diff --git a/scripts/perform_PCA.py b/scripts/perform_PCA.py
new file mode 100755
index 0000000000000000000000000000000000000000..3150d4a2f7ccc1b77c6edec9f06562c9b00806e1
--- /dev/null
+++ b/scripts/perform_PCA.py
@@ -0,0 +1,210 @@
+#!/usr/bin/env python
+
+# _____________________________________________________________________________
+# -----------------------------------------------------------------------------
+# import needed (external) modules
+# -----------------------------------------------------------------------------
+
+
+import sys
+import os
+import pandas as pd
+import numpy as np
+import random as rd
+from sklearn.decomposition import PCA
+from sklearn import preprocessing
+import matplotlib
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from argparse import ArgumentParser, RawTextHelpFormatter
+
+# _____________________________________________________________________________
+# -----------------------------------------------------------------------------
+# Main function
+# -----------------------------------------------------------------------------
+
+
+def main():
+ """ Main function """
+
+ __doc__ = "Perform PCA based on a table (rows are genes/transcripts, columns are samples)."
+
+ parser = ArgumentParser(
+ description=__doc__,
+ formatter_class=RawTextHelpFormatter
+ )
+
+ parser.add_argument(
+ "--tpm",
+ dest="tpm",
+ help="TPM table (tsv)",
+ required=True,
+ metavar="FILE"
+ )
+
+ parser.add_argument(
+ "--out",
+ dest="out",
+ help="Output directory",
+ required=True,
+ metavar="FILE"
+ )
+
+ parser.add_argument(
+ "-v",
+ "--verbose",
+ action="store_true",
+ dest="verbose",
+ default=False,
+ required=False,
+ help="Verbose"
+ )
+
+ # _________________________________________________________________________
+ # -------------------------------------------------------------------------
+ # get the arguments
+ # -------------------------------------------------------------------------
+ try:
+ options = parser.parse_args()
+ except(Exception):
+ parser.print_help()
+
+ if len(sys.argv) == 1:
+ parser.print_help()
+ sys.exit(1)
+
+ if options.verbose:
+ sys.stdout.write(f"Creating output directory: {options.out} {os.linesep}")
+
+ if not os.path.exists(options.out):
+ os.makedirs(options.out)
+
+ if options.verbose:
+ sys.stdout.write(f"Reading: {options.tpm} {os.linesep}")
+ df = pd.read_csv(options.tpm, header=0, sep="\t")
+ df.set_index(["Name"], inplace=True)
+
+ if options.verbose:
+ sys.stdout.write(f"Performing PCA {os.linesep}")
+
+ scaled_data = preprocessing.scale(df.T)
+
+ pca = PCA()
+ pca.fit(scaled_data)
+ pca_data = pca.transform(scaled_data)
+
+ per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)
+ labels = ['PC' + str(x) for x in range(1, len(per_var)+1)]
+ plt.figure()
+ plt.rcParams["figure.figsize"] = (20,20)
+ plt.bar(x=range(1,len(per_var)+1), height=per_var, tick_label=labels)
+ plt.ylabel('Percentage of Explained Variance')
+ plt.xlabel('Principal Component')
+ plt.title('Scree Plot')
+ plt.xticks(rotation='vertical')
+ plt.savefig(os.path.join(options.out, "scree_plot.pdf"))
+ plt.close()
+
+ pca_df = pd.DataFrame(pca_data, index=[*df], columns=labels)
+
+ if options.verbose:
+ sys.stdout.write(f"Generating plots in: {options.out} {os.linesep}")
+
+ # -------------------------------------------------------------------------
+ # PCA 1st and 2nd component
+ # -------------------------------------------------------------------------
+
+ plt.figure()
+ plt.rcParams["figure.figsize"] = (20,20)
+ plt.scatter(pca_df.PC1, pca_df.PC2)
+ plt.xlabel('PC1 - {0}%'.format(per_var[0]), fontsize=22)
+ plt.ylabel('PC2 - {0}%'.format(per_var[1]), fontsize=22)
+ plt.title('PCA components 1 & 2', fontsize=22)
+ for sample in pca_df.index:
+ plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample]))
+ plt.savefig(os.path.join(options.out, "PCA_1_2.pdf"))
+ plt.close()
+
+ # -------------------------------------------------------------------------
+ # PCA 2nd and 3rd component
+ # -------------------------------------------------------------------------
+
+ plt.figure()
+ plt.rcParams["figure.figsize"] = (20,20)
+ plt.scatter(pca_df.PC2, pca_df.PC3)
+ plt.xlabel('PC2 - {0}%'.format(per_var[1]), fontsize=22)
+ plt.ylabel('PC3 - {0}%'.format(per_var[2]), fontsize=22)
+ plt.title('PCA components 2 & 3', fontsize=22)
+ for sample in pca_df.index:
+ plt.annotate(sample, (pca_df.PC2.loc[sample], pca_df.PC3.loc[sample]))
+ plt.savefig(os.path.join(options.out, "PCA_2_3.pdf"))
+ plt.close()
+
+
+ # -------------------------------------------------------------------------
+ # PCA 1st and 3rd component
+ # -------------------------------------------------------------------------
+
+ plt.figure()
+ plt.rcParams["figure.figsize"] = (20,20)
+ plt.scatter(pca_df.PC1, pca_df.PC3)
+ plt.xlabel('PC1 - {0}%'.format(per_var[0]), fontsize=22)
+ plt.ylabel('PC3 - {0}%'.format(per_var[2]), fontsize=22)
+ plt.title('PCA components 1 & 3', fontsize=22)
+ for sample in pca_df.index:
+ plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC3.loc[sample]))
+ plt.savefig(os.path.join(options.out, "PCA_1_3.pdf"))
+ plt.close()
+
+
+ # -------------------------------------------------------------------------
+ # PCA 3D
+ # -------------------------------------------------------------------------
+
+ labels = []
+ pc1 = []
+ pc2 = []
+ pc3 = []
+ for i, row in pca_df.iterrows():
+ labels.append(i)
+ pc1.append(row["PC1"])
+ pc2.append(row["PC2"])
+ pc3.append(row["PC3"])
+
+ fig = plt.figure(figsize=(20,20))
+ ax = fig.add_subplot(111, projection='3d')
+ ax.scatter(pc1, pc2, pc3)
+
+ ax.set_xlabel('PC1 - {0}%'.format(per_var[0]))
+ ax.set_ylabel('PC2 - {0}%'.format(per_var[1]))
+ ax.set_zlabel('PC3 - {0}%'.format(per_var[2]))
+ for label, x, y, z in zip(labels, pc1, pc2, pc3):
+ ax.text(x, y, z, label)
+ plt.savefig(os.path.join(options.out, "PCA_3D.pdf"))
+
+ # -------------------------------------------------------------------------
+ # Write loading scores
+ # -------------------------------------------------------------------------
+
+ if options.verbose:
+ sys.stdout.write(f"Writing loading scores in: {options.out} {os.linesep}")
+
+ loading_scores = pd.Series(pca.components_[0], index=list(df.index))
+ sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
+ sorted_loading_scores.to_csv(os.path.join(options.out, "loading_scores.tsv"), header=False, sep="\t")
+
+ if options.verbose:
+ sys.stdout.write(f"Done {os.linesep}")
+
+# _____________________________________________________________________________
+# -----------------------------------------------------------------------------
+# Call the Main function and catch Keyboard interrups
+# -----------------------------------------------------------------------------
+
+
+if __name__ == '__main__':
+ try:
+ main()
+ except KeyboardInterrupt:
+ sys.stderr.write("User interrupt!" + os.linesep)
+ sys.exit(0)