If it's not too much trouble, can someone tell me as to how to perform differential expression analysis on this Illumina microarray data that I got from GEO dataset (GSE48624) and get the list of upregulated and downregulated genes.Thank You !

> library(GEOquery)
> library(limma)
> z <- getGEO("GSE48624")[[1]]
<snip>
> names(pData(z))
 [1] "title"                   "geo_accession"          
 [3] "status"                  "submission_date"        
 [5] "last_update_date"        "type"                   
 [7] "channel_count"           "source_name_ch1"        
 [9] "organism_ch1"            "characteristics_ch1"    
[11] "characteristics_ch1.1"   "characteristics_ch1.2"  
[13] "molecule_ch1"            "extract_protocol_ch1"   
[15] "label_ch1"               "label_protocol_ch1"     
[17] "taxid_ch1"               "hyb_protocol"           
[19] "scan_protocol"           "description"            
[21] "data_processing"         "platform_id"            
[23] "contact_name"            "contact_email"          
[25] "contact_phone"           "contact_department"     
[27] "contact_institute"       "contact_address"        
[29] "contact_city"            "contact_zip/postal_code"
[31] "contact_country"         "supplementary_file"     
[33] "data_row_count"         

## What are the characteristics of these samples?
> apply(pData(z)[,10:12], 2, table)
$characteristics_ch1

tissue: peripheral blood
                      96

$characteristics_ch1.1

ethnicity: Finnish
                96

$characteristics_ch1.2

treatment: baseline    treatment: music
                 47                  49
## the only phenotype that differs is the music/baseline treatment
## subset the featureData to something tractable - most of this stuff isn't of interest

> names(fData(z))
 [1] "ID"                    "Species"               "Source"               
 [4] "Search_Key"            "Transcript"            "ILMN_Gene"            
 [7] "Source_Reference_ID"   "RefSeq_ID"             "Unigene_ID"           
[10] "Entrez_Gene_ID"        "GI"                    "Accession"            
[13] "Symbol"                "Protein_Product"       "Probe_Id"             
[16] "Array_Address_Id"      "Probe_Type"            "Probe_Start"          
[19] "SEQUENCE"              "Chromosome"            "Probe_Chr_Orientation"
[22] "Probe_Coordinates"     "Cytoband"              "Definition"           
[25] "Ontology_Component"    "Ontology_Process"      "Ontology_Function"    
[28] "Synonyms"              "Obsolete_Probe_Id"     "GB_ACC"               
> fData(z) <- fData(z)[,c(1,10,13)]

## fit model
> design <- model.matrix(~pData(z)[,12])

> fit <- lmFit(z, design)
> fit2 <- eBayes(fit)

> topTable(fit2,2)
                       ID Entrez_Gene_ID    Symbol       logFC  AveExpr
ILMN_3236932 ILMN_3236932         353133     LCE1C -0.14815313 6.721839
ILMN_3239103 ILMN_3239103         340357 LOC340357 -0.17065860 7.381326
ILMN_1721415 ILMN_1721415           9425      CDYL  0.09895831 6.055042
ILMN_1812702 ILMN_1812702           6495      SIX1  0.09901370 6.172894
ILMN_1675433 ILMN_1675433         646914 LOC646914  0.13058373 7.080659
ILMN_1693001 ILMN_1693001         642607 LOC642607  0.12772460 6.874270
ILMN_1681221 ILMN_1681221          84904  C9orf100 -0.13552653 6.986433
ILMN_1731707 ILMN_1731707         441601 LOC441601 -0.14621298 6.817454
ILMN_1853705 ILMN_1853705             NA            0.14548676 6.773012
ILMN_1787540 ILMN_1787540         122183  FLJ40296 -0.12145411 6.844140
                     t      P.Value adj.P.Val           B
ILMN_3236932 -4.009277 0.0001169953 0.9997305  0.79210743
ILMN_3239103 -3.996187 0.0001227126 0.9997305  0.75411573
ILMN_1721415  3.911712 0.0001665612 0.9997305  0.51099215
ILMN_1812702  3.746906 0.0002986704 0.9997305  0.04713268
ILMN_1675433  3.657310 0.0004074674 0.9997305 -0.19905602
ILMN_1693001  3.621101 0.0004613228 0.9997305 -0.29732324
ILMN_1681221 -3.599539 0.0004965258 0.9997305 -0.35550123
ILMN_1731707 -3.591168 0.0005108654 0.9997305 -0.37801822
ILMN_1853705  3.584049 0.0005233669 0.9997305 -0.39713584
ILMN_1787540 -3.529596 0.0006290267 0.9997305 -0.54244947
>

There's no evidence to indicate that listening to classical music for 20 minutes has an effect on the expression of any genes in white blood cells.