Source: https://github.com/markziemann/dftd
Here I’m reading in some SNP files from the cancer cell article: https://www.cell.com/cancer-cell/fulltext/S1535-6108(18)30117-X to identify SNPs in exons that could be used to distinguish the lines.
The two files are SNPs which are present in the DFTD1 and DFTD2 cell types respectively. Although I’m not sure if these are mutually exclusive, the SNP sets may have an overlap, which I will test.
There are 8 sample which are of interest to us.
| Sample type | Sample label |
|---|---|
| DFT1 | 86T |
| DFT1 | 88T |
| DFT2 | 202T2 |
| DFT2 | 203T3 |
| Normal | 91H |
| Normal | 31H |
| Normal | 202H1 |
| Normal | 203H |
suppressPackageStartupMessages({
library("eulerr")
library("kableExtra")
library("GenomicRanges")
library("readxl")
})Here I’m loading the data in and only selecting the DFTD lines.
d1 <- read.table("DFT1_SNVs.txt",header=TRUE)
head(d1)## SCAFFOLD POS REF ALT X86T X88T X202T2 X203T3 X202H1
## 1 Chr1_supercontig_000000000 442678 T C 0/70 32/58 0/31 0/36 0/39
## 2 Chr1_supercontig_000000000 482371 G A 0/82 48/82 0/26 0/53 0/55
## 3 Chr1_supercontig_000000000 699656 C T 36/80 0/72 0/41 0/29 0/53
## 4 Chr1_supercontig_000000000 708655 G T 0/65 29/61 0/30 0/33 0/42
## 5 Chr1_supercontig_000000000 1515865 C T 33/77 0/65 0/37 0/44 0/56
## 6 Chr1_supercontig_000000000 1801031 C T 1/75 25/55 0/30 0/36 0/36
## X203H X91H X31H X110H X112H X115H1 X117H X119H X11H X122H1 X124H X133H X134H
## 1 0/44 0/68 0/9 0/2 0/1 0/0 0/2 0/2 0/1 0/2 0/1 0/1 0/3
## 2 0/71 0/70 0/15 0/3 0/1 0/1 0/1 0/3 0/0 0/3 0/0 0/3 0/1
## 3 0/39 1/73 0/21 0/2 0/1 0/1 0/3 0/0 0/0 0/0 0/0 0/5 0/1
## 4 0/68 0/55 0/21 0/2 0/1 0/1 0/0 0/1 0/1 0/2 0/1 0/1 0/1
## 5 0/41 0/64 0/23 0/2 0/0 0/1 0/2 0/2 0/1 0/0 0/3 0/0 0/5
## 6 0/37 0/51 0/24 0/1 0/0 0/1 0/1 0/3 0/0 0/1 0/0 0/0 0/3
## X238H X244H X264H X265H X266H X267H X268H X269H X270H X271H X317H X347H X379H
## 1 0/6 0/0 0/2 0/0 0/0 0/2 0/1 0/1 0/0 0/1 0/0 0/0 0/1
## 2 0/2 0/3 0/1 0/3 0/0 0/0 0/2 0/3 0/2 0/3 0/3 0/0 0/1
## 3 0/2 0/1 0/1 0/2 0/2 0/1 0/3 0/2 0/2 0/0 0/5 0/1 0/2
## 4 0/0 0/2 0/0 0/0 0/1 0/2 0/0 0/1 0/0 0/3 0/6 0/0 0/1
## 5 0/2 0/2 0/1 0/2 0/3 0/2 0/1 0/3 0/2 0/3 0/3 0/0 0/2
## 6 0/0 0/2 0/0 0/0 0/2 0/2 0/1 0/0 0/1 0/2 0/3 0/0 0/1
## X398H X420H X442H X443H X444H X63H1 X811H ERS1202857 ERS1202858 ERS1202859
## 1 0/1 0/2 0/1 0/1 0/1 0/0 0/1 0/6 0/15 0/8
## 2 0/4 0/1 0/0 0/2 0/0 0/3 0/2 0/7 0/14 0/14
## 3 0/0 0/0 0/2 0/3 0/1 0/2 0/2 0/3 0/12 0/7
## 4 0/1 0/3 0/1 0/6 0/0 0/2 0/1 0/7 0/13 0/14
## 5 0/0 0/4 0/1 0/0 0/0 0/3 0/1 0/2 0/11 0/9
## 6 0/3 0/2 0/1 0/1 0/0 0/0 0/1 0/4 0/6 0/10
## ERS1202860 ERS1202861 ERS682204 ERS682205 ERS682206 ERS682207 ERS682208
## 1 0/9 0/5 0/6 0/11 0/7 0/14 0/8
## 2 0/6 0/8 0/16 0/14 0/6 0/19 0/23
## 3 0/4 0/10 0/16 0/18 0/5 0/19 0/14
## 4 0/5 0/10 0/15 0/14 0/7 0/18 0/17
## 5 0/8 0/10 0/22 0/15 0/9 0/13 0/9
## 6 0/8 0/12 0/7 0/11 0/11 0/12 0/16
## ERS682209 ERS682210
## 1 0/13 0/5
## 2 0/10 0/19
## 3 0/14 0/13
## 4 0/16 0/11
## 5 0/8 0/21
## 6 0/12 0/12str(d1)## 'data.frame': 9608 obs. of 54 variables:
## $ SCAFFOLD : chr "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" ...
## $ POS : int 442678 482371 699656 708655 1515865 1801031 1815761 1858650 2116270 2122511 ...
## $ REF : chr "T" "G" "C" "G" ...
## $ ALT : chr "C" "A" "T" "T" ...
## $ X86T : chr "0/70" "0/82" "36/80" "0/65" ...
## $ X88T : chr "32/58" "48/82" "0/72" "29/61" ...
## $ X202T2 : chr "0/31" "0/26" "0/41" "0/30" ...
## $ X203T3 : chr "0/36" "0/53" "0/29" "0/33" ...
## $ X202H1 : chr "0/39" "0/55" "0/53" "0/42" ...
## $ X203H : chr "0/44" "0/71" "0/39" "0/68" ...
## $ X91H : chr "0/68" "0/70" "1/73" "0/55" ...
## $ X31H : chr "0/9" "0/15" "0/21" "0/21" ...
## $ X110H : chr "0/2" "0/3" "0/2" "0/2" ...
## $ X112H : chr "0/1" "0/1" "0/1" "0/1" ...
## $ X115H1 : chr "0/0" "0/1" "0/1" "0/1" ...
## $ X117H : chr "0/2" "0/1" "0/3" "0/0" ...
## $ X119H : chr "0/2" "0/3" "0/0" "0/1" ...
## $ X11H : chr "0/1" "0/0" "0/0" "0/1" ...
