[BioC] detectionCall usefulness

[Francisco Ortuno [guest]]

Hi,

I'm trying to analyze the differential expression between control and patient groups in a microarray from Illumina HumanHT12_V4. I would like to know what advantages or disadvantages have the use of the "detectionCall" function from "lumi" package. 

Once I've removed outliers and normalized, I've tried to reduce the number of genes with "detectionCall" in order to filter possible false positives. Then, I obtain the list of differentially expressed genes by applying the "limma" package ("lmFit" and "eBayes" functions). However, that list usually includes a list with more differentially expressed genes when I'm using the "detectionCall" function. Is this usual? If I've reduced the number of false positive genes, how is it possible that I obtain a higher list? Is my interpretation of "detectionCall" correct?

Thanks in advance,
Francisco.


 -- output of sessionInfo(): 

R version 2.15.0 (2012-03-30)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=Spanish_Spain.1252  LC_CTYPE=Spanish_Spain.1252   
[3] LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C                  
[5] LC_TIME=Spanish_Spain.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lumiHumanIDMapping_1.10.0  arrayQualityMetrics_3.12.0
 [3] lumiHumanAll.db_1.18.0     org.Hs.eg.db_2.7.1        
 [5] RSQLite_0.11.3             DBI_0.2-6                 
 [7] annotate_1.34.1            AnnotationDbi_1.18.4      
 [9] lumi_2.8.0                 nleqslv_2.0               
[11] methylumi_2.2.0            ggplot2_0.9.3.1           
[13] reshape2_1.2.2             scales_0.2.3              
[15] Biobase_2.16.0             BiocGenerics_0.2.0        
[17] limma_3.12.3              

loaded via a namespace (and not attached):
 [1] affy_1.34.0           affyio_1.24.0         affyPLM_1.32.0       
 [4] beadarray_2.6.0       BeadDataPackR_1.8.0   bigmemory_4.2.11     
 [7] BiocInstaller_1.4.9   Biostrings_2.24.1     bitops_1.0-5         
[10] BSgenome_1.24.0       Cairo_1.5-2           cluster_1.14.4       
[13] colorspace_1.2-2      dichromat_2.0-0       digest_0.6.3         
[16] DNAcopy_1.30.0        genefilter_1.38.0     GenomicRanges_1.8.13 
[19] genoset_1.6.0         grid_2.15.0           gtable_0.1.2         
[22] hdrcde_2.16           Hmisc_3.10-1          hwriter_1.3          
[25] IRanges_1.14.4        KernSmooth_2.23-10    labeling_0.1         
[28] lattice_0.20-6        latticeExtra_0.6-24   MASS_7.3-23          
[31] Matrix_1.0-12         mgcv_1.7-22           munsell_0.4          
[34] nlme_3.1-108          plyr_1.8              preprocessCore_1.18.0
[37] proto_0.3-10          RColorBrewer_1.0-5    RCurl_1.95-4.1       
[40] Rsamtools_1.8.6       rtracklayer_1.16.3    setRNG_2011.11-2     
[43] splines_2.15.0        stats4_2.15.0         stringr_0.6.2        
[46] survival_2.37-4       SVGAnnotation_0.93-1  tools_2.15.0         
[49] vsn_3.24.0            XML_3.96-1.1          xtable_1.7-1         
[52] zlibbioc_1.2.0

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