Statistics and Data Analysis for Microarrays Using R and Bioconductor, 2nd Edition

Statistics and Data Analysis for Microarrays Using R and Bioconductor, 2nd Edition

by Sorin Draghici
Released April 2016
Publisher(s): Chapman and Hall/CRC
ISBN: 9781439809761

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Book description

Richly illustrated in color, this bestselling text provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that explains the basics of R and micr

Table of contents

  1. Front Cover (1/2)
  2. Front Cover (2/2)
  3. Dedication
  4. Contents (1/4)
  5. Contents (2/4)
  6. Contents (3/4)
  7. Contents (4/4)
  8. List of Figures (1/2)
  9. List of Figures (2/2)
  10. List of Tables
  11. Preface (1/2)
  12. Preface (2/2)
  13. 1. Introduction
  14. 2. The cell and its basic mechanisms (1/7)
  15. 2. The cell and its basic mechanisms (2/7)
  16. 2. The cell and its basic mechanisms (3/7)
  17. 2. The cell and its basic mechanisms (4/7)
  18. 2. The cell and its basic mechanisms (5/7)
  19. 2. The cell and its basic mechanisms (6/7)
  20. 2. The cell and its basic mechanisms (7/7)
  21. 3. Microarrays (1/6)
  22. 3. Microarrays (2/6)
  23. 3. Microarrays (3/6)
  24. 3. Microarrays (4/6)
  25. 3. Microarrays (5/6)
  26. 3. Microarrays (6/6)
  27. 4. Reliability and reproducibility issues in DNA microarray measurements (1/4)
  28. 4. Reliability and reproducibility issues in DNA microarray measurements (2/4)
  29. 4. Reliability and reproducibility issues in DNA microarray measurements (3/4)
  30. 4. Reliability and reproducibility issues in DNA microarray measurements (4/4)
  31. 5. Image processing (1/6)
  32. 5. Image processing (2/6)
  33. 5. Image processing (3/6)
  34. 5. Image processing (4/6)
  35. 5. Image processing (5/6)
  36. 5. Image processing (6/6)
  37. 6. Introduction to R (1/15)
  38. 6. Introduction to R (2/15)
  39. 6. Introduction to R (3/15)
  40. 6. Introduction to R (4/15)
  41. 6. Introduction to R (5/15)
  42. 6. Introduction to R (6/15)
  43. 6. Introduction to R (7/15)
  44. 6. Introduction to R (8/15)
  45. 6. Introduction to R (9/15)
  46. 6. Introduction to R (10/15)
  47. 6. Introduction to R (11/15)
  48. 6. Introduction to R (12/15)
  49. 6. Introduction to R (13/15)
  50. 6. Introduction to R (14/15)
  51. 6. Introduction to R (15/15)
  52. 7. Bioconductor: principles and illustrations (1/3)
  53. 7. Bioconductor: principles and illustrations (2/3)
  54. 7. Bioconductor: principles and illustrations (3/3)
  55. 8. Elements of statistics (1/11)
  56. 8. Elements of statistics (2/11)
  57. 8. Elements of statistics (3/11)
  58. 8. Elements of statistics (4/11)
  59. 8. Elements of statistics (5/11)
  60. 8. Elements of statistics (6/11)
  61. 8. Elements of statistics (7/11)
  62. 8. Elements of statistics (8/11)
  63. 8. Elements of statistics (9/11)
  64. 8. Elements of statistics (10/11)
  65. 8. Elements of statistics (11/11)
  66. 9. Probability distributions (1/8)
  67. 9. Probability distributions (2/8)
  68. 9. Probability distributions (3/8)
  69. 9. Probability distributions (4/8)
  70. 9. Probability distributions (5/8)
  71. 9. Probability distributions (6/8)
  72. 9. Probability distributions (7/8)
  73. 9. Probability distributions (8/8)
  74. 10. Basic statistics in R (1/8)
  75. 10. Basic statistics in R (2/8)
  76. 10. Basic statistics in R (3/8)
  77. 10. Basic statistics in R (4/8)
  78. 10. Basic statistics in R (5/8)
  79. 10. Basic statistics in R (6/8)
  80. 10. Basic statistics in R (7/8)
  81. 10. Basic statistics in R (8/8)
