下面這個是在關(guān)于在要下載安裝包之前做的切換鏡像,搜索到http和https有什么區(qū)別
一厘擂、HTTP和HTTPS的基本概念
HTTP：是互聯(lián)網(wǎng)上應(yīng)用最為廣泛的一種網(wǎng)絡(luò)協(xié)議昆淡，是一個客戶端和服務(wù)器端請求和應(yīng)答的標(biāo)準(zhǔn)（TCP），用于從WWW服務(wù)器傳輸超文本到本地瀏覽器的傳輸協(xié)議刽严，它可以使瀏覽器更加高效瘪撇，使網(wǎng)絡(luò)傳輸減少。
HTTPS：是以安全為目標(biāo)的HTTP通道港庄，簡單講是HTTP的安全版，即HTTP下加入SSL層恕曲，HTTPS的安全基礎(chǔ)是SSL鹏氧，因此加密的詳細(xì)內(nèi)容就需要SSL。
HTTPS協(xié)議的主要作用可以分為兩種：一種是建立一個信息安全通道佩谣，來保證數(shù)據(jù)傳輸?shù)陌踩鸦梗涣硪环N就是確認(rèn)網(wǎng)站的真實(shí)性。
二茸俭、HTTP與HTTPS有什么區(qū)別吊履？
HTTP協(xié)議傳輸?shù)臄?shù)據(jù)都是未加密的，也就是明文的调鬓，因此使用HTTP協(xié)議傳輸隱私信息非常不安全艇炎，為了保證這些隱私數(shù)據(jù)能加密傳輸，于是網(wǎng)景公司設(shè)計了SSL（Secure Sockets Layer）協(xié)議用于對HTTP協(xié)議傳輸?shù)臄?shù)據(jù)進(jìn)行加密腾窝，從而就誕生了HTTPS缀踪。
關(guān)于GEOquery和getGEO簡略的前世今生,getGEO來自GEOquery這個包,那GEOquery這個包是從biobase包來的,要學(xué)會看幫助文檔,包括Description居砖、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
GEOA character string representing a GEO object for download and parsing. (eg., 'GDS505','GSE2','GSM2','GPL96') filenameThe 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. destdirThe 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! GSElimitsThis 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. GSEMatrixA 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. AnnotGPLA 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 getGPLA 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. parseCharacteristicsA 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
getGEOfileExamples
gds <- getGEO("GDS10") gds gse <- getGEO('GSE10') # Returns a list, so look at first item gse[[1]]
#在下載GEO數(shù)據(jù)時,是這么下載的
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獲得平臺的注釋信息驴娃，但下載速度會慢很多
####而且注釋文件格式大多不如bioconductor包好用
if(!file.exists(f)){
gset<-getGEO('GSE54839',destdir='.',
AnnotGPL=F,
getGPL=F)
save(gset,file=f)
}
#數(shù)據(jù)提取
load('GSE54839.Rdata')
class(gset)
> class(gset)
[1] "list"
#取列表中的元素
> gset[[1]]
這時就要去了解 這Biobase和ExpressionSet是什么呢,可以
?ExpressionSet,也可以去Bioconductor官網(wǎng)查看Biobase包,下面這張圖片記錄bioconductor 上面幾個非常有幫助的模塊,如箭頭所示,其中common work flows可以看到各個主流分析的HTML文檔,按操作可以出圖.
其實(shí)呢,在library(GEOquery)也可以看到,如下圖所示
現(xiàn)在知道得到的數(shù)據(jù)是一個ExpressionSet,關(guān)于ExpressionSet的解釋,在bioconductor也有官方文檔解釋,網(wǎng)址是:https://bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf
現(xiàn)在問題是如何獲取ExpressionSet里面的注入phenoData奏候、experimentData呢?
那就去看ExpressionSet里面的幫助文檔
但是這個phenoData的信息如何提取沒有說,繼續(xù)找
好了,那就繼續(xù)在代碼去輸入
ex<- exprs(gset[[1]])#表達(dá)矩陣
pd <- pData(gset[[1]])#臨床信息
參考:https://github.com/bioconductor-china/basic/blob/master/ExpressionSet.md
