autoHeatmap: heatmaplot

#' #'@import magrittr dplyr tibble tidyr xlsx
#'
#' data_demo <- 'data/trans_demo.xlsx'
#'
#' #'@title transform a perth mfi excel file
#' #'@description transform a perth mfi excel file
#' #'@param data a excel file
#' #'@export
#' perth_table_trans <- function(data){
#'
#'   # read-in data, with only Subject as char, others as numeric
#'   dat <- read.xlsx(data, sheetIndex = 1,
#'                          colClasses = (c('character',
#'                                           rep('numeric', 20)))) %>%
#'     as_tibble() %>%
#'     select(-contains('NA')) %>% # delete NA column which is used as delim
#'     drop_na() %>% # drop row without information
#'     # select useful column
#'     select(-c('X.Pacific.Blue.A.', 'X.Alexa.Fluor.700.A.', 'X.PE.Cy7.A.')) %>%
#'     select(Subject = `Sample..Set.1.blood.PMN.old`, everything())
#'
#'   # need to create new var panel first
#'   dat$panel = regmatches(dat$Subject,
#'                          regexpr(dat$Subject,
#'                                  pattern = '(?U)((?<=FL_)[^_]+(?=_.+.fcs))',
#'                                  perl = T))
#'
#'   # extract meaningful name to muate original Subject var
#'   dat$Subject = regmatches(dat$Subject,
#'                            regexpr(dat$Subject,
#'                                    pattern = '(?U)(?<=_)[^_]+((?=__WB)|(?=_WB))',
#'                                    perl = T))
#'
#'   # transform the tibble into a long format, new columns to indicate FITC or APC
#'   dat.long <- dat %>% pivot_longer(cols = c('X.FITC.A.', 'X.APC.A.'),
#'                                    names_to = 'FITC or APC',
#'                                    values_to = 'intensity',
#'   )
#'
#'   # re-transform the tibble into a wide format, use `FITC or APC`and panel
#'   #togather to create multiple columns
#'   dat.wide <-  dat.long %>% pivot_wider(names_from = c(`FITC or APC`, panel),
#'                                         values_from = intensity
#'   )
#'
#'   # summarize(average) all the columns grouped by Subject
#'   dat.tranformed <- dat.wide %>%
#'     group_by(Subject) %>%
#'     summarise_all(mean, na.rm = T) %>%
#'     mutate(
#'            HNE = matches('X.FITC.A._9PR', ignore.case = T),
#'            CD91 = matches('X.FITC.A._10Tcel', ignore.case = T),
#'            `PD-1` = X.FITC.A._11PD1,
#'            CD47 = rowSums(.[grep('X.FITC.A._12Ap', ignore.case = T),], na.rm = T),
#'            EXO = X.FITC.A._13EV,
#'            ###################
#'            CD163 = X.APC.A._9PR,
#'            CDD172a = X.APC.A._10Tcel,
#'            CD36 = X.APC.A._11PD1,
#'            FLICA = X.APC.A._12Ap,
#'            `TIMP-1` = X.APC.A._13EV,
#'            )
#'   # %>%
#'   #   select(Subject, CD45,	CD63,	CD16,	CD66b,	CD33,	HNE,	CD91,
#'   #          `PD-1`,	CD47,	EXO,	CD163,	CDD172a,	CD36,	FLICA,	`TIMP-1`,
#'   #          everything()
#'   #          )
#'
#'   # for FITC
#'   # 9PR HNE
#'   # 10Tcel CD91
#'   # 11PD1  PD-1
#'   # 12Ap CD47
#'   # 13EV EXO
#'
#'   ## For APC
#'   # 9PR CD163
#'   # 10Tcel CDD172a
#'   # 11PD1  CD36
#'   # 12Ap FLICA
#'   # 13EV TIMP-1
#'
#'
#'
#'   return(dat.tranformed)
#' }
#'