Struct SummaryConfig 

pub struct SummaryConfig { /* private fields */ }

Configuration for summary processes. It it used to filter the k-mers during and after graph construction and to make statistical analyses based on k-mer occurrence in the sample groups.

For any statistical analyses regarding the sample groups, the SummaryData requires a SampleInfo.

The available options are:

• filter the k-mers by
  • their number of occurrences
  • their quality based on the phred scores from the reads
  • their p-values regarding sample groups
  • if they occurr in at least a specific fraction of one or both of the sample groups
• filter the k-mer occurrences by the quality - should this disconnect the k-mer, alll occurrences will be used
• set the number of occurrences which is stored with the k-mers to be rounded to a number of significant digits
• choose a statistical test on which the p-value calculation is based, by default this is set to Welch’s t-test; Student’s t-test and the Mann-Whitney U test are also available

By default, all filter options are turned off.

use debruijn::summarizer::{GroupFrac, SummaryConfig, SampleInfo, StatTest};
use debruijn::BaseQuality;
 
let sample_info = SampleInfo::new(0b1100, 0b0011, vec![100, 100, 100, 100]);
let summary_config = SummaryConfig::new(sample_info.clone())
    .with_min_kmer_obs(2)
    .with_min_quality(BaseQuality::Medium)
    .with_max_p(Some(0.05)) // can also be none which can avoid p-value calculations and save time
    .with_group_frac(GroupFrac::One, 0.3);
 
let summary_config2 = SummaryConfig::new(sample_info.clone())
    .with_min_kmer_obs(4)
    .with_min_quality_for_edge(BaseQuality::Marginal);
 
let summary_config3 = SummaryConfig::new(sample_info)
    .with_significant(Some(5))
    .with_stat_test(StatTest::StudentsTTest);

To filter an already constructed graph, please use the original SummaryConfig and change the settings in plase with the set_... methods. This is so the program knows that some settings have been changed and corresponding values have to be re-calculated.

use debruijn::summarizer::{SummaryConfig, SampleInfo, StatTest};
use debruijn::BaseQuality;
 
let sample_info = SampleInfo::new(0b1100, 0b0011, vec![100, 100, 100, 100]);
let mut summary_config = SummaryConfig::new(sample_info);
 
// ... construct the graph
 
summary_config.set_max_p(Some(0.05));
summary_config.set_stat_test(StatTest::StudentsTTest);
 
// ...

By setting the with_min_kmer_obs to 0, k-mers for which all occurences were filtered out with with_min_quality_for_edge, can still be included, with empty k-mer data.

Implementations

impl SummaryConfig

pub fn new(sample_info: SampleInfo) -> Self

make a new SummaryConfig

arguments:

• sample_info: a SampleInfo with information about the sample groups, which is required for any statistical analysis

pub fn empty() -> Self

make an empty SummaryConfig. A proper SampleInfo is required for any statistical analysis regarding sample groups.

pub fn with_min_kmer_obs(&self, min_kmer_obs: usize) -> Self

produce a new SummaryConfig which will filter k-mers by their number of observations

pub fn set_min_kmer_obs(&mut self, min_kmer_obs: usize)

modify the number of k-mers observations required for each k-mer to be included in the graph

pub fn with_significant(&self, significant_digits: Option<u32>) -> Self

produce a new SummaryConfig which will round the number of observations of the k-mer to significant_digits

pub fn set_significant(&mut self, significant_digits: Option<u32>)

modify the number signigicant digits the number of observations stored with the k-mer will be rounded to

pub fn with_group_frac(&self, group_frac: GroupFrac, frac_cutoff: f32) -> Self

produce a new SummaryConfig which will require the k-mer to be ovserved in at least a fraction of frac_cutoff of either one, both or none of the sample groups

pub fn set_group_frac(&mut self, group_frac: GroupFrac, frac_cutoff: f32)

modify the group fract settings which require the k-mer to be ovserved in at least a fraction of frac_cutoff of either one, both or none of the sample groups

pub fn with_max_p(&self, max_p: Option<f32>) -> Self

produce a new SummaryConfig which will filter k-mers by their p-value regarding occurrence in the sample groups

pub fn set_max_p(&mut self, max_p: Option<f32>)

modify the maximum p-value a k-mer is allowed to have

pub fn with_stat_test(&self, stat_test: StatTest) -> Self

produce a new SummaryConfig which will calculate the p-values based on the given statistical test

pub fn set_stat_test(&mut self, stat_test: StatTest)

modify the statistical test, which is used to calculate p-values regarding observations in the two sample groups

pub fn with_min_quality(&self, min_quality: BaseQuality) -> Self

produce a new SummaryConfig which will filter k-mers by their quality

pub fn set_min_quality(&mut self, min_quality: BaseQuality)

modify the minimum quality required for each k-mer to be included in the graph

pub fn with_min_quality_for_edge( &self, min_quality_for_edge: BaseQuality, ) -> Self

produce a new SummaryConfig which will keep k-mers from being counted if it’s quality is too low - should this disconnect the k-mer it will still be counted

pub fn set_min_quality_for_edge(&mut self, min_quality_for_edge: BaseQuality)

modify the minimum quality required for each k-mer observation for its edges to be counted

pub fn get_markers(&self) -> (Marker, Marker)

get the binary encoded group affiliation of tags

pub fn sample_info(&self) -> &SampleInfo

get the SampleInfo stored in the SummaryConfig

Trait Implementations

impl Clone for SummaryConfig

fn clone(&self) -> SummaryConfig

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SummaryConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for SummaryConfig

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for SummaryConfig

fn eq(&self, other: &SummaryConfig) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for SummaryConfig

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for SummaryConfig