mirror of
https://github.com/msberends/AMR.git
synced 2025-12-25 10:30:18 +01:00
202 lines
6.7 KiB
Markdown
202 lines
6.7 KiB
Markdown
# Calculate the Mean AMR Distance
|
||
|
||
Calculates a normalised mean for antimicrobial resistance between
|
||
multiple observations, to help to identify similar isolates without
|
||
comparing antibiograms by hand.
|
||
|
||
## Usage
|
||
|
||
``` r
|
||
mean_amr_distance(x, ...)
|
||
|
||
# S3 method for class 'sir'
|
||
mean_amr_distance(x, ..., combine_SI = TRUE)
|
||
|
||
# S3 method for class 'data.frame'
|
||
mean_amr_distance(x, ..., combine_SI = TRUE)
|
||
|
||
amr_distance_from_row(amr_distance, row)
|
||
```
|
||
|
||
## Arguments
|
||
|
||
- x:
|
||
|
||
A vector of class [sir](https://amr-for-r.org/reference/as.sir.md),
|
||
[mic](https://amr-for-r.org/reference/as.mic.md) or
|
||
[disk](https://amr-for-r.org/reference/as.disk.md), or a
|
||
[data.frame](https://rdrr.io/r/base/data.frame.html) containing
|
||
columns of any of these classes.
|
||
|
||
- ...:
|
||
|
||
Variables to select. Supports [tidyselect
|
||
language](https://tidyselect.r-lib.org/reference/starts_with.html)
|
||
such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and
|
||
can thus also be [antimicrobial
|
||
selectors](https://amr-for-r.org/reference/antimicrobial_selectors.md).
|
||
|
||
- combine_SI:
|
||
|
||
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
|
||
all values of S, SDD, and I must be merged into one, so the input only
|
||
consists of S+I vs. R (susceptible vs. resistant) - the default is
|
||
`TRUE`.
|
||
|
||
- amr_distance:
|
||
|
||
The outcome of `mean_amr_distance()`.
|
||
|
||
- row:
|
||
|
||
An index, such as a row number.
|
||
|
||
## Details
|
||
|
||
The mean AMR distance is effectively [the
|
||
Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised
|
||
numeric value to compare AMR test results which can help to identify
|
||
similar isolates, without comparing antibiograms by hand.
|
||
|
||
MIC values (see [`as.mic()`](https://amr-for-r.org/reference/as.mic.md))
|
||
are transformed with [`log2()`](https://rdrr.io/r/base/Log.html) first;
|
||
their distance is thus calculated as
|
||
`(log2(x) - mean(log2(x))) / sd(log2(x))`.
|
||
|
||
SIR values (see [`as.sir()`](https://amr-for-r.org/reference/as.sir.md))
|
||
are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If
|
||
`combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
|
||
|
||
For data sets, the mean AMR distance will be calculated per column,
|
||
after which the mean per row will be returned, see *Examples*.
|
||
|
||
Use `amr_distance_from_row()` to subtract distances from the distance of
|
||
one row, see *Examples*.
|
||
|
||
## Interpretation
|
||
|
||
Isolates with distances less than 0.01 difference from each other should
|
||
be considered similar. Differences lower than 0.025 should be considered
|
||
suspicious.
|
||
|
||
## Examples
|
||
|
||
``` r
|
||
sir <- random_sir(10)
|
||
sir
|
||
#> Class 'sir'
|
||
#> [1] R R I R I I S R S I
|
||
mean_amr_distance(sir)
|
||
#> [1] 1.1618950 1.1618950 -0.7745967 1.1618950 -0.7745967 -0.7745967
|
||
#> [7] -0.7745967 1.1618950 -0.7745967 -0.7745967
|
||
|
||
mic <- random_mic(10)
|
||
mic
|
||
#> Class 'mic'
|
||
#> [1] 0.032 0.5 1 >=8 4 0.016 >=8 1 0.004 0.008
|
||
mean_amr_distance(mic)
|
||
#> [1] -0.7311752 0.2104422 0.4478776 1.1601837 0.9227483 -0.9686106
|
||
#> [7] 1.1601837 0.4478776 -1.4434813 -1.2060459
|
||
# equal to the Z-score of their log2:
|
||
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
|
