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## nu-TRLan User Guide (2008)

Citations: | 50 - 7 self |

### Citations

8739 |
Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing
- Benjamini, Hochberg
- 1995
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Citation Context ....06 3.93e-06 2.69e-04 GGAACTGTGA -3.61 10.83 5.17e-06 3.23e-04 CGCGTCACTA 4.77 10.18 5.24e-06 3.23e-04 By default, Benjamini and Hochberg’s algorithm is used to control the false discovery rate (FDR) =-=[2]-=-. The table below shows the counts per million for the tags that edgeR has identified as the most differentially expressed. There are pronounced differences between the groups: > detags <- rownames(to... |

3173 |
Generalized Linear Models
- McCullagh, Nelder
- 1989
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Citation Context ... 2.9 More complex experiments (glm functionality) 2.9.1 Generalized linear models Generalized linear models (GLMs) are an extension of classical linear models to nonnormally distributed response data =-=[14, 13]-=-. GLMs specify probability distributions according to their mean-variance relationship, for example the quadratic mean-variance relationship specified 16 above for read counts. Assuming that an estima... |

983 |
The symmetric eigenvalue problem
- Parlett
- 1980
(Show Context)
Citation Context ...y ipar is mapped to the elements of TRL.INFO.T, \Gammasipar(1) = stat, \Gammasipar(2) = lohi, \Gammasipar(3) = ned, \Gammasipar(4) = nec, \Gammasipar(5) = maxlan, \Gammasipar(6) = restart, \Gammasipar=-=(7)-=- = maxmv, \Gammasipar(8) = mpicom, \Gammasipar(9) = verbose, \Gammasipar(10) = log.io, \Gammasipar(11) = iguess, \Gammasipar(12) = cpflag, \Gammasipar(13) = cpio, \Gammasipar(14) = mvop, \Gammasipar(2... |

622 |
Numerical Methods for Large Eigenvalue Problems
- Saad
- 1992
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Citation Context ...ipar(3) = ned, \Gammasipar(4) = nec, \Gammasipar(5) = maxlan, \Gammasipar(6) = restart, \Gammasipar(7) = maxmv, \Gammasipar(8) = mpicom, \Gammasipar(9) = verbose, \Gammasipar(10) = log.io, \Gammasipar=-=(11)-=- = iguess, \Gammasipar(12) = cpflag, \Gammasipar(13) = cpio, \Gammasipar(14) = mvop, \Gammasipar(24) = locked, \Gammasipar(25) = matvec, \Gammasipar(26) = nloop, \Gammasipar(27) = north, \Gammasipar(2... |

315 |
Implicit application of polynomial filters in a k-step Arnoldi method
- Sorensen
- 1992
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Citation Context ...(4) = nec, \Gammasipar(5) = maxlan, \Gammasipar(6) = restart, \Gammasipar(7) = maxmv, \Gammasipar(8) = mpicom, \Gammasipar(9) = verbose, \Gammasipar(10) = log.io, \Gammasipar(11) = iguess, \Gammasipar=-=(12)-=- = cpflag, \Gammasipar(13) = cpio, \Gammasipar(14) = mvop, \Gammasipar(24) = locked, \Gammasipar(25) = matvec, \Gammasipar(26) = nloop, \Gammasipar(27) = north, \Gammasipar(28) = nrand, \Gammasipar(29... |

308 |
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
- Robinson, Smyth, et al.
- 2010
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Citation Context ...apter 1 Introduction 1.1 Scope This guide provides an overview of the Bioconductor package edgeR for differential expression analyses of read counts arising from RNA-Seq, SAGE or similar technologies =-=[17]-=-. The package can be applied to any technology that produces read counts for genomic features. Of particular interest are summaries of short reads from massively parallel sequencing technologies such ... |

285 |
Lanczos Algorithms for Large Symmetric Eigenvalue Computations, Volume 2: Programs, Birkhäuser
- Cullum, Willoughby
- 1985
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Citation Context ...rth Restart Time(ave) 5.2985E-01 9.4129E-03 2.1143E-01 2.3685E-01 Rate(tot) 1.2001E+02 7.1990E+01 1.8801E+02 8.0930E+01 E(1) = 0.99999999997742750 E(2) = 3.9999999999816311 E(3) = 8.9999999999916049 E=-=(4)-=- = 16.000000000026944 E(5) = 25.000000000089663 E(6) = 36.000000000367905 In short, to use TRLAN to find some extreme eigenvalues, the user defines a matrixvector multiplication routine with the same ... |

