Tingwei Adeck November 26, 2023
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For quick starters, please visit the {video tutorial}
for an illustration on how to effectively use the package. The video tutorial only covers DAT files based on the assumption that MOST users will be interested in using the package for DAT files.
{normfluodbf}
is used to clean and normalize DBF and DAT files obtained from liposome flux assay experiments performed with the FLUOstar microplate reader. The expectation is this package is not limited to this assay type but other assay types performed with the same instrument. The final data frames obtained from this package are ready for insightful data analysis and for the creation of amazing visuals (using ggplot2) that help in making scientific deductions or making presentations to project stakeholders. This project firmly represents my core belief in the public dissemination of scientific information. My convictions on this idea of public dissemination were driven by the book “What Mad Pursuit” by Francis Crick. Visit {my page}
for details on the concept behind the project.
The development version of {normfluodbf}
can be installed as illustrated below:
The CRAN version of {normfluodbf}
can be installed as illustrated below:
{normfluodbf_builds}
to download the zip files into your desired directory.#library(normfluodbf)
liposomes_214 <- system.file("extdata", "liposomes_214.dbf", package = "normfluodbf")
normalized_data <- norm_tidy_dbf(liposomes_214, norm_scale = 'hundred')
library(normfluodbf)
liposomes_214 <- system.file("extdata", "liposomes_214.dbf", package = "normfluodbf")
normalized_data <- normfluordbf(liposomes_214)
normalized_data <- normfluordbf(liposomes_214)
library(normfluodbf)
liposomes_214 <- system.file("extdata", "liposomes_214.dbf", package = "normfluodbf")
normalized_data <- norm_tidy_dbf(liposomes_214, norm_scale = 'one')
normalized_data100 <- norm_tidy_dbf(liposomes_214, norm_scale = 'hundred')
normalized_dataz <- norm_tidy_dbf(liposomes_214, norm_scale = 'z-score')
# The user can add a transformation parameter
normalized_datazt <- norm_tidy_dbf(liposomes_214, norm_scale = 'z-score', transformed = 'log')
library(normfluodbf)
liposomes_214 <- system.file("extdata", "liposomes_214.dbf", package = "normfluodbf")
normalized_data <- normfluordbf(liposomes_214, norm_scale = 'one')
normalized_data100 <- normfluordbf(liposomes_214, norm_scale = 'hundred')
normalized_dataz <- normfluordbf(liposomes_214, norm_scale = 'z-score')
# The user can add a transformation parameter
normalized_datazt <- normfluordbf(liposomes_214, norm_scale = 'z-score', transformed = 'log')
library(normfluodbf)
dat1 <- system.file("extdata", "dat_1.dat", package = "normfluodbf")
normalized_data <- normfluodat(dat1, tnp = 3, cycles = 40, rows_used = c('A','B','C'))
library(normfluodbf)
dat2 <- system.file("extdata", "dat_2.dat", package = "normfluodbf")
normalized_data <- normfluordat(dat2, tnp = 3, cycles = 40, rows_used = c('A','B','C'))
library(normfluodbf)
dat2 <- system.file("extdata", "dat_2.dat", package = "normfluodbf")
n <- c('A','B','C') #rows used
# Cycle_Number attribute is included below
normalized_data <- normfluodat(dat2, tnp = 3, cycles = 40, n)
# Cycle_Number & Time attributes are included below
normalized_data <- normfluodat(dat2, tnp = 3, cycles = 40, n, interval = 30)
Sample_Type (TNP) | 96 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | A | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | X12 |