## $ X122H1 : chr "0/2" "0/3" "0/0" "0/2" ...
## $ X124H : chr "0/1" "0/0" "0/0" "0/1" ...
## $ X133H : chr "0/1" "0/3" "0/5" "0/1" ...
## $ X134H : chr "0/3" "0/1" "0/1" "0/1" ...
## $ X238H : chr "0/6" "0/2" "0/2" "0/0" ...
## $ X244H : chr "0/0" "0/3" "0/1" "0/2" ...
## $ X264H : chr "0/2" "0/1" "0/1" "0/0" ...
## $ X265H : chr "0/0" "0/3" "0/2" "0/0" ...
## $ X266H : chr "0/0" "0/0" "0/2" "0/1" ...
## $ X267H : chr "0/2" "0/0" "0/1" "0/2" ...
## $ X268H : chr "0/1" "0/2" "0/3" "0/0" ...
## $ X269H : chr "0/1" "0/3" "0/2" "0/1" ...
## $ X270H : chr "0/0" "0/2" "0/2" "0/0" ...
## $ X271H : chr "0/1" "0/3" "0/0" "0/3" ...
## $ X317H : chr "0/0" "0/3" "0/5" "0/6" ...
## $ X347H : chr "0/0" "0/0" "0/1" "0/0" ...
## $ X379H : chr "0/1" "0/1" "0/2" "0/1" ...
## $ X398H : chr "0/1" "0/4" "0/0" "0/1" ...
## $ X420H : chr "0/2" "0/1" "0/0" "0/3" ...
## $ X442H : chr "0/1" "0/0" "0/2" "0/1" ...
## $ X443H : chr "0/1" "0/2" "0/3" "0/6" ...
## $ X444H : chr "0/1" "0/0" "0/1" "0/0" ...
## $ X63H1 : chr "0/0" "0/3" "0/2" "0/2" ...
## $ X811H : chr "0/1" "0/2" "0/2" "0/1" ...
## $ ERS1202857: chr "0/6" "0/7" "0/3" "0/7" ...
## $ ERS1202858: chr "0/15" "0/14" "0/12" "0/13" ...
## $ ERS1202859: chr "0/8" "0/14" "0/7" "0/14" ...
## $ ERS1202860: chr "0/9" "0/6" "0/4" "0/5" ...
## $ ERS1202861: chr "0/5" "0/8" "0/10" "0/10" ...
## $ ERS682204 : chr "0/6" "0/16" "0/16" "0/15" ...
## $ ERS682205 : chr "0/11" "0/14" "0/18" "0/14" ...
## $ ERS682206 : chr "0/7" "0/6" "0/5" "0/7" ...
## $ ERS682207 : chr "0/14" "0/19" "0/19" "0/18" ...
## $ ERS682208 : chr "0/8" "0/23" "0/14" "0/17" ...
## $ ERS682209 : chr "0/13" "0/10" "0/14" "0/16" ...
## $ ERS682210 : chr "0/5" "0/19" "0/13" "0/11" ...# subset to obtain DFTD lines only
d1 <- d1[,1:8]
head(d1)## SCAFFOLD POS REF ALT X86T X88T X202T2 X203T3
## 1 Chr1_supercontig_000000000 442678 T C 0/70 32/58 0/31 0/36
## 2 Chr1_supercontig_000000000 482371 G A 0/82 48/82 0/26 0/53
## 3 Chr1_supercontig_000000000 699656 C T 36/80 0/72 0/41 0/29
## 4 Chr1_supercontig_000000000 708655 G T 0/65 29/61 0/30 0/33
## 5 Chr1_supercontig_000000000 1515865 C T 33/77 0/65 0/37 0/44
## 6 Chr1_supercontig_000000000 1801031 C T 1/75 25/55 0/30 0/36Now look at the SNPS which are common to the two DFTD1 lines 86 and 88
First need to parse out the data into numerical format.
g1 <- d1[,5:ncol(d1)]
x86 <- do.call(rbind,strsplit(g1$X86T,"/"))
x88 <- do.call(rbind,strsplit(g1$X88T,"/"))
x202 <- do.call(rbind,strsplit(g1$X202T2,"/"))
x203 <- do.call(rbind,strsplit(g1$X203T3,"/"))
g1 <- cbind(x86,x88,x202,x203)
g1 <- apply(g1,2,as.numeric)
colnames(g1) <- c("x86_alt","x86_total",
"x88_alt","x88_total",
"x202_alt","x202_total",
"x203_alt","x203_total")
head(g1)## x86_alt x86_total x88_alt x88_total x202_alt x202_total x203_alt
## [1,] 0 70 32 58 0 31 0
## [2,] 0 82 48 82 0 26 0
## [3,] 36 80 0 72 0 41 0
## [4,] 0 65 29 61 0 30 0
## [5,] 33 77 0 65 0 37 0
## [6,] 1 75 25 55 0 30 0
## x203_total
## [1,] 36
## [2,] 53
## [3,] 29
## [4,] 33
## [5,] 44
## [6,] 36x1 <- cbind(d1,g1)
str(x1)## 'data.frame': 9608 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" ...
## $ POS : int 442678 482371 699656 708655 1515865 1801031 1815761 1858650 2116270 2122511 ...
## $ REF : chr "T" "G" "C" "G" ...
## $ ALT : chr "C" "A" "T" "T" ...
## $ X86T : chr "0/70" "0/82" "36/80" "0/65" ...
## $ X88T : chr "32/58" "48/82" "0/72" "29/61" ...
## $ X202T2 : chr "0/31" "0/26" "0/41" "0/30" ...
## $ X203T3 : chr "0/36" "0/53" "0/29" "0/33" ...
## $ x86_alt : num 0 0 36 0 33 1 0 0 0 0 ...
## $ x86_total : num 70 82 80 65 77 75 80 94 98 101 ...
## $ x88_alt : num 32 48 0 29 0 25 31 33 35 33 ...
## $ x88_total : num 58 82 72 61 65 55 61 64 81 81 ...
## $ x202_alt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ x202_total: num 31 26 41 30 37 30 32 30 37 40 ...
## $ x203_alt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ x203_total: num 36 53 29 33 44 36 43 37 35 43 ...Now we can filter further.
