illumina是大厂家，所以芯片包括人类的，小鼠以及大鼠的，然后对于人来说，经历了V1~V4的进化过程，最新版是 V4。

GEO里面是这样介绍illumina bead V4这个芯片的：

The HumanHT-12 v4 Expression BeadChip provides high throughput processing of 12 samples per BeadChip without the need for expensive, specialized automation. The BeadChip is designed to support flexible usage across a wide-spectrum of experiments.

可以在官网下载芯片探针的详情，manifest数据文件：http://support.illumina.com/array/array_kits/humanht-12_v4_expression_beadchip_kit/downloads.html 这些文件写清楚了芯片用的探针的详情，包括使用了哪些control探针，主要是给它自己的BeadStudio 软件来使用的。

NCBI的GEO也提供大批量的公共数据：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL10558

芯片厂家illumina本身提供数据处理软件BeadStudio, GenomeStudio，在R/bioconductor上面也有开源的包做同样的事情，Illuminaio ，beadarray，lumi

数据的前期处理有3个层次，都在bioconductor有对应的包可以来处理

Data can be in raw form, where pixel-level data are available from TIFF images, allowing the complete data processing pipeline, including image analysis, to be carried out in R. 这种图片格式的数据，基本上没有人愿意去开始处理的，TIFF格式的图片压缩包，在BeadArrayUseCases包里面附带有一个测试数据

The next level, referred to as bead-level, refers to the availability of intensity and location information for individual beads. In this format, a given probe will have a variable number of replicate intensities per sample. Processed data, where replicate intensities have been summarized and outliers removed to give a mean, a measure of variability, and a number of observations per probe in each sample, is the most commonly available format.

数据处理流程如下：

其实对芯片数据处理最重要的过程，就是如何做QC以及拿到表达量矩阵，后面的差异分析，功能富集分析其实是大同小异的。我比较喜欢用bioconductor包，会讲如何用 lumi包来处理这个芯片数据。