Data exploration
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';
Allele frequency table
allelefreqtable(data::PopData; by::Union{String, Symbol} = "global")
Return a table of the observed global (default) or population allele frequencies in a PopData object. Use this if you want to see what the frequencies are for every allele at every locus.
<Tabs
block={true}
defaultValue="g"
values={[
{ label: 'global', value: 'g', },
{ label: 'population', value: 'p', },
]
}>
julia> cats = @nancycats ;
julia> allelefreqtable(cats)
108×4 DataFrame
Row │ locus allele count frequency
│ String Int16? Int64 Float64
─────┼───────────────────────────────────
1 │ fca8 135 105 0.241935
2 │ fca8 143 44 0.101382
3 │ fca8 133 33 0.0760369
4 │ fca8 137 83 0.191244
⋮ │ ⋮ ⋮ ⋮ ⋮
105 │ fca37 226 2 0.00421941
106 │ fca37 216 7 0.0147679
107 │ fca37 224 2 0.00421941
108 │ fca37 204 6 0.0126582
100 rows omitted
julia> cats = @nancycats ;
julia> allelefreqtable(cats, by = "population")
839×5 DataFrame
Row │ locus population allele count frequency
│ String String Int16? Int64 Float64
─────┼──────────────────────────────────────────────
1 │ fca8 1 135 9 0.5625
2 │ fca8 1 143 4 0.25
3 │ fca8 1 133 2 0.125
4 │ fca8 1 137 1 0.0625
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
836 │ fca37 16 210 5 0.208333
837 │ fca37 17 208 22 0.846154
838 │ fca37 17 182 3 0.115385
839 │ fca37 17 220 1 0.0384615
831 rows omitted
Genotype frequency table
genofreqtable(data::PopData; by::Union{String, Symbol} = "global")
Return a table of the observed global (default) or population genotype frequencies in a PopData object. Use this if you want to see what the frequencies are for every genotype at every locus.
<Tabs
block={true}
defaultValue="g"
values={[
{ label: 'global', value: 'g', },
{ label: 'population', value: 'p', },
]
}>
julia> cats = @nancycats ;
julia> genofreqtable(cats)
341×4 DataFrame
Row │ locus genotype count frequency
│ String Tuple… Int64 Float64
─────┼───────────────────────────────────────
1 │ fca8 (135, 143) 16 0.0737327
2 │ fca8 (133, 135) 9 0.0414747
3 │ fca8 (135, 135) 23 0.105991
4 │ fca8 (137, 143) 8 0.0368664
⋮ │ ⋮ ⋮ ⋮ ⋮
338 │ fca37 (206, 220) 1 0.00421941
339 │ fca37 (208, 218) 1 0.00421941
340 │ fca37 (184, 184) 3 0.0126582
341 │ fca37 (208, 210) 3 0.0126582
333 rows omitted
julia> cats = @nancycats ;
julia> genofreqtable(cats, by = "population")
1094×5 DataFrame
Row │ locus population genotype count frequency
│ String String Tuple… Int64 Float64
──────┼──────────────────────────────────────────────────
1 │ fca8 1 (135, 143) 3 0.375
2 │ fca8 1 (133, 135) 2 0.25
3 │ fca8 1 (135, 135) 2 0.25
4 │ fca8 1 (137, 143) 1 0.125
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
1091 │ fca37 17 (208, 208) 10 0.769231
1092 │ fca37 17 (182, 182) 1 0.0769231
1093 │ fca37 17 (182, 208) 1 0.0769231
1094 │ fca37 17 (208, 220) 1 0.0769231
1086 rows omitted
Missing Data
missingdata(data::PopData; by::Union{String, Symbol} = "sample")
Get missing genotype information in a PopData object. Specify a mode of operation using the by = keyword to return a table corresponding with that missing information type.