Use a 25% allele frequency filter to identify the SNPs for each DFTD line.
x86 <- x1[(which(x1$x86_alt / x1$x86_total > 0.25)),]
x88 <- x1[(which(x1$x88_alt / x1$x88_total > 0.25)),]
x202 <- x1[(which(x1$x202_alt / x1$x202_total > 0.25)),]
x203 <- x1[(which(x1$x203_alt / x1$x203_total > 0.25)),]
str(x86)## 'data.frame': 5678 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000002" "Chr1_supercontig_000000002" ...
## $ POS : int 699656 1515865 323555 357826 703114 188150 732837 972351 1112036 1377188 ...
## $ REF : chr "C" "C" "T" "G" ...
## $ ALT : chr "T" "T" "G" "A" ...
## $ X86T : chr "36/80" "33/77" "39/86" "30/66" ...
## $ X88T : chr "0/72" "0/65" "0/54" "20/41" ...
## $ X202T2 : chr "0/41" "0/37" "0/33" "0/28" ...
## $ X203T3 : chr "0/29" "0/44" "0/45" "0/33" ...
## $ x86_alt : num 36 33 39 30 38 42 30 34 34 29 ...
## $ x86_total : num 80 77 86 66 71 68 57 76 73 54 ...
## $ x88_alt : num 0 0 0 20 0 0 20 27 0 25 ...
## $ x88_total : num 72 65 54 41 66 55 42 52 77 50 ...
## $ x202_alt : num 0 0 0 0 0 0 0 0 0 1 ...
## $ x202_total: num 41 37 33 28 31 25 23 19 62 24 ...
## $ x203_alt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ x203_total: num 29 44 45 33 41 33 26 31 78 24 ...str(x88)## 'data.frame': 6231 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" "Chr1_supercontig_000000000" ...
## $ POS : int 442678 482371 708655 1801031 1815761 1858650 2116270 2122511 332336 357826 ...
## $ REF : chr "T" "G" "G" "C" ...
## $ ALT : chr "C" "A" "T" "T" ...
## $ X86T : chr "0/70" "0/82" "0/65" "1/75" ...
## $ X88T : chr "32/58" "48/82" "29/61" "25/55" ...
## $ X202T2 : chr "0/31" "0/26" "0/30" "0/30" ...
## $ X203T3 : chr "0/36" "0/53" "0/33" "0/36" ...
## $ x86_alt : num 0 0 0 1 0 0 0 0 0 30 ...
## $ x86_total : num 70 82 65 75 80 94 98 101 79 66 ...
## $ x88_alt : num 32 48 29 25 31 33 35 33 32 20 ...
## $ x88_total : num 58 82 61 55 61 64 81 81 62 41 ...
## $ x202_alt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ x202_total: num 31 26 30 30 32 30 37 40 25 28 ...
## $ x203_alt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ x203_total: num 36 53 33 36 43 37 35 43 33 33 ...str(x202)## 'data.frame': 6 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr3_supercontig_000000088" "Chr3_supercontig_000001581" "Chr3_supercontig_000001581" "Chr4_supercontig_000000337" ...
## $ POS : int 3752 18502 18503 62371 146017 42492
## $ REF : chr "T" "C" "A" "C" ...
## $ ALT : chr "C" "G" "C" "T" ...
## $ X86T : chr "6/6" "6/12" "6/12" "9/23" ...
## $ X88T : chr "3/3" "0/7" "0/7" "5/15" ...
## $ X202T2 : chr "4/4" "3/8" "4/8" "4/11" ...
## $ X203T3 : chr "4/7" "2/6" "2/6" "4/16" ...
## $ x86_alt : num 6 6 6 9 7 7
## $ x86_total : num 6 12 12 23 7 47
## $ x88_alt : num 3 0 0 5 0 1
## $ x88_total : num 3 7 7 15 0 26
## $ x202_alt : num 4 3 4 4 4 3
## $ x202_total: num 4 8 8 11 4 10
## $ x203_alt : num 4 2 2 4 0 0
## $ x203_total: num 7 6 6 16 0 12str(x203)## 'data.frame': 7 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr2_supercontig_000000157" "Chr2_supercontig_000000457" "Chr3_supercontig_000000088" "Chr3_supercontig_000001581" ...
## $ POS : int 1561315 2526161 3752 18502 18503 719185 40142
## $ REF : chr "T" "A" "T" "C" ...
## $ ALT : chr "G" "T" "C" "G" ...
## $ X86T : chr "5/23" "5/5" "6/6" "6/12" ...
## $ X88T : chr "3/11" "2/2" "3/3" "0/7" ...
## $ X202T2 : chr "1/11" "0/1" "4/4" "3/8" ...
## $ X203T3 : chr "3/9" "4/7" "4/7" "2/6" ...
## $ x86_alt : num 5 5 6 6 6 8 5
## $ x86_total : num 23 5 6 12 12 50 8
## $ x88_alt : num 3 2 3 0 0 3 2
## $ x88_total : num 11 2 3 7 7 53 19
## $ x202_alt : num 1 0 4 3 4 2 0
## $ x202_total: num 11 1 4 8 8 19 7
## $ x203_alt : num 3 4 4 2 2 4 1
## $ x203_total: num 9 7 7 6 6 13 3Importantly, there were approx 6000 SNPs that could identify DFTD1 lines but only 7 which could identify DFTD2 lines.
