feat: add function to calculate mean and variance

Add function to calculate mean and variance from a directory with files of gene expression(Work in progress)

src/meanvariancefunction.py

+import csv
+from glob import glob
+import io
+import os
+
+import matplotlib.pyplot as plt
+
+
+def mean_variance(filepath: str, output_dir: str = os.getcwd()+'/') -> str:
+    """Given the counts observed for a given gene across all cells,
+    calculate the mean and variance.
+
+    ! At the moment the function does not check the import files format,
+    be careful, and only add text files in the format geneid number_of_transcipts !
+
+    Args:
+        directory with text files of gene expression counts in individual cells.
+    Returns:
+        1. Path to Csv-formatted table with GeneID, Mean, Variance of the count, and
+        Scatterplot of mean vs variance for all genes.
+
+    Raises:
+        ValueError: If there are no files in directory
+    """
+    # Open each file in the input directory, raises error if no file is found
+    files = [file for file in glob(filepath)]
+
+    if len(files) == 0:
+        raise ValueError('No files in directory:', filepath)
+
+    # Creates all required dictionaries to construct the mean and variance
+    gene_counts = {}
+    occurence = {}
+    mean = {}
+    variance = {}
+
+    # Adds together all gene counts in gene_counts, and occurences in occurence
+    for file_name in files:
+        with io.open(file_name, 'r') as fh:
+            for line in fh:
+                geneid, copies = str(line.split()[0]), int(line.split()[1])
+                if geneid not in gene_counts:
+                    gene_counts[geneid] = [copies]
+                    occurence[geneid] = 1
+                else:
+                    gene_counts[geneid] += [copies]
+                    occurence[geneid] += 1
+    # Calculates mean of each gene
+    for i in gene_counts:
+        mean[i] = sum(gene_counts[i])/occurence[i]
+
+    # Calculates the variance
+    for i in gene_counts:
+        for j in range(0, len(gene_counts[i])):
+            variance[i] = (gene_counts[i][j]-mean[i])**2/occurence[i]
+
+    # Plots mean against variance
+    plt.scatter(mean.values(), variance.values())
+    for value in list(mean.keys()):
+        plt.text(mean[value], variance[value], value)
+    plt.xlabel('mean')
+    plt.ylabel('variance')
+    plt.title('Mean gene expression vs. variance')
+    plt.savefig(output_dir+'/meanvarianceplot.png')
+    plt.show()
+    # Constructs csv file and saves it in the users directory
+    path = output_dir+'/results_mean_var_function.csv'
+    with open(path, 'w') as csv_file:
+        filewriter = csv.writer(csv_file, delimiter=',', quotechar='|',
+                                quoting=csv.QUOTE_MINIMAL)
+        filewriter.writerow(['geneid', 'mean', 'variance'])
+        for id in gene_counts.keys():
+            filewriter.writerow([id, mean[id], variance[id]])
+    return output_dir