Biology Reference
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principles in their treatment of the data; and to set a lowest meaningful fold
change in data comparison, in contrast to the usual twofold change as recom-
mended in the literature.
In the recent past, many new pre-processing methods for Affymetrix GeneChip
data have been developed, and there are conflicting reports about the performance
of each algorithm [41-43]. Because there is no consensus about the most accu-
rate analysis methods, contrasting methods can be combined for gene discovery
[44]. We used the following three methods in data analyses: the microarray suite
software (MAS; Affymetrix) and Genspring; the DNA Chip analyzer (dCHIP)
package [45]; and GC robust multi-array average analysis (gcRMA) [46]. MAS
uses a nonparametric statistical method in data analyses, whereas dCHIP uses
an intensity modeling approach [47]. dCHIP removes outlier probe intensities,
and reduces the between-replicate variation [48]. A more recent method, gcRMA
uses a model-based background correction and a robust linear model to calcu-
late signal intensities. Depending on the particular question to be addressed, one
may wish to identify genes that are expressed in the embryo sac with the highest
probability possible and to use a very stringent statistical treatment (for example,
dCHIP), or one may wish to obtain the widest possible range of genes that are
potentially expressed in the embryo sac and employ a less stringent method (for
example, MAS). We did not wish to discriminate between the three methods in
our analysis, and we provide data for all of them.
Although conventionally twofold change criteria have been followed in a num-
ber of microarray studies, it has been disputed whether fold change should be used
at all to study differential gene expression (for review, see [49]). Based on studies
correlating both microarray and quantitative RT-PCR data, it was suggested that
genes exhibiting 1.4-fold change could be used reliably [50,51]. Tung and cowork-
ers used a minimum fold change as low as 1.2 in order to identify differentially
expressed genes in Arabidopsis pistils within specific cell types, and the results were
spatially validated [52]. In order to make a decision on our fold change criterion in
the data analysis, we examined the dataset for validation of embryo sac expressed
genes that had previously been reported. We found that genes such as CyclinA2;4
(coa dataset) and ORC2 (spl dataset) were identified at a fold change of 1.28. In
addition, out of the 43 predicted genes at 1.28-fold change from coa and spl data-
sets, 33% were present in triplicate datasets from laser captured central cells (Wuest
S, Vijverberg K, Grossniklaus U, unpublished data), independently confirming
their expression in at least one cell of the embryo sac. Therefore, the baseline cut-
of for subtraction was set at 1.28-fold in the wild type, and a total of 1,260 genes
were identified as putative candidates for expression in the female gametophyte.
However, it must be noted that lowering the fold change potentially increases
the incidence of false-positive findings. By setting the baseline to 1.28, we could
