fastleng 0.2.0

fastleng - read length statistics tool
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329


extern crate serde;

use serde::{Deserialize, Serialize};
use std::collections::BTreeMap;

/// This will compute the total number of bases and sequences by iterating over the length stats and return a tuple (`total_bases`, `total_seqs`).
/// # Arguments
/// * `length_counts` - a BTreeMap with the sequence length as the key, and the value the total number of sequences with that length
/// # Examples
/// ```
/// use std::collections::BTreeMap;
/// use fastleng::length_stats::compute_total_counts;
/// let length_counts: BTreeMap<usize, u64> = [
///     (5, 10),
///     (10, 3)
/// ].iter().cloned().collect();
/// let (total_bases, total_seqs) = compute_total_counts(&length_counts);
/// assert_eq!(total_bases, 80);
/// assert_eq!(total_seqs, 13);
/// ```
pub fn compute_total_counts(length_counts: &BTreeMap<usize, u64>) -> (u64, u64) {
    let mut total_bases: u64 = 0;
    let mut total_seqs: u64 = 0;
    for (length, count) in length_counts.iter() {
        total_bases += (*length as u64) * count;
        total_seqs += count;
    }
    (total_bases, total_seqs)
}

/// This will compute the median length of the sequences captured by some length statistics.
/// This metric is imprecise for some instances of an even number of sequences (e.g. does not take the mean).
/// # Arguments
/// * `length_counts` - a BTreeMap with the sequence length as the key, and the value the total number of sequences with that length
/// * `total_seqs` - the total number of sequences represented by `length_counts`, this can be computed by `compute_total_counts(...)`
/// # Examples
/// ```
/// use std::collections::BTreeMap;
/// use fastleng::length_stats::{compute_median_length,compute_total_counts};
/// let length_counts: BTreeMap<usize, u64> = [
///     (5, 10),
///     (10, 3)
/// ].iter().cloned().collect();
/// let (_total_bases, total_seqs) = compute_total_counts(&length_counts);
/// let median_length = compute_median_length(&length_counts, total_seqs);
/// assert_eq!(median_length, 5.0);
/// ```
pub fn compute_median_length(length_counts: &BTreeMap<usize, u64>, total_seqs: u64) -> f64 {
    //find the middle index
    let middle_seq_index: u64 = total_seqs / 2;
    let mut total_observed = 0;
    for (seq_len, seq_count) in length_counts.iter() {
        total_observed += seq_count;

        //loop until we observe more than the target index
        if total_observed > middle_seq_index {
            return *seq_len as f64;
        }
    }

    //this case only happens with empty files
    assert!(total_seqs == 0 && length_counts.is_empty());
    0.0
}

/// This will compute the N-score (e.g. N50) for the sequence lengths provided. 
/// For details on this measure, see <https://www.molecularecologist.com/2017/03/29/whats-n50/>.
/// # Arguments
/// * `length_counts` - a BTreeMap with the sequence length as the key, and the value the total number of sequences with that length
/// * `total_bases` - the total number of bases represented by the `length_counts` parameter, this can be computed by `compute_total_counts(...)`
/// * `target` - the score target; e.g. for N50, N75, and N90, this parameter should be 50, 75, and 90 respectively
/// # Examples
/// ```
/// use std::collections::BTreeMap;
/// use fastleng::length_stats::{compute_n_score,compute_total_counts};
/// let length_counts: BTreeMap<usize, u64> = [
///     (5, 10),
///     (10, 3)
/// ].iter().cloned().collect();
/// let (total_bases, _total_seqs) = compute_total_counts(&length_counts);
/// let n50_score = compute_n_score(&length_counts, total_bases, 50);
/// assert_eq!(n50_score, 5);
/// ```
pub fn compute_n_score(length_counts: &BTreeMap<usize, u64>, total_bases: u64, target: usize) -> usize {
    //make sure this is in our allowed range
    assert!((1..=99).contains(&target));

