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I want to detect rare contamination signals in sequencing data obtained in the lab. Which methods are appropriate when there are almost no contamination labels available beforehand?
①Learn using large-scale supervised deep learning.
②Unsupervised anomaly detection and outlier detection using clustering.
③Periodically label all data using batch inference.
④Learning detection strategies using reinforcement learning
⑤Process using only a simple threshold filter.