Hello everyone,I have been struggling to the problem about how to read in bead-level txt file provided by GEO using beadarray::readIllumina, but I have no more idea to solve this.
The documents by beadarray didn't show an example for bead-level txt but other formats such as .bab and .tiff in 'BeadArrayUseCases'.
My data from GEO raw data is as follows:
#file: GSM1105686_8839265065_J.txt
#format
Code Grn GrnX GrnY
10008 2530 697.5355 3672.499
10008 2881 1112.538 6927.974
<font face="sans-serif, Arial, Verdana, Trebuchet MS">
</font>
My error is as follows:
Warning messages: 1: Unknown input format in fileD:\test\GSE45495\1/8839265065_J.txt This is probably not a bead-level text file and was ignored
My sessionInfo:
R version 3.2.2 (2015-08-14)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 7 (build 7601) Service Pack 1
locale:
[1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936
[2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936
[3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_People's Republic of China.936
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] beadarrayExampleData_1.8.0 beadarray_2.20.1
[3] ggplot2_2.1.0 Biobase_2.30.0
[5] BiocGenerics_0.16.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.3 AnnotationDbi_1.32.3 XVector_0.10.0
[4] magrittr_1.5 GenomicRanges_1.22.4 zlibbioc_1.16.0
[7] IRanges_2.4.8 munsell_0.4.3 colorspace_1.2-6
[10] stringr_1.0.0 plyr_1.8.3 GenomeInfoDb_1.6.3
[13] tools_3.2.2 base64_1.1 grid_3.2.2
[16] gtable_0.2.0 DBI_0.3.1 reshape2_1.4.1
[19] S4Vectors_0.8.11 RSQLite_1.0.0 limma_3.26.8
[22] stringi_1.0-1 BeadDataPackR_1.22.0 scales_0.4.0
[25] stats4_3.2.2 illuminaio_0.12.0
The solution I could think of is using 'Bioconductor:lumi' to read in 'array_address_id' and 'intensity' and then preprocess to get the final expression profile.
Thank you very much! It suffers me for a long time but it is an unexpected solution. By the way, whether if it is a right way that I preprocess the data using just two columns 'Code' and ‘Grn' by lumi?