bioset

latest GitHub-Release: https://github.com/randomchars42/bioset/releases

bioset is intended to help you working with sets of raw data.

Working in a lab it is not uncommon to have a data set of raw values (because your measuring device spat it out) and you now need to somehow transform and organise the data so that you can work with it.

Installation

A stable version of bioset is available on CRAN: https://cran.r-project.org/package=bioset

So all you need to do is:

install.packages("bioset")

You can find the latest additions and changes on GitHub. To spare CRAN administrators' time it is requested of all package authors not to submit changes too frequently.

Consequently, I will make new features available on GitHub first. Packages I have not yet submitted to CRAN will be labelled vX.Y.Z-pre.N and appear under: https://github.com/randomchars42/bioset/releases.

To install those packages you can use githubinstall

# install.packages("githubinstall")
gh_install_packages("bioset", ref = "vX.Y.Z-pre.N")

You can install the very latest changes in bioset-master from github with:

# install.packages("devtools")
devtools::install_github("randomchars42/bioset")

Why? What bioset can do for you

bioset lets you:

import raw data organised in matrices, e.g. measured values of a 8 x 12 (96-well) bio-assay plate
calculate concentrations using samples with known concentrations (calibrators) in your dataset
calculate means and variability for duplicates / triplicates / ...
convert your concentrations to (more or less) arbitrary units of concentration

Data import

Suppose you have an ods / xls(x) file with raw values obtained from a measurement like this:

	1	2	3	4	5	6
A	102	107	156	145	360	342
B	198	203	101	121	231	226
C	296	291	276	283	430	413
D	430	386	325	298	110	119

Save them as set_1.csv- thats like an ods / xls(x) file but its basically a text file with the values separated by commas. In the current versions of LibreOffice / OpenOffice / Microsoft office theres an option "Save as" > "csv".

Load the package.

library("bioset")

Then you can use set_read() to get all values with their position as name in a nice tibble:

set_read()

set	position	sample_id	name	value
1	A1	A1	A1	102
1	B1	B1	B1	198
1	C1	C1	C1	296
1	D1	D1	D1	430
1	A2	A2	A2	107
1	B2	B2	B2	203
1	C2	C2	C2	291
1	D2	D2	D2	386
1	A3	A3	A3	156
1	B3	B3	B3	101
1	C3	C3	C3	276
1	D3	D3	D3	325
1	A4	A4	A4	145
1	B4	B4	B4	121
1	C4	C4	C4	283
1	D4	D4	D4	298
1	A5	A5	A5	360
1	B5	B5	B5	231
1	C5	C5	C5	430
1	D5	D5	D5	110
1	A6	A6	A6	342
1	B6	B6	B6	226
1	C6	C6	C6	413
1	D6	D6	D6	119

set_read() automagically reads set_1.csv in your current directory. If you have more than one set use set_read(num = 2) to read set 2, etc.

If your files are called plate_1.csv, plate_2.csv, ..., (run_1.csv, run_1.csv) you can set file_name = "plate_#NUM#.csv" (run_#NUM#.csv, ...).

If your files are stored in ./files/ tell set_read() where to look via path = "./files/".

Naming the values

Before feeding your samples into your measuring device you most likely drafted some sort of plan which position corresponds to which sample (didn't you?).

	1	2	3	4	5	6
A	CAL1	CAL1	A	A	B	B
B	CAL2	CAL2	C	C	D	D
C	CAL3	CAL3	E	E	F	F
D	CAL4	CAL4	G	G	H	H

So you had some calibrators (1-4) and samples A, B, C, D, E, F, G, H, each in duplicates.

