This R markdown example demonstrates the basic functionality of the lans2r package.
To load data into R, export it from LANS which creates a folder for
each analysis with sub folders dat containing the
aggregated information about the different ROIs (in text file format)
and mat containing the raw ion maps (in Matlab file
format). Both of these can be imported easily with this package. For
easier demonstration lans2r bundles a set of 3 analyses
(folders analysis1
, analysis2
and
analysis3
) with the package sources.
library(lans2r)
library(dplyr)
library(knitr)
<- system.file("extdata", "nanosims_data", package = "lans2r") # data base directory folder
This loads the ROI overview data for the 3 analyses and assigns some
additional information to the analyses (here rather random, column
info
). Since the parameters quiet=F
indicates
that information messages should be provided, it also outputs a summary
of the loaded data.
<-
data load_LANS_summary (
analysis = c("analysis1", "analysis2", "analysis3"), # the analysis folders
base_dir = folder, # the data base director
load_zstacks = TRUE, # whether to load z-stacks as well (have to be exported from LANS!)
info = c("turtle", "jetpack", "pizza"), # any additional information about the analyses
quiet = FALSE # output information about the files
)
## INFO: folder 'analysis1' read successfully.
## Data for 37 ROIs with 4 ions recovered: 12C, 13C, 14N12C, 15N12C.
## Z-stacks were loaded. Recovered 2 planes.
## INFO: folder 'analysis2' read successfully.
## Data for 33 ROIs with 4 ions recovered: 12C, 13C, 14N12C, 15N12C.
## Z-stacks were loaded. Recovered 2 planes.
## INFO: folder 'analysis3' read successfully.
## Data for 34 ROIs with 4 ions recovered: 12C, 13C, 14N12C, 15N12C.
## Z-stacks were loaded. Recovered 2 planes.
To calculate ratios and abundances, simply specify which ions you
would like to ratio. Note: for convenience, we make use of the pipe
operator %>%
for chaining multiple operations. For more
information on the pipe, take a look at the magrittr
package.
<- data %>%
data calculate_sums(c(`13C`, `12C`), c(`15N12C`, `14N12C`)) %>%
calculate_ratios(c(`13C`, `12C`), c(`15N12C`, `14N12C`), c(`13C+12C`, `15N12C+14N12C`)) %>%
calculate_abundances(c(`13C`, `12C`), c(`15N12C`, `14N12C`))
## INFO: 624 'ion_sum' values + errors calculated, 624 added (subset: all)
## values added (stored in 'variable' column): '13C+12C' (312x), '15N12C+14N12C' (312x)
## INFO: 936 'ratio' values + errors calculated, 936 added (subset: all)
## values added (stored in 'variable' column): '13C+12C/15N12C+14N12C' (312x), '13C/12C' (312x), '15N12C/14N12C' (312x)
## INFO: 624 'abundance' values + errors calculated, 624 added (subset: all)
## values added (stored in 'variable' column): '13C F' (312x), '15N12C F' (312x)
For additional operations, one can use the more generic
calculate
function and provide custom functions for value
and error calculations and name construction. Here, we have APE (atom
percent enrichment) as an example. For additional examples on
calculate
, see the
vignette("lans2r-calculate")
.
<- data %>%
data mutate(F13C_natural = 1.11/100, F15N_natural = 0.366/100) %>%
calculate(
data_type = "APE",
c(`13C F`, `F13C_natural`, `13C F sigma`),
c(`15N12C F`, `F15N_natural`, `15N12C F sigma`),
# calculate the APE value as the fractional abundance enrichment above natural
value_fun = function(val, nat, sigma) 100*(val - nat),
# assume the error in natural abundance is negligible so only F error propagates
error_fun = function(val, nat, sigma) 100*sigma,
# replace the F in the existing columnes with APE to make the new variable
name_fun = function(val, ...) sub("F", "APE [%]", deparse(substitute(val)))
)
## INFO: 624 'APE' values + errors calculated, 624 added (subset: all)
## values added (stored in 'variable' column): '13C APE [%]' (312x), '15N12C APE [%]' (312x)
Let’s take a look at the first couple of rows of the data frame.
