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).[……]

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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[……]

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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://ww[……]

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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 ful[……]

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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[……]

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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 cruc[……]

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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 captu[……]

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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[……]

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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-[……]

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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[……]

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