diff --git a/notebooks/analysis/mapping_analysis.ipynb b/notebooks/analysis/mapping_analysis.ipynb index 00c4dd1..6bc2365 100644 --- a/notebooks/analysis/mapping_analysis.ipynb +++ b/notebooks/analysis/mapping_analysis.ipynb @@ -667,23 +667,23 @@ " 1018091\n", " \n", " \n", - " Minimum Number of Variants in a Score Set\n", + " Minimum Number of Variants (functional measurements) in a Score Set\n", " 1\n", " \n", " \n", - " Maximum Number of Variants in a Score Set\n", + " Maximum Number of Variants (functional measurements) in a Score Set\n", " 648022\n", " \n", " \n", - " Average Number of Variants in a Score Set\n", + " Average Number of Variants (functional measurements) in a Score Set\n", " 2833\n", " \n", " \n", - " Median Number of Variants in a Score Set\n", + " Median Number of Variants (functional measurements) in a Score Set\n", " 1289\n", " \n", " \n", - " Interquartile Range for Variants in a Score Set (25-75%)\n", + " Interquartile Range for Variants (functional measurements) in a Score Set (25-75%)\n", " (920, 1794)\n", " \n", " \n", @@ -697,11 +697,11 @@ "Number of Pairs of Reference Mismatch 2839573 (31.56%)\n", "Number of Unique Pre-Mapped Variants 1048823\n", "Number of Unique Post-Mapped Variants 1018091\n", - "Minimum Number of Variants in a Score Set 1\n", - "Maximum Number of Variants in a Score Set 648022\n", - "Average Number of Variants in a Score Set 2833\n", - "Median Number of Variants in a Score Set 1289\n", - "Interquartile Range for Variants in a Score Set... (920, 1794)" + "Minimum Number of Variants (functional measurem... 1\n", + "Maximum Number of Variants (functional measurem... 648022\n", + "Average Number of Variants (functional measurem... 2833\n", + "Median Number of Variants (functional measureme... 1289\n", + "Interquartile Range for Variants (functional me... (920, 1794)" ] }, "execution_count": 16, @@ -716,11 +716,11 @@ " \"Number of Pairs of Reference Mismatch\": \"2839573 (31.56%)\",\n", " \"Number of Unique Pre-Mapped Variants\": 1048823,\n", " \"Number of Unique Post-Mapped Variants\": 1018091,\n", - " \"Minimum Number of Variants in a Score Set\": 1,\n", - " \"Maximum Number of Variants in a Score Set\": 648022,\n", - " \"Average Number of Variants in a Score Set\": 2833,\n", - " \"Median Number of Variants in a Score Set\": 1289,\n", - " \"Interquartile Range for Variants in a Score Set (25-75%)\": \"(920, 1794)\"\n", + " \"Minimum Number of Variants (functional measurements) in a Score Set\": 1,\n", + " \"Maximum Number of Variants (functional measurements) in a Score Set\": 648022,\n", + " \"Average Number of Variants (functional measurements) in a Score Set\": 2833,\n", + " \"Median Number of Variants (functional measurements) in a Score Set\": 1289,\n", + " \"Interquartile Range for Variants (functional measurements) in a Score Set (25-75%)\": \"(920, 1794)\"\n", "}\n", "stats_data = pd.DataFrame.from_dict(stats_data, orient=\"index\", columns=[\"Value\"])\n", "stats_data" @@ -743,7 +743,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -764,8 +764,8 @@ "ax = sns.histplot(log_variant_counts, bins=50, edgecolor=\"black\")\n", "plt.xticks(np.arange(0, 14, step=1))\n", "plt.yticks(np.arange(0, 300, step=50))\n", - "plt.xlabel(\"Ln(Score Set Variant Counts)\")\n", - "plt.ylabel(\"Variant Count Frequency\")\n", + "plt.xlabel(\"Ln(Score Set Functional Measurement Counts)\")\n", + "plt.ylabel(\"Functional Measurement Count Frequency\")\n", "\n", "plt.savefig(\"variant_counts.png\", dpi=300)\n", "plt.show()"