Negative | B | X13 | X14 | X15 | X16 | X17 | X18 | X19 | X20 | X21 | X22 | X23 | X24 |
Positive | C | X25 | X26 | X27 | X28 | X29 | X30 | X31 | X32 | X33 | X34 | X35 | X36 |
D | |||||||||||||
E | |||||||||||||
F | |||||||||||||
G | |||||||||||||
H |
A1 (Test) | B1 (Negative) | C1 (Positive) | Cycle_No |
---|---|---|---|
A1 | B1 | C1 | 1 |
A1 | B1 | C1 | 2 |
A1 | B1 | C1 | 3 |
A1 | B1 | C1 | 4 |
A1 | B1 | C1 | 5 |
A1 | B1 | C1 | 6 |
A1 | B1 | C1 | …38 |
A1 | B1 | C1 | …39 |
A1 | B1 | C1 | …40 |
library(normfluodbf)
dat2 <- system.file("extdata", "dat_2.dat", package = "normfluodbf")
n <- c('A','B','C')
c <- c(5,6,7)
normalized_data <- normfluodat(dat2, tnp = 3, cycles = 40, rows_used=n, cols_used=c)
A5 (Test) | B5 (Negative) | C5 (Positive) | Cycle_No |
---|---|---|---|
A5 | B5 | C6 | 1 |
A5 | B5 | C6 | 2 |
A5 | B5 | C6 | 3 |
library(normfluodbf)
dat2 <- system.file("extdata", "dat_2.dat", package = "normfluodbf")
manual_cols <- c('A1','B1','C1')
normalized_data <- normfluodat(dat2, tnp = 3, cycles = 40, user_specific_labels = manual_cols)
Hypothetically, if the user uses the rows and columns indicated in the examples in Using The rows_used and cols_used parameter but skips some wells, then the user can simply go ahead and use the user_specific_labels to add the correct column names for the final data frame.
However, if the scenario above occurs where the user indicates 9 wells should be used but the program detects fewer than 9 samples, then the program will inform the user and ask the user to choose from a list of column names. Illustration below:
Sample_Type (TNP) | 96 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | A | X1 | X4 | X3 | |||||||||
Negative | B | X2 | X29 | ||||||||||
Positive | C | X25 | X28 | ||||||||||
D | |||||||||||||
E | |||||||||||||
F |
library(normfluodbf)
dat2 <- system.file("extdata", "dat_2.dat", package = "normfluodbf")
manual_cols <- c('A1','B1','C1')
normalized_data <- normfluodat(dat2, tnp = 3, cycles = 40, n, read_direction = 'horizontal')
Sample_Type (TNP) | 96 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | A | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | X12 |
Negative | B | X13 | X14 | X15 | X16 | X17 | X18 | X19 | X20 | X21 | X22 | X23 | X24 |
Positive | C | X25 | X26 | X27 | X28 | X29 | X30 | X31 | X32 | X33 | X34 | X35 | X36 |
D | |||||||||||||
E | |||||||||||||
F | |||||||||||||
G | |||||||||||||
H |
A1 (Test) | A2 (Test) | A3 (Test) | Cycle_No |
---|---|---|---|
A1 | A2 | A3 | 1 |
A1 | A2 | A3 | 2 |
A1 | A2 | A3 | 3 |
A1 | A2 | A3 | 4 |
A1 | A2 | A3 | 5 |
A1 | A2 | A3 | 6 |
A1 | A2 | A3 | …38 |
A1 | A2 | A3 | …39 |
A1 | A2 | A3 | …40 |
library(normfluodbf)
dat2 <- system.file("extdata", "dat_2.dat", package = "normfluodbf")
manual_cols <- c('A1','B1','C1')
normalized_data <- normfluodat(dat2, tnp = 3, cycles = 40, n, read_direction = 'horizontal', norm_scale = 'hundred')
Sample_Type | 96 | Test | Negative | Positive | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | X12 | |
B | X13 | X14 | X15 | X16 | X17 | X18 | X19 | X20 | X21 | X22 | X23 | X24 | |
C | X25 | X26 | X27 | X28 | X29 | X30 | X31 | X32 | X33 | X34 | X35 | X36 | |
D | |||||||||||||
E | |||||||||||||
F | |||||||||||||
G | |||||||||||||
H |
Sample_Type | Cycle_No | Machine_data_verbose | machine_data_numeric |
---|---|---|---|
Test-1 | 1 | A1 | 1 |
Test-2 | 1 | B1 | 2 |
Test-3 | 1 | C1 | 3 |
Test-1 | 2 | A1 | 4 |
