1 Introduction
Vince is looking to plot the various intersections/unions of some existing RNASeq data of mice infected with Pseudomonas aeruginosa. The table in question is rather complex; comprising samples of: mouse bladder data, LB with various additions, etc etc. Thus a great majority of what follows is my attempt to simplify the table so that it is possible to follow; then I load it into one of R’s venn libraries and print some images.
2 Step 1, look at table
In order to load this most easily, I am changing the following:
- Remove top rows
- shorten names to a combination of condition+column: Eg:
- ID (gene id)
- hupbst_basemean – human urine vs. pbs-t
- hupbst_lfc
- hupbst_p
- hupbst_padj
- mupbst_lfc – mouse urine vs. pbs-t
- mupbst_p
- mupbst_padj
- mbpbst_lfc – mouse bladder vs. pbs-t
- mbpbst_p
- mbpbst_padj
- lbu_ID – LB with LB urine
- lbu_basemean
- lbu_lfc
- lbu_p
- lbu_adjp
- Remove blank columns
Save it as shrunken.csv
3 Step 2, load it up
Unfortunately, I have no guarantee that ID’s order and lbu_ID’s order are the same, so split this into two sets and merge them back.
all_together <- read.csv("external_data/shrunken.csv")
## 3 rows are missing data... drop them.
##all_together <- all_together[complete.cases(all_together), ]
rownames(all_together) <- make.names(all_together[["ID"]], unique=TRUE)
all_together$lbu_lfc <- as.numeric(all_together$lbu_lfc)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercionall_together[is.na(all_together)] <- 0
## Human urine, PBS-T up/down (hupbst_stuff)
## The goal is 4 fold, yesno? So 2 log fold changes.
## First pull the up
hupbst_up <- all_together[all_together[["hupbst_lfc"]] >= 2, ]
## Vince's sheet shows 161 here
dim(hupbst_up)## [1] 161 16## Take only the low adjusted p-values
hupbst_pup <- hupbst_up[hupbst_up[["hupbst_padj"]] <= 0.05, ]
## How many?
dim(hupbst_pup)## [1] 160 16## Get the names of these genes
hupbst_up_genes <- rownames(hupbst_up)
hupbst_pup_genes <- rownames(hupbst_pup)
## Repeat going down, <= 4 fold
hupbst_down <- all_together[all_together[["hupbst_lfc"]] <= -2, ]
hupbst_pdown <- hupbst_down[hupbst_down[["hupbst_padj"]] <= 0.05, ]
dim(hupbst_down)## [1] 275 16## [1] 274 16hupbst_pdown_genes <- rownames(hupbst_pdown)
## now repeat the above for Mouse urine, PBS-T (mupbst_stuff)
mupbst_up <- all_together[all_together[["mupbst_lfc"]] >= 2, ]
## Take only the low adjusted p-values
mupbst_pup <- mupbst_up[mupbst_up[["mupbst_padj"]] <= 0.05, ]
## How many?
dim(mupbst_up)## [1] 321 16## [1] 321 16## Get the names of these genes
mupbst_pup_genes <- rownames(mupbst_pup)
## Repeat going down, <= 4 fold
mupbst_down <- all_together[all_together[["mupbst_lfc"]] <= -2, ]
mupbst_pdown <- mupbst_down[mupbst_down[["mupbst_padj"]] <= 0.05, ]
dim(mupbst_down)## [1] 611 16## [1] 611 16mupbst_pdown_genes <- rownames(mupbst_pdown)
## Repeat for mouse bladder (mbpbst_stuff)
mbpbst_up <- all_together[all_together[["mbpbst_lfc"]] >= 2, ]