To easily set the names for your samples just copy the names into your set_1.csv:

	1	2	3	4	5	6
A	102	107	156	145	360	342
B	198	203	101	121	231	226
C	296	291	276	283	430	413
D	430	386	325	298	110	119
E	CAL1	CAL1	A	A	B	B
F	CAL2	CAL2	C	C	D	D
G	CAL3	CAL3	E	E	F	F
H	CAL4	CAL4	G	G	H	H

Tell set_read() your data contains the names and which column should hold those names by setting additional_vars = c("name").

set_read(
  additional_vars = c("name")
)

This will get you:

set	position	sample_id	name	value
1	A1	CAL1	CAL1	102
1	B1	CAL2	CAL2	198
1	C1	CAL3	CAL3	296
1	D1	CAL4	CAL4	430
1	A2	CAL1	CAL1	107
1	B2	CAL2	CAL2	203
1	C2	CAL3	CAL3	291
1	D2	CAL4	CAL4	386
1	A3	A	A	156
1	B3	C	C	101
1	C3	E	E	276
1	D3	G	G	325
1	A4	A	A	145
1	B4	C	C	121
1	C4	E	E	283
1	D4	G	G	298
1	A5	B	B	360
1	B5	D	D	231
1	C5	F	F	430
1	D5	H	H	110
1	A6	B	B	342
1	B6	D	D	226
1	C6	F	F	413
1	D6	H	H	119

Encoding additional properties

Suppose samples A, B, C, D were taken at day 1 and E, F, G, H were taken from the same rats / individuals / patients on day 2.

It would be more elegant to encode that into the data:

	1	2	3	4	5	6
A	102	107	156	145	360	342
B	198	203	101	121	231	226
C	296	291	276	283	430	413
D	430	386	325	298	110	119
E	CAL1	CAL1	A_1	A_1	B_1	B_1
F	CAL2	CAL2	C_1	C_1	D_1	D_1
G	CAL3	CAL3	A_2	A_2	B_2	B_2
H	CAL4	CAL4	C_2	C_2	D_2	D_2

Now, tell set_read() your data contains the names and day by setting additional_vars = c("name", "day"). This will get you:

set_read(
  additional_vars = c("name", "day")
)

set	position	sample_id	name	day	value
1	A1	CAL1	CAL1	NA	102
1	B1	CAL2	CAL2	NA	198
1	C1	CAL3	CAL3	NA	296
1	D1	CAL4	CAL4	NA	430
1	A2	CAL1	CAL1	NA	107
1	B2	CAL2	CAL2	NA	203
1	C2	CAL3	CAL3	NA	291
1	D2	CAL4	CAL4	NA	386
1	A3	A_1	A	1	156
1	B3	C_1	C	1	101
1	C3	A_2	A	2	276
1	D3	C_2	C	2	325
1	A4	A_1	A	1	145
1	B4	C_1	C	1	121
1	C4	A_2	A	2	283
1	D4	C_2	C	2	298
1	A5	B_1	B	1	360
1	B5	D_1	D	1	231
1	C5	B_2	B	2	430
1	D5	D_2	D	2	110
1	A6	B_1	B	1	342
1	B6	D_1	D	1	226
1	C6	B_2	B	2	413
1	D6	D_2	D	2	119

Calculating concentrations

Propably, your measuring device only gave you raw values (extinction rates / relative light units / ...). You know the concentrations of CAL1, CAL2, CAL3 and CAL4. Conveniently, the concentrations follow a linear relationship. To get the concentrations for the rest of the samples you need to interpolate between those calibrators.

set_calc_concentrations() does exactly this for you:

set_calc_concentrations(
  data,
  cal_names = c("CAL1", "CAL2", "CAL3", "CAL4"),
  cal_values = c(1, 2, 3, 4) # ng / ml
)

