| experimentDesignTable {designGG} | R Documentation |
This function generates two .csv files which descibe how samples are allocated
samples into different conditions and paired on arrays.
experimentDesignTable( array.allocation, condition.allocation,
nEnvFactors, nLevels, Level, fileName,envFactorNames,
directory )
array.allocation |
matrix with nArray rows and nRIL columns. Elements of 1/0 indicate this RIL is/not selected for this array. |
condition.allocation |
matrix with nCondition rows and nRIL columns. Elements of 1/0 indicate this RIL is/not selected for this condition. |
nEnvFactors |
number of environmental factors, an integer bewteen 1 and 3.
When nEnvFactors is 1 and the number of levels for the enviromental factor
(nLevels)is 1, there is one condition in the experiment (i.e. no
enviromental perturbation) and thus only genetic factor will be considered in the algorithm.
When nEnvFactors is 1 and nLevels is larger than 1 or nEnvFactors is
larger than 1, all main factor(s) and interacting facotr(s) will be included.
Examples:
If there is a temperature perturbation, then nEnvFactors is 1;
If there is both temperature and drug treatment perturbation,
then nEnvFactors is 2.
|
nLevels |
number of levels for each factor, a vector with each
component being integer. The length of it should equal
nEnvFactors.
|
Level |
a list which specifies the levels for each factor in the
experiment. There are in total nEnvFactors elements in the list and each
element correpsond to certain envrironmental factor. The emlemet is a
vector describing all levels of the environmental factor.
default setting for the level of each factor is 1, 2, ...nLevels[i]. (Here
nLevels[i] is the ith element of nLevels, which gives the total number of
levels for i environmental facotor).
|
fileName |
the final optimal design table(s) in csv format and a plot (in png
format) of the all scores during SA process (if plotScores = T) will be
produced. The users can specify the table and plot name by setting
fileName.
If NULL (default) it produces files starting with "myDesignGG".
|
envFactorNames |
a vector with names for all environmental factor(s). For example, for the
experiment with two environmental factors of temperature and drug treatment:
envFactorNames <- c( "Temperature", "Dosage" ) Default = NULL, then the output will use "F1" and "F2" to indicate the
environmental factors.
|
directory |
It tells where the resulting optimal design tables are to be stored.
If NULL (default), it will use the currect working directory.
|
Based on nEnvFactors and nLevels, nConditions is calculated.
Two tables report the results: table "pair design" which is only used for
two-channel experiments and describes how samples are paired together on the
slide (e.g. microarray chip), and table "envi-ronment design" which is used
when there are more environments evolved in the experiment. With these two
tables, the experimenters can set up the environmental treatment and
follow-up profiling measurement.
Examples:
1. conditionDesign.csv
| Temperature | Cell Type | Selected Samples | |||||
| condition1 | 15 | A | RIL28 | RIL81 | RIL18 | RIL61 | |
| condition2 | 24 | A | RIL72 | RIL40 | RIL83 | RIL44 | RIL10 |
| condition3 | 29 | A | RIL22 | RIL89 | RIL3 | RIL30 | RIL58 |
| condition4 | 15 | B | RIL70 | RIL47 | RIL4 | RIL59 | |
| condition5 | 24 | B | RIL93 | RIL97 | RIL49 | RIL14 |
| Channel 1 | Channel 2 | |
| array1 | RIL28 | RIL92 |
| array2 | RIL70 | RIL47 |
| array3 | RIL22 | RIL89 |
| array4 | RIL45 | RIL15 |
| array5 | RIL52 | RIL41 |
The optimal design results are described in two tables. One is called "array design" which is only used for two-channel experiments. It describes how samples are paired together on the slide (e.g. microarray chip). The other table is called "condition design" which is used when there is more than one environmental factor involved in the experiment. Each cell in condition design table represents a combination of different levels of environmental factors and the selected sample names (e.g. RIL names) for this condition are shown. Based on these two tables, the experimenters can set up the environmental treatment and follow-up profiling measurement.
Yang Li <yang.li@rug.nl>, Gonzalo Vera <gonzalo.vera.rodriguez@gmail.com>
Rainer Breitling <r.breitling@rug.nl>, Ritsert Jansen <r.c.jansen@rug.nl>
Y. Li, R. Breitling and R.C. Jansen. Generalizing genetical
genomics: the added value from environmental perturbation, Trends Genet
(2008) 24:518-524.
Y. Li, M. Swertz, G. Vera, J. Fu, R. Breitling, and R.C. Jansen. designGG:
An R-package and Web tool for the optimal design of genetical genomics
experiments. (submitted)
http://gbic.biol.rug.nl/designGG
designGG, exampleArrayDesignTable, exampleConditionDesignTable