In todays session we will work with some of the ATAC-seq data of T-regulatory cells from Christina Leslie’s lab.
Aligned data as a BAM file can be found here.
The peak calls for ATAC-seq data can be found here
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
load(file="data/chipqc_Treg.RData")
<- QCmetrics(qcRes)
myMetrics c("RiP%")] myMetrics[
## RiP%
## 43.9
<- flagtagcounts(qcRes)
flgCounts <- flgCounts["DuplicateByChIPQC"]/flgCounts["Mapped"]
DupRate *100 DupRate
## DuplicateByChIPQC
## 6.743219
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
Counts are in *data/myCounts.RData**
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.
## final dispersion estimates
## fitting model and testing
## Warning in submitGreatJob(LiverMinusHindbrain, species = "mm10", request_interval = 1, : GREAT gives a warning:
## Your set hits a large fraction of the genes in the genome, which often
## does not work well with the GREAT Significant by Both view due to a
## saturation of the gene-based hypergeometric test.
##
## See our tips for handling large datasets or try the Significant By
## Region-based Binomial view.
## [1] "GO" "Phenotype Data and Human Disease"
## [3] "Pathway Data" "Gene Expression"
## [5] "Regulatory Motifs" "Gene Families"
## The default enrichment tables contain no associated genes for the input
## regions. You can set `download_by = 'tsv'` to download the complete
## table, but note only the top 500 regions can be retreived. See the
## following link:
##
## https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655401/Export#Export-GlobalExport
## [1] "PANTHER Pathway" "BioCyc Pathway" "MSigDB Pathway"
## ID
## 1 PWY-5920
## 2 PWY-5189
## 3 PWY3DJ-11470
## 4 PWY-6358
## name
## 1 heme biosynthesis II
## 2 tetrapyrrole biosynthesis II
## 3 sphingosine and sphingosine-1-phosphate metabolism
## 4 superpathway of D-<i>myo</i>-inositol (1,4,5)-trisphosphate metabolism
## Binom_Genome_Fraction Binom_Expected Binom_Observed_Region_Hits
## 1 2.378908e-04 2.3282380 26
## 2 9.254901e-05 0.9057772 14
## 3 5.385445e-04 5.2707350 25
## 4 4.999543e-04 4.8930530 23
## Binom_Fold_Enrichment Binom_Region_Set_Coverage Binom_Raw_PValue
## 1 11.167240 0.002656585 8.974517e-19
## 2 15.456340 0.001430469 1.224402e-12
## 3 4.743171 0.002554409 4.530668e-10
## 4 4.700542 0.002350056 2.592819e-09
## Binom_Adjp_BH Hyper_Total_Genes Hyper_Expected Hyper_Observed_Gene_Hits
## 1 2.898769e-16 9 3.137796 8
## 2 1.977409e-10 5 1.743220 4
## 3 4.878019e-08 9 3.137796 5
## 4 2.093701e-07 11 3.835084 8
## Hyper_Fold_Enrichment Hyper_Gene_Set_Coverage Hyper_Term_Gene_Coverage
## 1 2.549561 0.0010476690 0.8888889
## 2 2.294605 0.0005238345 0.8000000
## 3 1.593475 0.0006547931 0.5555556
## 4 2.086004 0.0010476690 0.7272727
## Hyper_Raw_PValue Hyper_Adjp_BH
## 1 0.001353018 0.2185124
## 2 0.053250040 0.6307870
## 3 0.169392700 0.8255783
## 4 0.011911010 0.2993989