View source: R/seqpac_S4classes.R
reanno-class | R Documentation |
Holds the imported information from mapping using the map_reanno function. All information are held in tibble class (tbl/tbl_df) tables from the tibble package.
reanno(Overview, Full_anno)
Overview |
A tibble data.frame with summarized results from mapping
using the |
Full_anno |
A multi-level list with tibble data.frames that contains all
that was imported by |
Contains two slots: 1. Overview: A table holding a summary of the mapping. 2. Full_anno: Lists of tables holding the full imports per mismatch cycle (mis0, mis1 etc) and reference (mi0-ref1, mis0-ref2, mis1-ref1, mis1-ref2 etc).
##### Create an reanno object
## First load a PAC- object
load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata",
package = "seqpac", mustWork = TRUE))
## Then specify paths to fasta references
# If you are having problem see the vignette small RNA guide for more info.
trna_file <- system.file("extdata/trna", "tRNA.fa",
package = "seqpac", mustWork = TRUE)
trna_dir <- dirname(trna_file)
if(!sum(stringr::str_count(list.files(trna_dir), ".ebwt")) ==6){
Rbowtie::bowtie_build(trna_file,
outdir=trna_dir,
prefix="tRNA", force=TRUE)
}
ref_paths <- list(trna= trna_file)
## Add output path of your choice.
# Here we use the R temporary folder depending on platform
output <- paste0(tempdir(),"/seqpac/test")
## Make sure it is empty (otherwise you will be prompted for a question)
out_fls <- list.files(output, recursive=TRUE)
closeAllConnections()
suppressWarnings(file.remove(paste(output, out_fls, sep="/")))
## Then map your PAC-object against the fasta references
map_reanno(pac, ref_paths=ref_paths, output_path=output,
type="internal", mismatches=0, import="biotype",
threads=2, keep_temp=FALSE, override=TRUE)
## Then import and generate a reanno-object of the temporary bowtie-files
reanno <- make_reanno(output, PAC=pac, mis_fasta_check = TRUE)
## Now make some search terms against reference names to create shorter names
# Theses can be used to create factors in downstream analysis
# One search hit (regular expressions) gives one new short name
bio_search <- list(trna =c("_tRNA", "mt:tRNA"))
## You can merge directly with your PAC-object by adding your original
# PAC-object, that you used with map_reanno, to merge_pac option.
pac <- add_reanno(reanno, bio_search=bio_search,
type="biotype", bio_perfect=FALSE,
mismatches = 0, merge_pac=pac)
## Turn your S3 list to an S4 reanno-object
isS4(reanno)
names(reanno)
table(overview(reanno)$trna)
reanno_s3 <- as(reanno, "list")
isS4(reanno_s3)
# Turns S3 reanno object into a S4
reanno_s4 <- as.reanno(reanno_s3)
isS4(reanno_s4)
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