README.md
In mirkko-hub/jasco2: Handling of Jasco Spectrophotometer data
jasco2
Warning: This package is still under development !
A package for easy Spectrophotometer data handling.
Installation
You can install the released version of jasco2 from GitHub.
devtools::install_github("mirkko-hub/jasco2")
Example
Easy data handling of Jasco spectrophotometer (JASCO Corp., V-560,
Rev. 1.00) files - you only need three things:
- data (.txt output from spectrophotometer)
- design (record of individual files and grouping information +
optional flagging for removal, notes)
- treatmemt (additional information specifying the different
groups)
Import files
library(tidyverse, warn.conflicts = FALSE)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
#> ✓ tibble 3.0.3 ✓ dplyr 1.0.2
#> ✓ tidyr 1.1.2 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.0
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(magrittr, warn.conflicts = FALSE)
library(jasco2)
Given a bunch of .txt files from Jasco Spec,
files <- system.file("extdata", paste0(1:28, ".txt") , package = "jasco2", mustWork = TRUE)
head(files)
#> [1] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/1.txt"
#> [2] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/2.txt"
#> [3] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/3.txt"
#> [4] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/4.txt"
#> [5] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/5.txt"
#> [6] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/6.txt"
… some design tibble,
design <- readr::read_csv(system.file("extdata", "design.csv", package = "jasco2", mustWork = TRUE))
#>
#> ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> cols(
#> Exp_ID = col_double(),
#> Group = col_character(),
#> FLAG = col_double(),
#> Note = col_character()
#> )
head(design)
#> # A tibble: 6 x 4
#> Exp_ID Group FLAG Note
#> <dbl> <chr> <dbl> <chr>
#> 1 1 blank NA <NA>
#> 2 2 A 1 wrong tube
#> 3 3 A NA <NA>
#> 4 4 A NA <NA>
#> 5 5 A NA <NA>
#> 6 6 B NA <NA>
… and and a treatment tibble,
treatment <- readr::read_csv(system.file("extdata", "treatment.csv", package = "jasco2", mustWork = TRUE))
#>
#> ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> cols(
#> Group = col_character(),
#> Label = col_character(),
#> Protein = col_character()
#> )
head(treatment)
#> # A tibble: 6 x 3
#> Group Label Protein
#> <chr> <chr> <chr>
#> 1 A Control <NA>
#> 2 B Condition 1 A
#> 3 C Condition 2 A
#> 4 D Condition 3 A
#> 5 E Condition 1 B
#> 6 F Condition 2 B
we can easily combine all information in a tidy tibble, that
facilitates further analysis!
df <- jasco_tibble(filenames = files, design, treatment, rmflag = FALSE, rmblank = TRUE)
head(df)
#> # A tibble: 6 x 10
#> Set_ID Instrument Exp_ID Time_s Absorbance Group FLAG Note
#> <dttm> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
#> 1 2018-01-18 19:54:37 JASCO Cor… 2 0 1.81 A 1 wron…
#> 2 2018-01-18 19:54:37 JASCO Cor… 2 1 1.82 A 1 wron…
#> 3 2018-01-18 19:54:37 JASCO Cor… 2 2 1.81 A 1 wron…
#> 4 2018-01-18 19:54:37 JASCO Cor… 2 3 1.81 A 1 wron…
#> 5 2018-01-18 19:54:37 JASCO Cor… 2 4 1.81 A 1 wron…
#> 6 2018-01-18 19:54:37 JASCO Cor… 2 5 1.81 A 1 wron…
#> # … with 2 more variables: Label <chr>, Protein <chr>
Process data
In the given example, we performed an enzyme coupled ATP hydrolysis
assay. So we measured NADH oxidation (the consumption of NADH is
recorded at 340 nm) as a response to ATP hydrolysis via the depicted
reaction - each oxidized NADH molecule reports the hydrolysis of one
molecule of ATP.
Let’s say we want to calculate turnover rates of the ATPase of interest.
