Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.
Array data processing was done in R (version 2.14.1) using the limma (version 3.10.2) Bioconductor package. The 'normexp' method with offset=10 was used for background-correction, and followed with within-array global 'loess' normalization, with span=1/3 and values from probe-spots with flag-values >1 ignored, and then, between-array 'quantile' normalization. For probe-set summarization, the median of the values from multiple spots was used when the ratio of the maximum and minimum values was >1.5; else, the mean was used.