Hello,
This is first time I'm dealing with Micro-array data. I've affymetrix human exon arrays. Cases and control in total are 223.
I tried few things but kind of lost in between.
I perform rma at probe level and at core level. But the output matrix is same in both the cases is same.
22,000 rows (one per probe) and column count is 223. I'm unable to understand why is that and how?
Default:
Core level:
Dimensions for PD_RMAnormalized.txt and PD_RMAnormalized_core.txt are same.
I'm assming that RMA by default normalizes at probe level.
?rma doesn't show me what's the default method it applies.
This is first time I'm dealing with Micro-array data. I've affymetrix human exon arrays. Cases and control in total are 223.
I tried few things but kind of lost in between.
I perform rma at probe level and at core level. But the output matrix is same in both the cases is same.
22,000 rows (one per probe) and column count is 223. I'm unable to understand why is that and how?
Default:
Code:
celFiles <- list.celfiles("../CEL")
rawData <- read.celfiles(celFiles)
geneSummaries <- rma(rawData)
PDmatrixRMA<-exprs(geneSummaries)
getexpression_core<-as.data.frame(as.ffdf(PDmatrixRMA_core))
write.table(getexpression_core, "PD_RMAnormalized.txt")
Code:
celFiles <- list.celfiles("../CEL")
rawData <- read.celfiles(celFiles)
geneSummaries <- rma(rawData, target="core")
PDmatrixRMA_core<-exprs(geneSummaries)
getexpression_core<-as.data.frame(as.ffdf(PDmatrixRMA_core))
write.table(getexpression_core, "PD_RMAnormalized_core.txt")
I'm assming that RMA by default normalizes at probe level.
?rma doesn't show me what's the default method it applies.
other attached packages:
[1] pd.huex.1.0.st.v2_3.14.1 DBI_0.8 RSQLite_2.1.0
[4] ff_2.2-13 bit_1.1-12 oligo_1.42.0
[7] Biostrings_2.46.0 XVector_0.18.0 IRanges_2.12.0
[10] S4Vectors_0.16.0 Biobase_2.38.0 oligoClasses_1.40.0
[13] BiocGenerics_0.24.0 dplyr_0.7.4 stringr_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 BiocInstaller_1.28.0
[3] pillar_1.2.1 compiler_3.4.2
[5] GenomeInfoDb_1.14.0 bindr_0.1.1
[7] bitops_1.0-6 iterators_1.0.9
[9] tools_3.4.2 zlibbioc_1.24.0
[11] digest_0.6.15 memoise_1.1.0
[13] preprocessCore_1.40.0 tibble_1.4.2
[15] lattice_0.20-35 pkgconfig_2.0.1
[17] rlang_0.2.0 Matrix_1.2-12
[19] foreach_1.4.4 DelayedArray_0.4.1
[21] bindrcpp_0.2.2 GenomeInfoDbData_1.0.0
[23] affxparser_1.50.0 bit64_0.9-7
[25] grid_3.4.2 glue_1.2.0
[27] R6_2.2.2 blob_1.1.1
[29] magrittr_1.5 splines_3.4.2
[31] codetools_0.2-15 matrixStats_0.53.1
[33] GenomicRanges_1.30.3 assertthat_0.2.0
[35] SummarizedExperiment_1.8.1 stringi_1.1.7
[37] RCurl_1.95-4.10 affyio_1.48.0
[1] pd.huex.1.0.st.v2_3.14.1 DBI_0.8 RSQLite_2.1.0
[4] ff_2.2-13 bit_1.1-12 oligo_1.42.0
[7] Biostrings_2.46.0 XVector_0.18.0 IRanges_2.12.0
[10] S4Vectors_0.16.0 Biobase_2.38.0 oligoClasses_1.40.0
[13] BiocGenerics_0.24.0 dplyr_0.7.4 stringr_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 BiocInstaller_1.28.0
[3] pillar_1.2.1 compiler_3.4.2
[5] GenomeInfoDb_1.14.0 bindr_0.1.1
[7] bitops_1.0-6 iterators_1.0.9
[9] tools_3.4.2 zlibbioc_1.24.0
[11] digest_0.6.15 memoise_1.1.0
[13] preprocessCore_1.40.0 tibble_1.4.2
[15] lattice_0.20-35 pkgconfig_2.0.1
[17] rlang_0.2.0 Matrix_1.2-12
[19] foreach_1.4.4 DelayedArray_0.4.1
[21] bindrcpp_0.2.2 GenomeInfoDbData_1.0.0
[23] affxparser_1.50.0 bit64_0.9-7
[25] grid_3.4.2 glue_1.2.0
[27] R6_2.2.2 blob_1.1.1
[29] magrittr_1.5 splines_3.4.2
[31] codetools_0.2-15 matrixStats_0.53.1
[33] GenomicRanges_1.30.3 assertthat_0.2.0
[35] SummarizedExperiment_1.8.1 stringi_1.1.7
[37] RCurl_1.95-4.10 affyio_1.48.0
