News and Announcements » |

**Description:**

This script calculates the conditional uncovered probability for each sample in an OTU table. It uses the methods introduced in Lladser, Gouet, and Reeder, “Extrapolation of Urn Models via Poissonization: Accurate Measurements of the Microbial Unknown” PLoS 2011.

Specifically, it computes a point estimate and a confidence interval using two different methods. Thus it can happen that the PE is actually outside of the CI.

We only provide the ability to generate 95% (alpha=0.95) CIs. The CIs are ULCL CIs; they provide an upper and lower bound, where the lower bound is conservative. The CIs are constructed using an upper-to-lower bound ratio of 10.

The CI method requires precomputed constants that depend on the lookahead. We only provide constants for r=3..25,30,40,50.

**Usage:** `conditional_uncovered_probability.py [options]`

**Input Arguments:**

Note

**[OPTIONAL]**

- -i, --input_path
- Input OTU table filepath. [default: None]
- -o, --output_path
- Output filepath to store the predictions. [default: None]
- -r, --look_ahead
- Number of unobserved, new colors necessary for prediction. [default: 25]
- -m, --metrics
- CUP metric(s) to use. A comma-separated list should be provided when multiple metrics are specified. [default: lladser_pe,lladser_ci]
- -s, --show_metrics
- Show the available CUP metrics and exit.

**Output:**

The resulting file(s) is a tab-delimited text file, where the columns correspond to estimates of the cond. uncovered probability and the rows correspond to samples. The output file is compatible with the alpha_diversity output files and thus could be tied into the rarefaction workflow.

Example Output:

PE | Lower Bound | Upper Bound | |
---|---|---|---|

PC.354 | 0.111 | 0.0245 | 0.245 |

PC.124 | 0.001 | 0.000564 | 0.00564 |

**Default case:**

To calculate the cond. uncovered probability with the default values, you can use the following command:

```
conditional_uncovered_probability.py -i otu_table.biom -o cup.txt
```

**Change lookahead:**

To change the accuracy of the prediction change the lookahead value. Larger values of r lead to more precise predictions, but might be unfeasable for small samples. For deeply sequenced samples, try increasing r to 50:

```
conditional_uncovered_probability.py -i otu_table.biom -o cup_r50.txt -r 50
```