Let’s see whether these are specific to one cell line.
x86_2 <- x1[which(x1$x86_alt / x1$x86_total > 0.25 & x1$x202_alt / x1$x202_total < 0.25 & x1$x203_alt / x1$x203_total < 0.25 ),]
dim(x86)## [1] 5678 16dim(x86_2)## [1] 5667 16x88_2 <- x1[which(x1$x88_alt / x1$x88_total > 0.25 & x1$x202_alt / x1$x202_total < 0.25 & x1$x203_alt / x1$x203_total < 0.25 ),]
dim(x88)## [1] 6231 16dim(x88_2)## [1] 6222 16This shows that of the 5678 SNPs specific to x86, 5667 were specific to this line and not present in x202 or s203.
Of the 6231 SNPs specific to x88, 6222 were specific to this line and not present in x202 or s203.
This means that 5000-6000 SNPs can be used to distinguish genomes.
d1_snps <- x1[which(x1$x86_alt / x1$x86_total > 0.25 & x1$x88_alt / x1$x88_total > 0.25),]
str(d1_snps)## 'data.frame': 2745 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr1_supercontig_000000002" "Chr1_supercontig_000000004" "Chr1_supercontig_000000005" "Chr1_supercontig_000000005" ...
## $ POS : int 357826 732837 972351 1377188 3665329 291757 293015 866865 855884 1593561 ...
## $ REF : chr "G" "G" "A" "G" ...
## $ ALT : chr "A" "A" "C" "A" ...
## $ X86T : chr "30/66" "30/57" "34/76" "29/54" ...
## $ X88T : chr "20/41" "20/42" "27/52" "25/50" ...
## $ X202T2 : chr "0/28" "0/23" "0/19" "1/24" ...
## $ X203T3 : chr "0/33" "0/26" "0/31" "0/24" ...
## $ x86_alt : num 30 30 34 29 41 26 45 40 39 26 ...
## $ x86_total : num 66 57 76 54 75 57 79 79 81 55 ...
## $ x88_alt : num 20 20 27 25 35 23 33 28 33 22 ...
## $ x88_total : num 41 42 52 50 55 50 65 55 59 44 ...
## $ x202_alt : num 0 0 0 1 0 0 0 0 0 0 ...
## $ x202_total: num 28 23 19 24 29 26 37 38 61 53 ...
## $ x203_alt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ x203_total: num 33 26 31 24 34 24 36 33 70 57 ...d2_snps <- x1[which(x1$x202_alt / x1$x202_total > 0.25 & x1$x203_alt / x1$x203_total > 0.25),]
str(d2_snps)## 'data.frame': 3 obs. of 16 variables:
## $ SCAFFOLD : chr "Chr3_supercontig_000000088" "Chr3_supercontig_000001581" "Chr3_supercontig_000001581"
## $ POS : int 3752 18502 18503
## $ REF : chr "T" "C" "A"
## $ ALT : chr "C" "G" "C"
## $ X86T : chr "6/6" "6/12" "6/12"
## $ X88T : chr "3/3" "0/7" "0/7"
## $ X202T2 : chr "4/4" "3/8" "4/8"
## $ X203T3 : chr "4/7" "2/6" "2/6"
## $ x86_alt : num 6 6 6
## $ x86_total : num 6 12 12
## $ x88_alt : num 3 0 0
## $ x88_total : num 3 7 7
## $ x202_alt : num 4 3 4
## $ x202_total: num 4 8 8
## $ x203_alt : num 4 2 2
## $ x203_total: num 7 6 6This indicates 2745 SNPs which are common to the DFTD1 cell lines, while only 3 SNPs were common to DFTD2 cell lines.
Given that there are are only 2745 which are common to DFTD1 lines, we better focus on the 6222 which can distinguish x88.
Next, lets look at the 6222 SNPs that are present in x88 and not in the DFTD2 lines and see how many are located in exons.
The exon bedfile was generated with the gff2bed.sh script. That script extracts CDS coordinates the GTF file. We use the GTF as input because it has the gene ID number which can be matched to the RNA-seq.
cds <- read.table("../gene_annotation/Sarcophilus_harrisii.DEVIL7.0.90.gtf.cds.bed")
colnames(cds) <- c("seqname","start","end","gene_name")
# GRanges()
cdsr <- GRanges(seqnames=cds$seqname,ranges=IRanges(start=cds$start, end=cds$end) )
cdsr$gene_name <- cds$gene_name
head(cdsr)## GRanges object with 6 ranges and 1 metadata column:
## seqnames ranges strand | gene_name
## <Rle> <IRanges> <Rle> | <character>
## [1] GL834412.1 183603-183680 * | ENSSHAG00000005817
## [2] GL834412.1 184756-184957 * | ENSSHAG00000005817
## [3] GL834412.1 189295-189467 * | ENSSHAG00000005817
## [4] GL834412.1 189884-189945 * | ENSSHAG00000005817
## [5] GL834412.1 191912-191969 * | ENSSHAG00000005817
## [6] GL834412.1 193138-193190 * | ENSSHAG00000005817
## -------
## seqinfo: 3620 sequences from an unspecified genome; no seqlengthsThe problem is that exons have this sort of seq name (GL834412.1) while the variants have this type (Chr1_supercontig_000000000)
The correspondence map of these naming conventions is given here: https://plos.figshare.com/articles/dataset/Genome_coordinates_/13283417/1
head(x88_2)## SCAFFOLD POS REF ALT X86T X88T X202T2 X203T3 x86_alt
## 1 Chr1_supercontig_000000000 442678 T C 0/70 32/58 0/31 0/36 0
## 2 Chr1_supercontig_000000000 482371 G A 0/82 48/82 0/26 0/53 0
## 4 Chr1_supercontig_000000000 708655 G T 0/65 29/61 0/30 0/33 0
## 6 Chr1_supercontig_000000000 1801031 C T 1/75 25/55 0/30 0/36 1
## 7 Chr1_supercontig_000000000 1815761 T A 0/80 31/61 0/32 0/43 0
## 8 Chr1_supercontig_000000000 1858650 T G 0/94 33/64 0/30 0/37 0
## x86_total x88_alt x88_total x202_alt x202_total x203_alt x203_total
## 1 70 32 58 0 31 0 36
## 2 82 48 82 0 26 0 53
## 4 65 29 61 0 30 0 33
## 6 75 25 55 0 30 0 36
## 7 80 31 61 0 32 0 43
## 8 94 33 64 0 30 0 37map_tbl <- read.table("../gene_annotation/supercontig2gl.tsv",header=TRUE)
head(map_tbl)## ENSEMBL_ID TCG_ID