  82. 11. Statistical hypothesis testing (1/5)
  83. 11. Statistical hypothesis testing (2/5)
  84. 11. Statistical hypothesis testing (3/5)
  85. 11. Statistical hypothesis testing (4/5)
  86. 11. Statistical hypothesis testing (5/5)
  87. 12. Classical approaches to data analysis (1/7)
  88. 12. Classical approaches to data analysis (2/7)
  89. 12. Classical approaches to data analysis (3/7)
  90. 12. Classical approaches to data analysis (4/7)
  91. 12. Classical approaches to data analysis (5/7)
  92. 12. Classical approaches to data analysis (6/7)
  93. 12. Classical approaches to data analysis (7/7)
  94. 13. Analysis of Variance – ANOVA (1/8)
  95. 13. Analysis of Variance – ANOVA (2/8)
  96. 13. Analysis of Variance – ANOVA (3/8)
  97. 13. Analysis of Variance – ANOVA (4/8)
  98. 13. Analysis of Variance – ANOVA (5/8)
  99. 13. Analysis of Variance – ANOVA (6/8)
  100. 13. Analysis of Variance – ANOVA (7/8)
  101. 13. Analysis of Variance – ANOVA (8/8)
  102. 14. Linear models in R (1/6)
  103. 14. Linear models in R (2/6)
  104. 14. Linear models in R (3/6)
  105. 14. Linear models in R (4/6)
  106. 14. Linear models in R (5/6)
  107. 14. Linear models in R (6/6)
  108. 15. Experiment design (1/6)
  109. 15. Experiment design (2/6)
  110. 15. Experiment design (3/6)
  111. 15. Experiment design (4/6)
  112. 15. Experiment design (5/6)
  113. 15. Experiment design (6/6)
  114. 16. Multiple comparisons (1/5)
  115. 16. Multiple comparisons (2/5)
  116. 16. Multiple comparisons (3/5)
  117. 16. Multiple comparisons (4/5)
  118. 16. Multiple comparisons (5/5)
  119. 17. Analysis and visualization tools (1/11)
  120. 17. Analysis and visualization tools (2/11)
  121. 17. Analysis and visualization tools (3/11)
  122. 17. Analysis and visualization tools (4/11)
  123. 17. Analysis and visualization tools (5/11)
  124. 17. Analysis and visualization tools (6/11)
  125. 17. Analysis and visualization tools (7/11)
  126. 17. Analysis and visualization tools (8/11)
  127. 17. Analysis and visualization tools (9/11)
  128. 17. Analysis and visualization tools (10/11)
  129. 17. Analysis and visualization tools (11/11)
  130. 18. Cluster analysis (1/14)
  131. 18. Cluster analysis (2/14)
  132. 18. Cluster analysis (3/14)
  133. 18. Cluster analysis (4/14)
  134. 18. Cluster analysis (5/14)
  135. 18. Cluster analysis (6/14)
  136. 18. Cluster analysis (7/14)
  137. 18. Cluster analysis (8/14)
  138. 18. Cluster analysis (9/14)
  139. 18. Cluster analysis (10/14)
  140. 18. Cluster analysis (11/14)
  141. 18. Cluster analysis (12/14)
  142. 18. Cluster analysis (13/14)
  143. 18. Cluster analysis (14/14)
  144. 19. Quality control (1/12)
  145. 19. Quality control (2/12)
  146. 19. Quality control (3/12)
  147. 19. Quality control (4/12)
  148. 19. Quality control (5/12)
  149. 19. Quality control (6/12)
  150. 19. Quality control (7/12)
  151. 19. Quality control (8/12)
  152. 19. Quality control (9/12)
  153. 19. Quality control (10/12)
  154. 19. Quality control (11/12)
  155. 19. Quality control (12/12)
  156. 20. Data preprocessing and normalization (1/12)
  157. 20. Data preprocessing and normalization (2/12)
  158. 20. Data preprocessing and normalization (3/12)
  159. 20. Data preprocessing and normalization (4/12)
  160. 20. Data preprocessing and normalization (5/12)
  161. 20. Data preprocessing and normalization (6/12)
  162. 20. Data preprocessing and normalization (7/12)
  163. 20. Data preprocessing and normalization (8/12)
  164. 20. Data preprocessing and normalization (9/12)
  165. 20. Data preprocessing and normalization (10/12)
