August « 2012 « 小生这厢有礼了(BioFaceBook Personal Blog)

Using prcomp/princomp for PCA in R （三）

Testing i.pca ~ prcomp(), m.eigensystem ~ princomp()

1. Briefly about PCA 2. The modules/functions that implement PCA in GRASS & R 3. My claims (Entitled Comments) 4. Evidence (=the numbers derived from i.pca, prcomp, princomp, m.eigensystem using some MODIS surface reflectance bands).

Finally all is clear _but_ one thing: the only “unknown” variable (to […]

Using prcomp/princomp for PCA in R （二）

###############################

PCA ############################### install.packages(“vegan”) library(vegan)

> STpcoa<-read.table(file=”bactera_16s_final.subsample.phylip.tre1.weighted.phylip.pcoa.axes”, header=T,row.names=1) > STpcoa axis1 axis2 axis3 axis4 Cellulose -0.020878 -0.234601 0.167454 0 Foodwaste -0.234592 0.221741 0.085802 0 Sludge 0.368882 0.100725 -0.010570 0 Xylan -0.113413 -0.087865 -0.242686 0 >pl.STpcoa<-princomp(STpcoa) > summary(pl.STpcoa) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 0.2260563 0.1746944 0.1536319 0 Proportion of Variance 0.4856521 0.2900347 […]

Using prcomp/princomp for PCA in R （一）

Difference between prcomp and princomp:

‘princomp’ can only be used with more units than variables”

prcomp是基于SVD分解（svd()函数，princomp是基于特征向量eigen()函数)

Good video source:

http://www.youtube.com/watch?v=oZ2nfIPdvjY

http://www.youtube.com/watch?v=I5GxNzKLIoU&feature=relmfu

http://www.planta.cn/forum/viewtopic.php?t=16754&highlight=%D3%EF%D1%D4

###########################################

以下所有代码包括练习数据，都可在R平台上直接运行。

#主成分分析和主成分回归主成分分析的思想是Pearson 1901年提出的，Hotelling 1933进一步发展在R中，进行主成分分析用到princomp() 函数

用法 princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), …)

# 分析用数据 # cor 是否用样本的协方差矩阵作主成分分析 prcomp() 二 summary()函数三 […]

Bio3D in R Utilities for the analysis of protein structure and sequence data

http://users.mccammon.ucsd.edu/~bgrant/bio3d/user_guide/user_guide.html#example

Some Beginner Examples

library(bio3d) # load the bio3d package

lbio3d() # list the functions within the package

## See the help pages of individual functions for full documentation and worked examples.

help(read.pdb) # type “q” to exit help page and return to the R prompt

example(read.pdb)

## Read a PDB […]

Positive-Unlabeled Learning for Disease Gene Identification

Background: Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the […]

RazerS 3: Faster, fully sensitive read mapping

Motivation: During the last years NGS sequencing has become a key technology for many applications in the biomedical sciences. Throughput continues to increase and new protocols provide longer reads than currently available. In almost all applications, read mapping is a first step. Hence, it is crucial to have algorithms and implementations that perform fast, […]

FacPad: Bayesian Sparse Factor Modeling for the Inference of Pathways Responsive to Drug Treatment

Motivation: It is well recognized that the effects of drugs are far beyond targeting individual proteins, but rather influencing the complex interactions among many relevant biological pathways. Genome-wide expression profiling before and after drug treatment has become a powerful approach for capturing a global snapshot of cellular response to drugs, as well as to […]

Qualimap: evaluating next generation sequencing alignment data

Motivation: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol, and/or the selected mapping algorithm introduce some unwanted biases in […]

MEGA-CC: Computing Core of Molecular Evolutionary Genetics Analysis program for automated and iterative data analysis

Summary: There is a growing need in the research community to apply the Molecular Evolutionary Genetics Analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. We now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an […]

HSPIR: A manually annotated Heat Shock Protein Information Resource

Summary: HSPIR is a concerted database of six major Heat ShockProteins (HSPs) namely Hsp70, Hsp40, Hsp60, Hsp90, Hsp100 and sHsp (small HSP). The HSPs are essential for the survival of all living organisms which protects the conformations of proteins upon exposure to various stress conditions. They are highly conserved group of proteins involved in […]

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