## 1 GL834412.1 Chr1_supercontig_000000000
## 2 GL834413.1 Chr1_supercontig_000000001
## 3 GL834414.1 Chr1_supercontig_000000002
## 4 GL834415.1 Chr1_supercontig_000000003
## 5 GL834416.1 Chr1_supercontig_000000004
## 6 GL834417.1 Chr1_supercontig_000000005x88_2$SCAFFOLD <- map_tbl[match(x88_2$SCAFFOLD,map_tbl$TCG_ID),"ENSEMBL_ID"]
head(x88_2)## SCAFFOLD POS REF ALT X86T X88T X202T2 X203T3 x86_alt x86_total x88_alt
## 1 GL834412.1 442678 T C 0/70 32/58 0/31 0/36 0 70 32
## 2 GL834412.1 482371 G A 0/82 48/82 0/26 0/53 0 82 48
## 4 GL834412.1 708655 G T 0/65 29/61 0/30 0/33 0 65 29
## 6 GL834412.1 1801031 C T 1/75 25/55 0/30 0/36 1 75 25
## 7 GL834412.1 1815761 T A 0/80 31/61 0/32 0/43 0 80 31
## 8 GL834412.1 1858650 T G 0/94 33/64 0/30 0/37 0 94 33
## x88_total x202_alt x202_total x203_alt x203_total
## 1 58 0 31 0 36
## 2 82 0 26 0 53
## 4 61 0 30 0 33
## 6 55 0 30 0 36
## 7 61 0 32 0 43
## 8 64 0 30 0 37Now we can intersect
x88_r <- GRanges(seqnames=x88_2$SCAFFOLD, ranges=IRanges(start=x88_2$POS, end=x88_2$POS+1) )
x88_r## GRanges object with 6222 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] GL834412.1 442678-442679 *
## [2] GL834412.1 482371-482372 *
## [3] GL834412.1 708655-708656 *
## [4] GL834412.1 1801031-1801032 *
## [5] GL834412.1 1815761-1815762 *
## ... ... ... ...
## [6218] GL868375.1 6686-6687 *
## [6219] GL868600.1 1630-1631 *
## [6220] GL868618.1 2752-2753 *
## [6221] GL868784.1 1362-1363 *
## [6222] GL869864.1 3928-3929 *
## -------
## seqinfo: 2255 sequences from an unspecified genome; no seqlengthsol <- findOverlaps(x88_r,cdsr)## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
## - in 'x': GL834431.1, GL834434.1, GL834449.1, GL834478.1, GL834499.1, GL834515.1, GL834518.1, GL834519.1, GL834529.1, GL834530.1, GL834532.1, GL834534.1, GL834536.1, GL834559.1, GL834599.1, GL834601.1, GL834616.1, GL834634.1, GL834663.1, GL834664.1, GL834675.1, GL834681.1, GL834700.1, GL834737.1, GL834738.1, GL834754.1, GL834755.1, GL834756.1, GL834758.1, GL834786.1, GL834791.1, GL834796.1, GL834827.1, GL834828.1, GL834832.1, GL834837.1, GL834846.1, GL834847.1, GL834851.1, GL834855.1, GL834866.1, GL834880.1, GL834908.1, GL834910.1, GL834911.1, GL834918.1, GL834924.1, GL834925.1, GL834930.1, GL834938.1, GL834941.1, GL834947.1, GL834972.1, GL834977.1, GL834994.1, GL834998.1, GL835005.1, GL835008.1, GL835032.1, GL835035.1, GL835036.1, GL835041.1, GL835042.1, GL835071.1, GL835073.1, GL835077.1, GL835081.1, GL835085.1, GL835086.1, GL835095.1, GL835106.1, GL835119.1, GL835126.1, GL835136.1, GL835137.1, GL835170.1, GL835177.1, GL835179.1, GL835201.1, GL835203.1, GL835208.1, GL835211.1, GL835216.1, GL835231.1, GL835242.1, GL835259.1, GL835262.1, GL835263.1, GL835271.1, GL835272.1, GL835279.1, GL835282.1, GL835283.1, GL835287.1, GL835289.1, GL835305.1, GL835306.1, GL835308.1, GL835344.1, GL835374.1, GL835388.1, GL835401.1, GL835403.1, GL835409.1, GL835419.1, GL835428.1, GL835435.1, GL835466.1, GL835476.1, GL835487.1, GL835495.1, GL835517.1, GL835524.1, GL835537.1, GL835544.1, GL835567.1, GL835578.1, GL835586.1, GL835603.1, GL835621.1, GL835630.1, GL835647.1, GL835669.1, GL835684.1, GL835706.1, GL835710.1, GL835718.1, GL835730.1, GL835752.1, GL835767.1, GL835783.1, GL835787.1, GL835808.1, GL835878.1, GL835913.1, GL835915.1, GL835961.1, GL836333.1, GL836800.1, GL836875.1, GL837065.1, GL837081.1, GL837084.1, GL837343.1, GL837493.1, GL838201.1, GL838268.1, GL839052.1, GL839418.1, GL839478.1, GL839531.1, GL839782.1, GL840005.1, GL840161.1, GL840390.1, GL840392.1, GL841144.1, GL841149.1, GL841152.1, GL841154.1, GL841168.1, GL841203.1, GL841215.1, GL841216.1, GL841217.1, GL841219.1, GL841224.1, GL841225.1, GL841226.1, GL841228.1, GL841229.1, GL841230.1, GL841233.1, GL841236.1, GL841238.1, GL841270.1, GL841276.1, GL841280.1, GL841281.1, GL841282.1, GL841292.1, GL841319.1, GL841320.1, GL841321.1, GL841322.1, GL841328.1, GL841335.1, GL841361.1, GL841383.1, GL841464.1, GL841477.1, GL841479.1, GL841508.1, GL841509.1, GL841531.1, GL841534.1, GL841542.1, GL841567.1, GL841574.1, GL841576.1, GL841604.1, GL841641.1, GL841652.1, GL841655.1, GL841666.1, GL841671.1, GL841674.1, GL841677.1, GL841678.1, GL841679.1, GL841688.1, GL841693.1, GL841695.1, GL841697.1, GL841700.1, GL841707.1, GL841711.1, GL841714.1, GL841715.1, GL841720.1, GL841721.1, GL841722.1, GL841728.1, GL841743.1, GL841745.1, GL841753.1, GL841757.1, GL841761.1, GL841763.1, GL841767.1, GL841773.1, GL841777.1, GL841778.1