README.md
In benjbuch/summerrband: Analyse Gel-shift Assays
summerrband
summerrband plays for you gel-shift data from “tapes” recorded on
Cytiva’s ImageQuant TL. Wherever the data comes from, once it’s in a
table (data.frame), this package gives you also a handle on fitting
dissociation constants in various ways.
Installation
Since this package is not part of CRAN,
you must install and update the development version from
GitHub with:
# install.packages("devtools")
devtools::install_github("benjbuch/summerrband")
Showcases
library(summerrband)
#> Loading required package: summerr
#>
#> Verbose logging: `options(summerr.log = TRUE)`
#>
#> Attaching package: ‘summerr’
#> The following object is masked from ‘package:base’:
#>
#> normalizePath
A Song on Import-Me
This is how a raw IQTL file looks like (truncated):
#> V1 V2 V3 V4 V5 V6 V7
#> 1: Lane 1 Lane 2
#> 2: Band No Volume Vol+BkGnd Calib Vol(ug) MW (kd) Rf Band No
#> 3: 1 1836717.76 10316698.58 - - 0.318 1
#> 4: 2 708701.15 11036571.63 - - 0.711 2
#> V8 V9 V10 V11 V12 V13 V14
#> 1: Lane 3
#> 2: Volume Vol+BkGnd Calib Vol(ug) MW (kd) Rf Band No Volume
#> 3: 1952433.94 10790313.74 - - 0.305 1 1579557.00
#> 4: 1058242.09 13741043.81 - - 0.688 2 1212406.97
This is how the same file will look like after tidy importing. As an
extra benefit of calling iqtl_view instead of iqtl_read, we’ll play
a preview alongside:
iqtl_view(my_file)
#> lane_id band_id vol vol_frac vol+bkg CAL MW Rf
#> 1: lane_1 band_0 708701.2 0.27842221 11036572 NA NA 0.711
#> 2: lane_1 band_1 1836717.8 0.72157779 10316699 NA NA 0.318
#> 3: lane_2 band_0 1058242.1 0.35149650 13741044 NA NA 0.688
#> 4: lane_2 band_1 1952433.9 0.64850350 10790314 NA NA 0.305
#> 5: lane_3 band_0 1212407.0 0.43424879 12996290 NA NA 0.685
#> 6: lane_3 band_1 1579557.0 0.56575121 9462001 NA NA 0.282
#> 7: lane_4 band_0 1836166.1 0.58849169 17137079 NA NA 0.672
#> 8: lane_4 band_1 1283956.3 0.41150831 8149479 NA NA 0.266
#> 9: lane_5 band_0 1790299.8 0.67452721 15580045 NA NA 0.646
#> 10: lane_5 band_1 863855.2 0.32547279 7848150 NA NA 0.273
#> 11: lane_6 band_0 2555067.5 0.77664741 14939456 NA NA 0.649
#> 12: lane_6 band_1 734800.5 0.22335259 7719780 NA NA 0.299
#> 13: lane_7 band_0 2921065.4 0.85142851 15641146 NA NA 0.630
#> 14: lane_7 band_1 509716.4 0.14857149 7455754 NA NA 0.289
#> 15: lane_8 band_0 2971834.7 0.84904462 15490070 NA NA 0.627
#> 16: lane_8 band_1 528375.6 0.15095538 6659347 NA NA 0.305
#> 17: lane_9 band_0 3165996.8 0.91124556 16399084 NA NA 0.614
#> 18: lane_9 band_1 308365.0 0.08875444 7112698 NA NA 0.269
#> 19: lane_10 band_0 3193473.6 0.93275097 15649350 NA NA 0.633
#> 20: lane_10 band_1 230241.5 0.06724903 7311010 NA NA 0.273
#> 21: lane_11 band_0 3199637.7 0.95902255 15825951 NA NA 0.627
#> 22: lane_11 band_1 136715.2 0.04097745 7497813 NA NA 0.263
#> 23: lane_12 band_0 3382234.8 0.94492546 16349369 NA NA 0.656
#> 24: lane_12 band_1 197132.0 0.05507454 8754550 NA NA 0.292
#> lane_id band_id vol vol_frac vol+bkg CAL MW Rf
#> file
#> 1: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 2: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 3: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 4: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 5: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 6: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 7: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 8: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 9: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 10: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 11: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 12: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 13: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 14: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 15: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 16: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 17: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 18: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 19: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 20: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 21: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 22: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 23: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 24: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> file