%>% head(n=10) %>% knitr::kable() data
analysis | info | plane | ROI | data_type | variable | value | sigma | coord_x | coord_y | size | pixels | LW_ratio | F13C_natural | F15N_natural |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
analysis1 | turtle | all | 1 | ion_count | 12C | 1895850 | 1376.89869 | 17.38 | 192.93 | 0.83 | 353 | 2.45 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 2 | ion_count | 12C | 1273919 | 1128.68020 | 18.39 | 175.25 | 0.75 | 290 | 1.57 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 3 | ion_count | 12C | 1315417 | 1146.91630 | 40.38 | 168.26 | 0.70 | 250 | 1.66 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 4 | ion_count | 12C | 1289955 | 1135.76186 | 42.05 | 207.80 | 0.72 | 267 | 2.16 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 5 | ion_count | 12C | 1756159 | 1325.20149 | 42.92 | 147.66 | 0.81 | 334 | 2.50 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 6 | ion_count | 12C | 1023934 | 1011.89624 | 46.56 | 103.93 | 0.67 | 232 | 2.11 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 7 | ion_count | 12C | 5509 | 74.22264 | 52.29 | 22.77 | 1.32 | 893 | 1.09 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 8 | ion_count | 12C | 1117870 | 1057.29372 | 61.77 | 120.06 | 0.62 | 195 | 2.39 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 9 | ion_count | 12C | 1122839 | 1059.64098 | 63.38 | 146.02 | 0.65 | 217 | 2.02 | 0.0111 | 0.00366 |
analysis1 | turtle | all | 10 | ion_count | 12C | 1046665 | 1023.06647 | 74.39 | 98.29 | 0.66 | 221 | 2.23 | 0.0111 | 0.00366 |
Since this is now in long format so it’s easy to have both
value
and the sigma
error, it’s hard to see
line by line what is going on, let’s look just at analysis1
and recast the values into a wide format using the
spread_data
function.
%>% spread_data() %>% head(n=10) %>% kable() data
analysis | info | plane | ROI | coord_x | coord_y | size | pixels | LW_ratio | F13C_natural | F15N_natural | 12C | 13C | 13C APE [%] | 13C F | 13C+12C | 13C+12C/15N12C+14N12C | 13C/12C | 14N12C | 15N12C | 15N12C APE [%] | 15N12C F | 15N12C+14N12C | 15N12C/14N12C | 12C sigma | 13C APE [%] sigma | 13C F sigma | 13C sigma | 13C+12C sigma | 13C+12C/15N12C+14N12C sigma | 13C/12C sigma | 14N12C sigma | 15N12C APE [%] sigma | 15N12C F sigma | 15N12C sigma | 15N12C+14N12C sigma | 15N12C/14N12C sigma |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
analysis1 | turtle | all | 1 | 17.38 | 192.93 | 0.83 | 353 | 2.45 | 0.0111 | 0.00366 | 1895850 | 24315 | 0.1562974 | 0.0126630 | 1920165 | 0.2413707 | 0.0128254 | 7839685 | 115568 | 1.0867256 | 0.0145273 | 7955253 | 0.0147414 | 1376.89869 | 0.0080692 | 0.0000807 | 155.932678 | 1385.70018 | 0.0001941 | 0.0000828 | 2799.9437 | 0.0042422 | 0.0000424 | 339.95294 | 2820.5058 | 0.0000437 |
