在R中，我想通过比较2个变量同时保留系统发育信号来产生相关系数。
我认为这样做的最初方式不是计算效率，我认为有一个更简单，但我没有R的技能来做它。
我有一个csv文件，如下所示：
+-------------------------------+-----+----------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
| Species | OGT | Domain | A | C | D | E | F | G | H | I | K | L | M | N | P | Q | R | S | T | V | W | Y |
+-------------------------------+-----+----------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
| Aeropyrum pernix | 95 | Archaea | 9.7659115711 | 0.6720465616 | 4.3895390781 | 7.6501943794 | 2.9344881615 | 8.8666657183 | 1.5011817208 | 5.6901432494 | 4.1428307243 | 11.0604191603 | 2.21143353 | 1.9387130928 | 5.1038552753 | 1.6855017182 | 7.7664358772 | 6.266067034 | 4.2052190807 | 9.2692433532 | 1.318690698 | 3.5614200159 |
| Argobacterium fabrum | 26 | Bacteria | 11.5698896021 | 0.7985475923 | 5.5884500155 | 5.8165463343 | 4.0512504104 | 8.2643271309 | 2.0116736244 | 5.7962804605 | 3.8931525401 | 9.9250463349 | 2.5980609708 | 2.9846761128 | 4.7828063605 | 3.1262365491 | 6.5684282943 | 5.9454781844 | 5.3740045968 | 7.3382308193 | 1.2519739683 | 2.3149400984 |
| Anaeromyxobacter dehalogenans | 27 | Bacteria | 16.0337898849 | 0.8860252895 | 5.1368827707 | 6.1864992608 | 2.9730203513 | 9.3167603253 | 1.9360386851 | 2.940143349 | 2.3473650439 | 10.898494736 | 1.6343905351 | 1.5247123262 | 6.3580285706 | 2.4715303021 | 9.2639057482 | 4.1890063803 | 4.3992339725 | 8.3885969061 | 1.2890166336 | 1.8265589289 |
| Aquifex aeolicus | 85 | Bacteria | 5.8730327277 | 0.795341216 | 4.3287799008 | 9.6746388172 | 5.1386954322 | 6.7148035486 | 1.5438364179 | 7.3358775924 | 9.4641440609 | 10.5736658776 | 1.9263080969 | 3.6183861236 | 4.0518679067 | 2.0493569604 | 4.9229955632 | 4.7976564501 | 4.2005259246 | 7.9169763709 | 0.9292167138 | 4.1438942987 |
| Archaeoglobus fulgidus | 83 | Archaea | 7.8742687687 | 1.1695110027 | 4.9165979364 | 8.9548767369 | 4.568636662 | 7.2640358917 | 1.4998752909 | 7.2472039919 | 6.8957233203 | 9.4826333048 | 2.6014466253 | 3.206476915 | 3.8419576418 | 1.7789787933 | 5.7572748236 | 5.4763351139 | 4.1490633048 | 8.6330814159 | 1.0325605451 | 3.6494619148 |
+-------------------------------+-----+----------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
我想要做的是，对于20个单字母列中的百分比的每种可能组合（氨基酸，所以1000万种组合）。是计算CSV中每个不同组合和OGT变量之间的相关性....（同时保留系统发育信号）
我目前的代码是：
library(parallel)
library(dplyr)
library(tidyr)
library(magrittr)
library(ape)
library(geiger)
library(caper)
taxonomynex <- read.nexus("taxonomyforzeldospecies.nex")
zeldodata <- read.csv("COMPLETECOPYFORR.csv")
Species <- dput(zeldodata)
SpeciesLong <-
Species %>%
gather(protein, proportion,
A:Y) %>%
arrange(Species)
S <- unique(SpeciesLong$protein)
Scombi <- unlist(lapply(seq_along(S),
function(x) combn(S, x, FUN = paste0, collapse = "")))