#> gel_id
#> 1: gel_01
#> 2: gel_01
#> 3: gel_01
#> 4: gel_01
#> 5: gel_01
#> 6: gel_01
#> 7: gel_01
#> 8: gel_01
#> 9: gel_01
#> 10: gel_01
#> 11: gel_01
#> 12: gel_01
#> 13: gel_01
#> 14: gel_01
#> 15: gel_01
#> 16: gel_01
#> 17: gel_01
#> 18: gel_01
#> 19: gel_01
#> 20: gel_01
#> 21: gel_01
#> 22: gel_01
#> 23: gel_01
#> 24: gel_01
#> gel_id
Let’s assign some metadata along with the tunes:
my_data <- iqtl_meta(my_file, meta_data = list(conc = c(2^seq(10, 0), 1),
protein = "protA",
ligand = "DNA1"))
head(my_data)
#> lane_id band_id vol vol_frac vol+bkg CAL MW Rf
#> 1: lane_1 band_0 708701.2 0.2784222 11036572 NA NA 0.711
#> 2: lane_1 band_1 1836717.8 0.7215778 10316699 NA NA 0.318
#> 3: lane_2 band_0 1058242.1 0.3514965 13741044 NA NA 0.688
#> 4: lane_2 band_1 1952433.9 0.6485035 10790314 NA NA 0.305
#> 5: lane_3 band_0 1212407.0 0.4342488 12996290 NA NA 0.685
#> 6: lane_3 band_1 1579557.0 0.5657512 9462001 NA NA 0.282
#> file
#> 1: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 2: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 3: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 4: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 5: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 6: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> gel_id conc protein ligand
#> 1: gel_01 1024 protA DNA1
#> 2: gel_01 1024 protA DNA1
#> 3: gel_01 512 protA DNA1
#> 4: gel_01 512 protA DNA1
#> 5: gel_01 256 protA DNA1
#> 6: gel_01 256 protA DNA1
Of course, there is a batch function (iqtl_import_all) too.
Fitting the Tune and Tuning the Fit
Four lines to sing:
library(magrittr) # for the pipe operator
my_data %>%
dplyr::filter(band_id == "band_1") %>%
dplyr::group_by(protein, ligand) %>%
model_cleanly_groupwise(fit_Kd, formula = vol_frac ~ conc,
newdata = data.frame(conc = 10^seq(0, 3, length.out = 100))) %>%
model_display(color = ligand) + ggplot2::facet_wrap(ggplot2::vars(protein))
my_data %>%
dplyr::filter(band_id == "band_1") %>%
dplyr::group_by(protein, ligand) %>%
model_cleanly_groupwise(fit_Kd, formula = vol_frac ~ conc) %>%
dplyr::select(tidy) %>% tidyr::unnest(tidy)
#> Adding missing grouping variables: `protein`, `ligand`
#> # A tibble: 3 × 7
#> # Groups: protein, ligand [1]
#> protein ligand term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 protA DNA1 lower 0.0614 0.00983 6.25 1.50e- 4
#> 2 protA DNA1 upper 0.790 0.0244 32.3 1.27e-10
#> 3 protA DNA1 K_d 119. 14.4 8.25 1.73e- 5
We can also employ a model that doesn’t assume excess of the
non-titrated species R. So, when we provide its intial (total)
concentration R0 before the equilibrium is reached:
my_data %>%
dplyr::filter(band_id == "band_1") %>%
dplyr::group_by(protein, ligand) %>%
model_cleanly_groupwise(fit_Kd, formula = vol_frac ~ conc, R0 = 2.0) %>%
dplyr::select(tidy) %>% tidyr::unnest(tidy)
#> Adding missing grouping variables: `protein`, `ligand`
#> # A tibble: 3 × 7
#> # Groups: protein, ligand [1]
#> protein ligand term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 protA DNA1 lower 0.0617 0.00985 6.26 1.48e- 4
#> 2 protA DNA1 upper 0.789 0.0245 32.2 1.33e-10
#> 3 protA DNA1 K_d 118. 14.4 8.15 1.91e- 5
It is further possible to include a Hill slope if need be and to switch
between differen fitting algorithms in R.
benjbuch/summerrband documentation built on Dec. 19, 2021, 8:43 a.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.
summerrband
summerrband plays for you gel-shift data from “tapes” recorded on
Cytiva’s ImageQuant TL. Wherever the data comes from, once it’s in a
table (data.frame), this package gives you also a handle on fitting
dissociation constants in various ways.