analysis1 | turtle | all | 2 | 18.39 | 175.25 | 0.75 | 290 | 1.57 | 0.0111 | 0.00366 | 1273919 | 16087 | 0.1370485 | 0.0124705 | 1290006 | 0.2259213 | 0.0126280 | 5625982 | 83997 | 1.1050562 | 0.0147106 | 5709979 | 0.0149302 | 1128.68020 | 0.0097706 | 0.0000977 | 126.834538 | 1135.78431 | 0.0002202 | 0.0001002 | 2371.9153 | 0.0050382 | 0.0000504 | 289.82236 | 2389.5562 | 0.0000519 |
analysis1 | turtle | all | 3 | 40.38 | 168.26 | 0.70 | 250 | 1.66 | 0.0111 | 0.00366 | 1315417 | 17084 | 0.1721004 | 0.0128210 | 1332501 | 0.2262195 | 0.0129875 | 5802914 | 87388 | 1.1175912 | 0.0148359 | 5890302 | 0.0150593 | 1146.91630 | 0.0097460 | 0.0000975 | 130.705777 | 1154.34007 | 0.0002170 | 0.0001000 | 2408.9238 | 0.0049813 | 0.0000498 | 295.61461 | 2426.9944 | 0.0000513 |
analysis1 | turtle | all | 4 | 42.05 | 207.80 | 0.72 | 267 | 2.16 | 0.0111 | 0.00366 | 1289955 | 16221 | 0.1318694 | 0.0124187 | 1306176 | 0.2229323 | 0.0125749 | 5773380 | 85690 | 1.0965188 | 0.0146252 | 5859070 | 0.0148423 | 1135.76186 | 0.0096900 | 0.0000969 | 127.361690 | 1142.88057 | 0.0002157 | 0.0000994 | 2402.7859 | 0.0049595 | 0.0000496 | 292.72854 | 2420.5516 | 0.0000511 |
analysis1 | turtle | all | 5 | 42.92 | 147.66 | 0.81 | 334 | 2.50 | 0.0111 | 0.00366 | 1756159 | 22267 | 0.1420622 | 0.0125206 | 1778426 | 0.2427387 | 0.0126794 | 7218762 | 107741 | 1.1045652 | 0.0147057 | 7326503 | 0.0149251 | 1325.20149 | 0.0083379 | 0.0000834 | 149.221312 | 1333.57639 | 0.0002029 | 0.0000855 | 2686.7754 | 0.0044471 | 0.0000445 | 328.23924 | 2706.7514 | 0.0000458 |
analysis1 | turtle | all | 6 | 46.56 | 103.93 | 0.67 | 232 | 2.11 | 0.0111 | 0.00366 | 1023934 | 13166 | 0.1595015 | 0.0126950 | 1037100 | 0.2303558 | 0.0128583 | 4434979 | 67186 | 1.1263043 | 0.0149230 | 4502165 | 0.0151491 | 1011.89624 | 0.0109934 | 0.0001099 | 114.743191 | 1018.38107 | 0.0002509 | 0.0001128 | 2105.9390 | 0.0057142 | 0.0000571 | 259.20262 | 2121.8306 | 0.0000589 |
analysis1 | turtle | all | 7 | 52.29 | 22.77 | 1.32 | 893 | 1.09 | 0.0111 | 0.00366 | 5509 | 75 | 0.2331232 | 0.0134312 | 5584 | 0.1079555 | 0.0136141 | 51019 | 706 | 0.9989106 | 0.0136491 | 51725 | 0.0138380 | 74.22264 | 0.1540455 | 0.0015405 | 8.660254 | 74.72617 | 0.0015207 | 0.0015827 | 225.8739 | 0.0510173 | 0.0005102 | 26.57066 | 227.4313 | 0.0005244 |
analysis1 | turtle | all | 8 | 61.77 | 120.06 | 0.62 | 195 | 2.39 | 0.0111 | 0.00366 | 1117870 | 13971 | 0.1243607 | 0.0123436 | 1131841 | 0.2294823 | 0.0124979 | 4858391 | 73758 | 1.1294536 | 0.0149545 | 4932149 | 0.0151816 | 1057.29372 | 0.0103784 | 0.0001038 | 118.198985 | 1063.88016 | 0.0002392 | 0.0001064 | 2204.1758 | 0.0054651 | 0.0000547 | 271.58424 | 2220.8442 | 0.0000563 |
