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add cutoff_predictor to mlg vignette #184

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124 changes: 47 additions & 77 deletions vignettes/mlg.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -386,89 +386,59 @@ the biology of your organism.

To help visualize this, there is the function `filter_stats()`, which will plot
the output of the filtering algorithm. For simplicity, we will use the `Pinf`
data set.
data set representing *Phytophthora infestans* samples from Mexico and South America `r citep(bib["goss2009population"])`.

```{r}
```{r plot_replen}
data(Pinf)
Pinf
```
```{r, eval = FALSE}
pinfreps <- fix_replen(Pinf, c(2, 2, 6, 2, 2, 2, 2, 2, 3, 3, 2))
pinf_filtered <- filter_stats(Pinf, distance = bruvo.dist, replen = pinfreps, plot = TRUE)
```
```{r, echo = FALSE}
pinfreps <- fix_replen(Pinf, c(2, 2, 6, 2, 2, 2, 2, 2, 3, 3, 2))
pinf_dist <- bruvo.dist(Pinf, replen = pinfreps)
pinf_filtered <- structure(list(farthest = structure(list(THRESHOLDS = c(0.0126262626262626,
0.0218986742424242, 0.0227272727272727, 0.0227272727272727, 0.0353535353535354,
0.0416666666666667, 0.0426136363636364, 0.0454545440998944, 0.0469933712121212,
0.0475852031900425, 0.0568181818181818, 0.0568181818181818, 0.0583570075757576,
0.0645123106060606, 0.0653409090909091, 0.0681818181818182, 0.0787168560606061,
0.0795454545454545, 0.0877274792603772, 0.0887784090909091, 0.09375,
0.0946969696969697, 0.0972222218459303, 0.101444128787879, 0.125,
0.130997474747475, 0.1359375, 0.13740234375, 0.144886363636364,
0.149999997019768, 0.156565656565657, 0.166883656472871, 0.18115234375,
0.193170719526031, 0.210227261890065, 0.215909090909091, 0.21874982660467,
0.22561553030303, 0.23115234375, 0.232954502105713, 0.234374994039536,
0.23532196969697, 0.24147722937844, 0.258500315926292, 0.272017021070827,
0.274999999592546, 0.283380660143766, 0.29208984375, 0.299999999999997,
0.300423362038352, 0.303269896844421, 0.305499606455366, 0.306322496341461,
0.3132568359375, 0.336390339246273, 0.337091619318182, 0.340897971256213,
0.351708152074769, 0.356196582317352, 0.360792333090847, 0.36487923968922,
0.396464635627438, 0.402803457144535, 0.403320301662627, 0.423251064663584,
0.437319652600722, 0.454361644319513, 0.470472120576435, 0.508276028103299,
0.514966882581328, 0.574741401973021)), .Names = "THRESHOLDS"),
average = structure(list(THRESHOLDS = c(0.0126262626262626,
0.0218986742424242, 0.0227272727272727, 0.0227272727272727,
0.0258838383838384, 0.0416666666666667, 0.0426136363636364,
0.0454545440998944, 0.0455137310606061, 0.0466678503787879,
0.0475852031900425, 0.0511659564393939, 0.0545526883417508,
0.0568181818181818, 0.0568181818181818, 0.0653409090909091,
0.0681818181818182, 0.0746970385413379, 0.0776515151515152,
0.0795454545454545, 0.0838660037878788, 0.0866477272727273,
0.0915404036641121, 0.09375, 0.0990451334702848, 0.117836346931329,
0.125, 0.1359375, 0.13740234375, 0.144886363636364, 0.149999997019768,
0.166883656472871, 0.169220816635022, 0.18115234375, 0.182812413302335,
0.189831002331002, 0.193170719526031, 0.206485256010836,
0.210227261890065, 0.210983060586332, 0.215909090909091,
0.222481511544012, 0.226043217397815, 0.231752027784559,
0.232947945592142, 0.234374967488376, 0.239980004751746,
0.256210318427872, 0.256249999592546, 0.256803937821849,
0.265303876476758, 0.270312499999997, 0.274878910364527,
0.284377216447246, 0.287522453250307, 0.288763944769339,
0.289024162970308, 0.289442437948814, 0.303269896844421,
0.30955901687567, 0.320306661564573, 0.32450982493207, 0.324653779309478,
0.326784648203577, 0.337955069560145, 0.343659817474595,
0.353714804095389, 0.358177516571547, 0.360592245250208,
0.387454092730527, 0.412903107125552)), .Names = "THRESHOLDS"),
nearest = structure(list(THRESHOLDS = c(0.0126262626262626,
0.0218986742424242, 0.0227272727272727, 0.0227272727272727,
0.0227272727272727, 0.0340909090909091, 0.0397727272727273,