Installation
Since this package is not part of CRAN, you must install and update the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("benjbuch/summerrband")
Showcases
library(summerrband)
#> Loading required package: summerr
#>
#> Verbose logging: `options(summerr.log = TRUE)`
#>
#> Attaching package: ‘summerr’
#> The following object is masked from ‘package:base’:
#>
#> normalizePath
A Song on Import-Me
This is how a raw IQTL file looks like (truncated):
#> V1 V2 V3 V4 V5 V6 V7
#> 1: Lane 1 Lane 2
#> 2: Band No Volume Vol+BkGnd Calib Vol(ug) MW (kd) Rf Band No
#> 3: 1 1836717.76 10316698.58 - - 0.318 1
#> 4: 2 708701.15 11036571.63 - - 0.711 2
#> V8 V9 V10 V11 V12 V13 V14
#> 1: Lane 3
#> 2: Volume Vol+BkGnd Calib Vol(ug) MW (kd) Rf Band No Volume
#> 3: 1952433.94 10790313.74 - - 0.305 1 1579557.00
#> 4: 1058242.09 13741043.81 - - 0.688 2 1212406.97
This is how the same file will look like after tidy importing. As an
extra benefit of calling iqtl_view instead of iqtl_read, we’ll play
a preview alongside:
iqtl_view(my_file)
#> lane_id band_id vol vol_frac vol+bkg CAL MW Rf
#> 1: lane_1 band_0 708701.2 0.27842221 11036572 NA NA 0.711
#> 2: lane_1 band_1 1836717.8 0.72157779 10316699 NA NA 0.318
#> 3: lane_2 band_0 1058242.1 0.35149650 13741044 NA NA 0.688
#> 4: lane_2 band_1 1952433.9 0.64850350 10790314 NA NA 0.305
#> 5: lane_3 band_0 1212407.0 0.43424879 12996290 NA NA 0.685
#> 6: lane_3 band_1 1579557.0 0.56575121 9462001 NA NA 0.282
#> 7: lane_4 band_0 1836166.1 0.58849169 17137079 NA NA 0.672
#> 8: lane_4 band_1 1283956.3 0.41150831 8149479 NA NA 0.266
#> 9: lane_5 band_0 1790299.8 0.67452721 15580045 NA NA 0.646
#> 10: lane_5 band_1 863855.2 0.32547279 7848150 NA NA 0.273
#> 11: lane_6 band_0 2555067.5 0.77664741 14939456 NA NA 0.649
#> 12: lane_6 band_1 734800.5 0.22335259 7719780 NA NA 0.299
#> 13: lane_7 band_0 2921065.4 0.85142851 15641146 NA NA 0.630
#> 14: lane_7 band_1 509716.4 0.14857149 7455754 NA NA 0.289
#> 15: lane_8 band_0 2971834.7 0.84904462 15490070 NA NA 0.627
#> 16: lane_8 band_1 528375.6 0.15095538 6659347 NA NA 0.305
#> 17: lane_9 band_0 3165996.8 0.91124556 16399084 NA NA 0.614
#> 18: lane_9 band_1 308365.0 0.08875444 7112698 NA NA 0.269
#> 19: lane_10 band_0 3193473.6 0.93275097 15649350 NA NA 0.633
#> 20: lane_10 band_1 230241.5 0.06724903 7311010 NA NA 0.273
#> 21: lane_11 band_0 3199637.7 0.95902255 15825951 NA NA 0.627
#> 22: lane_11 band_1 136715.2 0.04097745 7497813 NA NA 0.263
#> 23: lane_12 band_0 3382234.8 0.94492546 16349369 NA NA 0.656
#> 24: lane_12 band_1 197132.0 0.05507454 8754550 NA NA 0.292
#> lane_id band_id vol vol_frac vol+bkg CAL MW Rf
#> file
#> 1: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 2: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 3: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 4: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 5: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 6: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 7: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 8: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 9: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 10: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 11: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 12: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 13: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 14: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 15: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 16: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 17: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 18: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 19: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 20: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 21: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 22: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 23: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 24: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> file