analysis1 | turtle | all | 9 | 63.38 | 146.02 | 0.65 | 217 | 2.02 | 0.0111 | 0.00366 | 1122839 | 14201 | 0.1389446 | 0.0124894 | 1137040 | 0.2325619 | 0.0126474 | 4818643 | 70550 | 1.0769784 | 0.0144298 | 4889193 | 0.0146411 | 1059.64098 | 0.0104149 | 0.0001041 | 119.167949 | 1066.32078 | 0.0002421 | 0.0001068 | 2195.1408 | 0.0053933 | 0.0000539 | 265.61250 | 2211.1520 | 0.0000555 |
analysis1 | turtle | all | 10 | 74.39 | 98.29 | 0.66 | 221 | 2.23 | 0.0111 | 0.00366 | 1046665 | 13408 | 0.1548186 | 0.0126482 | 1060073 | 0.2328937 | 0.0128102 | 4484536 | 67210 | 1.1105762 | 0.0147658 | 4551746 | 0.0149871 | 1023.06647 | 0.0108538 | 0.0001085 | 115.792919 | 1029.59847 | 0.0002512 | 0.0001113 | 2117.6723 | 0.0056534 | 0.0000565 | 259.24892 | 2133.4821 | 0.0000582 |
Or for more specific overviews, for example, only the abundance and APE, only the data values (excluding the errors) and only the first plane of the first few ROIs
%>% filter(data_type %in% c("abundance", "APE"), plane == "1", ROI < 4) %>%
data spread_data(errors = FALSE) %>% kable()
analysis | info | plane | ROI | coord_x | coord_y | size | pixels | LW_ratio | F13C_natural | F15N_natural | 13C APE [%] | 13C F | 15N12C APE [%] | 15N12C F |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
analysis1 | turtle | 1 | 1 | 17.38 | 192.93 | 0.83 | 353 | 2.45 | 0.0111 | 0.00366 | 0.1645876 | 0.0127459 | 1.058593 | 0.0142459 |
analysis1 | turtle | 1 | 2 | 18.39 | 175.25 | 0.75 | 290 | 1.57 | 0.0111 | 0.00366 | 0.1446352 | 0.0125464 | 1.090331 | 0.0145633 |
analysis1 | turtle | 1 | 3 | 40.38 | 168.26 | 0.70 | 250 | 1.66 | 0.0111 | 0.00366 | 0.1711441 | 0.0128114 | 1.092983 | 0.0145898 |
analysis2 | jetpack | 1 | 1 | 25.28 | 128.10 | 1.38 | 976 | 1.04 | 0.0111 | 0.00366 | 0.2299359 | 0.0133994 | 2.633973 | 0.0299997 |
analysis2 | jetpack | 1 | 2 | 28.10 | 203.98 | 0.66 | 221 | 2.29 | 0.0111 | 0.00366 | 0.5019285 | 0.0161193 | 3.283767 | 0.0364977 |
analysis2 | jetpack | 1 | 3 | 40.89 | 21.97 | 0.66 | 225 | 1.73 | 0.0111 | 0.00366 | 0.4311799 | 0.0154118 | 3.361709 | 0.0372771 |
analysis3 | pizza | 1 | 1 | 31.42 | 209.93 | 0.82 | 348 | 3.32 | 0.0111 | 0.00366 | 0.4568719 | 0.0156687 | 3.324170 | 0.0369017 |
analysis3 | pizza | 1 | 2 | 32.36 | 37.12 | 0.73 | 276 | 1.89 | 0.0111 | 0.00366 | 0.2947689 | 0.0140477 | 3.316543 | 0.0368254 |
analysis3 | pizza | 1 | 3 | 40.78 | 151.29 | 1.50 | 1158 | 1.55 | 0.0111 | 0.00366 | 0.0854406 | 0.0119544 | 2.731920 | 0.0309792 |
Plot all the data using the ggplot package.