0.0416666666666667, 0.0426136363636364, 0.0440340909090909,
0.0440340909090909, 0.0454545440998944, 0.0475852031900425,
0.0568181818181818, 0.0568181818181818, 0.0568181818181818,
0.0653409090909091, 0.0681818181818182, 0.0681818181818182,
0.0681818181818182, 0.071969696969697, 0.0795454545454545,
0.0823863636363636, 0.0858585854822939, 0.0883838383838384,
0.09375, 0.1248046875, 0.125, 0.134706415311255, 0.1359375,
0.13740234375, 0.144886363636364, 0.146875, 0.149999997019768,
0.151909718910853, 0.15625, 0.15625, 0.159326171875, 0.161024305141634,
0.166883656472871, 0.168797348484848, 0.172743055555556,
0.173295454545455, 0.177680120286014, 0.18115234375, 0.181818168271672,
0.181818180463531, 0.1841796875, 0.184374998509884, 0.186618041992188,
0.190625, 0.193170719526031, 0.193749997019768, 0.20625,
0.210227261890065, 0.21240234375, 0.21484375, 0.215277776949935,
0.215902837837348, 0.218747171488675, 0.223487314551768,
0.224902342259884, 0.224902342632413, 0.240624999999997,
0.241050253876231, 0.241137550292405, 0.246679684519768,
0.249129393034511, 0.259232954545455, 0.278124998509884,
0.287377917766571)), .Names = "THRESHOLDS")), .Names = c("farthest",
"average", "nearest"))
plot_filter_stats(Pinf, pinf_filtered, pinf_dist, breaks = "Scott")
```

We can see that the different algorithms behave quite differently.

We can see that the different algorithms behave similarly for small thresholds,
but begin to differ over larger thresholds.

This function is useful for finding all thresholds at which MLLs are collapsed,
which can help with choosing a threshold that collapses putative clones in our
sample into lineages.

### Choosing a threshold

After you have chosen a genetic distance and a filtering algorithm, you must
then decide on the threshold to represent the **minimum genetic distance at
which two individuals would be considered from different clonal lineages**.

One method described in the literature of choosing a threshold is to look for an
initial, small peak in the histogram of pairwise genetic distances and set the
threshold to be between that peak and the larger peak `r citep(bib[c("arnaud2007standardizing", "bailleul2016rclone")])`.
This initial peak likely represents clones differentiated by a small set of
random mutations. You can see this in the figure above at a threshold of ~0.11
for the "farthest neighbor" algorithm.

However, if this peak is not obvious, then another method is to look for the largest gap between all putative thresholds. For this, you can use the `cutoff_predictor()` function with the output of `filter_stats()`. It should be noted, however, that this method is not a perfect solution. If we take the results from above, we can find the threshold for each algorithm:

```{r cutof_predictor}
print(farthest_thresh <- cutoff_predictor(pinf_filtered$farthest$THRESHOLDS))
print(average_thresh <- cutoff_predictor(pinf_filtered$average$THRESHOLDS))
print(nearest_thresh <- cutoff_predictor(pinf_filtered$nearest$THRESHOLDS))
```

Now we can define multilocus lineages for *P. infestans* with the following criteria:

- Genetic Distance: Bruvo's Distance
- Filtering Algorithm: Farthest neighbor
- Threshold: `r signif(farthest_thresh, 3)`

```{r pinf_thresh}
mlg.filter(Pinf, distance = bruvo.dist, replen = pinfreps, algorithm = "f") <- farthest_thresh
Pinf
```

> Note: Please take care to critically evaluate the results and make sure it
> applies to your data. This function WILL give you an answer whether or not one
> truely exists. Additionally, For diploid organisms, another method of choosing a
> threshold is available in the **RClone** package that involves simulating
> outcrossing and inbreeding events `r citep(bib["bailleul2016rclone"])`.
## Custom ("custom")

Expand Down
174 changes: 95 additions & 79 deletions vignettes/the_bibliography.bib
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Expand Up @@ -7,6 +7,22 @@ @article{parks1993study
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Expand Down