## Take only the low adjusted p-values
mbpbst_pup <- mbpbst_up[mbpbst_up[["mbpbst_padj"]] <= 0.05, ]
## How many?
dim(mbpbst_up)## [1] 435 16## [1] 434 16## Get the names of these genes
mbpbst_pup_genes <- rownames(mbpbst_pup)
## Repeat going down, <= 4 fold
mbpbst_down <- all_together[all_together[["mbpbst_lfc"]] <= -2, ]
mbpbst_pdown <- mbpbst_down[mbpbst_down[["mbpbst_padj"]] <= 0.05, ]
dim(mbpbst_down)## [1] 432 16## [1] 432 16mbpbst_pdown_genes <- rownames(mbpbst_pdown)
## And last, LB + urea (lbu_stuff)
lbu_up <- all_together[all_together[["lbu_lfc"]] >= 2, ]
## Take only the low adjusted p-values
## oops, I made padj to adjp...
lbu_pup <- lbu_up[lbu_up[["lbu_adjp"]] <= 0.05, ]
## How many?
dim(lbu_up)## [1] 192 16## [1] 184 16## Get the names of these genes
lbu_pup_genes <- rownames(lbu_pup)
## Repeat going down, <= 4 fold
lbu_down <- all_together[as.numeric(all_together[["lbu_lfc"]]) <= -2, ]
lbu_pdown <- lbu_down[as.numeric(lbu_down[["lbu_adjp"]]) <= 0.05, ]
dim(lbu_down)## [1] 543 16## [1] 540 164 Make some venns…
## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:base':
##
## format.pval, units## Now make sure that the sets in the ups are not down downs and vice versa
unique_hupbst_up <- hupbst_pup_genes[hupbst_pup_genes %nin% hupbst_pdown_genes]
unique_hupbst_up <- unique_hupbst_up[unique_hupbst_up %nin% mupbst_pdown_genes]
unique_hupbst_up <- unique_hupbst_up[unique_hupbst_up %nin% mbpbst_pdown_genes]
unique_hupbst_up <- unique_hupbst_up[unique_hupbst_up %nin% lbu_pdown_genes]
unique_mupbst_up <- mupbst_pup_genes[mupbst_pup_genes %nin% hupbst_pdown_genes]
unique_mupbst_up <- unique_mupbst_up[unique_mupbst_up %nin% mupbst_pdown_genes]
unique_mupbst_up <- unique_mupbst_up[unique_mupbst_up %nin% mbpbst_pdown_genes]
unique_mupbst_up <- unique_mupbst_up[unique_mupbst_up %nin% lbu_pdown_genes]
unique_mbpbst_up <- mbpbst_pup_genes[mbpbst_pup_genes %nin% hupbst_pdown_genes]
unique_mbpbst_up <- unique_mbpbst_up[unique_mbpbst_up %nin% mupbst_pdown_genes]
unique_mbpbst_up <- unique_mbpbst_up[unique_mbpbst_up %nin% mbpbst_pdown_genes]
unique_mbpbst_up <- unique_mbpbst_up[unique_mbpbst_up %nin% lbu_pdown_genes]
unique_lbu_up <- lbu_pup_genes[lbu_pup_genes %nin% hupbst_pdown_genes]
unique_lbu_up <- unique_lbu_up[unique_lbu_up %nin% mupbst_pdown_genes]
unique_lbu_up <- unique_lbu_up[unique_lbu_up %nin% mbpbst_pdown_genes]
unique_lbu_up <- unique_lbu_up[unique_lbu_up %nin% lbu_pdown_genes]
unique_hupbst_down <- hupbst_pdown_genes[hupbst_pdown_genes %nin% hupbst_pup_genes]
unique_hupbst_down <- unique_hupbst_down[unique_hupbst_down %nin% mupbst_pup_genes]
unique_hupbst_down <- unique_hupbst_down[unique_hupbst_down %nin% mbpbst_pup_genes]
unique_hupbst_down <- unique_hupbst_down[unique_hupbst_down %nin% lbu_pup_genes]
unique_mupbst_down <- mupbst_pdown_genes[mupbst_pdown_genes %nin% hupbst_pup_genes]
unique_mupbst_down <- unique_mupbst_down[unique_mupbst_down %nin% mupbst_pup_genes]
unique_mupbst_down <- unique_mupbst_down[unique_mupbst_down %nin% mbpbst_pup_genes]