joint_protein <- function(protein_combo, data){
sum(data$proportion[vapply(data$protein,
grepl,
logical(1),
protein_combo)])
}
SplitSpecies <-
split(SpeciesLong,
SpeciesLong$Species)
cl <- makeCluster(detectCores() - 1)
clusterExport(cl, c("Scombi", "joint_protein"))
SpeciesAggregate <-
parLapply(cl,
X = SplitSpecies,
fun = function(data){
X <- lapply(Scombi,
joint_protein,
data)
names(X) <- Scombi
as.data.frame(X)
})
Species <- cbind(Species, SpeciesAggregate)
`
尝试将每种组合物加入到记忆中，然后计算每种酸的每种比例的总和，但这需要永远完成并在完成前崩溃。
我认为最好将相关联合效应转化为载体，然后打印出每个物种的每种不同组合的相对系数，但我不知道这样做的最佳方法在R。
我还打算使用ape包保留一个系统发育信号，使用类似的东西：
pglsModel <- gls(OGT ~ AminoAcidCombination, correlation = corBrownian(phy = taxonomynex),
data = zeldodata, method = "ML")
summary(pglsModel)
道歉，如果有人有任何建议，这是多么不清楚，非常感谢！
修改：Link to taxonomyforzeldospecies.nex
dput（Zeldodata）的输出：
1 Species OGT Domain A C D E F G H I K L M N P Q R S T V W Y
------------------------------- ----- ---------- --------------- -------------- -------------- -------------- -------------- -------------- -------------- -------------- -------------- --------------- -------------- -------------- -------------- -------------- -------------- -------------- -------------- -------------- -------------- --------------
2 Aeropyrum pernix 95 Archaea 9.7659115711 0.6720465616 4.3895390781 7.6501943794 2.9344881615 8.8666657183 1.5011817208 5.6901432494 4.1428307243 11.0604191603 2.21143353 1.9387130928 5.1038552753 1.6855017182 7.7664358772 6.266067034 4.2052190807 9.2692433532 1.318690698 3.5614200159
3 Argobacterium fabrum 26 Bacteria 11.5698896021 0.7985475923 5.5884500155 5.8165463343 4.0512504104 8.2643271309 2.0116736244 5.7962804605 3.8931525401 9.9250463349 2.5980609708 2.9846761128 4.7828063605 3.1262365491 6.5684282943 5.9454781844 5.3740045968 7.3382308193 1.2519739683 2.3149400984
4 Anaeromyxobacter dehalogenans 27 Bacteria 16.0337898849 0.8860252895 5.1368827707 6.1864992608 2.9730203513 9.3167603253 1.9360386851 2.940143349 2.3473650439 10.898494736 1.6343905351 1.5247123262 6.3580285706 2.4715303021 9.2639057482 4.1890063803 4.3992339725 8.3885969061 1.2890166336 1.8265589289
5 Aquifex aeolicus 85 Bacteria 5.8730327277 0.795341216 4.3287799008 9.6746388172 5.1386954322 6.7148035486 1.5438364179 7.3358775924 9.4641440609 10.5736658776 1.9263080969 3.6183861236 4.0518679067 2.0493569604 4.9229955632 4.7976564501 4.2005259246 7.9169763709 0.9292167138 4.1438942987
6 Archaeoglobus fulgidus 83 Archaea 7.8742687687 1.1695110027 4.9165979364 8.9548767369 4.568636662 7.2640358917 1.4998752909 7.2472039919 6.8957233203 9.4826333048 2.6014466253 3.206476915 3.8419576418 1.7789787933 5.7572748236 5.4763351139 4.1490633048 8.6330814159 1.0325605451 3.6494619148
答案 0 :(得分：1)
这将为您提供一个长数据框，每个组合和每个Species的总和（在我的机器上大约需要35秒）......
