How to use opm [Kompatibilitätsmodus]€¦ · Leibniz-Institut DSMZ-Deutsche Sammlung von...

Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH

opm – an R package to analyse

OmniLog Phenotype MicroArray data

Dr. Johannes Sikorski, Dr. Lea Vaas, Dr. Markus Göker


www.dsmz.de

An introduction to opm


You have numerous OmniLog Phenotype MicroArray data

and you want to explore them full-fledged and quantitatively

into diverse directions of analysis frameworks.

• of closely related organisms or cell lines

• of numerous well-defined mutants

• obtained under diverse physiological

test conditions

www.biolog.com


� 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

opm: Tools for analysing OmniLog(R) Phenotype Microarray data

enables you:


http://www.rstudio.org/

RStudio™ is a free and open source

integrated development environment (IDE) for R.

http://www.r-project.org/

R is a free software environment for statistical computing and graphics.

http://cran.r-project.org/web/packages/opm/index.html

add-on package opm:

Tools for analysing OmniLog(R) Phenotype Microarray data

Software requirements


The R code of this presentation is available on request from

Dr. Johannes Sikorski [email protected]

R Code of this presentation

Dr. Lea Vaas [email protected]

Dr. Markus Göker [email protected]

Feel free to contact us in case of any questions regarding usage of opm.


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


OPM

organizes your PM data in OPMS objects:

Plate 1 Plate 2 Plate 3









raw kinetic data

aggregated curve

parameters

metadata

An OPMS object stores:

Example: a set of 9 PM plates of the same

plate type

The size of the OPMS object is only limited by the amount of RAM memory

Hour00.0000.2500.50.30.00.60.00

lysin353337.

102.

328Hour

inte

nsity

per well:raw kinetic data

metadata Plate 3

Taxonomy Bacillus subtilis.habitat soilsampling place GPS coord.sampling date 2011-06-15sampling season summerhabitat [°C] 27.sporulation yes.PCR (gene xyz) positive.... as much and what you wish...

per plate:any metadata of interest to the user

lysin

mu 15.559078 lambda 5.798210A 305.989319AUC 23308.269348mu CI95 low 3.803466lambda CI95 low 1.080333A CI95 low 305.642353AUC CI95 low 23125.092442mu CI95 high 140.841704lambda CI95 high 11.819251A CI95 high 306.986123AUC CI95 high 23411.648024

per well:aggregated curve parameters, confidence-intervals from bootstrapping

Lag = lambda, Slope = mu, Max = A, Area Under the Curve = AUC


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().


read_opm()


Load Demo files that come

with the opm package

# 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")

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)



read_opm(names, convert = c("try", "no", "yes", "sep", "grp"),

gen.iii = FALSE, include = list(), ..., demo = FALSE)




# 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)





plates(example.opm)


show(example.opm)

dim(example.opm)

hours(example.opm)

length(example.opm)

max(example.opm)


seq(example.opm)



wells(example.opm)


returns the raw kinetic data





plates(example.opm)


show(example.opm)

dim(example.opm)

hours(example.opm)

length(example.opm)

max(example.opm)


seq(example.opm)



wells(example.opm)



do_aggr()

x <- do_aggr(example.opm, program = "opm-fast")

aggregate only “A“ and “AUC“ using a fast algorithm

x <- do_aggr(example.opm)

aggregate all 4 parameters using a spline fit algorithm (grofit package)

x <- do_aggr(example.opm, program = "opm-fast", boot = 100)

include 100x bootstrap replicates

x <- do_aggr(example.opm , boot = 100) Note: time consuming


aggregated(example.opm)check aggregated data

A01 A02 A03 A04 A07mu 4.242159 5.769109 0.02138581 0.2827407 0.2383062lambda -2.340620 12.799329 -465.46803431 20.0749555 -14.4573092A 47.923185 62.738943 11.51078807 19.4617762 18.2811191AUC 3914.852139 4154.830048 1070.20657323 1250.9426009 1396.9447154mu CI95 low 2.733574 3.045267 -1.10076311 -2.2050686 -4.8515830lambda CI95 low -38.403543 -10.300782 56.14216650 42.4248855 24.8184260A CI95 low 47.197513 58.940763 11.17285004 19.1992801 16.9627344AUC CI95 low 3875.243148 4093.577722 1056.62986435 1230.3571787 1352.9702303mu CI95 high 14.170557 13.689212 6.15737265 9.3063345 21.5309783lambda CI95 high 79.044830 50.248293 87.70587107 106.1197708 107.3697670A CI95 high 52.484756 67.456369 15.37628753 23.6590936 30.0717055AUC CI95 high 3941.361758 4183.239559 1077.02925382 1262.9208049 1432.5071603


�

�


OPM

organizes your PM data in OPMS objects:










raw kinetic data

aggregated curve

parameters

metadata

An OPMS object stores:

Example: a set of 9 PM plates of the same

plate type

You need to provide

the metadata

separately

Hour00.0000.2500.50.30.00.60.00

lysin353337.