unique_mupbst_down <- unique_mupbst_down[unique_mupbst_down %nin% lbu_pup_genes]
unique_mbpbst_down <- mbpbst_pdown_genes[mbpbst_pdown_genes %nin% hupbst_pup_genes]
unique_mbpbst_down <- unique_mbpbst_down[unique_mbpbst_down %nin% mupbst_pup_genes]
unique_mbpbst_down <- unique_mbpbst_down[unique_mbpbst_down %nin% mbpbst_pup_genes]
unique_mbpbst_down <- unique_mbpbst_down[unique_mbpbst_down %nin% lbu_pup_genes]
unique_lbu_down <- lbu_pdown_genes[lbu_pdown_genes %nin% hupbst_pup_genes]
unique_lbu_down <- unique_lbu_down[unique_lbu_down %nin% mupbst_pup_genes]
unique_lbu_down <- unique_lbu_down[unique_lbu_down %nin% mbpbst_pup_genes]
unique_lbu_down <- unique_lbu_down[unique_lbu_down %nin% lbu_pup_genes]
## a is hupbst
## b is mupbst
## c is mbpbst
## d is lbu
library(Vennerable)
input <- list(
"human_urine" = unique_hupbst_up,
"mouse_urine" = unique_mupbst_up,
"mouse_bladder" = unique_mbpbst_up,
"lb_urea" = unique_lbu_up)
venn_fun <- Venn(input)
start <- Weights(venn_fun)
venn_fun## A Venn object on 4 sets named
## human_urine,mouse_urine,mouse_bladder,lb_urea
## 0000 1000 0100 1100 0010 1010 0110 1110 0001 1001 0101 1101 0011 1011 0111
## 0 51 131 43 223 4 60 18 93 3 17 16 13 4 16
## 1111
## 12
pinput <- list(
"human_urine" = unique_hupbst_down,
"mouse_urine" = unique_mupbst_down,
"mouse_bladder" = unique_mbpbst_down,
"lb_urea" = unique_lbu_down)
venn_fun <- Venn(input)
venn_fun## A Venn object on 4 sets named
## human_urine,mouse_urine,mouse_bladder,lb_urea
## 0000 1000 0100 1100 0010 1010 0110 1110 0001 1001 0101 1101 0011 1011 0111
## 0 51 131 43 223 4 60 18 93 3 17 16 13 4 16
## 1111
## 12
5 Vince’s numbers
In order to use these, I think the venneuler package will be the best, as it is able to work with intersection names and numbers rather than the sets of items.
down upA Human Urine only 13 51 B Mouse Urine only 150 131 C Bladder (mouse) 190 222 D Urea 210 93 A+B Human and mouse 59 43 A+C Human + Bladder 8 4 A+D Human+Urea 7 3 B+C Mouse + Bladder 101 59 B+D Mouse+Urea 73 17 C+D Urea+Bladder 14 13 A+B+C All but urea 44 18 A+B+D All but bladder 90 16 A+C+D All but mouse 1 4 B+C+D All but human 26 16 A+B+C+D All 38 12
## Loading required package: rJavadown_sets <- c(
"A" = 13,
"B" = 150,
"C" = 101,
"D" = 210,
"A&B" = 59,
"A&C" = 8,
"A&D" = 7,
"B&C" = 101,
"B&D" = 73,
"C&D" = 14,
"A&B&C" = 44,
"A&B&D" = 90,
"A&C&D" = 1,
"B&C&D" = 26,
"A&B&C&D" = 38)
down_vince <- venneuler(down_sets)
plot(down_vince)
up_sets <- c(
"A" = 51,
"B" = 131,
"C" = 222,
"D" = 93,
"A&B" = 43,
"A&C" = 4,
"A&D" = 3,
"B&C" = 59,
"B&D" = 17,
"C&D" = 13,
"A&B&C" = 18,
"A&B&D" = 16,
"A&C&D" = 4,
"B&C&D" = 16,
"A&B&C&D" = 12)
up_vince = venneuler(down_sets)
plot(up_vince)
---
title: "20170425: Random Venn diagrams for VTLee."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: tango
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: cosmo
  toc: true
  toc_float:
    collapsed: false
    smooth_scroll: false
---

