The goal of Infernape is to identify and quantify APA events from scRNA-seq data.
You can install the development version of Infernape from GitHub with:
# install.packages("devtools")
devtools::install_github("kangbw702/Infernape")
This is a basic example which shows you how to solve a common problem. The data used for this example can be downloaded from https://www.dropbox.com/sh/pp9hoe128lfci7u/AABCtyOjxB8Ejb_ObcBw7k9ya?dl=0.
Function Infernape_cnt
outputs raw peaks, peak annotation (before and
after filtering), and peak by cell UMI count matrix.
library(Infernape)
genome = BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10
Infernape_cnt(genome.ref = '../data/ref.csv',
bam = '../data/TEGLU16.bam',
batch.start = 2901,
batch.end = 2920,
ncores = 1,
d = 31,
h = 5,
d.cut = 50,
hr = 160,
min.mode.prop = 0.05,
min.mode.cutoff = 5,
output.path = '../result',
pas.reference.file = '../data/PAS_withinfo.csv',
genome = genome,
pas.search.cut.1 = 0,
pas.search.cut.2 = 300,
polystretch_length = 13,
max_mismatch = 1,
motif.search.cut = 300,
invert_strand = FALSE,
q = c(110, 200),
whitelist.file = "../data/whitelist.TEGLU16.csv",
start.cid = NULL,
end.cid = NULL
)
Function Infernape_apa
performs hierarchical differential APA testing.
Infernape_apa(counts.dir = '../result/cnt_mat',
attr.file = '../data/attr.tbl.example.csv',
anno.file = '../result/anno_filtered.csv',
utr3.file = '../data/ref.utr3.anno.csv',
ctype.colname = 'ctype',
base_grp = 'c1',
alt_grp = 'c2',
cut.low.pct = 0.05,
cut.pval = 0.05,
cut.MPRO = 0.2,
test.type = 'gene',
out.dir = '../result'
)