Here, I tried to quickly reproduce the normalization factor and the relative fold change using an example of the geNorm method.
The following code shows the data in the attached file and compares the calculations from pcr_nf and pcr_genorm to the original calculations from the example (a clean version of the file is attached).
clean_example.xlsx
# load required libraries
library(tidyverse)
library(readxl)
library(FSA)
#> ## FSA v0.8.20. See citation('FSA') if used in publication.
#> ## Run fishR() for related website and fishR('IFAR') for related book.
library(pcr)
# load and show raw data from the example
raw_data <- read_excel('./clean_example.xlsx', sheet = 1)
print(raw_data)
#> # A tibble: 16 x 8
#> Target Sample `Cq value` `Mean Cq` `SD Cq` RQ `SD RQ` `SE RQ`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 REF1 Control 24.5 24.6 0.212 1 0.147 0.104
#> 2 <NA> Control 24.8 NA NA NA NA NA
#> 3 <NA> Treatment 25.4 25.6 0.283 0.518 0.101 0.0718
#> 4 <NA> Treatment 25.8 NA NA NA NA NA
#> 5 REF2 Control 18.3 18.4 0.0707 1 0.0490 0.0347
#> 6 <NA> Control 18.4 NA NA NA NA NA
#> 7 <NA> Treatment 18.8 18.8 0.0707 0.758 0.0371 0.0263
#> 8 <NA> Treatment 18.7 NA NA NA NA NA
#> 9 REF3 Control 32.3 32.3 0.0707 1 0.0490 0.0347
#> 10 <NA> Control 32.4 NA NA NA NA NA
#> 11 <NA> Treatment 33 33.2 0.354 0.536 0.131 0.0929
#> 12 <NA> Treatment 33.5 NA NA NA NA NA
#> 13 GOI1 Control 22.3 22.2 0.141 1 0.0980 0.0693
#> 14 <NA> Control 22.1 NA NA NA NA NA
#> 15 <NA> Treatment 26.8 26.7 0.141 0.0442 0.00433 0.00306
#> 16 <NA> Treatment 26.6 NA NA NA NA NA
# clean data
ct <- raw_data %>%
fill(Target) %>%
select(Target, Sample, `Cq value`) %>%
setNames(c('gene', 'group', 'ct')) %>%
group_by(gene, group) %>%
mutate(row_id = row_number()) %>%
spread(gene, ct) %>%
ungroup() %>%
select(-row_id, -group)
group <- rep(c('control', 'treatment'), each = 2)
# calculate a normalization factor
pcr_nf(ct,
group_var = group,
reference_group = 'control',
reference_gene = paste0('REF', 1:3))
#> Joining, by = c("group", "gene")
#> # A tibble: 2 x 4
#> group mean sd factor
#> <chr> <dbl> <dbl> <chr>
#> 1 control 1 0.0542 NF
#> 2 treatment 0.595 0.0630 NF
# load and show normalization factor from the example
read_excel('clean_example.xlsx', sheet = 2)
#> # A tibble: 2 x 4
#> `Normalization Factor (NF)` NF `NF SD` `NF SE`
#> <chr> <dbl> <dbl> <dbl>
#> 1 NF Control 1 0.0542 0.0383
#> 2 NF Treatment 0.595 0.0630 0.0445
# calculate relative expression change
pcr_genorm(ct,
group_var = group,
reference_group = 'control',
reference_gene = paste0('REF', 1:3))
#> Joining, by = c("group", "gene")
#> Joining, by = c("group", "gene")
#> Joining, by = "group"
#> # A tibble: 2 x 5
#> # Groups: group, gene [?]
#> group gene factor change error
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 control GOI1 NF 1 0.112
#> 2 treatment GOI1 NF 0.0743 0.0107
# load and show relative expression from the example
read_excel('clean_example.xlsx', sheet = 3)
#> # A tibble: 2 x 4
#> `Normalized Expression Level (NRQ)` NRQ `NRQ SD` `NRQ SE`
#> <chr> <dbl> <dbl> <dbl>
#> 1 GOInorm Control 1 0.112 0.0792
#> 2 GOInorm Treatment 0.0743 0.0107 0.00758
Related #4
Here, I tried to quickly reproduce the normalization factor and the relative fold change using an example of the geNorm method.
The following code shows the data in the attached file and compares the calculations from
pcr_nfandpcr_genormto the original calculations from the example (a clean version of the file is attached). clean_example.xlsxCreated on 2018-10-17 by the reprex package (v0.2.1)