set	position	sample_id	name	day	value	real	conc	recovery
1	A1	CAL1	CAL1	NA	102	1	1.0089686	1.0089686
1	B1	CAL2	CAL2	NA	198	2	1.9656203	0.9828102
1	C1	CAL3	CAL3	NA	296	3	2.9422023	0.9807341
1	D1	CAL4	CAL4	NA	430	4	4.2775286	1.0693822
1	A2	CAL1	CAL1	NA	107	1	1.0587942	1.0587942
1	B2	CAL2	CAL2	NA	203	2	2.0154459	1.0077230
1	C2	CAL3	CAL3	NA	291	3	2.8923767	0.9641256
1	D2	CAL4	CAL4	NA	386	4	3.8390633	0.9597658
1	A3	A_1	A	1	156	NA	1.5470852	NA
1	B3	C_1	C	1	101	NA	0.9990035	NA
1	C3	A_2	A	2	276	NA	2.7428999	NA
1	D3	C_2	C	2	325	NA	3.2311908	NA
1	A4	A_1	A	1	145	NA	1.4374689	NA
1	B4	C_1	C	1	121	NA	1.1983059	NA
1	C4	A_2	A	2	283	NA	2.8126557	NA
1	D4	C_2	C	2	298	NA	2.9621325	NA
1	A5	B_1	B	1	360	NA	3.5799701	NA
1	B5	D_1	D	1	231	NA	2.2944694	NA
1	C5	B_2	B	2	430	NA	4.2775286	NA
1	D5	D_2	D	2	110	NA	1.0886896	NA
1	A6	B_1	B	1	342	NA	3.4005979	NA
1	B6	D_1	D	1	226	NA	2.2446437	NA
1	C6	B_2	B	2	413	NA	4.1081216	NA
1	D6	D_2	D	2	119	NA	1.1783757	NA

Your calibrators are not so linear? Perhaps after a ln-ln transformation? You can use: model_func = fit_lnln and interpolate_func = interpolate_lnln. Basicallly, you can use any function as model_function that returns a model which is understood by your interpolate-func.

Duplicates / Triplicates / ...

So samples were measured in duplicates. For our further research you might want to use the mean and perhaps exclude samples with too much spread in their values.

set_calc_variability() to the rescue.

data <- set_calc_variability(
  data = data,
  ids = sample_id,
  value,
  conc
)

This will give you the mean and coefficient of variation (as well as n of the samples and the standard deviation) for the columns value and conc. It will use sample_id to determine which rows belong to the same sample.

set	position	sample_id	name	day	value	real	conc	recovery	value_n	value_mean	value_sd	value_cv	conc_n	conc_mean	conc_sd	conc_cv
1	A1	CAL1	CAL1	NA	102	1	1.0089686	1.0089686	2	104.5	3.535534	0.0338329	2	1.033881	0.0352320	0.0340774
1	B1	CAL2	CAL2	NA	198	2	1.9656203	0.9828102	2	200.5	3.535534	0.0176336	2	1.990533	0.0352320	0.0176998
1	C1	CAL3	CAL3	NA	296	3	2.9422023	0.9807341	2	293.5	3.535534	0.0120461	2	2.917289	0.0352320	0.0120770
1	D1	CAL4	CAL4	NA	430	4	4.2775286	1.0693822	2	408.0	31.112698	0.0762566	2	4.058296	0.3100418	0.0763970
1	A2	CAL1	CAL1	NA	107	1	1.0587942	1.0587942	2	104.5	3.535534	0.0338329	2	1.033881	0.0352320	0.0340774
1	B2	CAL2	CAL2	NA	203	2	2.0154459	1.0077230	2	200.5	3.535534	0.0176336	2	1.990533	0.0352320	0.0176998
1	C2	CAL3	CAL3	NA	291	3	2.8923767	0.9641256	2	293.5	3.535534	0.0120461	2	2.917289	0.0352320	0.0120770
1	D2	CAL4	CAL4	NA	386	4	3.8390633	0.9597658	2	408.0	31.112698	0.0762566	2	4.058296	0.3100418	0.0763970
1	A3	A_1	A	1	156	NA	1.5470852	NA	2	150.5	7.778175	0.0516822	2	1.492277	0.0775105	0.0519411
1	B3	C_1	C	1	101	NA	0.9990035	NA	2	111.0	14.142136	0.1274066	2	1.098655	0.1409281	0.1282733
1	C3	A_2	A	2	276	NA	2.7428999	NA	2	279.5	4.949747	0.0177093	2	2.777778	0.0493248	0.0177569
1	D3	C_2	C	2	325	NA	3.2311908	NA	2	311.5	19.091883	0.0612902	2	3.096662	0.1902529	0.0614381
1	A4	A_1	A	1	145	NA	1.4374689	NA	2	150.5	7.778175	0.0516822	2	1.492277	0.0775105	0.0519411
1	B4	C_1	C	1	121	NA	1.1983059	NA	2	111.0	14.142136	0.1274066	2	1.098655	0.1409281	0.1282733
1	C4	A_2	A	2	283	NA	2.8126557	NA	2	279.5	4.949747	0.0177093	2	2.777778	0.0493248	0.0177569
1	D4	C_2	C	2	298	NA	2.9621325	NA	2	311.5	19.091883	0.0612902	2	3.096662	0.1902529	0.0614381
1	A5	B_1	B	1	360	NA	3.5799701	NA	2	351.0	12.727922	0.0362619	2	3.490284	0.1268353	0.0363395
1	B5	D_1	D	1	231	NA	2.2944694	NA	2	228.5	3.535534	0.0154728	2	2.269557	0.0352320	0.0155237
1	C5	B_2	B	2	430	NA	4.2775286	NA	2	421.5	12.020815	0.0285191	2	4.192825	0.1197889	0.0285700
1	D5	D_2	D	2	110	NA	1.0886896	NA	2	114.5	6.363961	0.0555804	2	1.133533	0.0634176	0.0559469
1	A6	B_1	B	1	342	NA	3.4005979	NA	2	351.0	12.727922	0.0362619	2	3.490284	0.1268353	0.0363395
1	B6	D_1	D	1	226	NA	2.2446437	NA	2	228.5	3.535534	0.0154728	2	2.269557	0.0352320	0.0155237
1	C6	B_2	B	2	413	NA	4.1081216	NA	2	421.5	12.020815	0.0285191	2	4.192825	0.1197889	0.0285700
1	D6	D_2	D	2	119	NA	1.1783757	NA	2	114.5	6.363961	0.0555804	2	1.133533	0.0634176	0.0559469

