1.Start-GEO

下面这个是在关于在要下载安装包之前做的切换镜像,搜索到http和https有什么区别

一、HTTP和HTTPS的基本概念

HTTP：是互联网上应用最为广泛的一种网络协议，是一个客户端和服务器端请求和应答的标准（TCP），用于从WWW服务器传输超文本到本地浏览器的传输协议，它可以使浏览器更加高效，使网络传输减少。

HTTPS：是以安全为目标的HTTP通道，简单讲是HTTP的安全版，即HTTP下加入SSL层，HTTPS的安全基础是SSL，因此加密的详细内容就需要SSL。

HTTPS协议的主要作用可以分为两种：一种是建立一个信息安全通道，来保证数据传输的安全；另一种就是确认网站的真实性。

二、HTTP与HTTPS有什么区别？

HTTP协议传输的数据都是未加密的，也就是明文的，因此使用HTTP协议传输隐私信息非常不安全，为了保证这些隐私数据能加密传输，于是网景公司设计了SSL（Secure Sockets Layer）协议用于对HTTP协议传输的数据进行加密，从而就诞生了HTTPS。

关于GEOquery和getGEO简略的前世今生,getGEO来自GEOquery这个包,那GEOquery这个包是从biobase包来的,要学会看帮助文档,包括Description、Usage、Arguments(命令)等

Get a GEO object from NCBI or file

Description

This function is the main user-level function in the GEOquery package. It directs the download (if no filename is specified) and parsing of a GEO SOFT format file into an R data structure specifically designed to make access to each of the important parts of the GEO SOFT format easily accessible.

Usage

getGEO(GEO = NULL, filename = NULL, destdir = tempdir(),
GSElimits = NULL, GSEMatrix = TRUE, AnnotGPL = FALSE, getGPL = TRUE,
parseCharacteristics = TRUE)

Arguments

GEO	A character string representing a GEO object for download and parsing. (eg., 'GDS505','GSE2','GSM2','GPL96')
filename	The filename of a previously downloaded GEO SOFT format file or its gzipped representation (in which case the filename must end in .gz). Either one of GEO or filename may be specified, not both. GEO series matrix files are also handled. Note that since a single file is being parsed, the return value is not a list of esets, but a single eset when GSE matrix files are parsed.
destdir	The destination directory for any downloads. Defaults to the architecture-dependent tempdir. You may want to specify a different directory if you want to save the file for later use. Doing so is a good idea if you have a slow connection, as some of the GEO files are HUGE!
GSElimits	This argument can be used to load only a contiguous subset of the GSMs from a GSE. It should be specified as a vector of length 2 specifying the start and end (inclusive) GSMs to load. This could be useful for splitting up large GSEs into more manageable parts, for example.
GSEMatrix	A boolean telling GEOquery whether or not to use GSE Series Matrix files from GEO. The parsing of these files can be many orders-of-magnitude faster than parsing the GSE SOFT format files. Defaults to TRUE, meaning that the SOFT format parsing will not occur; set to FALSE if you for some reason need other columns from the GSE records.
AnnotGPL	A boolean defaulting to FALSE as to whether or not to use the Annotation GPL information. These files are nice to use because they contain up-to-date information remapped from Entrez Gene on a regular basis. However, they do not exist for all GPLs; in general, they are only available for GPLs referenced by a GDS
getGPL	A boolean defaulting to TRUE as to whether or not to download and include GPL information when getting a GSEMatrix file. You may want to set this to FALSE if you know that you are going to annotate your featureData using Bioconductor tools rather than relying on information provided through NCBI GEO. Download times can also be greatly reduced by specifying FALSE.
parseCharacteristics	A boolean defaulting to TRUE as to whether or not to parse the characteristics information (if available) for a GSE Matrix file. Set this to FALSE if you experience trouble while parsing the characteristics.

Details

getGEO functions to download and parse information available from NCBI GEO (http://www.ncbi.nlm.nih.gov/geo). Here are some details about what is avaible from GEO. All entity types are handled by getGEO and essentially any information in the GEO SOFT format is reflected in the resulting data structure.