, GL841786.1, GL841789.1, GL841791.1, GL841800.1, GL841801.1, GL841813.1, GL841838.1, GL841842.1, GL841844.1, GL841858.1, GL841868.1, GL841869.1, GL841870.1, GL841872.1, GL841893.1, GL841899.1, GL841900.1, GL841901.1, GL841902.1, GL841903.1, GL841905.1, GL841913.1, GL841919.1, GL841937.1, GL841941.1, GL841948.1, GL841950.1, GL841955.1, GL841970.1, GL841973.1, GL841975.1, GL841977.1, GL841986.1, GL841990.1, GL841991.1, GL842001.1, GL842003.1, GL842010.1, GL842013.1, GL842024.1, GL842027.1, GL842029.1, GL842039.1, GL842051.1, GL842057.1, GL842069.1, GL842074.1, GL842076.1, GL842091.1, GL842096.1, GL842100.1, GL842104.1, GL842105.1, GL842107.1, GL842108.1, GL842112.1, GL842115.1, GL842118.1, GL842119.1, GL842123.1, GL842131.1, GL842132.1, GL842152.1, GL842157.1, GL842160.1, GL842178.1, GL842198.1, GL842212.1, GL842213.1, GL842216.1, GL842227.1, GL842232.1, GL842243.1, GL842271.1, GL842279.1, GL842305.1, GL842311.1, GL842315.1, GL842335.1, GL842344.1, GL842350.1, GL842351.1, GL842352.1, GL842364.1, GL842377.1, GL842390.1, GL842395.1, GL842404.1, GL842413.1, GL842419.1, GL842468.1, GL842482.1, GL842489.1, GL842501.1, GL842503.1, GL842507.1, GL842539.1, GL842549.1, GL842560.1, GL842584.1, GL842596.1, GL842597.1, GL842603.1, GL842604.1, GL842607.1, GL842608.1, GL842648.1, GL842660.1, GL842664.1, GL842678.1, GL842686.1, GL842692.1, GL842702.1, GL842730.1, GL842734.1, GL842736.1, GL842749.1, GL842775.1, GL842776.1, GL842779.1, GL842808.1, GL842818.1, GL842834.1, GL842846.1, GL842853.1, GL842858.1, GL842885.1, GL842889.1, GL842903.1, GL842930.1, GL842939.1, GL842944.1, GL843001.1, GL843004.1, GL843030.1, GL843090.1, GL843110.1, GL843111.1, GL843130.1, GL843209.1, GL843237.1, GL843441.1, GL844235.1, GL844391.1, GL844497.1, GL844930.1, GL845152.1, GL845848.1, GL846800.1, GL847029.1, GL847103.1, GL847114.1, GL847156.1, GL847467.1, GL847594.1, GL848031.1, GL848944.1, GL849027.1, GL849144.1, GL849542.1, GL849551.1, GL849552.1, GL849553.1, GL849554.1, GL849561.1, GL849576.1, GL849595.1, GL849599.1, GL849605.1, GL849613.1, GL849615.1, GL849617.1, GL849618.1, GL849637.1, GL849642.1, GL849643.1, GL849646.1, GL849649.1, GL849651.1, GL849669.1, GL849670.1, GL849671.1, GL849672.1, GL849674.1, GL849675.1, GL849682.1, GL849689.1, GL849700.1, GL849718.1, GL849745.1, GL849796.1, GL849800.1, GL849802.1, GL849859.1, GL849862.1, GL849866.1, GL849874.1, GL849875.1, GL849877.1, GL849880.1, GL849882.1, GL849883.1, GL849884.1, GL849886.1, GL849908.1, GL849938.1, GL849942.1, GL849952.1, GL849959.1, GL849969.1, GL849997.1, GL850008.1, GL850011.1, GL850038.1, GL850045.1, GL850054.1, GL850099.1, GL850104.1, GL850107.1, GL850111.1, GL850114.1, GL850115.1, GL850121.1, GL850126.1, GL850134.1, GL850140.1, GL850144.1, GL850147.1, GL850177.1, GL850184.1, GL850193.1, GL850203.1, GL850207.1, GL850216.1, GL850226.1, GL850227.1, GL850230.1, GL850236.1, GL850240.1, GL850242.1, GL850274.1, GL850276.1, GL850279.1, GL850292.1, GL850304.1, GL850314.1, GL850320.1, GL850330.1, GL850340.1, GL850344.1, GL850350.1, GL850360.1, GL850362.1, GL850380.1, GL850393.1, GL850394.1, GL850398.1, GL850399.1, GL850435.1, GL850446.1, GL850452.1, GL850477.1, GL850479.1, GL850485.1, GL850488.1, GL850495.1, GL850496.1, GL850524.1, GL850530.1, GL850555.1, GL850572.1, GL850606.1, GL850618.1, GL850619.1, GL850626.1, GL850633.1, GL850641.1, GL850664.1, GL850680.1, GL850698.1, GL850700.1, GL850715.1, GL850720.1, GL850722.1, GL850726.1, GL850734.1, GL850755.1, GL850758.1, GL850770.1, GL850774.1, GL850807.1, GL850811.1, GL850819.1, GL850830.1, GL850851.1, GL850862.1, GL850871.1, GL850877.1, GL850889.1, GL850893.1, GL850925.1, GL850955.1, GL850960.1, GL850967.1, GL850979.1, GL851012.1, GL851032.1, GL851057.1, GL851162.1, GL851178.1, GL851189.1, GL851199.1, GL851230.1, GL851244.1, GL851245.1, GL851251.1, GL851264.1, GL851315.1, GL851692.1, GL852120.1, GL852631.1, GL852749.1, GL852794.1, GL852826.1, GL853644.1, GL853781.1, GL854030.1, GL854076.1, GL854353.1, GL854697.1, GL855472.1, GL855609.1, GL855816.1, GL855896.1, GL856222.1, GL856346.1, GL856356.1, GL856759.1, GL856760.1, GL856762.1, GL856765.1, GL856766.1, GL856768.1, GL856769.1, GL856834.1, GL856835.1, GL856838.1, GL856891.1, GL856903.1, GL856904.1, GL856927.1, GL856930.1, GL856931.1, GL856943.1, GL856957.1, GL856983.1, GL856985.1, GL856986.1, GL856987.1, GL857026.1, GL857036.1, GL857040.1, GL857051.1, GL857052.1, GL857073.1, GL857086.1, GL857103.1, GL857112.1, GL857114.1, GL857122.1, GL857123.1, GL857124.1, GL857130.1, GL857140.1, GL857160.1, GL857166.1, GL857171.1, GL857175.1, GL857195.1, GL857209.1, GL857216.1, GL857231.1, GL857233.1, GL857235.1, GL857248.1, GL857269.1, GL857280.1, GL857288.1, GL857290.1, GL857295.1, GL857318.1, GL857322.1, GL857345.1, GL857357.1, GL857363.1, GL857364.1, GL857377.1, GL857402.1, GL857403.1, GL857414.1, GL857419.1, GL857421.1, GL857440.1, GL857448.1, GL857454.1, GL857467.1, GL857470.1, GL857481.1, GL857487.1, GL857497.1, GL857515.1, GL857517.1, GL857523.1, GL857524.1, GL857537.1, GL857580.1, GL857592.1, GL857599.1, GL857602.1, GL857615.1, GL857623.1, GL857680.1, GL857694.1, GL857696.1, GL857704.1, GL857728.1, GL857735.1, GL857744.1, GL857756.1, GL857775.1, GL857800.1, GL857805.1, GL857828.1, GL857953.1, GL858154.1, GL858667.1, GL859091.1, GL859142.1, GL859307.1, GL859566.1, GL860155.1, GL860925.1, GL861043.x88_r[queryHits(ol)]## GRanges