The short way

If you need to read and transform multiple sets sets_read can do that for you.

It takes basically the same arguments as set_read, set_calc_concentrations and set_calc_variability combined and combines their functionality. The principal difference is, that sets_read takes sets - the number of sets to process.

It returns a list and may (write_data = TRUE) create two files in your current directory: data_all.csv and data_samples.csv with the processed data.

sets_read()'s list holds the following items:

$all: here you will find all the data , including calibrators, duplicates, ... (saved in data_all.csv if write_data = TRUE)
$samples: only one row per distinct sample here - no calibrators, no duplicates -> most often you will work with this data (saved in data_samples.csv if write_data = TRUE)
$set1: a list
- $plot: a plot showing you the function used to calculate the concentrations for this set. The points represent the calibrators.
- $model: the model as returned by model_func
($set2 - $setN): the same information for every set you have

Take a look at the data

# now you may run it :)
result_list <- sets_read(
  sets = 1,
  sep = ",",
  additional_vars = c("name", "day"),
  cal_names = c("CAL1", "CAL2", "CAL3", "CAL4"),
  cal_values = c(1, 2, 3, 4) # ng / ml
)

result_list$all

set	position	sample_id	name	day	value	real	recovery	n	raw	raw_mean	raw_sd	raw_cv	concentration	concentration_sd	concentration_cv
1	A1	CAL1	CAL1	NA	102	1	1.0089686	2	102	104.5	3.535534	0.0338329	1.033881	0.0352320	0.0340774
1	B1	CAL2	CAL2	NA	198	2	0.9828102	2	198	200.5	3.535534	0.0176336	1.990533	0.0352320	0.0176998
1	C1	CAL3	CAL3	NA	296	3	0.9807341	2	296	293.5	3.535534	0.0120461	2.917289	0.0352320	0.0120770
1	D1	CAL4	CAL4	NA	430	4	1.0693822	2	430	408.0	31.112698	0.0762566	4.058296	0.3100418	0.0763970
1	A2	CAL1	CAL1	NA	107	1	1.0587942	2	107	104.5	3.535534	0.0338329	1.033881	0.0352320	0.0340774
1	B2	CAL2	CAL2	NA	203	2	1.0077230	2	203	200.5	3.535534	0.0176336	1.990533	0.0352320	0.0176998
1	C2	CAL3	CAL3	NA	291	3	0.9641256	2	291	293.5	3.535534	0.0120461	2.917289	0.0352320	0.0120770
1	D2	CAL4	CAL4	NA	386	4	0.9597658	2	386	408.0	31.112698	0.0762566	4.058296	0.3100418	0.0763970
1	A3	A_1	A	1	156	NA	NA	2	156	150.5	7.778175	0.0516822	1.492277	0.0775105	0.0519411
1	B3	C_1	C	1	101	NA	NA	2	101	111.0	14.142136	0.1274066	1.098655	0.1409281	0.1282733
