mutate_p_value

Source: R/mutate_p_value.R

Calculate p values for variables.

mutate_p_value(
  object,
  control_sample_id,
  case_sample_id,
  method = c("t.test", "wilcox.test"),
  p_adjust_methods = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none"),
  return_mass_dataset = TRUE,
  ...
)

Arguments

object

tidymass-class object.

control_sample_id

A character vector.

case_sample_id

A character vector

method

t test or wilcox test.

p_adjust_methods

see ?p.adjust

return_mass_dataset

logical default TRUE

...

other parameters

Value

object with fold change in variable_info.

Author

Xiaotao Shen shenxt1990@outlook.com

Examples

library(massdataset)
library(magrittr)
library(dplyr)
data("liver_aging_pos")
liver_aging_pos
#> -------------------- 
#> massdataset version: 0.01 
#> -------------------- 
#> 1.expression_data:[ 21607 x 24 data.frame]
#> 2.sample_info:[ 24 x 4 data.frame]
#> 3.variable_info:[ 21607 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> -------------------- 
#> Processing information (extract_process_info())
#> 1 processings in total
#> Creation ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2021-12-23 00:24:02

w_78 =
  liver_aging_pos %>%
  activate_mass_dataset(what = "sample_info") %>%
  dplyr::filter(group == "78W") %>%
  dplyr::pull(sample_id)

w_24 =
  liver_aging_pos %>%
  activate_mass_dataset(what = "sample_info") %>%
  dplyr::filter(group == "24W") %>%
  dplyr::pull(sample_id)

control_sample_id = w_24
case_sample_id = w_78

liver_aging_pos =
  mutate_p_value(
    object = liver_aging_pos,
    control_sample_id = control_sample_id,
    case_sample_id = case_sample_id,
    method = "t.test",
    p_adjust_methods = "BH"
  )
#> 10 control samples.
#> 10 case samples.

head(extract_variable_info(liver_aging_pos))
#>          variable_id       mz       rt    p_value p_value_adjust
#> M60T1209    M60T1209 60.04340 1209.340 0.55112435      0.7073953
#> M60T1182    M60T1182 60.04341 1181.850 0.03031202      0.1195487
#> M60T1163    M60T1163 60.04340 1163.205 0.44663865      0.6206044
#> M60T1310    M60T1310 60.04335 1310.170 0.52215644      0.6839942
#> M60T1240    M60T1240 60.04339 1239.900 0.98676052      0.9926437
#> M60T1254    M60T1254 60.04340 1254.240 0.53146573      0.6912415

liver_aging_pos =
  mutate_p_value(
    object = liver_aging_pos,
    control_sample_id = control_sample_id,
    case_sample_id = case_sample_id,
    method = "wilcox.test",
    p_adjust_methods = "BH"
  )
#> 10 control samples.
#> 10 case samples.
#> Warning: cannot compute exact p-value with ties
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#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact p-value with ties

head(extract_variable_info(liver_aging_pos))
#>          variable_id       mz       rt    p_value p_value_adjust  p_value.1
#> M60T1209    M60T1209 60.04340 1209.340 0.55112435      0.7073953 0.85342831
#> M60T1182    M60T1182 60.04341 1181.850 0.03031202      0.1195487 0.02323064
#> M60T1163    M60T1163 60.04340 1163.205 0.44663865      0.6206044 0.97051246
#> M60T1310    M60T1310 60.04335 1310.170 0.52215644      0.6839942 0.97051246
#> M60T1240    M60T1240 60.04339 1239.900 0.98676052      0.9926437 0.97051246
#> M60T1254    M60T1254 60.04340 1254.240 0.53146573      0.6912415 0.63052891
#>          p_value_adjust.1
#> M60T1209       0.92295899
#> M60T1182       0.09287525
#> M60T1163       0.98668538
#> M60T1310       0.98668538
#> M60T1240       0.98668538
#> M60T1254       0.77858965

extract_variable_info(liver_aging_pos) %>%
  ggplot(aes(-log(p_value_adjust, 10), -log(p_value_adjust.1, 10))) +
  geom_point()
#> Warning: Removed 10 rows containing missing values (geom_point).