limma: Linear Models for Microarray Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2005-01-01

AUTHORS

G. K. Smyth

ABSTRACT

A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data. More... »

PAGES

397-420

Book

TITLE

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

ISBN

978-0-387-25146-2
978-0-387-29362-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/0-387-29362-0_23

DOI

http://dx.doi.org/10.1007/0-387-29362-0_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025432622


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "familyName": "Smyth", 
        "givenName": "G. K.", 
        "type": "Person"
      }
    ], 
    "datePublished": "2005-01-01", 
    "datePublishedReg": "2005-01-01", 
    "description": "A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the \u03b27 data.", 
    "editor": [
      {
        "familyName": "Gentleman", 
        "givenName": "Robert", 
        "type": "Person"
      }, 
      {
        "familyName": "Carey", 
        "givenName": "Vincent J.", 
        "type": "Person"
      }, 
      {
        "familyName": "Huber", 
        "givenName": "Wolfgang", 
        "type": "Person"
      }, 
      {
        "familyName": "Irizarry", 
        "givenName": "Rafael A.", 
        "type": "Person"
      }, 
      {
        "familyName": "Dudoit", 
        "givenName": "Sandrine", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/0-387-29362-0_23", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-0-387-25146-2", 
        "978-0-387-29362-2"
      ], 
      "name": "Bioinformatics and Computational Biology Solutions Using R and Bioconductor", 
      "type": "Book"
    }, 
    "keywords": [
      "test statistic", 
      "direct design", 
      "linear model", 
      "modeling features", 
      "quality weights", 
      "biological replication", 
      "more groups", 
      "linear modelling", 
      "microarray data", 
      "control spots", 
      "statistics", 
      "replicated design", 
      "modelling", 
      "differential expression analysis", 
      "background correction", 
      "design", 
      "model", 
      "experiments", 
      "package", 
      "correction", 
      "data", 
      "conjunction", 
      "factorial design", 
      "features", 
      "analysis", 
      "chapter", 
      "spots", 
      "use", 
      "weight", 
      "survey", 
      "group", 
      "time-course experiments", 
      "course experiments", 
      "replication", 
      "expression analysis", 
      "limma package"
    ], 
    "name": "limma: Linear Models for Microarray Data", 
    "pagination": "397-420", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025432622"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/0-387-29362-0_23"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/0-387-29362-0_23", 
      "https://app.dimensions.ai/details/publication/pub.1025432622"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-12-01T06:52", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/chapter/chapter_37.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/0-387-29362-0_23"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/0-387-29362-0_23'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/0-387-29362-0_23'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/0-387-29362-0_23'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/0-387-29362-0_23'


 

This table displays all metadata directly associated to this object as RDF triples.

110 TRIPLES      22 PREDICATES      59 URIs      52 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/0-387-29362-0_23 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N1e1bc158902b4c4bb4ac421a8b295d32
4 schema:datePublished 2005-01-01
5 schema:datePublishedReg 2005-01-01
6 schema:description A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.
7 schema:editor N2aee34b29dcf43d9b7aad291c8c7eff2
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf Na994fdcbd9544c7bb60f407c011811e8
11 schema:keywords analysis
12 background correction
13 biological replication
14 chapter
15 conjunction
16 control spots
17 correction
18 course experiments
19 data
20 design
21 differential expression analysis
22 direct design
23 experiments
24 expression analysis
25 factorial design
26 features
27 group
28 limma package
29 linear model
30 linear modelling
31 microarray data
32 model
33 modeling features
34 modelling
35 more groups
36 package
37 quality weights
38 replicated design
39 replication
40 spots
41 statistics
42 survey
43 test statistic
44 time-course experiments
45 use
46 weight
47 schema:name limma: Linear Models for Microarray Data
48 schema:pagination 397-420
49 schema:productId Nad3666c69ba94bc7b44de4f426f70352
50 Ne36edfb4f44e4fcf939dc84577036a84
51 schema:publisher N82ab3d91fec84625b1d860bbf8374e7b
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025432622
53 https://doi.org/10.1007/0-387-29362-0_23
54 schema:sdDatePublished 2022-12-01T06:52
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher Na3aa2222b83347dca29a4b4fa1fbde00
57 schema:url https://doi.org/10.1007/0-387-29362-0_23
58 sgo:license sg:explorer/license/
59 sgo:sdDataset chapters
60 rdf:type schema:Chapter
61 N17ee9b442da0475c9577085c43b5431a rdf:first N7981390184884a96951ac56a90d73277
62 rdf:rest Na51561f041c34d1e8d775dc1e8f1309c
63 N19b1650aaa374687bdf992a6dbe0ae3b rdf:first N6a9e119a370346c3bae57386fe5e22b6
64 rdf:rest rdf:nil
65 N1e1bc158902b4c4bb4ac421a8b295d32 rdf:first Nad4c4fd61d78422a94499b2216b0deca
66 rdf:rest rdf:nil
67 N2aee34b29dcf43d9b7aad291c8c7eff2 rdf:first N5810d00961ac4fcb801754a33422934a
68 rdf:rest N9f8683456e3043858675f19e99b211d8
69 N5810d00961ac4fcb801754a33422934a schema:familyName Gentleman
70 schema:givenName Robert
71 rdf:type schema:Person
72 N6a9e119a370346c3bae57386fe5e22b6 schema:familyName Dudoit
73 schema:givenName Sandrine
74 rdf:type schema:Person
75 N787f7631674e49a481936ab19bf2ea6c schema:familyName Irizarry
76 schema:givenName Rafael A.
77 rdf:type schema:Person
78 N7981390184884a96951ac56a90d73277 schema:familyName Huber
79 schema:givenName Wolfgang
80 rdf:type schema:Person
81 N82ab3d91fec84625b1d860bbf8374e7b schema:name Springer Nature
82 rdf:type schema:Organisation
83 N9f8683456e3043858675f19e99b211d8 rdf:first Nce69a7210b864170aea9c8dd69a9fe12
84 rdf:rest N17ee9b442da0475c9577085c43b5431a
85 Na3aa2222b83347dca29a4b4fa1fbde00 schema:name Springer Nature - SN SciGraph project
86 rdf:type schema:Organization
87 Na51561f041c34d1e8d775dc1e8f1309c rdf:first N787f7631674e49a481936ab19bf2ea6c
88 rdf:rest N19b1650aaa374687bdf992a6dbe0ae3b
89 Na994fdcbd9544c7bb60f407c011811e8 schema:isbn 978-0-387-25146-2
90 978-0-387-29362-2
91 schema:name Bioinformatics and Computational Biology Solutions Using R and Bioconductor
92 rdf:type schema:Book
93 Nad3666c69ba94bc7b44de4f426f70352 schema:name dimensions_id
94 schema:value pub.1025432622
95 rdf:type schema:PropertyValue
96 Nad4c4fd61d78422a94499b2216b0deca schema:familyName Smyth
97 schema:givenName G. K.
98 rdf:type schema:Person
99 Nce69a7210b864170aea9c8dd69a9fe12 schema:familyName Carey
100 schema:givenName Vincent J.
101 rdf:type schema:Person
102 Ne36edfb4f44e4fcf939dc84577036a84 schema:name doi
103 schema:value 10.1007/0-387-29362-0_23
104 rdf:type schema:PropertyValue
105 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
106 schema:name Mathematical Sciences
107 rdf:type schema:DefinedTerm
108 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
109 schema:name Statistics
110 rdf:type schema:DefinedTerm
 




Preview window. Press ESC to close (or click here)


...