Cell Cycle is an interesting little journal that publishes many papers open access. A nice little review of the statistical treatment of genome-wide RNAi is available freely.
The review focuses on noise and variance in RNAi screens, and doesn't explore some of the other key issues such as off-target effects, interferon response and appropriate cell lines. So it isn't a complete guide but a sharply focused one.
A recent Nature has a paper on genome-wide RNAi for targets increasing sensitivity to the key antitumor agent paclitaxel (alas, not free).
Genome-wide RNAi with siRNAs is a powerful technology, but it requires a pretty large investment in automation to make it work. That will slow the widespread adoption of the technology, which isn't entirely bad. In some ways, mRNA microarray technology spread too far too fast leading to many bad papers being published before the methodologies were well worked out. Of course, there are still lots of bad microarray papers being published, but you can't make the horse drink. Some bad papers have poor microarray analysis, and others are just atrocious experimental design. In the end, the technology has been besmirched, generally unfairly.
on the other hand, it's nice to be in a field before all the "rules" have been set down. microarray analysis has benefited as much or more by its widespread and rapid adoption than it has been hurt. Individual groups have been forced to make sense of their data as best they can. The most useful techniques survive (Eisen's cluster program),less fit approaches (interractomes)leave fewer offspring. The end result is that the more robust algorithms spread relatively quickly when a field is being driven. the
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