基于Affymetrix芯片的基因表达研究（导读版） 下载 pdf 百度网盘 epub 免费 2025 电子书 mobi 在线

Affymetrix

GeneChip系统是目前应用广泛的生物芯片平台。但是由于Aflymetrix芯片含有超大量的信息，很多Affymetrix芯片用户趋向于使用默认的分析设置，得到的常常不是*化的结论。分子生物学家和生物统计学家根据十余年的基因表达谱实验研究和数据分析的实践经验编写了《基于Affymetrix芯片的基因表达研究》，从理论概念到实验结果，解释了使用Affymetrix芯片进行基因表达研究的全部过程，拆除了分子生物学、生物信息学和生物统计学之间无处不在的语言障碍。

本书权威实用，介绍了Affymetrix芯片的重要技术、统计学易犯的错误和问题，同时涉及其他芯片平台的一般规则和应用。通过例证和全彩图例，描述了技术和统计方法的概念，为初学者提供详细指导。本领域的专家则可以了解芯片所涉及的其他学科知识，拓展基因芯片表达谱研究的认识。

附图目录

表格目录

BioBox目录

StatsBox目录

前言

缩写词和术语

1 生物学问题

1.1 为什么进行基因表达?

1.1.1 生物技术的进展

1.1.2 生物学相关的研究

1.2 研究问题

1.2.1 相关性和实验研究对比

1.3 研究课题的主要类型

1.3.1 两组间比较

1.3.2 多组间比较

1.3.3 不同治疗方式间的比较

1.3.4 多组与对照组的比较

1.3.5 研究主题内的变化

1.3.6 分类和预测样本

2 AffymetriX芯片技术

2.1 探针

2.2 探针组

2.2.1 标准探针组的定义

2.2.2 客户可选择的芯片描述文件(CDF)

2.3 芯片类型

2.3.1 标准表达检测芯片

2.3.2 外显子芯片

2.3.3 基因芯片

2.3.4 叠瓦芯片

2.3.5 用于某项研究的专用芯片

2.4 标准实验室芯片实验流程

2.4.1 体外转录分析

2.4.2 全转录本正义链标记

2.5 AffymetriX芯片的数据质量

2.5.1 分析数据的重复性

2.5.2 分析数据的稳定性

2.5.3 分析的敏感性

3 实验操作

3.1 生物学实验

3.1.1 生物学背景

3.1.1.1 实验目的/假设

3.1.1.2 技术平台

3.1.1.3 mRNA水平的预期改变

3.1.2 样本

3.1.2.1 选择合适的样品/组织

3.1.2.2 样本的类型

3.1.2.3 样本的异质性

3.1.2.4 性别

3.1.2.5 时间点

3.1.2.6 样本切割引起的误差

3.1.2.7 动物处理产生的误差

3.1.2.8 RNA的质量

3.1.2.9 RNA的数量

3.1.3 预实验

3.1.4 主实验

3.1.4.1 对照实验

3.1.4.2 实验处理

3.1.4.3 分批实验

3.1.4.4 随机化

3.1.4.5 标准化

3.1.4.6 选择对照

3.1.4.7 样品量/重复次数/费用

3.1.4.8 平衡设计

3.1.4.9 对照样本

3.1.4.10 样本混合

3.1.4.11 实验记录

3.1.5 实验数据分析验证

3.2 芯片实验

3.2.1 外源RNA对照

3.2.2 靶基因合成

3.2.3 批处理影响

3.2.4 全基因组芯片和用于某项研究的专用芯片比较

4 数据分析预处理

4.1 数据预处理

4.1.1 探针的信号强度

4.1.2 转换为log2的对数

4.1.3 背景校正

4.1.4 归一化

4.1.5 AffymetriX芯片概要

4.1.5.1 完全匹配(PM)和错配(MM)技术

4.1.5.2 只使用PM探针的技术

4.1.6 整体解决方案

4.1.7 信号检测方法

4.1.7.1 芯片分析系统MAS 5.0

4.1.7.2 背景和杂交信号检测(DABG)

4.1.7.3 检出/缺失比值(PANP)

4.1.8 标准化

4.2 质量控制

4.2.1 技术数据

4.2.2 虚拟图像

4.2.3 重复性评价

4.2.3.1 重复性评价方法

4.2.3.2 实例分析

4.2.4 批处理效应

4.2.5 批处理效应校正

5 数据分析

5.1 为什么我们需要统计学?