| by | what it does |
|---|---|
"sample" | returns a count of missing loci per individual (default) |
"population" | returns a count of missing genotypes per population |
"locus" | returns a count of missing genotypes per locus |
"locusxpopulation" | returns a count of missing genotypes per locus per population |
<Tabs
block={true}
defaultValue="s"
values={[
{ label: 'sample', value: 's', },
{ label: 'population', value: 'p', },
{ label: 'locus', value: 'l', },
{ label: 'locusxpopulation', value: 'f', },
]
}>
julia> sharks = @gulfsharks ;
julia> missingdata(sharks)
212×2 DataFrame
Row │ name missing
│ String Int64
─────┼──────────────────
1 │ cc_001 124
2 │ cc_002 94
3 │ cc_003 100
4 │ cc_005 0
5 │ cc_007 2
6 │ cc_008 1
7 │ cc_009 2
⋮ │ ⋮ ⋮
206 │ seg_025 0
207 │ seg_026 0
208 │ seg_027 2
209 │ seg_028 25
210 │ seg_029 0
211 │ seg_030 1
212 │ seg_031 1
198 rows omitted
julia> sharks = @gulfsharks ;
julia> missingdata(sharks, by = "population")
7×2 DataFrame
Row │ population missing
│ String Int64
─────┼─────────────────────────
1 │ Cape Canaveral 666
2 │ Georgia 423
3 │ South Carolina 233
4 │ Florida Keys 1241
5 │ Mideast Gulf 99
6 │ Northeast Gulf 472
7 │ Southeast Gulf 1504
julia> sharks = @gulfsharks ;
julia> missingdata(sharks, by = "locus")
2209×2 DataFrame
Row │ locus missing
│ String Int64
──────┼───────────────────────
1 │ contig_35208 0
2 │ contig_23109 6
3 │ contig_4493 3
4 │ contig_10742 2
5 │ contig_14898 0
6 │ contig_8483 0
7 │ contig_8065 0
⋮ │ ⋮ ⋮
2203 │ contig_18959 0
2204 │ contig_43517 6
2205 │ contig_27356 2
2206 │ contig_475 0
2207 │ contig_19384 5
2208 │ contig_22368 3
2209 │ contig_2784 7
2195 rows omitted
julia> sharks = @gulfsharks ;
julia> missingdata(sharks, by = "locusxpopulation")
15463×3 DataFrame
Row │ locus population missing
│ String String Int64
───────┼───────────────────────────────────────
1 │ contig_35208 Cape Canaveral 0
2 │ contig_35208 Georgia 0
3 │ contig_35208 South Carolina 0
4 │ contig_35208 Florida Keys 0
5 │ contig_35208 Mideast Gulf 0
6 │ contig_35208 Northeast Gulf 0
7 │ contig_35208 Southeast Gulf 0
⋮ │ ⋮ ⋮ ⋮
15457 │ contig_2784 Cape Canaveral 0
15458 │ contig_2784 Georgia 2
15459 │ contig_2784 South Carolina 1
15460 │ contig_2784 Florida Keys 2
15461 │ contig_2784 Mideast Gulf 1
15462 │ contig_2784 Northeast Gulf 0
15463 │ contig_2784 Southeast Gulf 1
15449 rows omitted
Pairwise Identical Genotypes
While not a substitute for a kinship analysis, it may be useful to know or verify how similar your data are in a very literal sense:
how many identical genotypes do two individuals have across all loci? To do this, we use pairwiseidentical() to perform an all x all comparison of identical genotypes. This can be done for all individuals in a PopData object, or restricted to a specific set of individuals:
<Tabs
block={true}
defaultValue="a"
values={[
{ label: 'all samples', value: 'a', },
{ label: 'some samples', value: 's', },
]
}>
julia> cats = @nancycats;
julia> pairwiseidentical(cats)
27966×4 DataFrame
Row │ sample_1 sample_2 identical n
│ String String Float64 Int64
───────┼──────────────────────────────────────
1 │ N215 N216 0.5 8
2 │ N215 N217 0.25 8
3 │ N215 N218 0.38 8
4 │ N215 N219 0.38 8
⋮ │ ⋮ ⋮ ⋮ ⋮
27963 │ N297 N290 0.29 7
27964 │ N281 N289 0.25 8
27965 │ N281 N290 0.43 7
27966 │ N289 N290 0.14 7
27958 rows omitted
julia> cats = @nancycats;
julia> interesting_cats = samplenames(cats)[1:5]
5-element Array{String,1}:
"N215"
"N216"
"N217"
"N218"
"N219"
julia> pairwiseidentical(cats, interesting_cats)
10×4 DataFrame
Row │ sample_1 sample_2 identical n
│ String String Float64 Int64
─────┼──────────────────────────────────────
1 │ N215 N216 0.5 8
2 │ N215 N217 0.25 8
3 │ N215 N218 0.38 8
4 │ N215 N219 0.38 8
5 │ N216 N217 0.12 8
6 │ N216 N218 0.25 8
7 │ N216 N219 0.38 8
8 │ N217 N218 0.0 9
9 │ N217 N219 0.11 9
10 │ N218 N219 0.33 9
Allelic Richness
If you were curious about allelic richness (number of alleles per locus), then you can use richness() to find that out. Use by = "population" to return a table by locus by population.
