General data accessing getter function to return any of:
data,
segmentation,
normalisation factors.
The function uses the what parameter to return the
appropriate type of information
Since the input formats are different, the outputs are also
different based on the value of what, but they are
always in tibble format.
Besides obvious columns, these information will also be available in the returned tibbles.
what = "data": modality and value_type, the latter reporting the likelihood
code associated to the modality.
what = "segmentation":
ATAC_nonzerovals and ATAC_peaks, reporting the number of ATAC entries mapped to a
segment, and the number of peaks these come from. Note that one non-zero entry is given by a cell that reported a
ATAC count in a peak mapping within the segment.
RNA_nonzerovals and RNA_genes, reporting the number of RNA entries mapped to a segment,
and the number of genes these come from. For RNA, one non-zero entry is given by a cell that reported an RNA count
in a gene mapping within the segment.
what = "normalisation": just modality.
get_input(x, what = "data")An object of class rcongasplus.
Any of "data", "segmentation" or
"normalisation".
A tibble; its format depends on what. See the examples.
data(example_object)
# Extract the input data, after mapping to segments
get_input(example_object, what = 'data')
#> # A tibble: 29,190 × 5
#> segment_id cell value modality value_type
#> <chr> <chr> <int> <chr> <chr>
#> 1 chr1:57781319:108512982 bcc.su008.pre.cd45_AAAGATG… 274 RNA NB
#> 2 chr1:57781319:108512982 bcc.su008.pre.cd45_GCAATCA… 227 RNA NB
#> 3 chr1:57781319:108512982 bcc.su008.pre.cd45_GCAGCCA… 209 RNA NB
#> 4 chr1:57781319:108512982 bcc.su008.pre.cd45_TCCACAC… 258 RNA NB
#> 5 chr1:57781319:108512982 bcc.su008.pre.tcell_AGCATA… 284 RNA NB
#> 6 chr1:57781319:108512982 bcc.su008.pre.tcell_AGCTCC… 56 RNA NB
#> 7 chr1:57781319:108512982 bcc.su008.pre.tcell_GGAAAG… 130 RNA NB
#> 8 chr1:57781319:108512982 bcc.su008.pre.tcell_GGTGCG… 195 RNA NB
#> 9 chr1:57781319:108512982 bcc.su008.pre.tumor_AAACCT… 251 RNA NB
#> 10 chr1:57781319:108512982 bcc.su008.pre.tumor_AAAGCA… 256 RNA NB
#> # ℹ 29,180 more rows
# e.g., in this way you can get input ATAC values
get_input(example_object, what = 'data') %>% dplyr::filter(modality == 'ATAC')
#> # A tibble: 7,770 × 5
#> segment_id cell value modality value_type
#> <chr> <chr> <int> <chr> <chr>
#> 1 chr1:57781319:108512982 SU008_Tumor_Pre_10 1961 ATAC NB
#> 2 chr1:57781319:108512982 SU008_Tumor_Pre_100 342 ATAC NB
#> 3 chr1:57781319:108512982 SU008_Tumor_Pre_104 602 ATAC NB
#> 4 chr1:57781319:108512982 SU008_Tumor_Pre_106 511 ATAC NB
#> 5 chr1:57781319:108512982 SU008_Tumor_Pre_110 335 ATAC NB
#> 6 chr1:57781319:108512982 SU008_Tumor_Pre_112 593 ATAC NB
#> 7 chr1:57781319:108512982 SU008_Tumor_Pre_114 794 ATAC NB
#> 8 chr1:57781319:108512982 SU008_Tumor_Pre_115 273 ATAC NB
#> 9 chr1:57781319:108512982 SU008_Tumor_Pre_116 1076 ATAC NB
#> 10 chr1:57781319:108512982 SU008_Tumor_Pre_118 910 ATAC NB
#> # ℹ 7,760 more rows
# Extract the input segmentation.
get_input(example_object, what = 'segmentation')
#> # A tibble: 30 × 12
#> chr from to copies segment_id ATAC_nonzerovals ATAC_peaks
#> <chr> <int> <int> <int> <chr> <dbl> <dbl>
#> 1 chr10 285200 135490954 2 chr10:285200:13… 176459 18251
#> 2 chr4 40332 190986668 2 chr4:40332:1909… 153390 16652
#> 3 chr11 17515668 134898011 3 chr11:17515668:… 180071 17098
#> 4 chr2 133074851 241531737 3 chr2:133074851:… 122575 13415
#> 5 chr2 39340 89319834 3 chr2:39340:8931… 130226 12956
#> 6 chr14 20323479 105411153 3 chr14:20323479:… 125378 12371
#> 7 chr15 28947721 102517302 2 chr15:28947721:… 132411 12616
#> 8 chr6 67340783 171046023 2 chr6:67340783:1… 105050 11560
#> 9 chr12 53343461 133801333 3 chr12:53343461:… 120836 11634
#> 10 chr5 110286431 180712485 3 chr5:110286431:… 105659 10740
#> # ℹ 20 more rows
#> # ℹ 5 more variables: RNA_nonzerovals <dbl>, RNA_genes <dbl>, E <dbl>, L <int>,
#> # `row_number() <= 30` <lgl>
# Extract the input normalisation factors
get_input(example_object, what = 'normalisation')
#> # A tibble: 973 × 3
#> cell normalisation_factor modality
#> <chr> <dbl> <chr>
#> 1 bcc.su008.pre.cd45_AAAGATGCAAAGGAAG 2.70 RNA
#> 2 bcc.su008.pre.cd45_GCAATCATCAAACAAG 1.89 RNA
#> 3 bcc.su008.pre.cd45_GCAGCCATCTTTAGTC 1.66 RNA
#> 4 bcc.su008.pre.cd45_TCCACACCACATCTTT 2.49 RNA
#> 5 bcc.su008.pre.tcell_AGCATACCAGCATGAG 1.59 RNA
#> 6 bcc.su008.pre.tcell_AGCTCCTAGGACAGAA 0.614 RNA
#> 7 bcc.su008.pre.tcell_GGAAAGCGTATTAGCC 1.15 RNA
#> 8 bcc.su008.pre.tcell_GGTGCGTCAGAGTGTG 1.74 RNA
#> 9 bcc.su008.pre.tumor_AAACCTGTCCGAGCCA 1.17 RNA
#> 10 bcc.su008.pre.tumor_AAAGCAAGTGTCTGAT 0.762 RNA
#> # ℹ 963 more rows