opm an R package to analyse OmniLog Phenotype MicroArray data

Transkript

1 An introduction to opm opm an R package to analyse OmniLog Phenotype MicroArray data Dr. Johannes Sikorski, Dr. Lea Vaas, Dr. Markus Göker Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH

2 You have numerous OmniLog Phenotype MicroArray data of closely related organisms or cell lines of numerous well-defined mutants obtained under diverse physiological test conditions and you want to explore them full-fledged and quantitatively into diverse directions of analysis frameworks.

3 opm: Tools for analysing OmniLog(R) Phenotype Microarray data enables you: to organize your PM data, curve parameters and metadata to subset and query your data graphical display of raw kinetics or aggregated curve parameters exploit the full statistics implemented in R export to third-party software using YAML

4 Software requirements R is a free software environment for statistical computing and graphics. RStudio is a free and open source integrated development environment (IDE) for R. add-on package opm: Tools for analysing OmniLog(R) Phenotype Microarray data

5 R Code of this presentation The R code of this presentation is available on request from Dr. Johannes Sikorski johannes.sikorski@dsmz.de Dr. Lea Vaas l.vaas@cbs.knaw.nl Dr. Markus Göker markus.goeker@dsmz.de Feel free to contact us in case of any questions regarding usage of opm.

6 opm enables you: to organize your PM data, curve parameters and metadata to subset and query your data graphical display of raw kinetics or aggregated curve parameters exploit the full statistics implemented in R export to third-party software using YAML

7 OPM organizes your PM data in OPMS objects: Example: a set of 9 PM plates of the same plate type intensity Hour Hour lysin per well: raw kinetic data An OPMS object stores: raw kinetic data aggregated curve parameters metadata Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 The size of the OPMS object is only limited by the amount of RAM memory lysin mu lambda A AUC mu CI95 low lambda CI95 low A CI95 low AUC CI95 low mu CI95 high lambda CI95 high A CI95 high AUC CI95 high metadata Plate 3 Taxonomy Bacillus subtilis. habitat soil sampling place GPS coord. sampling date sampling season summer habitat [ C] 27. sporulation yes. PCR (gene xyz) positive.... as much and what you wish... per well: aggregated curve parameters, confidenceintervals from bootstrapping per plate: any metadata of interest to the user Lag = lambda, Slope = mu, Max = A, Area Under the Curve = AUC

8

9

10 Batch-export of OmniLog kinetic data as CSV files for opm (1) Change WINDOWS software language settings to American English. (2) Start the OmniLog software PMM_Kinetic.exe. (3) Import *.D5E files by -> Load > Import -> Select Data Folder -> Populate Filters -> Import -> Close. (4) Add all plates or selected plates from the Worksheet List to the Data List. (5) Export the data by -> Export -> Export Data. You may either choose One-line Header or Multi-line Header, but you should choose Every Plate (Individual Files). (6) Upon pressing the button Export Data a pop-up window opens in which you must enter a new directory name. (7) Upon pressing the save button batch-export of the data as individual CSV files per plate into the new directory will take place These files can then be imported by opm using the function read_opm().

11

12 read_opm() Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH

13 Load Demo files that come with the opm package read_opm(names, convert = c("try", "no", "yes", "sep", "grp"), gen.iii = FALSE, include = list(),..., demo = FALSE) # Use the built-in function opm_files() to retrieve the paths where the example files in your R installation are located: (files <- opm_files("testdata")) # read in the files, which are zipped # using the include argument to select specific plates of interest # by this, three files are loaded into the object "example.opm" example.opm <- read_opm(files, include = "*Example_?.csv.xz")

14 Load Demo files that come with the opm package read_opm(names, convert = c("try", "no", "yes", "sep", "grp"), gen.iii = FALSE, include = list(),..., demo = FALSE) # read in all CSV raw data files in your working directory PM1 <- read_opm(".") # read in all CSV raw data files in your working directory and convert the plate type to GenIII plates GenIII <- read_opm(".", gen.iii = TRUE)

