+      'RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
+
+
+demo = gr.Interface(
+    create_correct_gene_plot,
+    [
+        gr.Dropdown(
+            gene_table.columns, value=["APP", "PSENEN"], multiselect=True, label="Genes", info="Select one or two genes to plot."
+        ),
+        gr.Checkbox(label="Normalize",
+                    info="Recenter and normalize the Gaussian for two genes."),
+    ],
+    "image",
+    # examples=[
+    #     [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
+    #     [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
+    #     [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
+    #     [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
+    # ]
+)
 
-demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
-    
 if __name__ == "__main__":
-    demo.launch()   
+  demo.launch()
+

gene_tpm_brain_cerebellar_hemisphere_log2minus1NEW.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7146a7abf52c322bbd46760cb393cbb4d0dc7ae20bd1ddc23c62e7553757537e
+size 99254578