hms-dbmi/UpSetR

Feature request:UpsetR: extracting intersections elements

robertamig opened this issue · 2 comments

robertamig commented

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 commented

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)

image

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