1	C3	A_2	A	2	276	NA	NA	2	276	279.5	4.949747	0.0177093	2.777778	0.0493248	0.0177569
1	D3	C_2	C	2	325	NA	NA	2	325	311.5	19.091883	0.0612902	3.096662	0.1902529	0.0614381
1	A4	A_1	A	1	145	NA	NA	2	145	150.5	7.778175	0.0516822	1.492277	0.0775105	0.0519411
1	B4	C_1	C	1	121	NA	NA	2	121	111.0	14.142136	0.1274066	1.098655	0.1409281	0.1282733
1	C4	A_2	A	2	283	NA	NA	2	283	279.5	4.949747	0.0177093	2.777778	0.0493248	0.0177569
1	D4	C_2	C	2	298	NA	NA	2	298	311.5	19.091883	0.0612902	3.096662	0.1902529	0.0614381
1	A5	B_1	B	1	360	NA	NA	2	360	351.0	12.727922	0.0362619	3.490284	0.1268353	0.0363395
1	B5	D_1	D	1	231	NA	NA	2	231	228.5	3.535534	0.0154728	2.269557	0.0352320	0.0155237
1	C5	B_2	B	2	430	NA	NA	2	430	421.5	12.020815	0.0285191	4.192825	0.1197889	0.0285700
1	D5	D_2	D	2	110	NA	NA	2	110	114.5	6.363961	0.0555804	1.133533	0.0634176	0.0559469
1	A6	B_1	B	1	342	NA	NA	2	342	351.0	12.727922	0.0362619	3.490284	0.1268353	0.0363395
1	B6	D_1	D	1	226	NA	NA	2	226	228.5	3.535534	0.0154728	2.269557	0.0352320	0.0155237
1	C6	B_2	B	2	413	NA	NA	2	413	421.5	12.020815	0.0285191	4.192825	0.1197889	0.0285700
1	D6	D_2	D	2	119	NA	NA	2	119	114.5	6.363961	0.0555804	1.133533	0.0634176	0.0559469

result_list$samples

position	sample_id	name	day	plate	n	raw	raw_sd	raw_cv	concentration	concentration_sd	concentration_cv
A3	A_1	A	1	1	2	150.5	7.778175	0.0516822	1.492277	0.0775105	0.0519411
B3	C_1	C	1	1	2	111.0	14.142136	0.1274066	1.098655	0.1409281	0.1282733
C3	A_2	A	2	1	2	279.5	4.949747	0.0177093	2.777778	0.0493248	0.0177569
D3	C_2	C	2	1	2	311.5	19.091883	0.0612902	3.096662	0.1902529	0.0614381
A5	B_1	B	1	1	2	351.0	12.727922	0.0362619	3.490284	0.1268353	0.0363395
B5	D_1	D	1	1	2	228.5	3.535534	0.0154728	2.269557	0.0352320	0.0155237
C5	B_2	B	2	1	2	421.5	12.020815	0.0285191	4.192825	0.1197889	0.0285700
D5	D_2	D	2	1	2	114.5	6.363961	0.0555804	1.133533	0.0634176	0.0559469

result_list$set1$plot

Name		Name	Last commit message	Last commit date
Latest commit History 102 Commits
R		R
inst/extdata		inst/extdata
man		man
revdep		revdep
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
badge_cran_release.svg		badge_cran_release.svg
badge_github_release.svg		badge_github_release.svg
badge_r_build_failing.svg		badge_r_build_failing.svg
badge_r_build_passing.svg		badge_r_build_passing.svg
badge_r_check_failing.svg		badge_r_check_failing.svg
badge_r_check_passing.svg		badge_r_check_passing.svg
bioset.Rproj		bioset.Rproj
cran-comments.md		cran-comments.md

