R-匹配索引中嵌套列表值和返回值的组合

问题描述

嗨，我有两个数据集。第一个是与给定簇（0-7）相关的基因列表：

# gene output

Cluster <- rep(0:7,each = 10)

Gene <- c("LMO3","NEUROD6","NFIB","SNAP25","RTN1","CPE","SOX11","CSRP2","VAMP2","ID2","EMX2","LHX5-AS1","PEG10","HES1","TRH","WLS","TPBG","RPS29","CRABP2","RSPO3","RPL17","RPL7","PTMA","RPL36A","HMGN2","H2AFZ","PABPC1","HNRNPH1","PTN","FABP7","IGFBP2","ID4","C1orf61","VIM","RPS27L","FABP5","SDCBP","BNIP3","TCF7L2","NEFL","HMGCS1","GAP43","GPM6A","sqlE","MSMO1","SCOC","BASP1","TTR","MEST","MDK","TMBIM6","RCN1","C8orf59","ID3","PKM","NCOR1","ELAVL4","NNAT","ETFB","STMN2","TUBA1A","GNG3","MALAT1","SOX4","TUBB2B","CRYAB","GFAP","CHCHD2","HOPX","LgalS1","SCRG1","ISG15","AC090498.1","B2M","CLU")

df <- data.frame(cbind(Cluster,Gene))

第二个是为特定基因组合提供细胞类型注释的索引：

# index

Type <- c("Radial Glia","Excitatory Neuron ","Inhibitory Neuron","IPC","Radial Glia","Microglia","Inhibitory Neuron")

Subtype <- c("early","Layer IV","sst-MGE1","IPC-div2","Parietal and Temporal","oRG/Astrocyte","IPC-new","MGE2")

Markers <- c("TOP2A AURK HMGB CTNNB1","PPP1R1B SCN2A RORB CRYM","dlx6-AS1 dlx1 sst DCX","ERBB4 sst dlx2 dlx5 dlx6-AS1","CCNB2 NEUROD4 KIF15 PENK HES6 ZFHX4 GLI3","MEF2C STMN2 FLT ROBO CRYM","AQP4 GFAP AGT dio2 IL33","C1QB aif1 ccl4 C1QC","CENPK EOMES","CCK LHX6 SCGN sst")

index <- data.frame(cbind(Type,Subtype,Markers))

我正在尝试从df基因列表中找到Markers中概述的特定组合。当找到这样的匹配项时，将返回相应的类型和子类型。但是，我发现有很多警告需要绕开我的头。

每个聚类的列表可能包含多个标记组合-因此该功能应迭代遍历每个标记组合，而不是在找到第一个匹配项时停止。
索引匹配过程应分别在每个聚类上进行-即检查聚类0中的基因是否存在标记匹配和返回类型/亚型，然后重复聚类1等步骤。

我的项目数据包含数十个类似df的输出，这些输出由不同数量的各个簇组成，每个簇包含数百至数千个基因。我已经尽力了在网上搜索解决方案，但很遗憾，我在这里画了一个空白。

任何帮助/建议/建议将不胜感激。

编辑：

输出看起来像这样：

  Cluster    Gene        Type Subtype
1       0    LMO3 Radial Glia   early
2       0 NEUROD6        <NA>    <NA>
3       0    NFIB        <NA>    <NA>
4       0  SNAP25        <NA>    <NA>
5       0    RTN1        <NA>    <NA>
6       0     CPE        <NA>    <NA>

正确的匹配会在df中添加一行，并为每个聚类添加相应的类型和子类型，而其余部分为空（NA）。

解决方法

执行此操作的方法可能更简单，但这里有一个循环；

output = data.frame(Cluster=as.character(),Gene=as.character(),Type=as.character(),Subtype=as.character())

for(i in 1:nrow(df)){
  cluster = df[i,1]
  gene = df[i,2]
  type = index[grep(gene,index$Markers),]
  n_types = nrow(type)
  tmp = data.frame(Cluster=rep(cluster,n_types),Gene=rep(gene,Type=type[,1],Subtype=type[,2])
  output = rbind(output,tmp)
}

我假设您想用以下类型注释每个基因簇当类型的所有标记都出现在集群的索引中时基因库。

我还将使用一些简化的数据集；两种简化的类型索引：

library(tidyverse)

index <- bind_rows(
  tibble(type = "AB",subtype = "X",markers = c("A","B")),tibble(type = "BC",subtype = "Y",markers = c("B","C")),)

index
#> # A tibble: 4 x 3
#>   type  subtype markers
#>   <chr> <chr>   <chr>  
#> 1 AB    X       A      
#> 2 AB    X       B      
#> 3 BC    Y       B      
#> 4 BC    Y       C

以及说明不同匹配方案的三个不同的集群：

clusters <- bind_rows(
  tibble(cluster = 0,genes = c("A","B",# 2 matches
  tibble(cluster = 1,genes = c("B","C","D")),# 1 match
  tibble(cluster = 2,genes = c("C","D","E")),# No matches
)

clusters
#> # A tibble: 9 x 2
#>   cluster genes
#>     <dbl> <chr>
#> 1       0 A    
#> 2       0 B    
#> 3       0 C    
#> 4       1 B    
#> 5       1 C    
#> 6       1 D    
#> 7       2 C    
#> 8       2 D    
#> 9       2 E

我将首先创建一个返回匹配类型的函数来解决这个问题对于给定的基因库：

match_index <- function(genes) {
  matches <- index %>% 
    group_by(type,subtype) %>% 
    filter(all(markers %in% genes)) %>% 
    distinct(type,subtype)

  # If none matched,return a row of NAs  
  if (nrow(matches)) matches else matches[NA_integer_,]
}

然后，您可以使用以下功能总结每个集群：

clusters %>% 
  group_by(cluster) %>% 
  summarise(match_index(genes))
#> `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
#> # A tibble: 4 x 3
#> # Groups:   cluster [3]
#>   cluster type  subtype
#>     <dbl> <chr> <chr>  
#> 1       0 AB    X      
#> 2       0 BC    Y      
#> 3       1 BC    Y      
#> 4       2 <NA>  <NA>

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