library(ggplot2)
%>%
data ggplot() +
aes(size, value, color = paste(analysis, info), shape = plane) +
geom_errorbar(aes(ymin = value - 2*sigma, ymax = value + 2*sigma), colour="black", width = 0) +
geom_point(size=3) +
labs(x = expression("ROI size ["*mu*"m"^2*"]"), y="",
title = expression("ROI summary (2"*sigma*" error bars, may be smaller than symbols)"),
color = "Analysis") +
facet_wrap(~variable, scales="free", nrow = 2) +
theme_bw()
Focus in on the combined counts (not the individual planes from the z-stack) and look just at ratios:
last_plot() %+% (data %>% filter(plane == "all", data_type == "ratio"))
Again, loading the ion maps for all 3 analyses.
<-
maps load_LANS_maps (
analysis = c("analysis1", "analysis2", "analysis3"),
base_dir = folder
)
## INFO: folder 'analysis1' read successfully.
## Ion map data for 256 x 256 pixel frame (10.014 microm^2) for 4 ions recovered: 12C, 13C, 14N12C, 15N12C.
## 37 ROIs identified in the frame.
## INFO: folder 'analysis2' read successfully.
## Ion map data for 256 x 256 pixel frame (10.014 microm^2) for 4 ions recovered: 12C, 13C, 14N12C, 15N12C.
## 33 ROIs identified in the frame.
## INFO: folder 'analysis3' read successfully.
## Ion map data for 256 x 256 pixel frame (10.014 microm^2) for 4 ions recovered: 12C, 13C, 14N12C, 15N12C.
## 34 ROIs identified in the frame.
The data in these looks similar to the summary data frame except that it is broken out pixel by pixel:
%>% head(n=10) %>% kable() maps
analysis | x.px | y.px | frame_size.px | x.um | y.um | frame_size.um | variable | data_type | value | sigma | ROI |
---|---|---|---|---|---|---|---|---|---|---|---|
analysis1 | 1 | 1 | 256 | 0.0391172 | 0.0391172 | 10.014 | 12C | ion_count | 1721 | 41.48494 | 0 |
analysis1 | 1 | 2 | 256 | 0.0391172 | 0.0782344 | 10.014 | 12C | ion_count | 1694 | 41.15823 | 0 |
analysis1 | 1 | 3 | 256 | 0.0391172 | 0.1173516 | 10.014 | 12C | ion_count | 1508 | 38.83298 | 0 |
analysis1 | 1 | 4 | 256 | 0.0391172 | 0.1564688 | 10.014 | 12C | ion_count | 1297 | 36.01389 | 0 |
analysis1 | 1 | 5 | 256 | 0.0391172 | 0.1955859 | 10.014 | 12C | ion_count | 1136 | 33.70460 | 0 |
analysis1 | 1 | 6 | 256 | 0.0391172 | 0.2347031 | 10.014 | 12C | ion_count | 786 | 28.03569 | 0 |
analysis1 | 1 | 7 | 256 | 0.0391172 | 0.2738203 | 10.014 | 12C | ion_count | 702 | 26.49528 | 0 |
analysis1 | 1 | 8 | 256 | 0.0391172 | 0.3129375 | 10.014 | 12C | ion_count | 453 | 21.28380 | 0 |
analysis1 | 1 | 9 | 256 | 0.0391172 | 0.3520547 | 10.014 | 12C | ion_count | 319 | 17.86057 | 0 |
analysis1 | 1 | 10 | 256 | 0.0391172 | 0.3911719 | 10.014 | 12C | ion_count | 220 | 14.83240 | 0 |
To make it easier to plot these kind of maps, lans2r
provides a convenience function plot_maps
but of course
this could be adjusted as needed (look at the source code to see how
this one is made). By default ion counts are normalized for each ion so
they can be visualized on the same scale.
plot_maps(maps)