#> gel_id
#> 1: gel_01
#> 2: gel_01
#> 3: gel_01
#> 4: gel_01
#> 5: gel_01
#> 6: gel_01
#> 7: gel_01
#> 8: gel_01
#> 9: gel_01
#> 10: gel_01
#> 11: gel_01
#> 12: gel_01
#> 13: gel_01
#> 14: gel_01
#> 15: gel_01
#> 16: gel_01
#> 17: gel_01
#> 18: gel_01
#> 19: gel_01
#> 20: gel_01
#> 21: gel_01
#> 22: gel_01
#> 23: gel_01
#> 24: gel_01
#> gel_id
Let’s assign some metadata along with the tunes:
my_data <- iqtl_meta(my_file, meta_data = list(conc = c(2^seq(10, 0), 1),
protein = "protA",
ligand = "DNA1"))
head(my_data)
#> lane_id band_id vol vol_frac vol+bkg CAL MW Rf
#> 1: lane_1 band_0 708701.2 0.2784222 11036572 NA NA 0.711
#> 2: lane_1 band_1 1836717.8 0.7215778 10316699 NA NA 0.318
#> 3: lane_2 band_0 1058242.1 0.3514965 13741044 NA NA 0.688
#> 4: lane_2 band_1 1952433.9 0.6485035 10790314 NA NA 0.305
#> 5: lane_3 band_0 1212407.0 0.4342488 12996290 NA NA 0.685
#> 6: lane_3 band_1 1579557.0 0.5657512 9462001 NA NA 0.282
#> file
#> 1: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 2: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 3: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 4: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 5: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> 6: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/summerrband/extdata/gel_01.txt
#> gel_id conc protein ligand
#> 1: gel_01 1024 protA DNA1
#> 2: gel_01 1024 protA DNA1
#> 3: gel_01 512 protA DNA1
#> 4: gel_01 512 protA DNA1
#> 5: gel_01 256 protA DNA1
#> 6: gel_01 256 protA DNA1
Of course, there is a batch function (iqtl_import_all) too.
Fitting the Tune and Tuning the Fit
Four lines to sing:
library(magrittr) # for the pipe operator
my_data %>%
dplyr::filter(band_id == "band_1") %>%
dplyr::group_by(protein, ligand) %>%
model_cleanly_groupwise(fit_Kd, formula = vol_frac ~ conc,
newdata = data.frame(conc = 10^seq(0, 3, length.out = 100))) %>%
model_display(color = ligand) + ggplot2::facet_wrap(ggplot2::vars(protein))
my_data %>%
dplyr::filter(band_id == "band_1") %>%
dplyr::group_by(protein, ligand) %>%
model_cleanly_groupwise(fit_Kd, formula = vol_frac ~ conc) %>%
dplyr::select(tidy) %>% tidyr::unnest(tidy)
#> Adding missing grouping variables: `protein`, `ligand`
#> # A tibble: 3 × 7
#> # Groups: protein, ligand [1]
#> protein ligand term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 protA DNA1 lower 0.0614 0.00983 6.25 1.50e- 4
#> 2 protA DNA1 upper 0.790 0.0244 32.3 1.27e-10
#> 3 protA DNA1 K_d 119. 14.4 8.25 1.73e- 5
We can also employ a model that doesn’t assume excess of the
non-titrated species R. So, when we provide its intial (total)
concentration R0 before the equilibrium is reached:
my_data %>%
dplyr::filter(band_id == "band_1") %>%
dplyr::group_by(protein, ligand) %>%
model_cleanly_groupwise(fit_Kd, formula = vol_frac ~ conc, R0 = 2.0) %>%
dplyr::select(tidy) %>% tidyr::unnest(tidy)
#> Adding missing grouping variables: `protein`, `ligand`
#> # A tibble: 3 × 7
#> # Groups: protein, ligand [1]
#> protein ligand term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 protA DNA1 lower 0.0617 0.00985 6.26 1.48e- 4
#> 2 protA DNA1 upper 0.789 0.0245 32.2 1.33e-10
#> 3 protA DNA1 K_d 118. 14.4 8.15 1.91e- 5
It is further possible to include a Hill slope if need be and to switch between differen fitting algorithms in R.
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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