235 |
RNA-Seq: an assessment of technical reproducibility and comparison with gene expression arrays,”
- Marioni, Mason, et al.
- 2008
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Citation Context ...each gene in each sample is estimated by the sequencing technology. If aliquots of the same RNA sample are sequenced, then the read counts for a particular gene should vary according to a Poisson law =-=[11]-=-. If sequencing variation is Poisson, then it can be shown that the squared coefficient of variation (CV) of each count between biological replicate libraries is the sum of the squared CVs for technic... |

230 |
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments,”
- Bullard, Purdom, et al.
- 2010
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Citation Context ...counts. Assuming that an estimate is available for φg, so the variance can be evaluated for any value of µgi, GLM theory can be used to fit a log-linear model log µgi = x T i βg + logNi for each gene =-=[9, 3]-=-. Here xi is a vector of covariates that specifies the treatment conditions applied to RNA sample i, and βg is a vector of regression coefficients by which the covariate effects are mediated for gene ... |

144 |
Eigenvalues of Matrices,
- Chatelin, Ahues
- 1993
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Citation Context ...E+01 MFLOPS -- Global summary -- Overall MATVEC Re-orth Restart Time(ave) 5.2985E-01 9.4129E-03 2.1143E-01 2.3685E-01 Rate(tot) 1.2001E+02 7.1990E+01 1.8801E+02 8.0930E+01 E(1) = 0.99999999997742750 E=-=(2)-=- = 3.9999999999816311 E(3) = 8.9999999999916049 E(4) = 16.000000000026944 E(5) = 25.000000000089663 E(6) = 36.000000000367905 In short, to use TRLAN to find some extreme eigenvalues, the user defines ... |

143 | Moderated statistical tests for assessing differences in tag abundance.
- Robinson, Smyth
- 2007
(Show Context)
Citation Context ...ferential expression in RNASeq experiments or differential marking in ChIP-Seq experiments. The package implements exact statistical methods for multigroup experiments developed by Robinson and Smyth =-=[19, 20]-=-. It also implements statistical methods based on generalized linear models (glms), suitable for multifactor experiments of any complexity, developed by McCarthy et al. [12] and Lund et al. [10]. Some... |

134 |
A scaling normalization method for differential expression analysis of RNA-seq data,”
- Robinson, Oshlack
- 2010
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Citation Context ...ry size, causing the remaining genes to be under-sampled in that sample. Unless this RNA composition effect is adjusted for, the remaining genes may falsely appear to be down-regulated in that sample =-=[18]-=-. The calcNormFactors function normalizes for RNA composition by finding a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes. The defau... |

130 |
Gene expression profiles in normal and cancer cells.
- Zhang, Zhou, et al.
- 1997
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Citation Context ...f data from a SAGE experiment to illustrate the data analysis pipeline for edgeR. The data come from a very early study using SAGE technology to analyse gene expression profiles in human cancer cells =-=[26]-=-. Zhang et al. [26] examined human colorectal and pancreatic cancer tumor tissue. In this case study, we analyse the data comparing primary colon tumor tissue with normal colon epithelial cells. Two t... |

115 | Small-sample estimation of negative binomial dispersion, with applications to SAGE data.
- Robinson, Smyth
- 2008
(Show Context)
Citation Context ...ferential expression in RNASeq experiments or differential marking in ChIP-Seq experiments. The package implements exact statistical methods for multigroup experiments developed by Robinson and Smyth =-=[19, 20]-=-. It also implements statistical methods based on generalized linear models (glms), suitable for multifactor experiments of any complexity, developed by McCarthy et al. [12] and Lund et al. [10]. Some... |

106 | A shifted Block Lanczos Algorithm for solving sparse symmetric generalized Eigen value problems”,
- Grimes, Levis, et al.
- 1994
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Citation Context ...01 2.3685E-01 Rate(tot) 1.2001E+02 7.1990E+01 1.8801E+02 8.0930E+01 E(1) = 0.99999999997742750 E(2) = 3.9999999999816311 E(3) = 8.9999999999916049 E(4) = 16.000000000026944 E(5) = 25.000000000089663 E=-=(6)-=- = 36.000000000367905 In short, to use TRLAN to find some extreme eigenvalues, the user defines a matrixvector multiplication routine with the same interface as diag.op, calls trl.init.info to specify... |