5.1.1 需要对数据作出解释

5.1.2 需要一个优秀的实验设计

5.1.3 统计学与生物信息学比较

5.2 高维数据的问题

5.2.1 分析结果的重复性

5.2.2 数据挖掘和验证

5.3 基因过滤

5.3.1 过滤方法

5.3.1.1 信号强度

5.3.1.2 两样品间变异

5.3.1.3 缺失/检出

5.3.1.4 含有效信息的/无有效信息的检出

5.3.2 数据过滤对检验和多重校正的影响

5.3.3 几种过滤方法的比较

5.4 无监督数据分析

5.4.1 进行无监督分析的原因

5.4.1.1 批次影响

5.4.1.2 技术或生物学的偏差

5.4.1.3 表型数据的质量校验

5.4.1.4 共调控基因的识别

5.4.2 聚类

5.4.2.1 距离和联系

5.4.2.2 聚类算法

5.4.2.3 聚类质量校验

5.4.3 多元投影方法

5.4.3.1 多元投影方法类型

5.4.3.2 基因和样本关系图

5.5 检测差异表达

5.5.1 复杂问题的简单解决方法

5.5.2 统计检验

5.5.2.1 倍数变化

5.5.2.2 t-检验类型

5.5.2.3 由t统计到p值

5.5.2.4 方法比较

5.5.2.5 线性模型

5.5.3 多重检验的校正

5.5.3.1 多重检验的问题

5.5.3.2 多重校正步骤

5.5.3.3 方法比较

5.5.3.4 事后比较

5.5.4 统计学意义与生物学相关性

5.5.5 样本数量估计

5.6 有监督的预测

5.6.1 分类与假设检验

5.6.2 芯片分类的挑战

5.6.2.1 过度拟合

5.6.2.2 偏执方差平衡

5.6.2.3 交叉效验

5.6.2.4 非分类解决方案

5.6.3 位点选择方法

5.6.4 分类方法

5.6.4.1 判别分析

5.6.4.2 近邻分析法

5.6.4.3 逻辑(Logistic)回归

5.6.4.4 神经网络

5.6.4.5 支持向量机

5.6.4.6 分类树

5.6.4.7 集成方法

5.6.4.8 芯片预测分析(PAM)

5.6.4.9 方法比较

5.6.5 复杂的预测问题

5.6.5.1 多级问题

5.6.5.2 生存预测

5.6.6 样本量

5.7 通路分析

5.7.1 通路分析的统计学方法

5.7.1.1 过表达分析

5.7.1.2 功能分类评分

5.7.1.3 基因集分析

5.7.1.4 方法比较

5.7.2 数据库

5.7.2.1 Gene ontology

5.7.2.2 京都基因与基因组百科全书(KEGG)

5.7.2.3 基因芯片通路分析(GenMAPP)

5.7.2.4 腺嘌呤富集元件数据库(ARED)

5.7.2.5 概念图(cMAP)

5.7.2.6 凋亡路径图(BioCarta)

5.7.2.7 染色体位置

5.8 其他分析方法

5.8.1 基因网络分析

5.8.2 元分析

5.8.3 染色体位置

6 分析结果表示

6.1 数据可视化

6.1.1 热图

6.1.2 强度图

6.1.3 基因表图

6.1.4 维恩图(Venn图)

6.1.5 散点图

6.1.5.1 火山图(Volcano plot)

6.1.5.2 MA图

6.1.5.3 高维数据的散点图

6.1.6 柱状图

6.1.7 盒图

6.1.8 小提琴图表

6.1.9 密度图

6.1.10 树状图

6.1.11 基因表达通路

6.1.12 出版用图表

6.2 生物学解释

6.2.1 重要数据库

6.2.1.1 Entrez Gene

6.2.1.2 AffymetriX网站(NetAffx)

6.2.1.3 OMIM

6.2.2 文献挖掘

6.2.3 数据整合

6.2.3.1 多种分子筛选数据

6.2.3.2 系统生物学

6.2.4 实时定量聚合酶反应(RTqPCR)验证

6.3 数据发表

6.3.1 ArrayExpress

6.3.2 基因表达文库(GEO)

