NanoSIMS data processing of LANS data in R

ROI overview data

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)

data %>% head(n=10) %>% knitr::kable()

data %>% spread_data() %>% head(n=10) %>% kable()

data %>% filter(data_type %in% c("abundance", "APE"), plane == "1", ROI < 4) %>% 
  spread_data(errors = FALSE) %>% kable()

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()

last_plot() %+% (data %>% filter(plane == "all", data_type == "ratio"))

Ion maps

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:

maps %>% head(n=10) %>% kable()

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)

Focusing in on just one ion, we can ditch the normalization, and let’s also not draw ROIs for a direct look. Also, because it’s a ggplot, all ggplot modifications of the plot are fair game.

plot_maps(maps %>% filter(variable == "14N12C", analysis %in% c("analysis1", "analysis2")), 
          normalize = FALSE, draw_ROIs = FALSE) + 
  theme(legend.position = "right") + labs(fill = "ion count")