104 |
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.
- DJ, Chen, et al.
- 2012
(Show Context)
Citation Context ... by Robinson and Smyth [19, 20]. It also implements statistical methods based on generalized linear models (glms), suitable for multifactor experiments of any complexity, developed by McCarthy et al. =-=[12]-=- and Lund et al. [10]. Sometimes we refer to the former exact methods as classic edgeR, and the latter as glm edgeR. However the two sets of methods are complementary and can often be combined in the ... |

86 | Rational Krylov algorithms for nonsymmetric eigenvalue problems II: matrix pairs. Linear Algevr.
- Ruhe
- 1984
(Show Context)
Citation Context ...Gammasipar(1) = stat, \Gammasipar(2) = lohi, \Gammasipar(3) = ned, \Gammasipar(4) = nec, \Gammasipar(5) = maxlan, \Gammasipar(6) = restart, \Gammasipar(7) = maxmv, \Gammasipar(8) = mpicom, \Gammasipar=-=(9)-=- = verbose, \Gammasipar(10) = log.io, \Gammasipar(11) = iguess, \Gammasipar(12) = cpflag, \Gammasipar(13) = cpio, \Gammasipar(14) = mvop, \Gammasipar(24) = locked, \Gammasipar(25) = matvec, \Gammasipa... |

78 |
Understanding mechanisms underlying human gene expression variation with RNA sequencing.
- Pickrell
- 2010
(Show Context)
Citation Context ... Unrelated Nigerian Individuals 4.6.1 Background RNA-Seq profiles were made from lymphoblastoid cell lines generated as part of the International HapMap project from 69 unrelated Nigerian individuals =-=[15]-=-. RNA from each individual was sequenced on at least two lanes of the Illumina Genome Analyser 2 platform, and mapped reads to the human genome using MAQ v0.6.8. The study group here is essentially an... |

72 |
Rational Krylov sequence methods for eigenvalue computation
- Ruhe
- 1984
(Show Context)
Citation Context ...n(diag.op, ! matrix-vector multiplication routine info, ! what eigenvalues to compute, etc. nrow, ! 100 rows on this processor mev, ! number of eigenpairs can be stored in ! eval and evec eval, ! real=-=(8)-=- :: eval(mev) ! array to store eigenvalue evec, ! real(8) :: evec(lde,mev) ! array to store the eigenvectors lde) ! the leading dimension of evec The content of info and the eigenvalues are printed se... |

67 |
The spectral transformation Lanczos method for the numerical solution of large sparse generalized symmetric eigenvalue problems
- Ericsson, Ruhe
- 1980
(Show Context)
Citation Context ...85E-01 9.4129E-03 2.1143E-01 2.3685E-01 Rate(tot) 1.2001E+02 7.1990E+01 1.8801E+02 8.0930E+01 E(1) = 0.99999999997742750 E(2) = 3.9999999999816311 E(3) = 8.9999999999916049 E(4) = 16.000000000026944 E=-=(5)-=- = 25.000000000089663 E(6) = 36.000000000367905 In short, to use TRLAN to find some extreme eigenvalues, the user defines a matrixvector multiplication routine with the same interface as diag.op, call... |

49 |
GC-Content Normalization for RNA-Seq Data,
- Risso, Schwartz, et al.
- 2011
(Show Context)
Citation Context ...xpected to have little effect on differential expression analyses to a first approximation. Recent publications, however, have demonstrated that sample-specific effects for GC-content can be detected =-=[16, 5]-=-. The EDASeq [16] and cqn [5] packages estimate correction factors that adjust for sample-specific GC-content effects in a way that is compatible with edgeR. In each case, the observation-specific cor... |

48 | The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
- Liao, Smyth, et al.
- 2013
(Show Context)
Citation Context ...iations on this process. Alignment needs to allow for the fact that reads may span multiple exons which may align to well separated locations on the genome. We find the subread-featureCounts pipeline =-=[7, 8]-=- to be very fast and effective for this purpose, but the Bowtie-TopHat-htseq pipeline is also very popular [1]. 2.3 Producing a table of read counts edgeR works on a table of integer read counts, with... |