6.4 可重复性研究

7 药物研发

7.1 早期标志物的需求

7.2 关键路径计划

7.3 药物发现

7.3.1 正常组织和病变组织的不同

7.3.2 疾病亚型的发现

7.3.3 分子靶标的识别

7.3.4 分子特征谱

7.3.5 疾病模型特征

7.3.6 化合物分析

7.3.7 剂量效应处理

7.4 药物开发

7.4.1 生物标志物

7.4.2 响应显著性

7.4.3 毒理基因组学

7.5 临床实验

7.5.1 功能指标

7.5.2 结果预测的意义

8 使用R和Bioconductor

8.1 R和Bioconductor

8.2 R和Sweave(R语言的一种函数)

8.3 R和Eclipse(一种代码)

8.4 自动芯片分析

8.4.1 装载文件包

8.4.2 基因过滤

8.4.3 无监督探索

8.4.4 差异表达检验

8.4.5 有监督分类

8.5 其他芯片分析软件

9 未来前景

9.1 同时分析不同数据类型

9.2 未来的芯片

9.3 新一代(二代)测序:芯片的终结?

参考文献

索引

附图目录

2.1 标准AffymetriX芯片图

2.2 GC含量对信号强度的影响

2.3 同一探针集中的探针之间信号强度的差别

2.4 使用客户选择的CDF时,探针集大小引起的差异

2.5 外显子芯片和3′端芯片探针覆盖范围的比较

2.6 外显子芯片的转录本注释

3.1 性别特异基因Xist(X染色体失活特异转录本)

3.2 样本切割产生误差示例

3.3 甲状腺素在小鼠纹状体的表达

3.4 小鼠结肠样本切割引起的误差

3.5 降解与非降解RNA对比

3.6 RNA的降解图显示3′偏差

3.7 不同批次芯片的批间效果

4.1 芯片扫描图像的一角

4.2 对数转换的分配效应

4.3 芯片数据中的两种噪音成分

4.4 归一化对强度依赖变异的影响

4.5 归一化对MA图的影响

4.6 MAS 5.0背景计算

4.7 由affyPLM产生的虚拟图像

4.8 两重复关联评估重复性

4.9 中心定位前后的成对一致性

4.10 光谱图评估重复性

4.11 由MAQC(生物芯片质量控制)得到的归一化前AffymetriX数据的盒式图

4.12 来自MAQC研究得到的AffymetriX芯片数据的SPM(谱图)

4.13 存在批次效应的差异表达基因的强度图

5.1 信息丰富的和不提供信息的探针集的探针比较

5.2 基因过滤对p值分布的影响

5.3 不同过滤技术排除基因的百分比

5.4 两种过滤技术的差异

5.5 基因过滤技术的分布差别

5.6 在聚类中的欧几里得(Euclidean)和皮尔森(Pearson)距离

5.7 基于欧几里得和皮尔森距离的ALL数据的分级聚类

5.8 分级聚类运算的示意图

5.9 k均值运算的示意图

5.10 ALL数据的主要成分分析

5.11 ALL数据的谱图

5.12 t-检验的可变性

5.13 t-检验

5.14 不良的t-检验:变异对显著性的影响

5.15 Δ=0.75的SAM图

5.16 t分布

5.17 使用大样本资料比较两种差异表达检验的方法(30 vs.30)

5.18 使用小样本资料比较两种差异表达检验的方法(3 vs.3)