102.

328Hour

inte

nsity

per well:raw kinetic data

metadata Plate 3

Taxonomy Bacillus subtilis.habitat soilsampling place GPS coord.sampling date 2011-06-15sampling season summerhabitat [°C] 27.sporulation yes.PCR (gene xyz) positive.... as much and what you wish...

per plate:any metadata of interest to the user

lysin

mu 15.559078 lambda 5.798210A 305.989319AUC 23308.269348mu CI95 low 3.803466lambda CI95 low 1.080333A CI95 low 305.642353AUC CI95 low 23125.092442mu CI95 high 140.841704lambda CI95 high 11.819251A CI95 high 306.986123AUC CI95 high 23411.648024

per well:aggregated curve parameters, confidence-intervals from bootstrapping

Lag = lambda, Slope = mu, Max = A, Area Under the Curve = AUC


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()

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.


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


collect_template()

data frame


CSV file (or *.txt, *.dat)


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


collect_template()

data frame




save tab separated and use

quotation marks as field protector

load file into R environment using

to_metadata()


collect_template()

data frame




save tab separated and use

quotation marks as field protector

metadata.example <- to_metadata("template.csv", sep = ",")

metadata.example <- to_metadata("template.csv")

metadata.example <- to_metadata("template.csv", strip.white = FALSE)

load file into R environment using


collect_template()

data frame




further added metadata columns

Note: mock metadata for demonstration purpose


example.opm.metadata <- include_metadata(example.opm, md = metadata)

OPMS object with

kinetic raw data

data frame with metadata

metadata

example.opm

include_metadata()


�

��


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"

$options $options$cutoffs [1] 100.7844 228.5482

$options$datasets [1] 3

0

5

10

15

20

25

30

60 80 100 120 140 160 180 200 220 240 260 280 300maximum height [A]

freq

uenc

y

k-means partitioning of maximum height values

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).

negative weak positive


Export discretized results for wells A01 to A012for wells A01 to A012for wells A01 to A012for wells A01 to A012:

# export as text

discretized(disc[, ,c(1:12)])

TRUE = positive reaction

NA = ambiguous (weak) reaction

FALSE = negative reaction


# export as html file formatted for IJSEM

disc.html <- phylo_data(disc, format = "html",

as.labels = c("Species", "strain"), outfile = "disc.html")

Strains: 1, Escherichia coli Donald; 2, Bacillus simplex Batman; 3, Escherichia coli Mickey. +, positive metabolic response; w, weak metabolic response; -, negative metabolic response.

1 2 3

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) + + +


xy_plot(example.opm)draw kinetic data


xy_plot(example.opm)


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


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


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

include = c("Species", "strain"))


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


xy_plot(example.opm[plates, time points, wells])

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[ , , ])

What about drawing only parts?


xy_plot(example.opm[ , , ])


xy_plot(example.opm[ 3, , ])


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


xy_plot(example.opm[ 3, 1:100 , c("A01", "A02", "E05",

"G08", "H10")])


Heatmaps compare

plates on the basis of

aggregated curve

parameters


The generation of heatmaps includes two steps:

(1) Extract the curve parameter values using

extract()

(2) Create the heatmap using

heat_map()


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


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))


heat_map(AUC,

as.labels = c("Species", "town"),

as.groups = "town",

cexRow = 1.2,

use.fun = "gplots",

main = "nice heatmap",

col = topo.colors(120))


Confidence interval plot

Do curves differ

significantly in aggregated

curve parameters?

We make use of the 95%

confidence intervals

calculated from 100

bootstrap replicates.



In which

aggregated curve parameters

do these curves

differ significantly ?


xy_plot(example.opm[ , ,"D10"],

include = list("Species","town"), neg.ctrl = FALSE)


ci_plot(example.opm[ , , c("D10")],

as.labels = list("Species","town"), subset = "A")



as.labels = list("Species","town"), subset = "AUC")



as.labels = list("Species","town"), subset = "lambda")



Do these curves differ in their

lag phase?

Try yourself


radial_plot(example.opm[, , 5:17], sep = " ", as.labels = c("Species", "town"),

draw.legend = FALSE, subset = "AUC")


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


data(vaas_et_al)

How to use opm [Kompatibilitätsmodus]€¦ · Leibniz-Institut DSMZ-Deutsche Sammlung von...

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