Cluster

For clustering samples into groups using genotype comparison data.

Takes as input the results from running biometrics genotype and clusters the samples together using the discordance rate. Done by thresholding the discordance rate into 0 or 1, where 1 means the sample pair came from the same patient. The default discordance rate threshold is 0.05, but you can change it via the --discordance-threshold argument. The tool then uses the networkx package to get the groups of samples that are connected.

When you run biometrics genotype, it automatically outputs two sets of clustering results: (1) the first set just clusters your input samples, and (2) the second set clusters your input samples and samples in the database.

Due to the limitations of the discordance rate metric, samples that have contamination can lead to many false positive matches using this clustering approach. Hence, you might want to consider removing contaminated samples before running this tool.

How to run the tool

You simply need to provide the CSV file that is outputted from running biometrics genotype.

biometrics cluster \
  -i genotype_comparison.csv \
  -o genotype_clusters.csv

You can also specify multiple inputs. It will automatically drop duplicate comparisons.

biometrics cluster \
  -i genotype_comparison_1.csv \
  -i genotype_comparison_2.csv \
  -i genotype_comparison_3.csv \
  -o genotype_clusters.csv

Output

Produces a CSV file that contains the clustering results. Each row corresponds to a different sample. The table below provides a description on each column.

Column Name	Description
sample_name	The sample name.
expected_sample_group	The expected group for the sample based on user input.
cluster_index	The integer cluster index. All rows with the same cluster_index are in the same cluster.
cluster_size	The size of the cluster this sample is in.
avg_discordance	The average discordance between this sample and all other samples in the cluster.
count_expected_matches	The count of expected matches when comparing the sample to all others in the cluster.
count_unexpected_matches	The count of unexpected matches when comparing the sample to all others in the cluster.
count_expected_mismatches	The count of expected mismatches when comparing the sample to all other samples (inside and outside its cluster).
count_unexpected_mismatches	The count of unexpected mismatches when comparing the sample to all other samples (inside and outside its cluster).