15 Load Demo files that come with the opm package # let us check some information on the files in this OPMS object plates(example.opm) summary(example.opm) show(example.opm) dim(example.opm) hours(example.opm) length(example.opm) max(example.opm) plate_type(example.opm) seq(example.opm) setup_time(example.opm) measurements(example.opm) wells(example.opm) wells(example.opm, full = TRUE)

23 Load Demo files that come with the opm package # let us check some information on the files in this OPMS object plates(example.opm) summary(example.opm) show(example.opm) dim(example.opm) hours(example.opm) length(example.opm) max(example.opm) plate_type(example.opm) seq(example.opm) setup_time(example.opm) measurements(example.opm) returns the raw kinetic data wells(example.opm) wells(example.opm, full = TRUE)

26

27 do_aggr() aggregate only A and AUC using a fast algorithm x <- do_aggr(example.opm, program = "opm-fast") aggregate all 4 parameters using a spline fit algorithm (grofit package) x <- do_aggr(example.opm) include 100x bootstrap replicates x <- do_aggr(example.opm, program = "opm-fast", boot = 100) x <- do_aggr(example.opm, boot = 100) Note: time consuming

28 check aggregated data aggregated(example.opm) A01 A02 A03 A04 A07 mu lambda A AUC mu CI95 low lambda CI95 low A CI95 low AUC CI95 low mu CI95 high lambda CI95 high A CI95 high AUC CI95 high

29

30 OPM organizes your PM data in OPMS objects: Example: a set of 9 PM plates of the same plate type intensity Hour Hour lysin per well: raw kinetic data An OPMS object stores: raw kinetic data aggregated curve parameters metadata Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 You need to provide the metadata separately lysin mu lambda A AUC mu CI95 low lambda CI95 low A CI95 low AUC CI95 low mu CI95 high lambda CI95 high A CI95 high AUC CI95 high metadata Plate 3 Taxonomy Bacillus subtilis. habitat soil sampling place GPS coord. sampling date sampling season summer habitat [ C] 27. sporulation yes. PCR (gene xyz) positive.... as much and what you wish... per well: aggregated curve parameters, confidenceintervals from bootstrapping per plate: any metadata of interest to the user Lag = lambda, Slope = mu, Max = A, Area Under the Curve = AUC

31 You need to provide the metadata separately One Problem Arises: Imagine, you have numerous plates with numerous metadata to each plate. How can we make sure that the metadata are matched CORRECTLY to the specific raw kinetic data? Solution: We need an identifier that perfectly matches metadata to raw kinetic data. We use as identifier the Setup Time and Position of the plate in the reader. Good news: opm allows to export these informations as a start for the metadata file using the function: collect_template()

32 collect_template() data frame add further metadata columns metadata <- collect_template(files, include = "*Example_?.csv.xz") Unique identifier to merge metadata and raw kinetic data

33 collect_template() data frame CSV file (or *.txt, *.dat) add further metadata columns add further metadata columns in a spreadsheed application collect_template(files, include = "*Example_?.csv.xz", outfile = "template.csv") note the FORMAT: columns are tab separated, fields protected by quotation marks

34 collect_template() data frame add further metadata columns CSV file (or *.txt, *.dat) add further metadata columns save tab separated and use quotation marks as field protector load file into R environment using to_metadata()

35 collect_template() data frame add further metadata columns CSV file (or *.txt, *.dat) add further metadata columns save tab separated and use quotation marks as field protector load file into R environment using metadata.example <- to_metadata("template.csv") metadata.example <- to_metadata("template.csv", strip.white = FALSE) metadata.example <- to_metadata("template.csv", sep = ",")

36 collect_template() data frame add further metadata columns CSV file (or *.txt, *.dat) add further metadata columns further added metadata columns Note: mock metadata for demonstration purpose

37 include_metadata() data frame with metadata metadata OPMS object with kinetic raw data example.opm example.opm.metadata <- include_metadata(example.opm, md = metadata)

38

39 Discretize the `maximum height value into positive, negative and ambiguous reactions # perform exact k-means partitioning disc <- do_disc(example.opm, cutoff = TRUE) # check the discretization settings disc_settings(disc[1]) $program [1] "kmeans" frequency k-means partitioning of maximum height values negative weak positive $options $options$cutoffs [1] $options$datasets [1] maximum height [A] k-means partitioning the algorithm divides the data into three groups by minimizing the sum of squares of within-cluster distances from each element to its corresponding cluster centre (mean).