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

bioset

Installation

Why? What bioset can do for you

Data import

Naming the values

Encoding additional properties

Calculating concentrations

Duplicates / Triplicates / ...

The short way

About

Releases 8

Packages

Languages

	1	2	3	4	5	6
A	102	107	156	145	360	342
B	198	203	101	121	231	226
C	296	291	276	283	430	413
D	430	386	325	298	110	119

set	position	sample_id	name	value
1	A1	A1	A1	102
1	B1	B1	B1	198
1	C1	C1	C1	296
1	D1	D1	D1	430
1	A2	A2	A2	107
1	B2	B2	B2	203
1	C2	C2	C2	291
1	D2	D2	D2	386
1	A3	A3	A3	156
1	B3	B3	B3	101
1	C3	C3	C3	276
1	D3	D3	D3	325
1	A4	A4	A4	145
1	B4	B4	B4	121
1	C4	C4	C4	283
1	D4	D4	D4	298
1	A5	A5	A5	360
1	B5	B5	B5	231
1	C5	C5	C5	430
1	D5	D5	D5	110
1	A6	A6	A6	342
1	B6	B6	B6	226
1	C6	C6	C6	413
1	D6	D6	D6	119

	1	2	3	4	5	6
A	102	107	156	145	360	342
B	198	203	101	121	231	226
C	296	291	276	283	430	413
D	430	386	325	298	110	119

set	position	sample_id	name	value
1	A1	A1	A1	102
1	B1	B1	B1	198
1	C1	C1	C1	296
1	D1	D1	D1	430
1	A2	A2	A2	107
1	B2	B2	B2	203
1	C2	C2	C2	291
1	D2	D2	D2	386
1	A3	A3	A3	156
1	B3	B3	B3	101
1	C3	C3	C3	276
1	D3	D3	D3	325
1	A4	A4	A4	145
1	B4	B4	B4	121
1	C4	C4	C4	283
1	D4	D4	D4	298
1	A5	A5	A5	360
1	B5	B5	B5	231
1	C5	C5	C5	430
1	D5	D5	D5	110
1	A6	A6	A6	342
1	B6	B6	B6	226
1	C6	C6	C6	413
1	D6	D6	D6	119

License

randomchars42/bioset

Folders and files

Latest commit

History

Repository files navigation

bioset

Installation

Why? What bioset can do for you

Data import

Naming the values

Encoding additional properties

Calculating concentrations

Duplicates / Triplicates / ...

The short way

About

Resources

License

Stars

Watchers

Forks

Releases 8

Packages 0

Languages

Packages

	1	2	3	4	5	6
A	102	107	156	145	360	342
B	198	203	101	121	231	226
C	296	291	276	283	430	413
D	430	386	325	298	110	119

set	position	sample_id	name	value
1	A1	A1	A1	102
1	B1	B1	B1	198
1	C1	C1	C1	296
1	D1	D1	D1	430
1	A2	A2	A2	107
1	B2	B2	B2	203
1	C2	C2	C2	291
1	D2	D2	D2	386
1	A3	A3	A3	156
1	B3	B3	B3	101
1	C3	C3	C3	276
1	D3	D3	D3	325
1	A4	A4	A4	145
1	B4	B4	B4	121
1	C4	C4	C4	283
1	D4	D4	D4	298
1	A5	A5	A5	360
1	B5	B5	B5	231
1	C5	C5	C5	430
1	D5	D5	D5	110
1	A6	A6	A6	342
1	B6	B6	B6	226
1	C6	C6	C6	413
1	D6	D6	D6	119