5.19 各种交互效应的假设方案

5.20 用GLUCO数据中具有不同表达方式的四个基因解释交互效应

5.21 多种检验校正方法及其如何处理假阳性和假阴性

5.22 ALL数据组中调整过和未调整过的p值

5.23 高维性和过度拟合在分离中的关联

5.24 过度拟合的问题

5.25 嵌套循环交叉验证

5.26 利用PAM基因组合秩次升高

5.27 利用LASSO基因组合秩次升高

5.28 交叉验证中的位点排列

5.29 进行分类的基因数量

5.30 惩罚回归:惩罚的系数关联

5.31 神经网络方案

5.32 支持向量机模型的二维可视框图

5.33 使用MLP包含高秩基因组的GO通路

5.34 利用GSA含有高秩基因组的GO通路

5.35 BioCarta通路

5.36 识别差异表达的染色体区域

6.1 热图

6.2 强度图

6.3 基因列表图

6.4 Venn(维恩)图

6.5 火山图

6.6 MA图

6.7 平滑散点图

6.8 柱状图

6.9 数据组HD的盒图

6.10 小提琴图

6.11 密度图

6.12 系统树图

6.13 重要基因组的GO通路

7.1 药物开发中的基因表达谱

7.2 Fos的剂量反应特征

9.1 二代测序排序可能出现的错误

表格目录

1.1 双通道ANOVA设计

2.1 AffymetriX探针集的类型和名称

2.2 已经不再使用的AffymetriX探针集和名称

2.3 原始AffymetriX探针集的注释级别

2.4 产生客户可选择的CDF的规则

2.5 基于Ensembl Gene数据库的HG U133 plus 2.0探针的使用

3.1 不同样本的RNA产率

4.1 背景微小差异的影响

5.1 修正p值的计算

5.2 分类和假设检验

5.3 采用LASSO和PAM选择的重要基因

5.4 惩罚回归:基因选择

5.5 采用MLP选择的重要基因

5.6 采用GSA选择的前5个上调基因组和前5个下调基因组

BioBox目录

1.1 基因表达芯片

1.2 分子生物学的中心法则

1.3 siRNA

1.4 表型

2.1 剪接变异

2.2 基因

3.1 Northern杂交

3.2 转录因子

3.3 血液

3.4 细胞培养

3.5 X染色体失活:Xist

3.6 凝胶电泳

3.7 生物分析仪进行RNA分析

3.8 RTqPCR(荧光定量PCR)

5.1 管家基因

7.1 生物标志物

7.2 EC50,ED50,IC50,LC50和LD50

7.3 生物标志物和临床意义

7.4 基因表达的意义

9.1 表观遗传学的实例:DNA甲基化

StatsBox目录

1.1 关联的两种解释

3.1 能力

4.1 准度和精度

4.2 贝叶斯统计

4.3 可重复性

4.4 关联假设

5.1 参数,变量,统计

5.2 完全拟合

5.3 有监督和无监督的研究

5.4 重取样技术

5.5 神经网络

5.6 多变量投影方法的步骤

5.7 确定差异表达的步骤

5.8 比值的对数=对数差异

5.9 零假设和p值

5.10 变异,标准偏差和标准误差

5.11 经验贝叶斯方法

5.12 显著性水平和能力

5.13 参数和非参数检验比较

5.14 Explanatory和响应变异

5.15 通用线性模型

5.16 测量规模

5.17 交互反应

5.18 规则化或惩罚

5.19 敏感性和特异性

5.20 多重检验校正步骤

5.21 信息并不是越多越好

5.22 核心技术

5.23 刀切法和自助法

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Chapter 1

Biological

question

All

experimental

work

starts

in

principle

with a

question.

This

also

applies

to

the

field

of

molecular

biology. A

molecular

scientist

is

using a

certain

technique

to

answer a

specific

question

such

as,

“Does

the

cell

produce

more

of a

given

protein

when

treated

in a

certain

way?

”

Questions

in

molecular

biology

are

indeed

regularly

focused

on

specific

proteins

or

genes,

often

because

the

applied

technique

cannot

measure

more.

Gene

expression

studies

that

make

use

of

microarrays

also

start

with a

biological

question.

The

largest

difference

to

many

other

molecular

biology

approaches

is,

however,

the

type

of

question

that

is

being

asked.

Scientists

will

typically

not

run

arrays

to

find

out

whether

the

expression

of a

specific

messenger

RNA

is

altered

in a

certain

condition.

More

often

they

will

focus

their

question

on

the

treatment

or

the

condition

of

interest.

Centering

the

question

on a

biological

phenomenon

or a

treatment

has

the

advantage

of

allowing

the

researcher

to

discover

hitherto

unknown

alterations.

On

the

other

hand,

it

poses

the

problem

that

one

needs

to

define

when

an“interesting”

alteration

occurs.

1.1

Why

gene

expression?

1.1.1

Biotechnological

advancements

Research

evolves

and

advances

not

only

through

the

compilation

of

knowledge

but

also

through

the

development

of

new

technologies.

Traditionally,

researchers

were

able

to

measure

only a

relatively

small

number

of

genes

at a

time.

The

emergence

of

microarrays

(see

BioBox

1.1)

now

allows

scientists

to

analyze

the

expression

of

many

genes

in a

single

experiment

quickly

and

efficiently.

1.1.2

Biological

relevance

Living

organisms

contain

information

on

how

to

develop

its

form

and

structure

and

how

to

build

the

tools

that

are

responsible

for

all

biological

processes

that

need

to

be

carried

out

by

the

organism.

This

information ?

the

genetic

..........................................