    //calculate the target number of bases
    let target_bases: f64 = (target as u64*total_bases) as f64 / 100.0;
    let mut current_bases: u64 = 0;
    for (seq_len, seq_count) in length_counts.iter().rev() {
        current_bases += (*seq_len as u64) * *seq_count;
        if current_bases as f64 >= target_bases {
            return *seq_len;
        }
    }

    //this only happens with empty files
    assert!(total_bases == 0 && length_counts.is_empty());
    0
}

/// This struct encapsulates the various statistics we return
#[derive(Serialize, Deserialize, Debug, PartialEq)]
pub struct LengthStats {
    /// The total number of bases analyzed
    pub total_bases: u64,
    /// The total number of sequences (i.e. strings) analyzed
    pub total_sequences: u64,
    /// The average length of the sequences
    pub mean_length: f64,
    /// The median length of the sequences
    pub median_length: f64,
    /// N10 - 10% of bases are in sequences of length greater than this value
    pub n10: usize,
    /// N25 - 25% of bases are in sequences of length greater than this value
    pub n25: usize,
    /// N50 - 50% of bases are in sequences of length greater than this value
    pub n50: usize,
    /// N75 - 75% of bases are in sequences of length greater than this value
    pub n75: usize,
    /// N90 - 90% of bases are in sequences of length greater than this value
    pub n90: usize
}

/// This will compute multiple different summary statistics based on the length BTreeMap and return a HashMap with all the various metrics
/// # Arguments
/// * `length_counts` - a BTreeMap with the sequence length as the key, and the value the total number of sequences with that length
/// # Examples
/// ```
/// use std::collections::BTreeMap;
/// use fastleng::length_stats::{compute_length_stats,LengthStats};
/// let length_counts: BTreeMap<usize, u64> = [
///     (5, 10),
///     (10, 3)
/// ].iter().cloned().collect();
/// let summary_stats: LengthStats = compute_length_stats(&length_counts);
/// assert_eq!(summary_stats.total_bases, 80);
/// assert_eq!(summary_stats.total_sequences, 13);
/// ```
pub fn compute_length_stats(length_counts: &BTreeMap<usize, u64>) -> LengthStats {
    //first get all the totals
    let (total_bases, total_seqs): (u64, u64) = compute_total_counts(length_counts);
    let median_length: f64 = compute_median_length(length_counts, total_seqs);
    let n10: usize = compute_n_score(length_counts, total_bases, 10);
    let n25: usize = compute_n_score(length_counts, total_bases, 25);
    let n50: usize = compute_n_score(length_counts, total_bases, 50);
    let n75: usize = compute_n_score(length_counts, total_bases, 75);
    let n90: usize = compute_n_score(length_counts, total_bases, 90);

    //now put the composite stats together
    let final_stats: LengthStats = LengthStats {
        total_bases, 
        total_sequences: total_seqs,
        mean_length: (total_bases as f64) / (total_seqs as f64),
        median_length,
        n10,
        n25,
        n50,
        n75,
        n90
    };
    final_stats
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_compute_total_counts() {
        let seq_lens: BTreeMap<usize, u64> = [
            (10, 100)
        ].iter().cloned().collect();

        let expected_total_bases: u64 = 1000;
        let expected_total_seqs: u64 = 100;

        let computed_results = compute_total_counts(&seq_lens);
        assert_eq!((expected_total_bases, expected_total_seqs), computed_results);
    }

    #[test]
    fn test_compute_median_length() {
        //odd one
        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1),
            (2, 1),
            (3, 1)
        ].iter().cloned().collect();
        let (_total_bases, total_seqs) = compute_total_counts(&seq_lens);
        let median: f64 = compute_median_length(&seq_lens, total_seqs);
        assert_eq!(median, 2.0);

