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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
www.dsmz.de
An introduction to opm
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
� 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:
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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.
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
OPM
organizes your PM data in OPMS objects:
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
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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().
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
read_opm()
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
# 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)
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
returns the raw kinetic data
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
�
�
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
OPM
organizes your PM data in OPMS objects:
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
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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.
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
collect_template()
data frame
add further metadata columns
CSV file (or *.txt, *.dat)
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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()
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
example.opm.metadata <- include_metadata(example.opm, md = metadata)
OPMS object with
kinetic raw data
data frame with metadata
metadata
example.opm
include_metadata()
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
�
��
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
# 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) + + +
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm)draw kinetic data
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2, legend.fmt = list(space = "right"))
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm, col = c("blue", "red", "green"), lwd = 2, legend.fmt = list(space = "right"),
include = c("Species", "strain"))
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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?
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm[ , , ])
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm[ 3, , ])
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm[ 3, 1:100 , ])
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm[ 3, 1:100 , c("A01", "A02", "E05",
"G08", "H10")])
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm[ 3, 1:100 , c("A01", "A02", "E05",
"G08", "H10")])
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Heatmaps compare
plates on the basis of
aggregated curve
parameters
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
The generation of heatmaps includes two steps:
(1) Extract the curve parameter values using
extract()
(2) Create the heatmap using
heat_map()
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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))
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
heat_map(AUC,
as.labels = c("Species", "town"),
as.groups = "town",
cexRow = 1.2,
use.fun = "gplots",
main = "nice heatmap",
col = topo.colors(120))
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Confidence interval plot
Do curves differ
significantly in aggregated
curve parameters?
We make use of the 95%
confidence intervals
calculated from 100
bootstrap replicates.
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm)
In which
aggregated curve parameters
do these curves
differ significantly ?
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm[ , ,"D10"],
include = list("Species","town"), neg.ctrl = FALSE)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
ci_plot(example.opm[ , , c("D10")],
as.labels = list("Species","town"), subset = "A")
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
ci_plot(example.opm[ , , c("D10")],
as.labels = list("Species","town"), subset = "AUC")
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
ci_plot(example.opm[ , , c("D10")],
as.labels = list("Species","town"), subset = "lambda")
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
xy_plot(example.opm)
Do these curves differ in their
lag phase?
Try yourself
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
radial_plot(example.opm[, , 5:17], sep = " ", as.labels = c("Species", "town"),
draw.legend = FALSE, subset = "AUC")
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
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
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
data(vaas_et_al)
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH
Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH