Feature request:UpsetR: extracting intersections elements

[robertamig]

Hello! Would it be possible to add a feature to extract the lists of elements making up the intersections, or also the elements unique to each group when plotting an UpsetR plot?

[pedriniedoardo]

here is my solution. It is bio-related. I start with a list of vectors. In my case a list of genes belonging to different signatures.

str(list_filter)
List of 9
 $ CellAge_Induces         : chr [1:153] "AAK1" "ABI3" "ADCK5" "AGT" ...
 $ CellAge_Inhibits        : chr [1:121] "ACLY" "AKR1B1" "ASPH" "ATF7IP" ...
 $ CLASSICAL_SASP          : chr [1:38] "BGN" "CCL2" "CCL20" "COL1A1" ...
 $ FRIDMAN_SENESCENCE_UP   : chr [1:77] "ALDH1A3" "CCND1" "CD44" "CDKN1A" ...
 $ ISM_SCORE               : chr [1:128] "HSH2D" "OTOF" "TRIM69" "PSME1" ...
 $ MOSERLE_IFNA_RESPONSE   : chr [1:31] "CD274" "CMPK2" "CXCL10" "DDX58" ...
 $ REACTOME_SENESCENCE_SASP: chr [1:110] "ANAPC1" "ANAPC10" "ANAPC11" "ANAPC15" ...
 $ SAEPHIA_CURATED_SASP    : chr [1:38] "IL1A" "IL1B" "CXCL10" "CXCL1" ...
 $ senmayo                 : chr [1:125] "ACVR1B" "ANG" "ANGPT1" "ANGPTL4" ...

From this list, I generate two tables: One with the unique gene names

df2 <- data.frame(gene=unique(unlist(list_filter)))

head(df2)
     gene
1    AAK1
2    ABI3
3   ADCK5
4     AGT
5    AKT1
6 ALOX15B

dim(df2)
[1] 671   1

One is simply a "dataframe" version of the list. With every gene in the signature and the name of every signature.

df1 <- lapply(list_filter,function(x){
  data.frame(gene = x)
}) %>% 
  bind_rows(.id = "path")

head(df1)
             path    gene
1 CellAge_Induces    AAK1
2 CellAge_Induces    ABI3
3 CellAge_Induces   ADCK5
4 CellAge_Induces     AGT
5 CellAge_Induces    AKT1
6 CellAge_Induces ALOX15B

dim(df1)
[1] 821   2

now I iterate the search of each unique gene name and save the identity of the signatures in a column.

df_int <- lapply(df2$gene,function(x){
  # pull the name of the intersections
  intersection <- df1 %>% 
    dplyr::filter(gene==x) %>% 
    arrange(path) %>% 
    pull("path") %>% 
    paste0(collapse = "|")

  # build the dataframe
  data.frame(gene = x,int = intersection)
}) %>% 
  bind_rows()

head(df_int,n=20)
      gene                                                            int
1     AAK1                                                CellAge_Induces
2     ABI3                                                CellAge_Induces
3    ADCK5                                                CellAge_Induces
4      AGT                                                CellAge_Induces
5     AKT1                                                CellAge_Induces
6  ALOX15B                                                CellAge_Induces
7       AR                                                CellAge_Induces
8   ARPC1B                                                CellAge_Induces
9    ASF1A                                                CellAge_Induces
10     AXL                                        CellAge_Induces|senmayo
11 BHLHE40                                                CellAge_Induces
12     BLK                                                CellAge_Induces
13    BRAF                                                CellAge_Induces
14    BRD7                                                CellAge_Induces
15    CAV1                                                CellAge_Induces
16   CCND1                          CellAge_Induces|FRIDMAN_SENESCENCE_UP
17   CDK18                                                CellAge_Induces
18  CDKN1A CellAge_Induces|FRIDMAN_SENESCENCE_UP|REACTOME_SENESCENCE_SASP
19  CDKN1C                          CellAge_Induces|FRIDMAN_SENESCENCE_UP
20  CDKN1B                       CellAge_Induces|REACTOME_SENESCENCE_SASP

dim(df_int)
[1] 671   2

the dataframe can be summarised and compared to the output provided by calling

df_int %>% 
  group_by(int) %>% 
  summarise(n=n()) %>% 
  arrange(desc(n))
# A tibble: 47 × 2
   int                                 n
   <chr>                           <int>
 1 CellAge_Induces                   126
 2 CellAge_Inhibits                  110
 3 REACTOME_SENESCENCE_SASP           95
 4 ISM_SCORE                          93
 5 senmayo                            77
 6 FRIDMAN_SENESCENCE_UP              44
 7 ISM_SCORE|MOSERLE_IFNA_RESPONSE    27
 8 CLASSICAL_SASP|senmayo             12
 9 CLASSICAL_SASP                      8
10 SAEPHIA_CURATED_SASP                8
# … with 37 more rows
# ℹ Use `print(n = ...)` to see more rows

upset(fromList(list_filter),nsets = 10) 

[ccgallen]

ComplexHeatmap has a function for this: extract_comb()

https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#upset-utility-functions