y - johns hopkins bloomberg school of public healthiruczins/teaching/misc/notes.bio.pdf · u s 1500...

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Biostatistics and Computational Biology

Some selected examples . . . and a bit of R and Bioconductor

Ingo Ruczinski

Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health

December 7, 2009

Ingo Ruczinski Biostatistics and Computational Biology 1

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Bioinformatics and computational biology

Wikipedia:

Bioinformatics and computational biology involve the use of

techniques including applied mathematics, informatics, statistics,

computer science, artificial intelligence, chemistry, and biochemistry

to solve biological problems usually on the molecular level.

· · ·Major research efforts in the field include sequence alignment, gene

finding, genome assembly, protein structure alignment, protein

structure prediction, prediction of gene expression and protein-protein

interactions, and the modeling of evolution.


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Wikipedia:

The terms bioinformatics and computational biology are often used

interchangeably. However bioinformatics more properly refers to the

creation and advancement of algorithms, computational and statistical

techniques, and theory to solve formal and practical problems

inspired from the management and analysis of biological data.

Computational biology, on the other hand, refers to hypothesis-driven

investigation of a specific biological problem using computers, carried

out with experimental or simulated data, with the primary goal of

discovery and the advancement of biological knowledge.


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NIH definition of Bioinformatics and Computational Biology:

Bioinformatics and computational biology are rooted in life sciences

as well as computer and information sciences and technologies. Both

of these interdisciplinary approaches draw from specific disciplines

such as mathematics, physics, computer science and engineering,

biology, and behavioral science.

· · ·Bioinformatics applies principles of information sciences and

technologies to make the vast, diverse, and complex life sciences

data more understandable and useful. Computational biology uses

mathematical and computational approaches to address theoretical

and experimental questions in biology. Although bioinformatics and

computational biology are distinct, there is also significant overlap

and activity at their interface.


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NIH definition of Bioinformatics and Computational Biology:

The NIH Biomedical Information Science and Technology InitiativeConsortium agreed on the following definitions of bioinformatics and

computational biology recognizing that no definition could completelyeliminate overlap with other activities or preclude variations in

interpretation by different individuals and organizations.

Bioinformatics: Research, development, or application ofcomputational tools and approaches for expanding the use of

biological, medical, behavioral or health data, including those toacquire, store, organize, archive, analyze, or visualize such data.

Computational Biology: The development and application of

data-analytical and theoretical methods, mathematical modeling and

computational simulation techniques to the study of biological,

behavioral, and social systems.


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2D gel electrophoresis


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A B


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A:1 A:2 A:3 A:4 B:1 B:2 B:3 B:4

A B


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!20

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Karyotypes


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Trisomy


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Karyotypes

General Cytogenetics Information http://members.aol.com/chrominfo/


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FISH

Courtesy of the Pevsner Laboratory


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DNA changes


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Single nucleotide polymorphisms


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SNP chip data


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Deletion

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Amplification

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Uniparental isodisomy

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A versus B plots

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log2(A)

log

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plate 1

plate 2

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plates ordered by median date


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A versus B plots

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SNP_A!8348190

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3 copies

2 copies


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Trisomy 21

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Samples from Aravinda Chakravarti and Betty Doan


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Trisomy 21

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Samples from Aravinda Chakravarti and Betty Doan


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Prediction regions for copy number

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De novo deletion


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De novo deletion

80.9

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MI!S

MI!D

UPI!M

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The statistical environment R

R is an environment for data analysis and visualization.

R is both open source and open development.

You can look at the source code and propose changes.

R is not in the public domain.

You are given a license to run the software (currently GPL).


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The R software

R is mainly written in C.

R is available for many platforms:

Unix of many flavors, including Linux, Solaris, FreeBSD.

Windows 95 and later.

MacOS X.

Binaries and source code are available from

www.r-project.org.

R “talks” to data bases, programming languages, and other

statistical packages.

R should be source code compatible with most of the

Splus code written.


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CRAN


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The R package system

Packages are self-contained units of code with

documentation.

The packages are simple to obtain and to understand, and

can easily be updated.

You can write your own packages!

All functions must have examples to run.

There are automatic testing features built in.


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CRAN packages


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Pros and cons

Advantages

Free

Available for all major platforms

Comprehensive

Powerful graphics

Well-designed programming language

Unlimited extensibility

Widely used by statisticians

Increasingly used for genomic analyses (Bioconductor)

Disadvantages

No dedicated support

Complex syntax

Not point-and-click

Some simple tasks are rather hard


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Bioconductor


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Bioconductor


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JHSPH Biostatistics classes

3 140.615 Biostatistics for Laboratory Scientists I

MWF 10:30 – 11.20 (Ingo Ruczinski)

140.644 Practical Machine Learning

MW 1:30 – 2.50 (Rafael Irizarry)

4 140.616 Biostatistics for Laboratory Scientists II

MWF 10:30 – 11.20 (Ingo Ruczinski)

140.688 Statistics for Genomics

MW 10:30 – 11.50 (Jeff Leek)


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Statistics and probability

What is statistics?

Data exploration and analysis.

Quantification of evidence and uncertainty.

Inductive inference with probability.

What is probability?

A branch of mathematics concerning the study of random

processes.


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Diagnostics

!

+

!+

TEST

DISEASE

FN TN

TP FP


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Diagnostics

!

+

!+

TEST

DISEASE

FN TN

TP FP

Sensitivity ! Pr ( positive test | disease )

Specificity ! Pr ( negative test | no disease )

Positive Predictive Value ! Pr ( disease | positive test )

Negative Predictive Value ! Pr ( no disease | negative test )

Accuracy ! Pr ( correct outcome )


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Diagnostics

Assume that some disease has a 0.1% prevalence in the

population. Assume we have a test kit for that disease that

works with 99% sensitivity and 99% specificity. What is the

probability of a person having the disease, given the test result

is positive, if we randomly select a subject from the general

population?


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Diagnostics

!

+

!+

TEST

DISEASE

1 98901

99 999


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Diagnostics

!

+

!+

TEST

DISEASE

1 98901

99 999

Sensitivity ! 99 / (99+1) = 99%

Specificity ! 98901 / (999+98901) = 99%

Positive Predictive Value ! 99 / (99+999) " 9%

Negative Predictive Value ! 98901 / (1+98901) > 99.9%

Accuracy ! (99+98901) / 100000 = 99%


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Calibration

Goal: Determine, by fluoresence, the concentration of quinine

in a sample of tonic water.

1 Obtain a stock solution with known concentration of quinine.

2 Create several dilutions of the stock.

3 Measure fluoresence intensity of each such standard.

4 Measure fluoresence intensity of the unknown.

5 Fit a line to the results for the standards.

6 Use line to estimate quinine concentration in the unknown.

Question: How precise is the resulting estimate?


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Calibration

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Summarizing data

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Summarizing data

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Summarizing data

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http: //biostat.jhsph.edu/#iruczins/

[email protected]


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