<Tabs
block={true}
defaultValue="l"
values={[
{ label: 'by locus', value: 'l', },
{ label: 'by locusxpopulation', value: 'p', },
]
}>
julia> cats = @nancycats;
julia> richness(cats)
9×2 DataFrame
Row │ locus richness
│ String Int64
─────┼──────────────────
1 │ fca8 16
2 │ fca23 11
3 │ fca43 10
4 │ fca45 9
5 │ fca77 12
6 │ fca78 8
7 │ fca90 12
8 │ fca96 12
9 │ fca37 18
julia> richness(cats, by = "population")
153×3 DataFrame
Row │ locus population richness
│ String String Int64
─────┼──────────────────────────────
1 │ fca8 1 4
2 │ fca8 2 6
3 │ fca8 3 7
4 │ fca8 4 10
⋮ │ ⋮ ⋮ ⋮
150 │ fca37 14 3
151 │ fca37 15 4
152 │ fca37 16 3
153 │ fca37 17 3
145 rows omitted
Average Number of Alleles
Similar to richness, if you wanted to know the average number of alleles per locus, use alleleavg(). Use rounding = false if you don't want the answer rounded to 4 decimal places.
julia> alleleavg(@nancycats)
(mean = 12.0, stdev = 0.2668)
julia> alleleavg(@nancycats, rounding = false)
(mean = 12.0, stdev = 0.2667968432263687)
Summary Statistics
Population genetics famously includes all manner of coefficients with which to summarize data. Use summary() to view FST, DST, HT, etc. (like Hierfstat::basic.stats).
<Tabs
block={true}
defaultValue="g"
values={[
{ label: 'global', value: 'g', },
{ label: 'by locus', value: 'l', },
]
}>
julia> summary(@nancycats)
1×10 DataFrame
Row │ Het_obs HS HT DST HT′ DST′ FST FST′ FIS DEST
│ Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64
─────┼──────────────────────────────────────────────────────────────────────────────────────────
1 │ 0.6299 0.7083 0.7717 0.0634 0.7757 0.0674 0.0821 0.0869 0.1108 0.231
julia> summary(@nancycats, by = "locus")
9×11 DataFrame
Row │ locus Het_obs HS HT DST HT′ DST′ FST FST′ FIS DEST
│ String Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────
1 │ fca8 0.667 0.779 0.8619 0.0829 0.8671 0.0881 0.0962 0.1016 0.1438 0.3987
2 │ fca23 0.6838 0.7439 0.7994 0.0555 0.8029 0.0589 0.0694 0.0734 0.0809 0.2302
3 │ fca43 0.6814 0.7442 0.7937 0.0495 0.7968 0.0526 0.0623 0.066 0.0844 0.2054
4 │ fca45 0.71 0.7085 0.7642 0.0557 0.7679 0.0594 0.0729 0.0774 -0.0021 0.2039
5 │ fca77 0.6295 0.7828 0.8659 0.0831 0.8711 0.0883 0.096 0.1014 0.1958 0.4067
6 │ fca78 0.5773 0.6339 0.6773 0.0434 0.6801 0.0462 0.0641 0.0679 0.0893 0.1261
7 │ fca90 0.6454 0.7408 0.8144 0.0736 0.819 0.0782 0.0904 0.0955 0.1287 0.3017
8 │ fca96 0.6259 0.6747 0.7657 0.091 0.7714 0.0967 0.1189 0.1254 0.0723 0.2973
9 │ fca37 0.4485 0.5671 0.6027 0.0356 0.6049 0.0379 0.0591 0.0626 0.2091 0.0874
The column names above use the unicode prime symbol ′ to better reflect the actual coefficient ("FST prime" etc.). To print that character, press \prime<TAB>, which reads "backslash, the word 'prime', and the TAB button".