object with 50 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] GL834436.1 3231122-3231123 *
## [2] GL834447.1 146798-146799 *
## [3] GL834592.1 128581-128582 *
## [4] GL834651.1 321559-321560 *
## [5] GL834658.1 599931-599932 *
## ... ... ... ...
## [46] GL865099.1 155927-155928 *
## [47] GL867574.1 180868-180869 *
## [48] GL867603.1 899665-899666 *
## [49] GL867628.1 176037-176038 *
## [50] GL867787.1 1746-1747 *
## -------
## seqinfo: 2255 sequences from an unspecified genome; no seqlengthscds_vars <- as.data.frame(cdsr[subjectHits(ol)])
cds_vars %>%
kbl(caption="x88 vars in protein coding genes") %>%
kable_paper("hover", full_width = F)| seqnames | start | end | width | strand | gene_name |
|---|---|---|---|---|---|
| GL834436.1 | 3230924 | 3231135 | 212 |
|
ENSSHAG00000017948 |
| GL834447.1 | 146291 | 147111 | 821 |
|
ENSSHAG00000005062 |
| GL834592.1 | 128564 | 128651 | 88 |
|
ENSSHAG00000003861 |
| GL834651.1 | 321536 | 321682 | 147 |
|
ENSSHAG00000007420 |
| GL834658.1 | 599327 | 600064 | 738 |
|
ENSSHAG00000009840 |
| GL834710.1 | 2881110 | 2881308 | 199 |
|
ENSSHAG00000017639 |
| GL835252.1 | 102723 | 103018 | 296 |
|
ENSSHAG00000004398 |
| GL841244.1 | 176971 | 177163 | 193 |
|
ENSSHAG00000005246 |
| GL841260.1 | 1800897 | 1802400 | 1504 |
|
ENSSHAG00000013921 |
| GL841325.1 | 205627 | 205880 | 254 |
|
ENSSHAG00000005425 |
| GL841340.1 | 89790 | 89861 | 72 |
|
ENSSHAG00000003834 |
| GL841442.1 | 623897 | 624021 | 125 |
|
ENSSHAG00000009381 |
| GL841548.1 | 191897 | 192798 | 902 |
|
ENSSHAG00000005938 |
| GL841556.1 | 374273 | 374576 | 304 |
|
ENSSHAG00000005525 |
| GL841565.1 | 688585 | 688800 | 216 |
|
ENSSHAG00000010397 |
| GL849532.1 | 855008 | 855991 | 984 |
|
ENSSHAG00000011474 |
| GL849632.1 | 3110392 | 3110584 | 193 |
|
ENSSHAG00000017921 |
| GL849666.1 | 81513 | 81787 | 275 |
|
ENSSHAG00000003707 |
| GL849739.1 | 1251257 | 1252816 | 1560 |
|
ENSSHAG00000013516 |
| GL849739.1 | 1622711 | 1623676 | 966 |
|
ENSSHAG00000014901 |
| GL849767.1 | 110423 | 110706 | 284 |
|
ENSSHAG00000003933 |
| GL849767.1 | 157218 | 158168 | 951 |
|
ENSSHAG00000005419 |
| GL849767.1 | 241259 | 242188 | 930 |
|
ENSSHAG00000006573 |
| GL849812.1 | 2017203 | 2017295 | 93 |
|
ENSSHAG00000016061 |
| GL849867.1 | 283244 | 284017 | 774 |
|
ENSSHAG00000007080 |
| GL849904.1 | 2927757 | 2927868 | 112 |
|
ENSSHAG00000017604 |
| GL850418.1 | 116798 | 117655 | 858 |
|
ENSSHAG00000004695 |
| GL850503.1 | 40321 | 40836 | 516 |
|
ENSSHAG00000002737 |
| GL856719.1 | 1404119 | 1404242 | 124 |
|
ENSSHAG00000014128 |
| GL856802.1 | 488812 | 489133 | 322 |
|
ENSSHAG00000008973 |
| GL856821.1 | 1533257 | 1533424 | 168 |
|
ENSSHAG00000014561 |
| GL856823.1 | 1120979 | 1121125 | 147 |
|
ENSSHAG00000012836 |
| GL856827.1 | 1771717 | 1771938 | 222 |
|
ENSSHAG00000015345 |
| GL856948.1 | 3601270 | 3601373 | 104 |
|
ENSSHAG00000018440 |
| GL856961.1 | 231866 | 232046 | 181 |
|
ENSSHAG00000002179 |
| GL856995.1 | 3322665 | 3323738 | 1074 |
|
ENSSHAG00000018182 |
| GL857007.1 | 350522 | 351686 | 1165 |
|
ENSSHAG00000007648 |
| GL857012.1 | 1388544 | 1388720 | 177 |
|
ENSSHAG00000013882 |
| GL857020.1 | 899522 | 900780 | 1259 |
|
ENSSHAG00000011731 |
| GL857297.1 | 136533 | 136734 | 202 |
|
ENSSHAG00000005049 |
| GL861544.1 | 312210 | 312292 | 83 |
|
ENSSHAG00000007070 |
| GL861691.1 | 824962 | 825450 | 489 |
|
ENSSHAG00000011266 |
| GL861723.1 | 796121 | 796171 | 51 |
|
ENSSHAG00000011079 |
| GL862090.1 | 81064 | 82227 | 1164 |
|
ENSSHAG00000003936 |
| GL864774.1 | 1676513 | 1677382 | 870 |
|
ENSSHAG00000015070 |
| GL865099.1 | 155640 | 156072 | 433 |
|
ENSSHAG00000005327 |
| GL867574.1 | 180857 | 180968 | 112 |
|
ENSSHAG00000005778 |
| GL867603.1 | 899431 | 900660 | 1230 |
|
ENSSHAG00000011738 |
| GL867628.1 | 176033 | 176076 | 44 |
|
ENSSHAG00000005392 |
| GL867787.1 | 657 | 2000 | 1344 |
|
ENSSHAG00000000367 |
length(unique(cds_vars$gene_name))## [1] 50This result shows that from the 6222 variants that can distinguish x88, only 50 are in protein-coding genes (0.08%).