<style>
  body .main-container {
    max-width: 1600px;
}
</style>

```{r options, include=FALSE}
## These are the options I tend to favor
library("hpgltools")
tt <- devtools::load_all("~/hpgltools")
knitr::opts_knit$set(
                   progress=TRUE,
                   verbose=TRUE,
                   width=90,
                   echo=TRUE)
knitr::opts_chunk$set(
                    error=TRUE,
                    fig.width=8,
                    fig.height=8,
                    dpi=96)
old_options <- options(
  digits=4,
  stringsAsFactors=FALSE,
  knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
set.seed(1)
```

```{r loadme, include=FALSE}
##tmp <- sm(loadme(filename=previous_save))
ver <- "20170425"
previous_file <- "venn.Rmd"
rmd_file <- "venn.Rmd"
previous_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
```

# Introduction

Vince is looking to plot the various intersections/unions of some existing
RNASeq data of mice infected with Pseudomonas aeruginosa.  The table in question
is rather complex; comprising samples of: mouse bladder data, LB with various
additions, etc etc.  Thus a great majority of what follows is my attempt to
simplify the table so that it is possible to follow; then I load it into one of
R's venn libraries and print some images.

# Step 1, look at table

In order to load this most easily, I am changing the following:

* Remove top rows
* shorten names to a combination of condition+column: Eg:
    * ID (gene id)
	* hupbst_basemean -- human urine vs. pbs-t
	* hupbst_lfc
	* hupbst_p
	* hupbst_padj
	* mupbst_lfc -- mouse urine vs. pbs-t
	* mupbst_p
	* mupbst_padj
	* mbpbst_lfc -- mouse bladder vs. pbs-t
	* mbpbst_p
	* mbpbst_padj
	* lbu_ID -- LB with LB urine
	* lbu_basemean
	* lbu_lfc
	* lbu_p
	* lbu_adjp
* Remove blank columns

Save it as shrunken.csv

# Step 2, load it up

Unfortunately, I have no guarantee that ID's order and lbu_ID's order are the same,
so split this into two sets and merge them back.

  ```{r venns}
all_together <- read.csv("external_data/shrunken.csv")
## 3 rows are missing data... drop them.
##all_together <- all_together[complete.cases(all_together), ]
rownames(all_together) <- make.names(all_together[["ID"]], unique=TRUE)
all_together$lbu_lfc <- as.numeric(all_together$lbu_lfc)
all_together$lbu_adjp <- as.numeric(all_together$lbu_adjp)
all_together[is.na(all_together)] <- 0

## Human urine, PBS-T up/down (hupbst_stuff)
## The goal is 4 fold, yesno?  So 2 log fold changes.
## First pull the up
hupbst_up <- all_together[all_together[["hupbst_lfc"]] >= 2, ]
## Vince's sheet shows 161 here
dim(hupbst_up)
## Take only the low adjusted p-values
hupbst_pup <- hupbst_up[hupbst_up[["hupbst_padj"]] <= 0.05, ]
## How many?
dim(hupbst_pup)
## Get the names of these genes
hupbst_up_genes <- rownames(hupbst_up)
hupbst_pup_genes <- rownames(hupbst_pup)
## Repeat going down, <= 4 fold
hupbst_down <- all_together[all_together[["hupbst_lfc"]] <= -2, ]
hupbst_pdown <- hupbst_down[hupbst_down[["hupbst_padj"]] <= 0.05, ]
dim(hupbst_down)
dim(hupbst_pdown)
hupbst_pdown_genes <- rownames(hupbst_pdown)

## now repeat the above for Mouse urine, PBS-T (mupbst_stuff)
mupbst_up <- all_together[all_together[["mupbst_lfc"]] >= 2, ]
## Take only the low adjusted p-values
mupbst_pup <- mupbst_up[mupbst_up[["mupbst_padj"]] <= 0.05, ]
## How many?
dim(mupbst_up)
dim(mupbst_pup)
## Get the names of these genes
mupbst_pup_genes <- rownames(mupbst_pup)
## Repeat going down, <= 4 fold
mupbst_down <- all_together[all_together[["mupbst_lfc"]] <= -2, ]
mupbst_pdown <- mupbst_down[mupbst_down[["mupbst_padj"]] <= 0.05, ]
dim(mupbst_down)
dim(mupbst_pdown)
mupbst_pdown_genes <- rownames(mupbst_pdown)

## Repeat for mouse bladder (mbpbst_stuff)
mbpbst_up <- all_together[all_together[["mbpbst_lfc"]] >= 2, ]
## Take only the low adjusted p-values
mbpbst_pup <- mbpbst_up[mbpbst_up[["mbpbst_padj"]] <= 0.05, ]
## How many?
dim(mbpbst_up)
dim(mbpbst_pup)
## Get the names of these genes
mbpbst_pup_genes <- rownames(mbpbst_pup)
## Repeat going down, <= 4 fold
mbpbst_down <- all_together[all_together[["mbpbst_lfc"]] <= -2, ]
mbpbst_pdown <- mbpbst_down[mbpbst_down[["mbpbst_padj"]] <= 0.05, ]
dim(mbpbst_down)
dim(mbpbst_pdown)
mbpbst_pdown_genes <- rownames(mbpbst_pdown)

## And last, LB + urea (lbu_stuff)
lbu_up <- all_together[all_together[["lbu_lfc"]] >= 2, ]
## Take only the low adjusted p-values
## oops, I made padj to adjp...
lbu_pup <- lbu_up[lbu_up[["lbu_adjp"]] <= 0.05, ]
## How many?
dim(lbu_up)
dim(lbu_pup)
## Get the names of these genes
lbu_pup_genes <- rownames(lbu_pup)
## Repeat going down, <= 4 fold
lbu_down <- all_together[as.numeric(all_together[["lbu_lfc"]]) <= -2, ]
lbu_pdown <- lbu_down[as.numeric(lbu_down[["lbu_adjp"]]) <= 0.05, ]
dim(lbu_down)
dim(lbu_pdown)
lbu_pdown_genes <- rownames(lbu_pdown)
```