Test-2 | 2 | B1 | 5 |
Test-3 | 2 | C1 | 6 |
Test-1 | 40 | A1 | …118 |
Test-2 | 40 | B1 | …119 |
Test-3 | 40 | C1 | …120 |
A1 (Test-1) | B1 (Test-2) | C1 (Test-3) | Cycle_No |
---|---|---|---|
A1 | B1 | C1 | 1 |
A1 | B1 | C1 | 2 |
A1 | B1 | C1 | 3 |
A1 | B1 | C1 | 4 |
A1 | B1 | C1 | 5 |
A1 | B1 | C1 | 6 |
A1 | B1 | C1 | …38 |
A1 | B1 | C1 | …39 |
A1 | B1 | C1 | …40 |
library(normfluodbf)
dat1 <- system.file("extdata", "dat_1.dat", package = "normfluodbf")
normalized_data <- normfluodat(dat1, tnp = 3, cycles = 40, rows_used = c('A','B','C') )
# Use the syntax below to obtain a Time attribute as well
normalized_data <- normfluodat(dat1, tnp = 3, cycles = 40, rows_used = c('A','B','C'), interval = 30)
Experimental issues should be investigated at very high or very low fluorescence values.
The most common experimental issues arise when ACMA concentrations are out of the tolerated range. Based on my experience, ACMA concentrations between 2 and 5 Micromolar will suffice to get fluorescence values within the tolerance threshold.
ACMA concentrations as low as 0.2 Micromolar or as high as 20 Micromolar have proven problematic based on my research experience. These ACMA concentrations have proven NOISY and provide the basis for determining the noise-signal regions.
Another issue linked to the FLUOstar instrument revolves around setting the right “gain” to ensure the right level of sensitivity in machine readings. A very high “gain” setting results in increased machine sensitivity even at the right ACMA concentrations and vice versa. In short, we want the machine to be primed to read exactly what we feed it, no more, no less.
This program boasts of a quality control function to help new researchers avoid pitfalls that can mar their experience performing experiments.
Within this package, a QC function is designed to check that fluorescence values do not exceed the upper limit (2^15 or 32768) OR fall below the lower limit (2^11 or 2048). Fluorescence values that exceed these thresholds are considered noisy and can lead to incorrect interpretation of analysis results.
The images presented below represent results obtained when experiments are conducted within the noise region. The X-axis is actually “Cycle_No” but seemed to have been mislabeled due to the tedious nature of my previous coding approach.
(Dowle and Srinivasan 2023) (Wickham, François, et al. 2023) (Yu 2021) (R Core Team 2022) (Wickham, Chang, et al. 2023) (Arnold 2021) (Wickham 2022) (Müller and Wickham 2023) (Wickham, Vaughan, and Girlich 2023)
Arnold, Jeffrey B. 2021. Ggthemes: Extra Themes, Scales and Geoms for Ggplot2. https://github.com/jrnold/ggthemes.
Dowle, Matt, and Arun Srinivasan. 2023. Data.table: Extension of ‘Data.frame‘. https://CRAN.R-project.org/package=data.table.
Müller, Kirill, and Hadley Wickham. 2023. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
R Core Team. 2022. Foreign: Read Data Stored by Minitab, s, SAS, SPSS, Stata, Systat, Weka, dBase, … https://svn.r-project.org/R-packages/trunk/foreign/.
Wickham, Hadley. 2022. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2023. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, Kirill Müller, and Davis Vaughan. 2023. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, Davis Vaughan, and Maximilian Girlich. 2023. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
Yu, Guangchuang. 2021. Emojifont: Emoji and Font Awesome in Graphics. https://github.com/GuangchuangYu/emojifont.