CLL 包

只是想做个记录。参考CLL官方说明书 http://bioconductor.org/packages/release/data/experiment/manuals/CLL/man/CLL.pdf

CLL 包 是chronic lymphocytic leukemia（CLL)慢性淋巴性白血病基因表达数据包，数据包的24个样本来自于Dr. Sabina Chiaretti at Division of Hematology, Department of Cellular Biotechnologies and Hematology, University La Sapienza, Rome, Italy and Dr. Jerome Ritz at Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts，归类于疾病中的发展期和稳定期

可以描述 ：ExperimentData（实验数据），CancerData（癌症数据）, Leukemia CancerData（白血病数据）,MicroarrayData（微阵列数据）

CLL-package # A package for CLL gene expression data（基因表达数据）
> data(sCLLex)
> sCLLex  # sCLLex  包含以下很多数据
ExpressionSet (storageMode: lockedEnvironment)
assayData: 12625 features, 22 samples 
element names: exprs 
protocolData: none
phenoData
sampleNames: CLL11.CEL CLL12.CEL ... CLL9.CEL (22 total)
varLabels: SampleID Disease
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu95av2 

CLLbatch # The AffyBatch object for the CLL microarray data （微阵列数据）
> data(CLLbatch)  #An AffyBatch object with 24 samples and 12,625 genes. 
# The Affymetrix hgu95av2 array was used.（用这个芯片hgu95av2）
# cdfName the name of the CDF file: HG\_U95Av2
# nrow the number of rows for each chip: 640
# ncol the number of columns for each chip: 640
# exprs the matrix containing one probe per row and one array per column (dimensions: 409,600 by 24)
# se.exprs the matrix for standard errors: not calculated yet so has dimensions 0 by 0
# description no information is available for the description slot, which is of class MIAME
# annotation "hgu95av2"
# notes there are no notes for this object
# reporterInfo unknown (NULL)
# phenoData a data frame with one variable: sample (more phenotype data can be found in the  disease data frame)
# classVersion no version

disease  # The phenotype data for the CLL microarray data 
#The disease data frame consists of two variables: SampleID, 
#which is the CEL file for the sample, and Disease, 
#which is whether the sample came from a patient that was stable or progressive in terms of CLL disease progression.
> data(disease)
nsFilter  # Nonspecific filtering boolean values for the sCLLex ExpressionSet object (sCLLex 表达数据中的非特定过滤布尔值）
#nsFilter is the nonspecific filtering boolean values for the sCLLex ExpressionSet object. One filter was use: genes with an IQR greater than or equal to the median IQR (IQR performed on the rows of the expression matrix) have a TRUE value (passed the filter) and those that had an IQR less than the median have a FALSE value (did not pass the filter). We only filtered on variation. （布尔值只有真或假中的一个）
> data(nsFilter)
> data(sCLLex)
> sCLLexF<-sCLLex[nsFilter, ]

sCLLex  #The ExpressionSet object for the CLL microarray data
data(sCLLex)


sFiltert   #Boolean values for specific filtering based on the t-test
> data(sFiltert)
> data(sCLLex)
> sCLLexSF<-sCLLex[sFiltert, ]

sFiltertBH # Boolean values for specific filtering based on the t-test
# sFiltert is a named vector of booleans indicating whether a gene passed the specific and non-specific filtering steps. The nonspecific filtering step was described in the nsFilter man page. The specific filtering was to perform row t-tests, then perform p-value adjustment using the Benjamini & Hochberg method (using the mt.rawp2adjp function in the multtest package with the "BH" procedure), and finally include the gene if its adjusted p-value was less than 0.35. So to have a TRUE value in sFiltertBH, the gene must have an IQR greater than or equal to the median IQR and must have a BH adjusted p-value less than 0.35. A TRUE value indicates that the gene passed the filtering step and should be included in further analysis.(执行t检验进行过滤)
> data(sFiltertBH)
> data(sCLLex)
> sCLLexSF<-sCLLex[sFiltertBH, ]

以上。

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