# Make some venns...

```{r vennerable}
library(Hmisc)
## Now make sure that the sets in the ups are not down downs and vice versa
unique_hupbst_up <- hupbst_pup_genes[hupbst_pup_genes %nin% hupbst_pdown_genes]
unique_hupbst_up <- unique_hupbst_up[unique_hupbst_up %nin% mupbst_pdown_genes]
unique_hupbst_up <- unique_hupbst_up[unique_hupbst_up %nin% mbpbst_pdown_genes]
unique_hupbst_up <- unique_hupbst_up[unique_hupbst_up %nin% lbu_pdown_genes]

unique_mupbst_up <- mupbst_pup_genes[mupbst_pup_genes %nin% hupbst_pdown_genes]
unique_mupbst_up <- unique_mupbst_up[unique_mupbst_up %nin% mupbst_pdown_genes]
unique_mupbst_up <- unique_mupbst_up[unique_mupbst_up %nin% mbpbst_pdown_genes]
unique_mupbst_up <- unique_mupbst_up[unique_mupbst_up %nin% lbu_pdown_genes]

unique_mbpbst_up <- mbpbst_pup_genes[mbpbst_pup_genes %nin% hupbst_pdown_genes]
unique_mbpbst_up <- unique_mbpbst_up[unique_mbpbst_up %nin% mupbst_pdown_genes]
unique_mbpbst_up <- unique_mbpbst_up[unique_mbpbst_up %nin% mbpbst_pdown_genes]
unique_mbpbst_up <- unique_mbpbst_up[unique_mbpbst_up %nin% lbu_pdown_genes]

unique_lbu_up <- lbu_pup_genes[lbu_pup_genes %nin% hupbst_pdown_genes]
unique_lbu_up <- unique_lbu_up[unique_lbu_up %nin% mupbst_pdown_genes]
unique_lbu_up <- unique_lbu_up[unique_lbu_up %nin% mbpbst_pdown_genes]
unique_lbu_up <- unique_lbu_up[unique_lbu_up %nin% lbu_pdown_genes]

unique_hupbst_down <- hupbst_pdown_genes[hupbst_pdown_genes %nin% hupbst_pup_genes]
unique_hupbst_down <- unique_hupbst_down[unique_hupbst_down %nin% mupbst_pup_genes]
unique_hupbst_down <- unique_hupbst_down[unique_hupbst_down %nin% mbpbst_pup_genes]
unique_hupbst_down <- unique_hupbst_down[unique_hupbst_down %nin% lbu_pup_genes]

unique_mupbst_down <- mupbst_pdown_genes[mupbst_pdown_genes %nin% hupbst_pup_genes]
unique_mupbst_down <- unique_mupbst_down[unique_mupbst_down %nin% mupbst_pup_genes]
unique_mupbst_down <- unique_mupbst_down[unique_mupbst_down %nin% mbpbst_pup_genes]
unique_mupbst_down <- unique_mupbst_down[unique_mupbst_down %nin% lbu_pup_genes]

unique_mbpbst_down <- mbpbst_pdown_genes[mbpbst_pdown_genes %nin% hupbst_pup_genes]
unique_mbpbst_down <- unique_mbpbst_down[unique_mbpbst_down %nin% mupbst_pup_genes]
unique_mbpbst_down <- unique_mbpbst_down[unique_mbpbst_down %nin% mbpbst_pup_genes]
unique_mbpbst_down <- unique_mbpbst_down[unique_mbpbst_down %nin% lbu_pup_genes]

unique_lbu_down <- lbu_pdown_genes[lbu_pdown_genes %nin% hupbst_pup_genes]
unique_lbu_down <- unique_lbu_down[unique_lbu_down %nin% mupbst_pup_genes]
unique_lbu_down <- unique_lbu_down[unique_lbu_down %nin% mbpbst_pup_genes]
unique_lbu_down <- unique_lbu_down[unique_lbu_down %nin% lbu_pup_genes]


## a is hupbst
## b is mupbst
## c is mbpbst
## d is lbu
library(Vennerable)
input <- list(
    "human_urine" = unique_hupbst_up,
    "mouse_urine" = unique_mupbst_up,
    "mouse_bladder" = unique_mbpbst_up,
    "lb_urea" = unique_lbu_up)
venn_fun <- Venn(input)
start <- Weights(venn_fun)
venn_fun

plot(venn_fun, doWeights=FALSE, type="ellipses")
plot(venn_fun, doWeights=FALSE, type="ChowRuskey")
plot(venn_fun, doWeights=FALSE, type="squares")

pinput <- list(
    "human_urine" = unique_hupbst_down,
    "mouse_urine" = unique_mupbst_down,
    "mouse_bladder" = unique_mbpbst_down,
    "lb_urea" = unique_lbu_down)
venn_fun <- Venn(input)
venn_fun
plot(venn_fun, doWeights=FALSE, type="ellipses")
```