40 Export discretized results for wells A01 to A012: # export as text discretized(disc[,,c(1:12)]) TRUE = positive reaction NA = ambiguous (weak) reaction FALSE = negative reaction

41 # export as html file formatted for IJSEM disc.html <- phylo_data(disc, format = "html", as.labels = c("species", "strain"), outfile = "disc.html") A01 (Negative Control) - - w A02 (L-Arabinose) A03 (N-Acetyl-D-Glucosamine) A04 (D-Saccharic Acid) A05 (Succinic Acid) - - w A06 (D-Galactose) A07 (L-Aspartic Acid) - - w A08 (L-Proline) - - w A09 (D-Alanine) - - w A10 (D-Trehalose) A11 (D-Mannose) A12 (Dulcitol) Strains: 1, Escherichia coli Donald; 2, Bacillus simplex Batman; 3, Escherichia coli Mickey. +, positive metabolic response; w, weak metabolic response; -, negative metabolic response.

42

43

44 draw kinetic data xy_plot(example.opm)

45 xy_plot(example.opm) Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH

46 xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2)

47 xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2, legend.fmt = list(space = "right"))

48 xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2, legend.fmt = list(space = "right"), include = c("species", "strain"))

49 xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2, legend.fmt = list(space = "right"), include = c("species", "strain")) Modify panel strip, strip text, and legend by using arguments from lattice

50 xy_plot(example.opm[,, ]) What about drawing only parts? It is possible to plot (1) specific plates, (2) time points, or (3) wells by indexing OPMS objects using square brackets. xy_plot(example.opm[plates, time points, wells])

51 xy_plot(example.opm[,, ])

52 xy_plot(example.opm[ 3,, ])

53 xy_plot(example.opm[ 3, 1:100, ])

54 xy_plot(example.opm[ 3, 1:100, c("a01", "A02", "E05", "G08", "H10")])

55 xy_plot(example.opm[ 3, 1:100, c("a01", "A02", "E05", "G08", "H10")])

56 Heatmaps compare plates on the basis of aggregated curve parameters

57 The generation of heatmaps includes two steps: (1) Extract the curve parameter values using extract() (2) Create the heatmap using heat_map()

58 First step: AUC <- extract(example.opm, dataframe = TRUE, as.labels = list("country", "Species", "strain", "town"), subset = "AUC") metadata of interest parameter and values from aggregating the curve parameters

59 Second step: heat_map(auc, as.labels = c("species", "town"), as.groups = "town", cexrow = 1.2, use.fun = "gplots", main = "nice heatmap", col = topo.colors(120))

60 heat_map(auc, as.labels = c("species", "town"), as.groups = "town", cexrow = 1.2, use.fun = "gplots", main = "nice heatmap", col = topo.colors(120))

61 Confidence interval plot Do curves differ significantly in aggregated curve parameters? We make use of the 95% confidence intervals calculated from 100 bootstrap replicates.

62 xy_plot(example.opm) In which aggregated curve parameters do these curves differ significantly?

63 xy_plot(example.opm[,,"d10"], include = list("species","town"), neg.ctrl = FALSE)

64 ci_plot(example.opm[,, c("d10")], as.labels = list("species","town"), subset = "A")

65 ci_plot(example.opm[,, c("d10")], as.labels = list("species","town"), subset = "AUC")

66 ci_plot(example.opm[,, c("d10")], as.labels = list("species","town"), subset = "lambda")

67 xy_plot(example.opm) Do these curves differ in their lag phase? Try yourself

68

69 radial_plot(example.opm[,, 5:17], sep = " ", as.labels = c("species", "town"), draw.legend = FALSE, subset = "AUC")

70

71 xy_plot(example.opm[plates, time points, wells]) data(vaas_et_al) -114 GenIII plates (run 96 hours) - numerous replicates of - each two strains of Escherichia coli and Pseudomonas aeruginosa, - including aggregated bootstrapped curve parameters and metadata

72 data(vaas_et_al)

73

74