All we need to do is:
- convert absorbance units into concentration
- apply a linear model (with customized interval, response and
predictor variables)
- extract the slopes (estimates) for all reaction
- and convert these into turnover rates
(with optional summary by specified groups)
like so:
data <- df %>%
convert_absorbance(., "NADH") %>%
jasco_extract_lm(., response = NADH, predictor = Time_s, min = 75, max = 175)
#> Warning: All elements of `...` must be named.
#> Did you want `data = c(Time_s, NADH, Absorbance)`?
head(data)
#> # A tibble: 6 x 14
#> Set_ID Instrument Exp_ID Group FLAG Note Label Protein data
#> <dttm> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <lis>
#> 1 2018-01-18 19:54:37 JASCO Cor… 2 A 1 wron… Cont… <NA> <tib…
#> 2 2018-01-18 20:02:06 JASCO Cor… 3 A NA <NA> Cont… <NA> <tib…
#> 3 2018-01-18 20:09:48 JASCO Cor… 4 A NA <NA> Cont… <NA> <tib…
#> 4 2018-01-18 20:17:15 JASCO Cor… 5 A NA <NA> Cont… <NA> <tib…
#> 5 2018-01-18 16:00:27 JASCO Cor… 6 B NA <NA> Cond… A <tib…
#> 6 2018-01-18 16:09:14 JASCO Cor… 7 B NA <NA> Cond… A <tib…
#> # … with 5 more variables: mod <list>, glance <list>, tidy <list>,
#> # augment <list>, rsq <dbl>
params <- data %>%
jasco_extract_params(., .bgcorr = FALSE, .protein_uM = 6, .unit = "sec", .factor = -1, Group)
head(params$data)
#> # A tibble: 6 x 20
#> Set_ID Instrument Exp_ID Group FLAG Note Label Protein data
#> <dttm> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <lis>
#> 1 2018-01-18 19:54:37 JASCO Cor… 2 A 1 wron… Cont… <NA> <tib…
#> 2 2018-01-18 20:02:06 JASCO Cor… 3 A NA <NA> Cont… <NA> <tib…
#> 3 2018-01-18 20:09:48 JASCO Cor… 4 A NA <NA> Cont… <NA> <tib…
#> 4 2018-01-18 20:17:15 JASCO Cor… 5 A NA <NA> Cont… <NA> <tib…
#> 5 2018-01-18 16:00:27 JASCO Cor… 6 B NA <NA> Cond… A <tib…
#> 6 2018-01-18 16:09:14 JASCO Cor… 7 B NA <NA> Cond… A <tib…
#> # … with 11 more variables: mod <list>, glance <list>, term <chr>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # augment <list>, rsq <dbl>, rate <dbl>, turnover <dbl>
head(params$summary)
#> # A tibble: 6 x 5
#> groups_Group rate_avg rate_sd turnover_avg turnover_sd
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 A -0.000000526 0.00000000909 5.26 0.0909
#> 2 B -0.000000618 0.0000000423 6.18 0.423
#> 3 C -0.00000119 0.0000000322 11.9 0.322
#> 4 D -0.00000119 0.0000000396 11.9 0.396
#> 5 E -0.000000393 0.0000000428 3.93 0.428
#> 6 F -0.000000980 0.0000000174 9.80 0.174
Plot data
ggplot() +
geom_point(data = data %>% unnest(data) %>% filter(., Label != "Control"),
mapping = aes(x = Time_s, y = NADH), size = 0.1) +
geom_path(data = data %>% unnest(augment) %>% filter(., Label != "Control"),
mapping = aes(x = Time_s, y = .fitted, group = Exp_ID), colour = "blue") +
facet_grid(Label ~ Protein)
ggplot() +
geom_boxplot(data = params$data %>% filter(Label != "Control"), aes(x = Label, y = turnover, colour = Protein)) +
geom_point(data = params$data %>% filter(Label != "Control"), aes(x = Label, y = turnover, colour = Protein)) +
facet_grid(. ~ Protein)
mirkko-hub/jasco2 documentation built on Jan. 1, 2021, 2:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
jasco2
Warning: This package is still under development !
