Last updated: 2019-02-14
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# SNPfile: "/project2/xinhe/simingz/CROP-seq/scRNA_seq_SNP_list.txt"
module load mysql
mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A -D hg19 -e '
select
K.name2,
K.name,
S.name,
S.avHet,
S.chrom,
S.chromStart,
K.txStart,
K.txEnd
from snp150 as S
left join refGene as K on
(S.chrom=K.chrom and not(K.txEnd+50000<S.chromStart or S.chromEnd+50000<K.txStart))
where
S.name in ("rs7148456","rs12895055","rs7170068","rs520843","rs12716973","rs2192932","rs17200916","rs1198588","rs324017","rs4151680","rs301791","rs324015","rs9882911","rs11633075","rs2027349","rs186132169","rs9661794","rs7936858","rs3861678","rs10933","rs6071578")' > /project2/xinhe/simingz/CROP-seq/cropseq/data/SNP_50000.txt
pcut <- 0.05
fisher_cisg <- function(resm, cisg, pcut){
res <- resm$table
res.sig <- res[res$PValue < pcut, ]
cisgres <- resm$table[cisg, ]
cisgres <- cisgres[complete.cases(cisgres), ]
cisgres.sig <- cisgres[cisgres$PValue < pcut, ]
ct <- matrix(c(dim(cisgres.sig)[1], dim(cisgres)[1] - dim(cisgres.sig)[1], dim(res.sig)[1], dim(res)[1]-dim(res.sig)[1]), nrow = 2)
print(ct)
fisher.test(ct)
}
cisgene <- read.table("data/SNP_50000.txt", stringsAsFactors = F,sep="\t", header=T)
cisg <- unique(cisgene$name2)
load("data/edgeR-qlf-10%filter_res.Rd")
fisher_cisg(resm, cisg, pcut)
Loading required package: edgeR
Loading required package: limma
[,1] [,2]
[1,] 4 612
[2,] 19 9009
Fisher's Exact Test for Count Data
data: ct
p-value = 0.0554
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7642145 9.3655805
sample estimates:
odds ratio
3.098467
load("data/edgeR-lrt-10%filter_res.Rd")
fisher_cisg(resm, cisg, pcut)
[,1] [,2]
[1,] 4 559
[2,] 19 9062
Fisher's Exact Test for Count Data
data: ct
p-value = 0.04201
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8413445 10.3169944
sample estimates:
odds ratio
3.412093
cisgene <- read.table("data/SNP_200000.txt", stringsAsFactors = F,sep="\t", header=T)
cisg <- unique(cisgene$name2)
load("data/edgeR-qlf-10%filter_res.Rd")
fisher_cisg(resm, cisg, pcut)
[,1] [,2]
[1,] 8 612
[2,] 69 9009
Fisher's Exact Test for Count Data
data: ct
p-value = 0.1557
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7054235 3.5761036
sample estimates:
odds ratio
1.70661
load("data/edgeR-lrt-10%filter_res.Rd")
fisher_cisg(resm, cisg, pcut)
[,1] [,2]
[1,] 8 559
[2,] 69 9062
Fisher's Exact Test for Count Data
data: ct
p-value = 0.08919
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7765322 3.9401042
sample estimates:
odds ratio
1.879465
cisgene <- read.table("data/SNP_500000.txt", stringsAsFactors = F,sep="\t", header=T)
cisg <- unique(cisgene$name2)
load("data/edgeR-qlf-10%filter_res.Rd")
fisher_cisg(resm, cisg, pcut)
[,1] [,2]
[1,] 13 612
[2,] 130 9009
Fisher's Exact Test for Count Data
data: ct
p-value = 0.1706
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7587635 2.6261486
sample estimates:
odds ratio
1.471976
load("data/edgeR-lrt-10%filter_res.Rd")
fisher_cisg(resm, cisg, pcut)
[,1] [,2]
[1,] 13 559
[2,] 130 9062
Fisher's Exact Test for Count Data
data: ct
p-value = 0.1044
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8351417 2.8937582
sample estimates:
odds ratio
1.620998
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] edgeR_3.24.3 limma_3.38.2
loaded via a namespace (and not attached):
[1] locfit_1.5-9.1 workflowr_1.1.1 Rcpp_1.0.0
[4] lattice_0.20-38 digest_0.6.18 rprojroot_1.3-2
[7] R.methodsS3_1.7.1 grid_3.5.1 backports_1.1.2
[10] git2r_0.23.0 magrittr_1.5 evaluate_0.12
[13] stringi_1.3.1 whisker_0.3-2 R.oo_1.22.0
[16] R.utils_2.7.0 rmarkdown_1.10 tools_3.5.1
[19] stringr_1.4.0 yaml_2.2.0 compiler_3.5.1
[22] htmltools_0.3.6 knitr_1.20
This reproducible R Markdown analysis was created with workflowr 1.1.1