Summarize regions

Summarize regions by minimum, maximum, mean, standard deviation of methylation rate in two probes and methylation diff rate withib two probes and estimated quantile basen od methylation diff rate

get_stats(data)

Arguments

data	dataframe with specific columns: chr, poz, prob, no, meth, unmeth, meth.rate, tiles and possible tiles.common columns. This dataframe is result of function create_tiles_min_gap or create_tiles_fixed_length

Value

data.frame which is summing-up regions specifed by tiles and tiles.common columns in data

Examples

data('schizophrenia')
control <- schizophrenia %>% filter(category == 'control') %>%
dplyr::select(-category)

disease <- schizophrenia %>% filter(category == 'disease') %>%
 dplyr::select(-category)

data <- preprocessing(control, disease)
data.tiles <- create_tiles_max_gap(data, gaps.length = 100)
head(get_stats(data.tiles))
#> # A tibble: 6 x 14
#> # Groups:   chr, start, end [6]
#>     chr start   end meth.cov meth.max_x meth.max_y meth.mean_x meth.mean_y
#>   <chr> <dbl> <dbl>    <dbl>      <dbl>      <dbl>       <dbl>       <dbl>
#> 1  chr1 81412 81442        2  0.8285714  0.8684211   0.8285714   0.8684211
#> 2  chr1 81546 81546        1  0.8285714  0.8684211   0.8285714   0.8684211
#> 3  chr1 81698 81863        4  0.8285714  0.8684211   0.8285714   0.8684211
#> 4  chr1 81984 82025        3  0.8285714  0.8684211   0.8252101   0.8672515
#> 5  chr1 82254 82254        1  0.8235294  0.8552632   0.8235294   0.8552632
#> 6  chr1 82603 82625        2  0.7272727  0.8846154   0.7272727   0.8846154
#> # ... with 6 more variables: meth.min_x <dbl>, meth.min_y <dbl>,
#> #   meth.sd_x <dbl>, meth.sd_y <dbl>, meth.diff <dbl>, quantile <dbl>