This page contains a summary of the different methods used to
access the information contained inside the UMI4C-class object. See
the details section for more information on the different accessors.
dgram(object) dgram(object) <- value groupsUMI4C(object, value) groupsUMI4C(object) <- value bait(object) trend(object) resultsUMI4C(object, format = "GRanges", counts = TRUE, ordered = FALSE) # S4 method for UMI4C dgram(object) # S4 method for UMI4C dgram(object) <- value # S4 method for UMI4C groupsUMI4C(object) # S4 method for UMI4C groupsUMI4C(object) <- value # S4 method for UMI4C bait(object) # S4 method for UMI4C trend(object) # S4 method for UMI4C resultsUMI4C(object, format = "GRanges", counts = FALSE, ordered = FALSE)
| object | a |
|---|---|
| value | Alternative list of dgrams to replace the current slot. |
| format | Either "GRanges" (default) or "data.frame", indicating the format output of the results. |
| counts | Logical indicating whether counts for the different region should be provided. Default: FALSE. |
| ordered | Logical indicating whether to sort output by significance (adjusted p-value). Default: FALSE. |
There are several accessors to easily retrive information from a
UMI4C-class object:
dgram: Returns a named list with the output domainograms for
each sample.
bait: Returns a GRanges object with the position
of the bait.
trend: Returns a data.frame in long format with the values of
the adapative smoothen trend.
resultsUMI4C: Returns a GRanges or data.frame with
the results of the differential analysis.
UMI4C, UMI4C-class
# Access the different information inside the UMI4C object data("ex_ciita_umi4c") ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition") dgram(ex_ciita_umi4c)#> List of length 4 #> names(4): ctrl_hi24_CIITA ctrl_hi32_CIITA cyt_hi24_CIITA cyt_hi32_CIITAbait(ex_ciita_umi4c)#> GRanges object with 1 range and 1 metadata column: #> seqnames ranges strand | name #> <Rle> <IRanges> <Rle> | <character> #> ctrl_hi24_CIITA chr16 10970996-10972544 * | CIITA #> ------- #> seqinfo: 1 sequence from an unspecified genome; no seqlengths#> geo_coord trend sd scale id_contact sample sampleID #> 1 10834435 0.1477273 0.08194435 44 frag_2481 ctrl_hi24_CIITA <NA> #> 2 10834681 0.1511628 0.08385003 43 frag_2482 ctrl_hi24_CIITA <NA> #> 3 10834926 0.1547619 0.08584646 42 frag_2483 ctrl_hi24_CIITA <NA> #> 4 10835168 0.1585366 0.08794028 41 frag_2484 ctrl_hi24_CIITA <NA> #> 5 10835417 0.1625000 0.09013878 40 frag_2485 ctrl_hi24_CIITA <NA> #> 6 10835672 0.1666667 0.09245003 39 frag_2486 ctrl_hi24_CIITA <NA> #> replicate viewpoint file #> 1 <NA> <NA> <NA> #> 2 <NA> <NA> <NA> #> 3 <NA> <NA> <NA> #> 4 <NA> <NA> <NA> #> 5 <NA> <NA> <NA> #> 6 <NA> <NA> <NA># Perform differential test enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700")) umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, filter_low = 20, resize = 5e3) resultsUMI4C(umi_dif)#> GRanges object with 2 ranges and 6 metadata columns: #> seqnames ranges strand | id pvalue odds_ratio #> <Rle> <IRanges> <Rle> | <character> <numeric> <numeric> #> [1] chr16 10924453-10929452 * | region_1 1.28944e-05 2.55569 #> [2] chr16 11100711-11105710 * | region_2 NA NA #> log2_odds_ratio padj sign #> <numeric> <numeric> <logical> #> [1] 1.35371 1.28944e-05 TRUE #> [2] NA NA <NA> #> ------- #> seqinfo: 1 sequence from an unspecified genome; no seqlengths