Statistics and Data Analysis for Microarrays Using R and Bioconductor
eBook - PDF

Statistics and Data Analysis for Microarrays Using R and Bioconductor

  1. 1,036 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Statistics and Data Analysis for Microarrays Using R and Bioconductor

About this book

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition 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 teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New to the Second Edition Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying downloadable resource.

With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

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Yes, you can access Statistics and Data Analysis for Microarrays Using R and Bioconductor by Sorin Draghici in PDF and/or ePUB format, as well as other popular books in Informatica & Programmazione di giochi. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
2016
Print ISBN
9781439809754
eBook ISBN
9781439809761
Edition
2
Topic
Informatica
Subtopic
Programmazione di giochi
Chapter
1
Introduction
If
we
begin
with
certainties,
we
shall
end
in
doubts;
but
if
we
begin
with
doubts,
and
are
patient
in
them,
we
shall
end
in
certainties.
—Francis
Bacon
1.1
Bioinformatics
–
an
emerging
discipline
Life
sciences
are
currently
at
the
center
of
an
informational
revolution.
Dra-
matic
changes
are
being
registered
as
a
consequence
of
the
development
of
techniques
and
tools
that
allow
the
collection
of
biological
information
at
an
unprecedented
level
of
detail
and
in
extremely
large
quantities.
The
human
genome
project
is
a
compelling
example.
Initially,
the
plan
to
sequence
the
hu-
man
genome
was
considered
extremely
ambitious,
on
the
border
of
feasibility.
The
first
serious
effort
was
planned
over
15
years
at
a
cost
of
$3
billion.
Soon
after,
the
schedule
was
revised
to
last
only
5
years.
Eventually,
the
genome
was
sequenced
in
less
than
3
years,
at
a
cost
much
lower
than
initially
expected
[361].
The
nature
and
amount
of
information
now
available
open
directions
1

Table of contents

  1. Front Cover
  2. Dedication
  3. Contents
  4. List of Figures
  5. List of Tables
  6. Preface
  7. 1. Introduction
  8. 2. The cell and its basic mechanisms
  9. 3. Microarrays
  10. 4. Reliability and reproducibility issues in DNA microarray measurements
  11. 5. Image processing
  12. 6. Introduction to R
  13. 7. Bioconductor: principles and illustrations
  14. 8. Elements of statistics
  15. 9. Probability distributions
  16. 10. Basic statistics in R
  17. 11. Statistical hypothesis testing
  18. 12. Classical approaches to data analysis
  19. 13. Analysis of Variance – ANOVA
  20. 14. Linear models in R
  21. 15. Experiment design
  22. 16. Multiple comparisons
  23. 17. Analysis and visualization tools
  24. 18. Cluster analysis
  25. 19. Quality control
  26. 20. Data preprocessing and normalization
  27. 21. Methods for selecting differentially expressed genes
  28. 22. The Gene Ontology (GO)
  29. 23. Functional analysis and biological interpretation of microarray data
  30. 24. Uses, misuses, and abuses in GO profiling
  31. 25. A comparison of several tools for ontological analysis
  32. 26. Focused microarrays – comparison and selection
  33. 27. ID Mapping issues
  34. 28. Pathway analysis
  35. 29. Machine learning techniques
  36. 30. The road ahead
  37. Bibliography
  38. Back Cover