Closed d4straub closed 1 year ago
It would be nice to have a sample filter before downstream analysis, such as diversity analysis, so that samples that have low counts will be removed at that point (default value: 50?). That would improve pipeline behavior so that low count samples can be skipped. Maybe it could be similarly implemented to the cutadapt parameters, e.g. https://nf-co.re/ampliseq/2.4.1/parameters#ignore_empty_input_files & https://nf-co.re/ampliseq/2.4.1/parameters#ignore_empty_input_files, using qiime feature-table filter-samples.
qiime feature-table filter-samples
This is in dev
d4straub
commented
1 year ago d4straub
commented
1 year ago
Description of feature
It would be nice to have a sample filter before downstream analysis, such as diversity analysis, so that samples that have low counts will be removed at that point (default value: 50?). That would improve pipeline behavior so that low count samples can be skipped. Maybe it could be similarly implemented to the cutadapt parameters, e.g. https://nf-co.re/ampliseq/2.4.1/parameters#ignore_empty_input_files & https://nf-co.re/ampliseq/2.4.1/parameters#ignore_empty_input_files, using
qiime feature-table filter-samples.