From the GEO website:

The Gene Expression Omnibus (GEO) from NCBI serves as a public repository for a wide range of high-throughput experimental data. These data include single and dual channel microarray-based experiments measuring mRNA, genomic DNA, and protein abundance, as well as non-array techniques such as serial analysis of gene expression (SAGE), and mass spectrometry proteomic data. At the most basic level of organization of GEO, there are three entity types that may be supplied by users: Platforms, Samples, and Series. Additionally, there is a curated entity called a GEO dataset.

A Platform record describes the list of elements on the array (e.g., cDNAs, oligonucleotide probesets, ORFs, antibodies) or the list of elements that may be detected and quantified in that experiment (e.g., SAGE tags, peptides). Each Platform record is assigned a unique and stable GEO accession number (GPLxxx). A Platform may reference many Samples that have been submitted by multiple submitters.

A Sample record describes the conditions under which an individual Sample was handled, the manipulations it underwent, and the abundance measurement of each element derived from it. Each Sample record is assigned a unique and stable GEO accession number (GSMxxx). A Sample entity must reference only one Platform and may be included in multiple Series.

A Series record defines a set of related Samples considered to be part of a group, how the Samples are related, and if and how they are ordered. A Series provides a focal point and description of the experiment as a whole. Series records may also contain tables describing extracted data, summary conclusions, or analyses. Each Series record is assigned a unique and stable GEO accession number (GSExxx).

GEO DataSets (GDSxxx) are curated sets of GEO Sample data. A GDS record represents a collection of biologically and statistically comparable GEO Samples and forms the basis of GEO's suite of data display and analysis tools. Samples within a GDS refer to the same Platform, that is, they share a common set of probe elements. Value measurements for each Sample within a GDS are assumed to be calculated in an equivalent manner, that is, considerations such as background processing and normalization are consistent across the dataset. Information reflecting experimental design is provided through GDS subsets.

Value

An object of the appropriate class (GDS, GPL, GSM, or GSE) is returned. If the GSEMatrix option is used, then a list of ExpressionSet objects is returned, one for each SeriesMatrix file associated with the GSE accesion. If the filename argument is used in combination with a GSEMatrix file, then the return value is a single ExpressionSet.

Warning

Some of the files that are downloaded, particularly those associated with GSE entries from GEO are absolutely ENORMOUS and parsing them can take quite some time and memory. So, particularly when working with large GSE entries, expect that you may need a good chunk of memory and that coffee may be involved when parsing....

Author(s)

Sean Davis

See Also

getGEOfile

Examples

gds <- getGEO("GDS10")
gds

gse <- getGEO('GSE10')
# Returns a list, so look at first item

gse[[1]]

#在下载GEO数据时,是这么下载的
rm(list = ls())
options("repos"="https://mirrors.ustc.edu.cn/CRAN/")
if(!require("BiocManager")) install.packages("BiocManager",update = F,ask = F)
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
library(GEOquery)
f<-'GSE54839.Rdata'
####getGPL获得平台的注释信息，但下载速度会慢很多
####而且注释文件格式大多不如bioconductor包好用
if(!file.exists(f)){
  gset<-getGEO('GSE54839',destdir='.',
               AnnotGPL=F,
               getGPL=F)
  save(gset,file=f)
}
#数据提取
load('GSE54839.Rdata')
class(gset)

> class(gset)
[1] "list"
#取列表中的元素
> gset[[1]]

image-20190805131454342

这时就要去了解 这Biobase和ExpressionSet是什么呢,可以?ExpressionSet,也可以去Bioconductor官网查看Biobase包,下面这张图片记录bioconductor 上面几个非常有帮助的模块,如箭头所示,其中common work flows可以看到各个主流分析的HTML文档,按操作可以出图.

image-20190805105359106 image-20190805133818559 文章名字是Orchestrationg~

其实呢,在library(GEOquery)也可以看到,如下图所示

image-20190805134701042

现在知道得到的数据是一个ExpressionSet,关于ExpressionSet的解释,在bioconductor也有官方文档解释,网址是:https://bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf

文档截图

现在问题是如何获取ExpressionSet里面的注入phenoData、experimentData呢?

那就去看ExpressionSet里面的帮助文档

expr()

但是这个phenoData的信息如何提取没有说,继续找

image-20190805140750925 image-20190805140914329

好了,那就继续在代码去输入

ex<- exprs(gset[[1]])#表达矩阵
pd <- pData(gset[[1]])#临床信息

ex pd

参考:https://github.com/bioconductor-china/basic/blob/master/ExpressionSet.md