Question: Suitable learning sets, gene selection methods and classification methods for low replicated microarray samples
4.8 years ago by
Guest User • 12k
Guest User • 12k wrote:
Dear grateful R helpers, I'm a biologist who is learning gene expression profile study, and have to deal with low replicated sample number (2-3 biological replicates per group). Due to my lack of background in bioinformatics, I find CMA as a very user-friendly package for supervised classification task. However, I'm suffering with the truth that I really have no clue what suitable choics to choose for my low replicated sample classfication. These are the choices to: 1. Select method to generate learning datasets 2. Select the gene selection methods 3. Select classification methods 4. Acquire generated learning datasets to be applied with other gene selection methods not available in CMA package (for example, Rank production and LPE) Any suggestions would be more than appreciated. With Respects, Kaj Chokeshaiusaha -- output of sessionInfo(): R version 3.1.0 (2014-04-10) Platform: x86_64-pc-linux-gnu (64-bit) locale:  LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C  LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8  LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8  LC_PAPER=en_GB.UTF-8 LC_NAME=C  LC_ADDRESS=C LC_TELEPHONE=C  LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C attached base packages:  parallel stats graphics grDevices utils datasets methods  base other attached packages:  BiocInstaller_1.14.2 CMA_1.22.0 Biobase_2.24.0  BiocGenerics_0.10.0 e1071_1.6-3 loaded via a namespace (and not attached):  class_7.3-10 tools_3.1.0 -- Sent via the guest posting facility at bioconductor.org.
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