The analysis was performed in both MNI space and the subject's OIS, due to potential concerns regarding the correctness of the measurements after data transformation and interpolation during the spatial normalization procedure.
Data transformation set measurements less than 0.01 to 0.01; 2. Each measurement was divided by the 50.0th percentile of all measurements in that sample; 3.
The normalization consists of the following three steps: first, data transformation (set measurements less than 300.0 to 300.0); second, per chip (normalize each chip to the 50th percentile of the measurements taken from that chip); and third, per gene (normalize each gene to the median of the measurements for that gene).
The following normalization steps were sequentially performed: (1) Data transformation: set measurements less than 0.01 to 0.01; (2) Per chip: normalize to a set of pre-determined genes, using all 15,820 P. sojae genes as control genes; (3) Per gene: normalize to median.
Normalisations included per spot and per chip intensity-dependent (Lowess) normalisations; data transformation set measurements less than 0.01 0.01 and per chip normalisations to the 50th percentile.
Microarray data were normalised using the genespring normalisation scenario for "Agilent FE one-color" which including "Data Transformation: Set measurements less than 5.0 to 5.0", "Per Chip: Normalise to 50th percentile" and "Per Gene: Normalise to median".
The intensity data sets for each time point were normalized according to the following three steps using the Subio platform basic plug-in program: data transformation (set measurements of less than 1 to 1), per chip normalization (normalization to the 75th percentile), and per gene normalization (using the wild-type log phase or stationary phase data).
The intensity data sets for each time point were normalized according to the following three steps using the Subio platform basic plug-in program: data transformation (set measurements of less than 1 to 1), per chip normalization (normalization to the 75th percentile), and per gene normalization (normalization to the mean).
The measurement data were obtained via a homogeneous transformation of the two-camera model in addition to measurement errors.
Abnormally distributed measurement data were converted to normally distributed data by logarithmic transformation and then analysed by variance analysis (t test).
The Z-score transformation above allowed us to convert the measurement data into rank data.