46 | Dynamic thick restarting of the Davidson and the implicitly restarted Arnoldi methods
- STATHOPOULOS, SAAD, et al.
- 1998
(Show Context)
Citation Context ...= maxlan, \Gammasipar(6) = restart, \Gammasipar(7) = maxmv, \Gammasipar(8) = mpicom, \Gammasipar(9) = verbose, \Gammasipar(10) = log.io, \Gammasipar(11) = iguess, \Gammasipar(12) = cpflag, \Gammasipar=-=(13)-=- = cpio, \Gammasipar(14) = mvop, \Gammasipar(24) = locked, \Gammasipar(25) = matvec, \Gammasipar(26) = nloop, \Gammasipar(27) = north, \Gammasipar(28) = nrand, \Gammasipar(29) = total time in millisec... |

46 |
Gene ontology analysis for RNA-seq: accounting for selection bias,”
- Young, Wakefield, et al.
- 2010
(Show Context)
Citation Context ...of one flow-cell. FASTA format files are available from http://yeolab.ucsd.edu/yeolab/Papers. html. 49 4.3.4 Read mapping Reads were mapped and summarized at the gene level as previously described by =-=[25]-=-. Reads were mapped to the NCBI36 build of the human genome using Bowtie, allowing up to two mismatches. Reads not mapping uniquely were discarded. The number of reads overlapping the genomic span of ... |

43 | Count-based differential expression analysis of RNA sequencing data using R and Bioconductor.
- Anders, DJ, et al.
- 2013
(Show Context)
Citation Context ... to well separated locations on the genome. We find the subread-featureCounts pipeline [7, 8] to be very fast and effective for this purpose, but the Bowtie-TopHat-htseq pipeline is also very popular =-=[1]-=-. 2.3 Producing a table of read counts edgeR works on a table of integer read counts, with rows corresponding to genes and columns to independent libraries. The counts represent the total number of re... |

34 |
Camera: a competitive gene set test accounting for inter-gene correlation,
- Wu, Smyth
- 2012
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Citation Context ...e FDR PValue.Mixed FDR.Mixed Y 12 0.0833 0.9167 Up 2e-04 1e-04 1e-04 5e-05 X 46 0.5217 0.0435 Down 2e-04 1e-04 1e-04 5e-05 The results from competitive camera gene sets tests are even more convincing =-=[24]-=-. The positive intergene correlations here show that the genes in each set tend to be biologically correlated: > camera(y,index,design) NGenes Correlation Direction PValue FDR Y 12 0.0846 Up 4.27e-39 ... |

31 |
Iterative methods for the computation of a few eigenvalues of a large symmetric matrix
- Baglama, Calvetti, et al.
- 1996
(Show Context)
Citation Context ...E: 5.29851E-01 sec, 3.00020E+01 MFLOPS -- Global summary -- Overall MATVEC Re-orth Restart Time(ave) 5.2985E-01 9.4129E-03 2.1143E-01 2.3685E-01 Rate(tot) 1.2001E+02 7.1990E+01 1.8801E+02 8.0930E+01 E=-=(1)-=- = 0.99999999997742750 E(2) = 3.9999999999816311 E(3) = 8.9999999999916049 E(4) = 16.000000000026944 E(5) = 25.000000000089663 E(6) = 36.000000000367905 In short, to use TRLAN to find some extreme eig... |

27 |
Removing technical variability in RNA-seq data using conditional quantile normalization,”
- Hansen, Irizarry, et al.
- 2012
(Show Context)
Citation Context ...xpected to have little effect on differential expression analyses to a first approximation. Recent publications, however, have demonstrated that sample-specific effects for GC-content can be detected =-=[16, 5]-=-. The EDASeq [16] and cqn [5] packages estimate correction factors that adjust for sample-specific GC-content effects in a way that is compatible with edgeR. In each case, the observation-specific cor... |

26 |
Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms,”
- Hoen, Ariyurek, et al.
- 2008
(Show Context)
Citation Context ...ls_3.1.0 4.2 deepSAGE of wild-type vs Dclk1 transgenic mice 4.2.1 Introduction This section provides a detailed analysis of data from an experiment using deep-sequenced tag-based expression profiling =-=[21]-=-. The biological question addressed was the identification of transcripts differentially expressed in the hippocampus between wild-type mice and transgenic mice over-expressing a splice variant of the... |