A package for easy Spectrophotometer data handling.
Installation
You can install the released version of jasco2 from GitHub.
devtools::install_github("mirkko-hub/jasco2")
Example
Easy data handling of Jasco spectrophotometer (JASCO Corp., V-560, Rev. 1.00) files - you only need three things:
- data (.txt output from spectrophotometer)
- design (record of individual files and grouping information + optional flagging for removal, notes)
- treatmemt (additional information specifying the different groups)
Import files
library(tidyverse, warn.conflicts = FALSE)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
#> ✓ tibble 3.0.3 ✓ dplyr 1.0.2
#> ✓ tidyr 1.1.2 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.0
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(magrittr, warn.conflicts = FALSE)
library(jasco2)
Given a bunch of .txt files from Jasco Spec,
files <- system.file("extdata", paste0(1:28, ".txt") , package = "jasco2", mustWork = TRUE)
head(files)
#> [1] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/1.txt"
#> [2] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/2.txt"
#> [3] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/3.txt"
#> [4] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/4.txt"
#> [5] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/5.txt"
#> [6] "/home/mirkko/R/x86_64-pc-linux-gnu-library/4.0/jasco2/extdata/6.txt"
… some design tibble,
design <- readr::read_csv(system.file("extdata", "design.csv", package = "jasco2", mustWork = TRUE))
#>
#> ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> cols(
#> Exp_ID = col_double(),
#> Group = col_character(),
#> FLAG = col_double(),
#> Note = col_character()
#> )
head(design)
#> # A tibble: 6 x 4
#> Exp_ID Group FLAG Note
#> <dbl> <chr> <dbl> <chr>
#> 1 1 blank NA <NA>
#> 2 2 A 1 wrong tube
#> 3 3 A NA <NA>
#> 4 4 A NA <NA>
#> 5 5 A NA <NA>
#> 6 6 B NA <NA>
… and and a treatment tibble,
treatment <- readr::read_csv(system.file("extdata", "treatment.csv", package = "jasco2", mustWork = TRUE))
#>
#> ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> cols(
#> Group = col_character(),
#> Label = col_character(),
#> Protein = col_character()
#> )
head(treatment)
#> # A tibble: 6 x 3
#> Group Label Protein
#> <chr> <chr> <chr>
#> 1 A Control <NA>
#> 2 B Condition 1 A
#> 3 C Condition 2 A
#> 4 D Condition 3 A
#> 5 E Condition 1 B
#> 6 F Condition 2 B
we can easily combine all information in a tidy tibble, that facilitates further analysis!
df <- jasco_tibble(filenames = files, design, treatment, rmflag = FALSE, rmblank = TRUE)
head(df)
#> # A tibble: 6 x 10
#> Set_ID Instrument Exp_ID Time_s Absorbance Group FLAG Note
#> <dttm> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
#> 1 2018-01-18 19:54:37 JASCO Cor… 2 0 1.81 A 1 wron…
#> 2 2018-01-18 19:54:37 JASCO Cor… 2 1 1.82 A 1 wron…
#> 3 2018-01-18 19:54:37 JASCO Cor… 2 2 1.81 A 1 wron…
#> 4 2018-01-18 19:54:37 JASCO Cor… 2 3 1.81 A 1 wron…
#> 5 2018-01-18 19:54:37 JASCO Cor… 2 4 1.81 A 1 wron…
#> 6 2018-01-18 19:54:37 JASCO Cor… 2 5 1.81 A 1 wron…
#> # … with 2 more variables: Label <chr>, Protein <chr>
Process data
In the given example, we performed an enzyme coupled ATP hydrolysis assay. So we measured NADH oxidation (the consumption of NADH is recorded at 340 nm) as a response to ATP hydrolysis via the depicted reaction - each oxidized NADH molecule reports the hydrolysis of one molecule of ATP.
