/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/fastcore/docscrape.py:230: UserWarning: Unknown section Parameters:
  else: warn(msg)
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/fastcore/docscrape.py:230: UserWarning: Unknown section Returns:
  else: warn(msg)

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add_ann_info_to_df

 add_ann_info_to_df (df, info_column='INFO')

Extract the first ANN annotation from the INFO field and add as separate columns to a DataFrame.

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extract_first_ann

 extract_first_ann (info_field)

Extract the first ANN annotation from a VCF INFO field.

# Test with the example
info_field = 'AB=0;ABP=0;AC=1;AF=0.142857;AN=7;AO=248;CIGAR=1X;DP=664;DPB=664;DPRA=0;EPP=13.1322;EPPR=3.03118;GTI=0;LEN=1;MEANALT=1;MQM=60;MQMR=60;NS=7;NUMALT=1;ODDS=61.2192;PAIRED=0.991935;PAIREDR=0.995192;PAO=0;PQA=0;PQR=0;PRO=0;QA=8364;QR=13932;RO=416;RPL=137;RPP=8.92931;RPPR=9.04449;RPR=111;RUN=1;SAF=122;SAP=3.15039;SAR=126;SRF=210;SRP=3.09382;SRR=206;TYPE=snp;technology.illumina=1;ANN=G|synonymous_variant|LOW|cgd1_340|cgd1_340|transcript|cgd1_340-RA|protein_coding|1/1|c.357A>G|p.Ser119Ser|570/1483|357/1194|119/397||,G|upstream_gene_variant|MODIFIER|cgd1_320|cgd1_320|transcript|cgd1_320-RA|protein_coding||c.-4699T>C|||||4675|,G|upstream_gene_variant|MODIFIER|cgd1_330|cgd1_330|transcript|cgd1_330-RA|protein_coding||c.-1174T>C|||||1095|,G|upstream_gene_variant|MODIFIER|cgd1_350|cgd1_350|transcript|cgd1_350-RA|protein_coding||c.-1490A>G|||||1323|,G|downstream_gene_variant|MODIFIER|cgd1_360|cgd1_360|transcript|cgd1_360-RA|protein_coding||c.*4665T>C|||||4665|'
result = extract_first_ann(info_field)
print(result)
# Should output: {'allele': 'G', 'type': 'synonymous_variant', 'impact': 'LOW', 'gene_id': 'cgd1_340'}

{'allele': 'G', 'type': 'synonymous_variant', 'impact': 'LOW', 'gene_id': 'cgd1_340'}

		REF	ALT	AF_M7	AF_M5	AF_M4	AF_M6	AF_C3	AF_C2	AF_C1	INFO	variant_type	impact	gene_id	allele
#CHROM	POS
CM000429	76625	A	G	0.269231	0.350000	0.404762	0.314607	0.566265	0.303571	0.437500	AB=0;ABP=0;AC=1;AF=0.142857;AN=7;AO=248;CIGAR=...	synonymous_variant	LOW	cgd1_340	G
	82019	A	T	0.908163	0.705882	0.779070	0.833333	0.628571	0.617284	0.690141	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=410;CIGAR=1X;DP=5...	synonymous_variant	LOW	cgd1_360	T
	82192	G	A	0.894231	0.701493	0.666667	0.745098	0.471698	0.619565	0.602410	AB=0;ABP=0;AC=6;AF=0.857143;AN=7;AO=398;CIGAR=...	missense_variant	MODERATE	cgd1_360	A
	106518	C	G	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=530;CIGAR=1X;DP=5...	missense_variant	MODERATE	cgd1_493	G
	116718	C	A	1.000000	1.000000	1.000000	0.989796	1.000000	1.000000	1.000000	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=525;CIGAR=1X;DP=5...	upstream_gene_variant	MODIFIER	cgd1_520	A
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
CM000436	1198389	T	A	1.000000	0.981818	1.000000	1.000000	0.987654	1.000000	1.000000	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=531;CIGAR=1X;DP=5...	missense_variant	MODERATE	cgd8_4800	A
	1198626	A	C	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.987013	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=626;CIGAR=1X;DP=6...	3_prime_UTR_variant	MODIFIER	cgd8_4800	C
	1198636	G	A	1.000000	1.000000	0.988889	1.000000	1.000000	1.000000	1.000000	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=650;CIGAR=1X;DP=6...	3_prime_UTR_variant	MODIFIER	cgd8_4800	A
	1198801	C	G	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=447;CIGAR=1X;DP=4...	3_prime_UTR_variant	MODIFIER	cgd8_4800	G
	1210605	C	T	0.827586	0.718310	0.711111	0.781818	0.673684	0.735294	0.747573	AB=0;ABP=0;AC=7;AF=1;AN=7;AO=464;CIGAR=1X;DP=6...	synonymous_variant	LOW	cgd8_4840	T

515 rows × 14 columns

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clean_axes

 clean_axes (ax, offset=10)

Customizes a matplotlib axes by removing top and right spines, and creating a broken axis effect where x and y axes don’t touch.

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mod_hist_legend

 mod_hist_legend (ax, title=False)

*Creates a cleaner legend for histogram plots by using line elements instead of patches.

Motivation: - Default histogram legends show rectangle patches which can be visually distracting - This function creates a more elegant legend with simple lines matching histogram edge colors - Positions the legend outside the plot to avoid overlapping with data*

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make_circos_plot

 make_circos_plot (data)

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elbow_point

 elbow_point (values)

Find the elbow point in a curve using the maximum curvature method.

	Type	Details
values	list	The y-values of the curve.
Returns	int	The index of the elbow point.

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kmeans_cluster_analysis

 kmeans_cluster_analysis (df, cluster_sizes, random_state=42,
                          features=None, figsize=(12, 6),
                          standardize=False, fill_na=False)

Perform K-means clustering analysis on a pandas DataFrame and visualize the results with both normalized inertia and silhouette scores on the same plot.

	Type	Default	Details
df	pandas.DataFrame		The input data to cluster.
cluster_sizes	list		List of cluster sizes (k values) to evaluate.
random_state	int	42	Random seed for reproducibility (default: 42).
features	NoneType	None	List of column names to use for clustering. If None, all columns are used.
figsize	tuple	(12, 6)	Figure size for the output plot (default: (12, 6)).
standardize	bool	False	Whether to standardize the features (default: False).
fill_na	bool	False	Whether to fill missing values with column means (default: False).
Returns	tuple		(figure, inertia_values, silhouette_values) - The matplotlib figure object, the list of inertia values, and the list of silhouette scores.