        //even one - this should be the average of index 1 & 2
        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1),
            (2, 1),
            (3, 1),
            (4, 1)
        ].iter().cloned().collect();
        let (_total_bases, total_seqs) = compute_total_counts(&seq_lens);
        let median: f64 = compute_median_length(&seq_lens, total_seqs);
        assert_eq!(median, 3.0);

        //even one - but both are in 2
        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1),
            (2, 2),
            (3, 1)
        ].iter().cloned().collect();
        let (_total_bases, total_seqs) = compute_total_counts(&seq_lens);
        let median: f64 = compute_median_length(&seq_lens, total_seqs);
        assert_eq!(median, 2.0);

        //even one - but both are in 2
        let seq_lens: BTreeMap<usize, u64> = [
            (2, 3),
            (3, 2),
            (4, 1)
        ].iter().cloned().collect();
        let (_total_bases, total_seqs) = compute_total_counts(&seq_lens);
        let median: f64 = compute_median_length(&seq_lens, total_seqs);
        assert_eq!(median, 3.0);
    }

    #[test]
    fn test_compute_n_score() {
        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1),
            (2, 1),
            (3, 1)
        ].iter().cloned().collect();
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        let n_score = compute_n_score(&seq_lens, total_bases, 50);
        assert_eq!(n_score, 3);
        
        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1),
            (2, 1),
            (3, 1),
            (4, 1)
        ].iter().cloned().collect();
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        let n_score = compute_n_score(&seq_lens, total_bases, 50);
        assert_eq!(n_score, 3);

        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1),
            (2, 2),
            (3, 1)
        ].iter().cloned().collect();
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        let n_score = compute_n_score(&seq_lens, total_bases, 50);
        assert_eq!(n_score, 2);

        let seq_lens: BTreeMap<usize, u64> = [
            (2, 3),
            (3, 2),
            (4, 1)
        ].iter().cloned().collect();
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        let n_score = compute_n_score(&seq_lens, total_bases, 50);
        assert_eq!(n_score, 3);

        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1000),
            (1000, 1)
        ].iter().cloned().collect();
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        let n_score = compute_n_score(&seq_lens, total_bases, 50);
        assert_eq!(n_score, 1000);
        let n_score = compute_n_score(&seq_lens, total_bases, 51);
        assert_eq!(n_score, 1);

        let seq_lens: BTreeMap<usize, u64> = [
            (1, 1001),
            (1000, 1)
        ].iter().cloned().collect();
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        let n_score = compute_n_score(&seq_lens, total_bases, 50);
        assert_eq!(n_score, 1);
        let n_score = compute_n_score(&seq_lens, total_bases, 49);
        assert_eq!(n_score, 1000);

        let mut seq_lens: BTreeMap<usize, u64> = BTreeMap::new();
        for x in 1..101 {
            seq_lens.insert(x, 1);
        }
        let (total_bases, _total_seqs) = compute_total_counts(&seq_lens);
        
        //do a test of all of the different n_values here
        for n_value in 1..100 {
            let n_score = compute_n_score(&seq_lens, total_bases, n_value);
            let target_value = (total_bases as f64) * (n_value as f64 / 100.0);
            let mut total_count: u64 = 0;
            for (seq_len, seq_count) in seq_lens.iter() {
                if *seq_len >= n_score {
                    total_count += (*seq_len as u64) * *seq_count;
                }
            }
            //println!("{} {} {} {}", n_value, n_score, total_count, target_value);
            assert!((total_count as f64) >= target_value);
        }
    }

    #[test]
    fn test_full_all_same() {
        let seq_lens: BTreeMap<usize, u64> = [
            (10, 100)
        ].iter().cloned().collect();

        let expected_stats: LengthStats = LengthStats {
            total_bases: 1000,
            total_sequences: 100,
            mean_length: 10.0,
            median_length: 10.0,
            n10: 10,
            n25: 10,
            n50: 10,
            n75: 10,
            n90: 10
        };

        let actual_stats: LengthStats = compute_length_stats(&seq_lens);
        assert_eq!(expected_stats, actual_stats);
    }
}