Let’s say we want to calculate turnover rates of the ATPase of interest. All we need to do is:
- convert absorbance units into concentration
- apply a linear model (with customized interval, response and predictor variables)
- extract the slopes (estimates) for all reaction
- and convert these into turnover rates
(with optional summary by specified groups)
like so:
data <- df %>%
convert_absorbance(., "NADH") %>%
jasco_extract_lm(., response = NADH, predictor = Time_s, min = 75, max = 175)
#> Warning: All elements of `...` must be named.
#> Did you want `data = c(Time_s, NADH, Absorbance)`?
head(data)
#> # A tibble: 6 x 14
#> Set_ID Instrument Exp_ID Group FLAG Note Label Protein data
#> <dttm> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <lis>
#> 1 2018-01-18 19:54:37 JASCO Cor… 2 A 1 wron… Cont… <NA> <tib…
#> 2 2018-01-18 20:02:06 JASCO Cor… 3 A NA <NA> Cont… <NA> <tib…
#> 3 2018-01-18 20:09:48 JASCO Cor… 4 A NA <NA> Cont… <NA> <tib…
#> 4 2018-01-18 20:17:15 JASCO Cor… 5 A NA <NA> Cont… <NA> <tib…
#> 5 2018-01-18 16:00:27 JASCO Cor… 6 B NA <NA> Cond… A <tib…
#> 6 2018-01-18 16:09:14 JASCO Cor… 7 B NA <NA> Cond… A <tib…
#> # … with 5 more variables: mod <list>, glance <list>, tidy <list>,
#> # augment <list>, rsq <dbl>
params <- data %>%
jasco_extract_params(., .bgcorr = FALSE, .protein_uM = 6, .unit = "sec", .factor = -1, Group)
head(params$data)
#> # A tibble: 6 x 20
#> Set_ID Instrument Exp_ID Group FLAG Note Label Protein data
#> <dttm> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <lis>
#> 1 2018-01-18 19:54:37 JASCO Cor… 2 A 1 wron… Cont… <NA> <tib…
#> 2 2018-01-18 20:02:06 JASCO Cor… 3 A NA <NA> Cont… <NA> <tib…
#> 3 2018-01-18 20:09:48 JASCO Cor… 4 A NA <NA> Cont… <NA> <tib…
#> 4 2018-01-18 20:17:15 JASCO Cor… 5 A NA <NA> Cont… <NA> <tib…
#> 5 2018-01-18 16:00:27 JASCO Cor… 6 B NA <NA> Cond… A <tib…
#> 6 2018-01-18 16:09:14 JASCO Cor… 7 B NA <NA> Cond… A <tib…
#> # … with 11 more variables: mod <list>, glance <list>, term <chr>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # augment <list>, rsq <dbl>, rate <dbl>, turnover <dbl>
head(params$summary)
#> # A tibble: 6 x 5
#> groups_Group rate_avg rate_sd turnover_avg turnover_sd
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 A -0.000000526 0.00000000909 5.26 0.0909
#> 2 B -0.000000618 0.0000000423 6.18 0.423
#> 3 C -0.00000119 0.0000000322 11.9 0.322
#> 4 D -0.00000119 0.0000000396 11.9 0.396
#> 5 E -0.000000393 0.0000000428 3.93 0.428
#> 6 F -0.000000980 0.0000000174 9.80 0.174
Plot data
ggplot() +
geom_point(data = data %>% unnest(data) %>% filter(., Label != "Control"),
mapping = aes(x = Time_s, y = NADH), size = 0.1) +
geom_path(data = data %>% unnest(augment) %>% filter(., Label != "Control"),
mapping = aes(x = Time_s, y = .fitted, group = Exp_ID), colour = "blue") +
facet_grid(Label ~ Protein)
ggplot() +
geom_boxplot(data = params$data %>% filter(Label != "Control"), aes(x = Label, y = turnover, colour = Protein)) +
geom_point(data = params$data %>% filter(Label != "Control"), aes(x = Label, y = turnover, colour = Protein)) +
facet_grid(. ~ Protein)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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