zeldodata <-
Species %>%
gather(protein, proportion, A:Y) %>%
group_by(Species) %>%
mutate(combo = sapply(1:n(), function(i) combn(protein, i, FUN = paste0, collapse = ""))) %>%
mutate(sum = sapply(1:n(), function(i) combn(proportion, i, FUN = sum))) %>%
unnest() %>%
select(-protein, -proportion)
分别计算每个物种并将数据保存到磁盘然后读取每个物种并将它们组合的示例...
library(readr)
library(dplyr)
library(tidyr)
library(purrr)
# read in CSV file
zeldodata <-
read_delim(
delim = "|",
trim_ws = TRUE,
col_names = TRUE,
col_types = "cicdddddddddddddddddddd",
file = "Species | OGT | Domain | A | C | D | E | F | G | H | I | K | L | M | N | P | Q | R | S | T | V | W | Y
Aeropyrum pernix | 95 | Archaea | 9.7659115711 | 0.6720465616 | 4.3895390781 | 7.6501943794 | 2.9344881615 | 8.8666657183 | 1.5011817208 | 5.6901432494 | 4.1428307243 | 11.0604191603 | 2.21143353 | 1.9387130928 | 5.1038552753 | 1.6855017182 | 7.7664358772 | 6.266067034 | 4.2052190807 | 9.2692433532 | 1.318690698 | 3.5614200159
Argobacterium fabrum | 26 | Bacteria | 11.5698896021 | 0.7985475923 | 5.5884500155 | 5.8165463343 | 4.0512504104 | 8.2643271309 | 2.0116736244 | 5.7962804605 | 3.8931525401 | 9.9250463349 | 2.5980609708 | 2.9846761128 | 4.7828063605 | 3.1262365491 | 6.5684282943 | 5.9454781844 | 5.3740045968 | 7.3382308193 | 1.2519739683 | 2.3149400984
Anaeromyxobacter dehalogenans | 27 | Bacteria | 16.0337898849 | 0.8860252895 | 5.1368827707 | 6.1864992608 | 2.9730203513 | 9.3167603253 | 1.9360386851 | 2.940143349 | 2.3473650439 | 10.898494736 | 1.6343905351 | 1.5247123262 | 6.3580285706 | 2.4715303021 | 9.2639057482 | 4.1890063803 | 4.3992339725 | 8.3885969061 | 1.2890166336 | 1.8265589289
Aquifex aeolicus | 85 | Bacteria | 5.8730327277 | 0.795341216 | 4.3287799008 | 9.6746388172 | 5.1386954322 | 6.7148035486 | 1.5438364179 | 7.3358775924 | 9.4641440609 | 10.5736658776 | 1.9263080969 | 3.6183861236 | 4.0518679067 | 2.0493569604 | 4.9229955632 | 4.7976564501 | 4.2005259246 | 7.9169763709 | 0.9292167138 | 4.1438942987
Archaeoglobus fulgidus | 83 | Archaea | 7.8742687687 | 1.1695110027 | 4.9165979364 | 8.9548767369 | 4.568636662 | 7.2640358917 | 1.4998752909 | 7.2472039919 | 6.8957233203 | 9.4826333048 | 2.6014466253 | 3.206476915 | 3.8419576418 | 1.7789787933 | 5.7572748236 | 5.4763351139 | 4.1490633048 | 8.6330814159 | 1.0325605451 | 3.6494619148"
)
# save an RDS file for each species
for(species in unique(zeldodata$Species)) {
zeldodata %>%
filter(Species == species) %>%
gather(protein, proportion, A:Y) %>%
mutate(combo = sapply(1:n(), function(i) combn(protein, i, FUN = paste0, collapse = ""))) %>%
mutate(sum = sapply(1:n(), function(i) combn(proportion, i, FUN = sum))) %>%
unnest() %>%
select(-protein, -proportion) %>%
saveRDS(file = paste0(species, ".RDS"))
}
# read in and combine all the RDS files
zeldodata <-
list.files(pattern = "\\.RDS") %>%
map(read_rds) %>%
bind_rows()