||
#> [1] -0.7311752 0.2104422 0.4478776 1.1601837 0.9227483 -0.9686106
|
||
#> [7] 1.1601837 0.4478776 -1.4434813 -1.2060459
|
||
|
||
disk <- random_disk(10)
|
||
disk
|
||
#> Class 'disk'
|
||
#> [1] 50 49 38 33 31 17 42 43 46 37
|
||
mean_amr_distance(disk)
|
||
#> [1] 1.15131286 1.05032051 -0.06059541 -0.56555720 -0.76754191 -2.18143490
|
||
#> [7] 0.34337401 0.44436637 0.74734344 -0.16158777
|
||
|
||
y <- data.frame(
|
||
id = LETTERS[1:10],
|
||
amox = random_sir(10, ab = "amox", mo = "Escherichia coli"),
|
||
cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
|
||
gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
|
||
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
|
||
)
|
||
y
|
||
#> id amox cipr gent tobr
|
||
#> 1 A I 27 >=2 8
|
||
#> 2 B S 28 1 8
|
||
#> 3 C R 33 1 8
|
||
#> 4 D R 32 1 16
|
||
#> 5 E I 25 0.5 16
|
||
#> 6 F I 19 0.5 8
|
||
#> 7 G S 23 0.5 16
|
||
#> 8 H R 27 0.5 8
|
||
#> 9 I S 29 1 8
|
||
#> 10 J R 32 0.5 16
|
||
mean_amr_distance(y)
|
||
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
|
||
#> and "tobr"
|
||
#> [1] 0.08471751 -0.21572693 0.55391610 0.98093500 -0.26046456 -1.08721162
|
||
#> [7] -0.37467261 -0.14625651 -0.15862291 0.62338653
|
||
y$amr_distance <- mean_amr_distance(y, is.mic(y))
|
||
#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"
|
||
y[order(y$amr_distance), ]
|
||
#> id amox cipr gent tobr amr_distance
|
||
#> 6 F I 19 0.5 8 -0.8163565
|
||
#> 8 H R 27 0.5 8 -0.8163565
|
||
#> 2 B S 28 1 8 -0.1012596
|
||
#> 3 C R 33 1 8 -0.1012596
|
||
#> 9 I S 29 1 8 -0.1012596
|
||
#> 5 E I 25 0.5 16 0.1518893
|
||
#> 7 G S 23 0.5 16 0.1518893
|
||
#> 10 J R 32 0.5 16 0.1518893
|
||
#> 1 A I 27 >=2 8 0.6138374
|
||
#> 4 D R 32 1 16 0.8669863
|
||
|
||
if (require("dplyr")) {
|
||
y %>%
|
||
mutate(
|
||
amr_distance = mean_amr_distance(y),
|
||
check_id_C = amr_distance_from_row(amr_distance, id == "C")
|
||
) %>%
|
||
arrange(check_id_C)
|
||
}
|
||
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
|
||
#> and "tobr"
|
||
#> id amox cipr gent tobr amr_distance check_id_C
|
||
#> 1 C R 33 1 8 0.55391610 0.00000000
|
||
#> 2 J R 32 0.5 16 0.62338653 0.06947042
|
||
#> 3 D R 32 1 16 0.98093500 0.42701889
|
||
#> 4 A I 27 >=2 8 0.08471751 0.46919859
|
||
#> 5 H R 27 0.5 8 -0.14625651 0.70017262
|
||
#> 6 I S 29 1 8 -0.15862291 0.71253901
|
||
#> 7 B S 28 1 8 -0.21572693 0.76964304
|
||
#> 8 E I 25 0.5 16 -0.26046456 0.81438066
|
||
#> 9 G S 23 0.5 16 -0.37467261 0.92858871
|
||
#> 10 F I 19 0.5 8 -1.08721162 1.64112773
|
||
if (require("dplyr")) {
|
||
# support for groups
|
||
example_isolates %>%
|
||
filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
|
||
select(mo, TCY, carbapenems()) %>%
|
||
group_by(mo) %>%
|
||
mutate(dist = mean_amr_distance(.)) %>%
|
||
arrange(mo, dist)
|
||
}
|
||
#> ℹ Using column 'mo' as input for `mo_genus()`
|
||
#> ℹ Using column 'mo' as input for `mo_species()`
|
||
#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
|
||
#> ℹ Calculating mean AMR distance based on columns "TCY", "IPM", and "MEM"
|
||
#> # A tibble: 63 × 5
|
||
#> # Groups: mo [4]
|
||
#> mo TCY IPM MEM dist
|
||
#> <mo> <sir> <sir> <sir> <dbl>
|
||
#> 1 B_ENTRC_AVIM S S NA 0
|
||
#> 2 B_ENTRC_AVIM S S NA 0
|
||
#> 3 B_ENTRC_CSSL NA S NA NA
|
||
#> 4 B_ENTRC_FACM S S NA -2.66
|
||
#> 5 B_ENTRC_FACM S R R -0.423
|
||
#> 6 B_ENTRC_FACM S R R -0.423
|
||
#> 7 B_ENTRC_FACM NA R R 0.224
|
||
#> 8 B_ENTRC_FACM NA R R 0.224
|
||
#> 9 B_ENTRC_FACM NA R R 0.224
|
||
#> 10 B_ENTRC_FACM NA R R 0.224
|
||
#> # ℹ 53 more rows
|
||
```
|