gen5helper: A Collection of Functions for Processing Gen5 2.06 Exported Data

Description

A collection of functions for processing Gen5 2.06 exported data. Gen 5 is a popular data analysis software for BioTek plate readers. This packages contains functions for data cleaning, modeling and plotting using exported data from Gen5 version 2.06. It exports technically correct data defined in (Edwin de Jonge and Mark van der Loo, 2013) for customized analysis. It contains Boltzmann fitting for general kinetic analysis. It also implement line plot and bar plot for generating publishable figures. See https://www.github.com/yanxianUCSB/gen5helper for more information, documentation and examples.

Boltzmann model for fitting time series data

Description

Boltzmann model for fitting time series data

Usage

Boltzmann(time_, val_, A0 = 1, k0 = 1, t20 = 1)

Arguments

Value

a model

Examples

Boltzmann(1:10, c(0,0,1,3,5,7,9,10,10,10), A0 = 10, k0 = 10, t20 = 5)

Cast an object to match class of another object

Description

Cast an object to match class of another object

Usage

as.is(x, vec)

Arguments

Examples

as.is(c("1", "2", "3"), 1:3)

this is a file for functions that are universally useful at common data manipulations Convert factor to numeric

Description

this is a file for functions that are universally useful at common data manipulations Convert factor to numeric

Usage

factor2num(x)

Arguments

Examples

factor2num(factor(c('1', '10', '100')))

Fit readings with Boltzmann model

Description

fit.boltzmann() using Boltzmann model to fit readings and time intervals with unit of hours, using start as initial guesses. It appends A, y0, k, t2 and val.predict, while preserving existing variables.

Usage

fit.boltzmann(.data, A0 = 1, k0 = 1, t20 = 1)

Arguments

Value

data.frame with fitted parameter and predicted value

Examples

fit.boltzmann(data.frame(x=1:10,y=c(0,0,1,3,5,7,9,10,10,10)), A0 = 10, k0 = 10, t20 = 5)

Annotate a cleaned dataset

Description

Add time interval in hour from the oldest timestamp

Usage

g5h.annotate(.data, by = "col")

Arguments

Value

data.frame

Examples

# suppose "gen5_export.txt" is the export from Gen5 2.06

g5h.clean2("gen5_export.txt") %>%
    g5h.annotate()

Clean Gen5 exported data

Description

g5h.clean2() returns technically correct data.frame from Gen5 2.06 exported tab-delim data. The exported data can be generated using default export protocol in Gen5 2.06. See Gen5 User Guide for more information.

Usage

g5h.clean2(files)

Arguments

Value

technically correct data.frame.

Examples

# suppose "exported_data_1.txt" and "exported_data_2.txt" are the exports from Gen5 2.06

# this line will clean one exported data
data <- g5h.clean2("exported_data_1.txt")

# this line will clean two exported data and return one appended dataset
data <- g5h.clean2(c("exported_data_1.txt", "exported_data_2.txt"))

Clean Gen5 exported data

Description

g5h.clean_() returns technically correct data.frame from Gen5 2.06 exported tab-delim data. The exported data can be generated using default export protocol in Gen5 2.06. See Gen5 User Guide for more information.

Usage

g5h.clean_(file)

Arguments

Value

technically correct data.frame.

Add time intervals to cleaned dataset

Description

g5h.set_time() preserves existing variables and add new variable, time, which are time intervals in hours.

Usage

g5h.set_time2(.data, units = "hours")

Arguments

Value

input data.frame appended with time

Get half time by linear fitting

Description

get half time according to http://www.amylofit.ch.cam.ac.uk The algorithm for the extraction of the half times proceeds as follows: first the middle part of the curve is selected, by determining when the average over several points is first above 0.3 and when the average is last below 0.7. The number of points to be averaged over depends on the number of points in the curve. A straight line is then fitted to this middle part of the curve, the point at which it crosses the value of 0.5 is recorded as the half time. (source: DOI: nprot.2016.010)

Usage

get.halftime(time, val)

Arguments

Value

half time

Examples

get.halftime(c(1:10), c(0,1,2,3,4,5,6,7,8,9))
get.halftime(c(1:10), c(0,0,1,3,5,7,9,10,10,10))

Map the unique values of a vector

Description

Map the unique values of a vector

Usage

mapvalues_(x, facs, bNaturalSort = FALSE)

Arguments

Value

factor

Examples

mapvalues_(c("A","A","B","C"), c("one", "two", "three"))
mapvalues_(c("apple", "apple", "banana", "pineable", "pineable"), c(3, 2, 1), bNaturalSort = TRUE)

Return most frequent numbers

Description

get the n most frequent elements in an array

Usage

most.freq(x, n = 1)

Arguments

Value

the most n elements

Examples

most.freq(c('a', 'a', 'b', 'b', 'b', 'c'), n = 2)
most.freq(c(1, 1, 2, 3, 3, 3, 4, 4), n = 2)

Scale a vector to 0-1 by min and max

Description

Scale a vector to 0-1 by min and max

Usage

normalize(x, na.rm = TRUE)

Arguments

Value

a normalized vector

Examples

normalize(0:10)
normalize(c(1, 100, NA, 10), na.rm = TRUE)

Return range of a vector

Description

This computes the range of a vector as a value.

Usage

range_(x, na.rm = TRUE)

Arguments

Value

numeric value

Examples

range_(c(1, 5, 10))
range_(c(1, 5, 10, NA), na.rm = TRUE)

saveRDS and return .data

Description

This function returns the object passed in, can be used for dplyr pipeline.

Usage

saveRDS_(.data, file, ...)

Arguments

Value

whatever object passed in.

Examples

data <- data.frame(a=1, b=2, c=3)
data %>%
    saveRDS_(file.path(tempdir(), "data.rds")) %>%
    print()

Smooth a vector using moving average

Description

Smooth a vector using moving average

Usage

smooth.mean(vec, naverage)

Arguments

Value

smoothed

Examples

smooth.mean(1:10, 2)
smooth.mean(1:10, 3)
smooth.mean(1:10, 5)

Run ungroup() and as.data.frame()

Description

Run ungroup() and as.data.frame()

Usage

ungroup_(.data)

Arguments

Value

an ungrouped data.frame()

Examples

data <- data.frame(m=c(1,2), n=c(2,3), group=c('a','b'))
data %>%
    group_by(group) %>%
    ungroup_()

write.csv without row.names

Description

This function returns the object passed in, can be used for dplyr pipeline.

Usage

write.csv_(x, file)

Arguments

Value

whatever object passed in

Examples

write.csv_(data.frame(a=1, b=2, c=3), file.path(tempdir(), "data.csv"))

data <- data.frame(a=1, b=2, c=3)
data %>%
    file.path(tempdir(), "data.csv") %>%
    print()