  166. 20. Data preprocessing and normalization (11/12)
  167. 20. Data preprocessing and normalization (12/12)
  168. 21. Methods for selecting differentially expressed genes (1/10)
  169. 21. Methods for selecting differentially expressed genes (2/10)
  170. 21. Methods for selecting differentially expressed genes (3/10)
  171. 21. Methods for selecting differentially expressed genes (4/10)
  172. 21. Methods for selecting differentially expressed genes (5/10)
  173. 21. Methods for selecting differentially expressed genes (6/10)
  174. 21. Methods for selecting differentially expressed genes (7/10)
  175. 21. Methods for selecting differentially expressed genes (8/10)
  176. 21. Methods for selecting differentially expressed genes (9/10)
  177. 21. Methods for selecting differentially expressed genes (10/10)
  178. 22. The Gene Ontology (GO) (1/3)
  179. 22. The Gene Ontology (GO) (2/3)
  180. 22. The Gene Ontology (GO) (3/3)
  181. 23. Functional analysis and biological interpretation of microarray data (1/5)
  182. 23. Functional analysis and biological interpretation of microarray data (2/5)
  183. 23. Functional analysis and biological interpretation of microarray data (3/5)
  184. 23. Functional analysis and biological interpretation of microarray data (4/5)
  185. 23. Functional analysis and biological interpretation of microarray data (5/5)
  186. 24. Uses, misuses, and abuses in GO profiling (1/4)
  187. 24. Uses, misuses, and abuses in GO profiling (2/4)
  188. 24. Uses, misuses, and abuses in GO profiling (3/4)
  189. 24. Uses, misuses, and abuses in GO profiling (4/4)
  190. 25. A comparison of several tools for ontological analysis (1/7)
  191. 25. A comparison of several tools for ontological analysis (2/7)
  192. 25. A comparison of several tools for ontological analysis (3/7)
  193. 25. A comparison of several tools for ontological analysis (4/7)
  194. 25. A comparison of several tools for ontological analysis (5/7)
  195. 25. A comparison of several tools for ontological analysis (6/7)
  196. 25. A comparison of several tools for ontological analysis (7/7)
  197. 26. Focused microarrays – comparison and selection (1/3)
  198. 26. Focused microarrays – comparison and selection (2/3)
  199. 26. Focused microarrays – comparison and selection (3/3)
  200. 27. ID Mapping issues (1/2)
  201. 27. ID Mapping issues (2/2)
  202. 28. Pathway analysis (1/10)
  203. 28. Pathway analysis (2/10)
  204. 28. Pathway analysis (3/10)
  205. 28. Pathway analysis (4/10)
  206. 28. Pathway analysis (5/10)
  207. 28. Pathway analysis (6/10)
  208. 28. Pathway analysis (7/10)
  209. 28. Pathway analysis (8/10)
  210. 28. Pathway analysis (9/10)
  211. 28. Pathway analysis (10/10)
  212. 29. Machine learning techniques (1/6)
  213. 29. Machine learning techniques (2/6)
  214. 29. Machine learning techniques (3/6)
  215. 29. Machine learning techniques (4/6)
  216. 29. Machine learning techniques (5/6)
  217. 29. Machine learning techniques (6/6)
  218. 30. The road ahead
  219. Bibliography (1/10)
  220. Bibliography (2/10)
  221. Bibliography (3/10)
  222. Bibliography (4/10)
  223. Bibliography (5/10)
  224. Bibliography (6/10)
  225. Bibliography (7/10)
  226. Bibliography (8/10)
  227. Bibliography (9/10)
  228. Bibliography (10/10)
  229. Back Cover

Product information

  • Title: Statistics and Data Analysis for Microarrays Using R and Bioconductor, 2nd Edition
  • Author(s): Sorin Draghici
  • Release date: April 2016
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781439809761