SUMMARY Computational algorithms for selecting subsets of regression variables are discussed. Only linear models and the least‐squares criterion are considered. The use of planar‐rotation algorithms, instead of Gauss–Jordan methods, is advocated. The advantages and disadvantages of a number of “cheap” search methods are described for use when it is not feasible to carry out an exhaustive search for the best‐fitting subsets. Hypothesis testing for three purposes is considered, namely (i) testing for zero regression coefficients for remaining variables, (ii) comparing subsets and (iii) testing for any predictive value in a selected subset. Three small data sets are used to illustrate these tests. Spj⊘tvoll's (1972a) test is discussed in detail, though an extension to this test appears desirable. Estimation problems have largely been overlooked in the past. Three types of bias are identified, namely that due to the omission of variables, that due to competition for selection and that due to the stopping rule. The emphasis here is on competition bias, which can be of the order of two or more standard errors when coefficients are estimated from the same data as were used to select the subset. Five possible ways of handling this bias are listed. This is the area most urgently requiring further research. Mean squared errors of prediction and stopping rules are briefly discussed. Competition bias invalidates the use of existing stopping rules as they are commonly applied to try to produce optimal prediction equations.

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There is increasing evidence of the important roles of glutamate receptors (GLRs) in plant development and in adaptation to stresses. However, the studies of these putative ion channels, both in planta and in Xenopus oocytes, may have been limited by our lack of knowledge of possible GLR heteromer formation in plants. We have developed a modification of the single-cell sampling technique to investigate GLR co-expression, and thus potential heteromer formation, in single cells of Arabidopsis thaliana leaves. MicroEXpression amplification (MEX) has allowed us to amplify gene transcripts from a single cell, enabling expression of up to 100 gene transcripts to be assayed. We measured, on average, the transcripts of five to six different AtGLRs in a single cell. However, no consistent patterns of co-expression or cell-type-specific expression were detected, except that cells sampled from the same plant showed similar expression profiles. The only discernible feature was the detection of AtGLR3.7 in every cell examined, an observation supported by GUS staining patterns in plants stably expressing promoter::uidA fusions. In addition, we found AtGLR3.7 expression in oocytes induces a Ba 2+ -, Ca 2+ -and Na + -permeable plasma membrane conductance.

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