# Vince's numbers

In order to use these, I think the venneuler package will be the best, as it
is able to work with intersection names and numbers rather than the sets of items.

                       down   up
A Human Urine only    13     51
B Mouse Urine only    150    131
C Bladder (mouse)     190    222
D Urea                210    93
A+B Human and mouse   59     43
A+C Human + Bladder   8      4
A+D Human+Urea        7      3
B+C Mouse + Bladder   101    59
B+D Mouse+Urea        73     17
C+D Urea+Bladder      14     13
A+B+C All but urea    44     18
A+B+D All but bladder 90     16
A+C+D All but mouse   1      4
B+C+D All but human   26     16
A+B+C+D All           38     12

```{r vinces_venn}
library(venneuler)

down_sets <- c(
    "A" = 13,
    "B" = 150,
    "C" = 101,
    "D" = 210,
    "A&B" = 59,
    "A&C" = 8,
    "A&D" = 7,
    "B&C" = 101,
    "B&D" = 73,
    "C&D" = 14,
    "A&B&C" = 44,
    "A&B&D" = 90,
    "A&C&D" = 1,
    "B&C&D" = 26,
    "A&B&C&D" = 38)
down_vince <- venneuler(down_sets)
plot(down_vince)

up_sets <- c(
    "A" = 51,
    "B" = 131,
    "C" = 222,
    "D" = 93,
    "A&B" = 43,
    "A&C" = 4,
    "A&D" = 3,
    "B&C" = 59,
    "B&D" = 17,
    "C&D" = 13,
    "A&B&C" = 18,
    "A&B&D" = 16,
    "A&C&D" = 4,
    "B&C&D" = 16,
    "A&B&C&D" = 12)
up_vince = venneuler(down_sets)
plot(up_vince)
```
