atacr
Helps with the analysis of count data from RNA-capture-seq and ATAC-capture-seq experiments. Using BioConductor RangedSummarizedExperiment objects, atacr implements a set of helper functions and quality control plots specific to the analysis of counts of reads in windows across genomes. Especially, atacr is useful for performing sample normalizations and for easily running bootstrap and Bayes factor tests for differentially accessible windows in common reference designs.
Installation
You can install atacr from github with:
# install.packages("devtools")
devtools::install_github("TeamMacLean/atacr")Documentation
You can read documentation on the following topics
- Tutorial - A worked example
- atacR - General Overview
- Loading Data
- Summaries of Data
- Normalising Data
- Differential Windows
- Subsetting Data
Quick start:
library(atacr)
summary(sim_counts)
#> ATAC-seq experiment of 2 treatments in 6 samples
#> Treatments: control,treatment
#> Samples: control_001,control_002,control_003,treatment_001,treatment_002,treatment_003
#> Bait regions used: 500
#> Total Windows: 1000
#>
#> On/Off target read counts:
#> sample off_target on_target percent_on_target
#> 1 control_001 312 15160 97.98345
#> 2 control_002 347 14777 97.70563
#> 3 control_003 339 15115 97.80639
#> 4 treatment_001 321 16955 98.14193
#> 5 treatment_002 346 16490 97.94488
#> 6 treatment_003 335 17064 98.07460
#> Quantiles:
#> $bait_windows
#> control_001 control_002 control_003 treatment_001 treatment_002
#> 1% 19.99 16.99 19 16.99 16.00
#> 5% 22.00 20.00 22 20.00 19.00
#> 95% 40.00 40.00 39 63.00 65.05
#> 99% 45.00 46.00 44 109.00 89.03
#> treatment_003
#> 1% 16.00
#> 5% 21.00
#> 95% 61.00
#> 99% 109.06
#>
#> $non_bait_windows
#> control_001 control_002 control_003 treatment_001 treatment_002
#> 1% 0 0 0.00 0 0.00
#> 5% 0 0 0.00 0 0.00
#> 95% 3 4 3.05 3 3.05
#> 99% 4 4 4.00 4 4.00
#> treatment_003
#> 1% 0
#> 5% 0
#> 95% 3
#> 99% 4
#>
#> Read depths:
#> sample off_target on_target
#> 1 control_001 0.624 30.320
#> 2 control_002 0.694 29.554
#> 3 control_003 0.678 30.230
#> 4 treatment_001 0.642 33.910
#> 5 treatment_002 0.692 32.980
#> 6 treatment_003 0.670 34.128plot(sim_counts)
#> Picking joint bandwidth of 0.0243
#> Picking joint bandwidth of 0.0582