25 | Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach. - Lu, Tomfohr, et al. - 2005 |

24 | Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.
- Lund, Nettleton, et al.
- 2012
(Show Context)
Citation Context ...h [19, 20]. It also implements statistical methods based on generalized linear models (glms), suitable for multifactor experiments of any complexity, developed by McCarthy et al. [12] and Lund et al. =-=[10]-=-. Sometimes we refer to the former exact methods as classic edgeR, and the latter as glm edgeR. However the two sets of methods are complementary and can often be combined in the course of a data anal... |

23 | Thick-restart Lanczos method for symmetric eigenvalue problems
- Wu, Simon
(Show Context)
Citation Context ... = restart, \Gammasipar(7) = maxmv, \Gammasipar(8) = mpicom, \Gammasipar(9) = verbose, \Gammasipar(10) = log.io, \Gammasipar(11) = iguess, \Gammasipar(12) = cpflag, \Gammasipar(13) = cpio, \Gammasipar=-=(14)-=- = mvop, \Gammasipar(24) = locked, \Gammasipar(25) = matvec, \Gammasipar(26) = nloop, \Gammasipar(27) = north, \Gammasipar(28) = nrand, \Gammasipar(29) = total time in milliseconds, \Gammasipar(30) = ... |

20 |
Roast: rotation gene set tests for complex microarray experiments,
- Wu, Lim, et al.
- 2010
(Show Context)
Citation Context ... %in% XiEgenes Roast gene set tests confirm that the male-specific genes are significantly up as a group in our comparison of males with females, whereas the X genes are significantly down as a group =-=[23]-=-. The p-values are at their minimum possible values given the number of rotations: > index <- list(Y=Ymale,X=Xescape) > mroast(y,index,design,nrot=9999) NGenes PropDown PropUp Direction PValue FDR PVa... |

16 |
featureCounts: an efficient general-purpose read summarization program
- Liao, Smyth, et al.
- 2014
(Show Context)
Citation Context ...iations on this process. Alignment needs to allow for the fact that reads may span multiple exons which may align to well separated locations on the genome. We find the subread-featureCounts pipeline =-=[7, 8]-=- to be very fast and effective for this purpose, but the Bowtie-TopHat-htseq pipeline is also very popular [1]. 2.3 Producing a table of read counts edgeR works on a table of integer read counts, with... |

10 |
Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer
- Li
- 2008
(Show Context)
Citation Context ...d from prostate cancer cells (LNCaP cell line) after treatment with an androgen hormone (100uM of DHT). Four replicate control samples were also collected from cells treated with an inactive compound =-=[6]-=-. 4.3.3 Sequencing 35bp reads were sequenced on an Illumina 1G Genome Analyzer using seven lanes of one flow-cell. FASTA format files are available from http://yeolab.ucsd.edu/yeolab/Papers. html. 49 ... |

8 | Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS One
- Tuch, Laborde, et al.
- 2010
(Show Context)
Citation Context ...sue 4.4.1 Introduction This section provides a detailed analysis of data from a paired design RNA-seq experiment, featuring oral squamous cell carcinomas and matched normal tissue from three patients =-=[22]-=-. The aim of the analysis is to detect genes differentially expressed between tumor and normal tissue, adjusting for any differences between the patients. This provides an example of the GLM capabilit... |

5 | Gene-counter: a computational pipeline for the analysis of rna-seq data for gene expression differences.
- Cumbie, Kimbrel, et al.
- 2011
(Show Context)
Citation Context .... 4.2.2 Reading in the data The tag counts for the eight individual libraries are stored in eight separate plain text files: > dir() [1] "GSE10782_Dataset_Summary.txt" "GSM272105.txt" "GSM272106.txt" =-=[4]-=- "GSM272318.txt" "GSM272319.txt" "GSM272320.txt" [7] "GSM272321.txt" "GSM272322.txt" "GSM272323.txt" [10] "Targets.txt" In each file, the tag IDs and counts for each tag are provided in a table. It is... |

3 | Dynamic Retarting Schemes For Thick-Restart Lanczos Method - Wu, Simon - 1998 |