Update hands-on z-score

Author: pubudu

content/6.Vectorized_Operations_in_NumPy.md

@@ -720,6 +720,27 @@ std_diff = diff_z_scores.std()
 z_score_ratios = diff_z_scores / std_diff
 print_array_info(z_score_ratios)
 print(z_score_ratios[:10])
+
+## Rank genes according to the Z score ratio
+
+### """ 
+# The Z-ratio provides a standardized measure of the difference between conditions for each gene. 
+# Dividing by the SD (difference - all genes) accounts for the overall variability in the experiment.
+# A gene showing a difference of, say, 0.5 in average Z-score might be highly significant if most genes show very little difference (small Z-score difference - SD), but not significant if many genes show large differences (large Z-score difference - SD).
+# It puts the individual gene's change in the context of the overall experimental variation.
+### """
+
+### Sort z_score_ratio in descending order and access indices
+### Rank genes using indices
+
+gene_list = ["ACTR3B", "ANLN", "APOBEC3G", "AURKA", "BAG1", "BCL2", "BIRC5", "BLVRA", "CCL5", "CCNB1", "CCNE1", "CCR2", "CD2", "CD27", "CD3D", "CD52", "CD68", "CDC20", "CDC6", "CDH3", "CENPF", "CEP55", "CORO1A", "CTSL2", "CXCL9", "CXXC5", "EGFR", "ERBB2", "ESR1", "EXO1", "FGFR4", "FOXA1", "FOXC1", "GAPDH", "GPR160", "GRB7", "GSTM1", "GUSB", "GZMA", "GZMK", "HLA-DMA", "IL2RG", "KIF2C", "KRT14", "KRT17", "KRT5", "LCK", "MAPT", "MDM2", "MELK", "MIA", "MKI67", "MLPH", "MMP11", "MRPL19", "MYBL2", "MYC", "NAT1", "NDC80", "NUF2", "ORC6", "PGR", "PHGDH", "PRKCB", "PSMC4", "PTPRC", "PTTG1", "RRM2", "SCUBE2", "SF3A1", "SFRP1", "SH2D1A", "SLC39A6", "TFRC", "TMEM45B", "TP53", "TYMS", "UBE2C", "UBE2T", "VEGFA"]
+
+### `np.argsort()` returns indices of the array that would sort in ascending order
+### slicing syntax [start:stop:step] with step -1 returns a reversed array
+
+gene_ranks = np.argsort(z_score_ratios)[::-1]
+print("Genes ranked according to Z-score ratio:")
+print(np.array(gene_list)[gene_ranks])
 ```
 
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