Each of these genes contained only 1 variant, so there are only 50 genes which can be distinguished.
This is consistent with only 1.1% of the genome being in expressed exons. And that exons are anticipated to accumulate variants at a rate slower than non-exonic regions due to their essential function.
From these 50 genes, not all of them will be expressed in the cells. We can look at the list of genes which are expressed in RNA-seq to give a realistic propostion of how many can be distinguished by RNA-seq.
rnaseq <- readxl::read_xlsx("../oncotarget-09-15895-s002.xlsx",col_names=TRUE,skip=1)
rnaseq <- as.data.frame(rnaseq)
rnaseq_expressed <- rnaseq[which(rowMeans(rnaseq[,grep("counts",colnames(rnaseq))])>10),]
dim(rnaseq_expressed)## [1] 12842 12head(rnaseq_expressed$gene_id )## [1] "ENSSHAG00000019817" "ENSSHAG00000019651" "ENSSHAG00000020414"
## [4] "ENSSHAG00000020043" "ENSSHAG00000020375" "ENSSHAG00000019115"cds_vars$gene_name %in% rnaseq_expressed$gene_id## [1] TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [13] FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [25] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [37] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE
## [49] TRUE FALSEcds_vars$gene_name[which(cds_vars$gene_name %in% rnaseq_expressed$gene_id)]## [1] "ENSSHAG00000017948" "ENSSHAG00000005062" "ENSSHAG00000003861"
## [4] "ENSSHAG00000009840" "ENSSHAG00000017639" "ENSSHAG00000013921"
## [7] "ENSSHAG00000005425" "ENSSHAG00000003834" "ENSSHAG00000009381"
## [10] "ENSSHAG00000005525" "ENSSHAG00000010397" "ENSSHAG00000017921"
## [13] "ENSSHAG00000003707" "ENSSHAG00000016061" "ENSSHAG00000002737"
## [16] "ENSSHAG00000014128" "ENSSHAG00000014561" "ENSSHAG00000012836"
## [19] "ENSSHAG00000015345" "ENSSHAG00000018440" "ENSSHAG00000002179"
## [22] "ENSSHAG00000007648" "ENSSHAG00000013882" "ENSSHAG00000011731"
## [25] "ENSSHAG00000007070" "ENSSHAG00000011079" "ENSSHAG00000003936"
## [28] "ENSSHAG00000005778" "ENSSHAG00000005392"length(cds_vars$gene_name[which(cds_vars$gene_name %in% rnaseq_expressed$gene_id)])## [1] 29In conclusion, there are only 29 genes where we will be able to use SNP variants to distinguish cells in co-culture.
This work supports the idea that cells need to be sorted prior to transcriptome analysis.
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] readxl_1.3.1 GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
## [4] IRanges_2.26.0 S4Vectors_0.30.2 BiocGenerics_0.38.0
## [7] kableExtra_1.3.4 eulerr_6.1.1
##
## loaded via a namespace (and not attached):
## [1] xfun_0.29 bslib_0.3.1 colorspace_2.0-2
## [4] vctrs_0.3.8 htmltools_0.5.2 viridisLite_0.4.0
## [7] yaml_2.2.1 utf8_1.2.2 rlang_0.4.12
## [10] jquerylib_0.1.4 pillar_1.6.4 glue_1.6.0
## [13] GenomeInfoDbData_1.2.6 lifecycle_1.0.1 stringr_1.4.0
## [16] zlibbioc_1.38.0 munsell_0.5.0 cellranger_1.1.0
## [19] rvest_1.0.2 evaluate_0.14 knitr_1.37
## [22] fastmap_1.1.0 fansi_1.0.0 highr_0.9
## [25] Rcpp_1.0.7 scales_1.1.1 webshot_0.5.2
## [28] jsonlite_1.7.2 XVector_0.32.0 systemfonts_1.0.3
## [31] digest_0.6.29 stringi_1.7.6 grid_4.1.2
## [34] cli_3.1.0 tools_4.1.2 bitops_1.0-7
## [37] magrittr_2.0.1 sass_0.4.0 RCurl_1.98-1.5
## [40] tibble_3.1.6 crayon_1.4.2 pkgconfig_2.0.3
## [43] ellipsis_0.3.2 xml2_1.3.3 rmarkdown_2.11
## [46] svglite_2.0.0 httr_1.4.2 rstudioapi_0.13
## [49] R6_2.5.1 compiler_4.1.2