with devtools:
> install.packages("devtools")
> require(devtools)
> install_github('dieterich-lab/CircTest')
> library(CircTest)CircTest is recommended to work with the output of DCC: https://github.com/dieterich-lab/DCC, but can also run on your own tables. All you need is one table with circular counts and one with host-gene counts. These tables should have the same order, i.e. circ[i,j] and linear[i,j] are read-counts for the same circle in the same sample.
Your table may have many columns describing the circle or just one column containing the circle ID followed by many columns of read counts.
Example tables:
Count table for back-spliced reads:
CircID Control_1 Control_2 Control_3 Treatment_1 Treatment_2 Treatment_3
chr1:100|800 0 2 1 5 4 0
chr1:1050|10080 20 22 21 10 13 0
chr2: 600|1000 0 1 0 10 0 1
chr10:4100|5400 55 54 52 56 53 50
chr11:600|1500 3 0 1 2 2 3Count table for host-gene reads:
CircID Control_1 Control_2 Control_3 Treatment_1 Treatment_2 Treatment_3
chr1:100|800 10 11 12 9 10 10
chr1:1050|10080 80 81 83 45 48 46
chr2: 600|1000 5 5 2 12 8 7
chr10:4100|5400 101 110 106 150 160 153
chr11:600|1500 20 21 18 19 20 20
- Read in tables
Circ <- read.delim('Circ.csv', header = T, as.is = T)
Linear <- read.delim('Linear.csv', header = T, as.is = T)- Filter tables To model expression data using the beta binomial distribution and testing for differences in groups, it is benefical to only test well supported circles. You can use the package's function Circ.filter to filter your tables.
Nreplicates specifies the number of replicates in each condition.
filter.sample specifies the number of samples the circle has to have enough circular reads in to be considered.
filter.count specifies the circular read count threshold.
percentage specifies the minimum circle to host-gene ratio.
circle_description tells the function which columns are NOT filled with read counts but the circle's annotation.
# filter circles by read counts
Circ_filtered <- Circ.filter(circ = Circ, linear = Linear, Nreplicates = 3, filter.sample = 3, filter.count = 5, percentage = 0.1, circle_description = 1)
# CircID Control_1 Control_2 Control_3 Treatment_1 Treatment_2 Treatment_3
# 2 chr1:1050|10080 20 22 21 10 13 0
# 4 chr10:4100|5400 55 54 52 56 53 50
# filter linear table by remaining circles
Linear_filtered <- Linear[rownames(Circ_filtered),]
# CircID Control_1 Control_2 Control_3 Treatment_1 Treatment_2 Treatment_3
# 2 chr1:1050|10080 80 81 83 45 48 46
# 4 chr10:4100|5400 101 110 106 150 160 153
- Test for changes
Circ.test uses the beta binomial distribution to model the data and performs an ANOVA to identify circles which differ in their relative expression between the groups.
It is important that the grouping is correct (group) and the non-read-count columuns are specified (circle_description).
test <- Circ.test(Circ_filtered, Linear_filtered, group=c(rep(1,3),rep(2,3)), circle_description = 1)
# $summary_table
# CircID sig_p
# 4 chr10:4100|5400 0.01747407
#
# $sig.dat
# CircID Control_1 Control_2 Control_3 Treatment_1 Treatment_2 Treatment_3
# 4 chr10:4100|5400 55 54 52 56 53 50
#
# $p.val
# [1] 0.153464107 0.008737037
#
# $p.adj
# [1] 0.15346411 0.01747407
#
# $sig_p
# [1] 0.01747407
# - Visualize data
The CircTest library has build in plot functions to view your significantly different genes.
To visualize the ratio as barplot try:
for (i in rownames(test$summary_table)) {
Circ.ratioplot(Circ_filtered, Linear_filtered, plotrow=i, groupindicator1=c(rep('Control',3),rep('Treatment',3)),
lab_legend='Condition', circle_description = 1 )
}To visualize the abundance of host-gene and circle separately in a line plot try:
for (i in rownames(test$summary_table)) {
Circ.lineplot(Circ_filtered, Linear_filtered, plotrow=i, groupindicator1=c(rep('Control',3),rep('Treatment',3)),
circle_description = 1 )
}More examples on how to deal with different data can be found in the example_data folder.
- Read and load DCC output into R
library(CircTest)
CircRNACount <- read.delim('CircRNACount',header=T)
LinearCount <- read.delim('LinearCount',header=T)
CircCoordinates <- read.delim('CircCoordinates',header=T)
CircRNACount_filtered <- Circ.filter(circ = CircRNACount, linear = LinearCount, Nreplicates = 6, filter.sample = 6, filter.count = 5, percentage = 0.1)
CircCoordinates_filtered <- CircCoordinates[rownames(CircRNACount_filtered),]
LinearCount_filtered <- LinearCount[rownames(CircRNACount_filtered),]Alternatively, load the processed Westholm et al. data from CircTest package.
library(CircTest)
data(Circ)
CircRNACount_filtered <- Circ
data(Coordinates)
CircCoordinates_filtered <- Coordinates
data(Linear)
LinearCount_filtered <- Linear- Test for host-independently regulated circRNAs
test=Circ.test(CircRNACount_filtered,LinearCount_filtered,CircCoordinates_filtered,group=c(rep(1,6),rep(2,6),rep(3,6)))
# Significant result show in a summary table
View(test$summary_table)- Visuallize the significantly host-independently regulated circRNAs
for (i in rownames(test$summary_table)) {
Circ.ratioplot( CircRNACount_filtered, LinearCount_filtered, CircCoordinates_filtered, plotrow=i,
groupindicator1=c(rep('1days',6),rep('4days',6),rep('20days',6)),
lab_legend='Ages', gene_column = 4 )
}If you have circle files generated by CIRI, you may but all sample files into one folder and run the python script:
python merge_ciri.py /path/to/all/ciri/filesThis will procude three files. Please use the junction_reads.txt as Circ, the non_junction_reads.txt as Linear, and change circle_description = c(1:6)