Calculate fold change.
mutate_fc(
object,
control_sample_id,
case_sample_id,
mean_median = c("mean", "median"),
return_mass_dataset = TRUE
)Arguments
- object
tidymass-class object.
- control_sample_id
A character vector.
- case_sample_id
A character vector
- mean_median
mean or median.
- return_mass_dataset
logical default TRUE
Value
object with fold change (fc) in variable_info.
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_fc(
object = liver_aging_pos,
control_sample_id = control_sample_id,
case_sample_id = case_sample_id,
mean_median = "mean"
)
#> 10 control samples.
#> 10 case samples.
head(extract_variable_info(liver_aging_pos))
#> variable_id mz rt fc
#> M60T1209 M60T1209 60.04340 1209.340 1.1733118
#> M60T1182 M60T1182 60.04341 1181.850 1.2331847
#> M60T1163 M60T1163 60.04340 1163.205 0.6996320
#> M60T1310 M60T1310 60.04335 1310.170 0.8301097
#> M60T1240 M60T1240 60.04339 1239.900 0.9963933
#> M60T1254 M60T1254 60.04340 1254.240 1.1172962
liver_aging_pos =
mutate_fc(
object = liver_aging_pos,
control_sample_id = control_sample_id,
case_sample_id = case_sample_id,
mean_median = "median"
)
#> 10 control samples.
#> 10 case samples.
head(extract_variable_info(liver_aging_pos))
#> variable_id mz rt fc fc.1
#> M60T1209 M60T1209 60.04340 1209.340 1.1733118 1.3044047
#> M60T1182 M60T1182 60.04341 1181.850 1.2331847 1.3044817
#> M60T1163 M60T1163 60.04340 1163.205 0.6996320 1.0927459
#> M60T1310 M60T1310 60.04335 1310.170 0.8301097 1.2033256
#> M60T1240 M60T1240 60.04339 1239.900 0.9963933 0.8143669
#> M60T1254 M60T1254 60.04340 1254.240 1.1172962 1.0093612
extract_variable_info(liver_aging_pos) %>%
ggplot(aes(fc, fc.1)) +
geom_point()