Geneexpressionmicroarrays.Inmicroarrays,thousandstomillionsofprobesarefixedtoorsynthesizedonasolidsur-

face,beingeitherglassorasiliconchip.Thelatterexplainswhymicroarraysarealsooftenreferredtoaschips.Thetar-

getsoftheprobes,themRNAsamples,arelabelledwithfluo-

rescentdyesandarehybridizedtotheirmatchingprobes.Thehybridizationintensity,whichestimatestherelativeamountsofthetargettranscripts,canafterwardsbemeasuredbytheamountoffluorescentemissionontheirrespectivespots.Therearevariousmicroarrayplatformsdifferinginarrayfabrication,

thenatureandlengthoftheprobes,thenumberoffluorescentdyesthatarebeingused,etc.

BioBox

1.1:

Gene

expression

microarrays

content ?

is

encoded

in

information

units

referred

to

as

genes.

The

whole

set

of

genes

of

an

organism

is

referred

to

as

its

genome.

The

vast

majority

of

genomes

are

encoded

in

the

sequence

of

chemical

building

blocks

made

from

deoxyribonucleic

acid

(DNA)

and a

smaller

number

of

genomes

are

composed

of

ribonucleic

acid

(RNA)

,

e.g.

,

for

certain

types

of

viruses.

The

genetic

information

is

encoded

in a

specific

sequence

made

from

four

different

nucleotide

bases:

adenine,

cytosine,

guanine

and

thymine. A

slighlty

different

composition

of

building

blocks

is

present

in

mRNA

where

the

base

thymine

is

replaced

by

uracil.

Genetic

information

encoding

the

building

plan

for

proteins

is

transferred

from

DNA

to

mRNA

to

proteins.

The

gene

sequence

can

range

in

length

typically

between

hundreds

and

thousands

of

nucleotides

up

to

even

millions

of

bases.

The

number

of

genes

that

contain

protein-coding

information

is

expected

to

be

between

25,000

to

30,000

when

looking

at

the

human

genome. A

protein

is

made

by

constructing a

string

of

protein

building

blocks

(amino

acids)

.

The

order

of

the

amino

acids

in a

protein

matches

the

sequence

of

the

nucleotides

in

the

gene.

In

other

words,

messenger

RNA

interconnects

DNA

and

protein,

and

also

has

some

important

practical

advantages

compared

to

both

DNA

and

proteins

(see

BioBox

1.2)

.

Increasing

our

knowlegde

about

the

dynamics

of

the

genome

as

manifested

in

the

alterations

in

gene

expression

of a

cell

upon

treatment,

disease,

development

or

other

external

stimuli,

should

enable

us

to

transform

this

knowledge

into

better

tools

for

the

diagnosis

and

treatment

of

diseases.

DNA

is

made

of

two

strands

forming

together a

chemical

structure

that

is

called

“double

helix.

”

The

two

strands

are

connected

with

one

another

via

pairs

of

bases

that

form

hydrogen

bonds

between

both

strands.

Such

pairing

of

so-called

“complementary”

bases

occurs

only

between

certain

pairs.

..........

Centraldogmaofmolecularbiology.Thedogmaofmolec-

ularbiologyexplainshowtheinformationtobuildproteinsistransferredinlivingorganisms.Thegeneralflowofbiologicalinformation(greenarrows)hasthreemajorcomponents:(1)

DNAtoDNA(replication)occursinthecellnucleus(drawninyellow)priortocelldevision,(2)DNAtomRNA(transcrip-

tion)takesplacewheneverthecell(drawninlightred)needstomakeaprotein(drawnaschainofreddots),and(3)mRNAtoproteins(translation)istheactualproteinsynthesisstepintheribosomes(drawningreen).Besidesthesegeneraltransfersthatoccurnormallyinmostcells,therearealsosomespecialinformationtransfersthatareknowntooccurinsomevirusesorinalaboratoryexperimentalsetting.

BioBox

1.2:

Central

dogma

of

molecular

biology

..........................................

Hydrogen

bonds

can

be

formed

between

cytosine

and

guanine

or

between

adenine

and

thymine.

The

pairing

of

the

two

strands

occurs

in a

process

called

“hybridization.

”

Compared

to

DNA,

mRNA

is

more

dynamic

and

less

redundant.

The

information

that

is

encoded

in

the

DNA

is

made

available

for

processing

in a

step

called

“gene

expression”

or

“transcription.

”

Gene

expression

is a

highly

complex

and

tightly

regulated

process

by

which a

working

copy

of

the

original

sequence

information

is

made.

This

allows a

cell

to

respond

dynamically

both

to

environmental

stimuli

and

to

its

own

changing

needs,

while

DNA

is

relatively

invariable.

Furthermore,

as

mRNA

constitutes

only

the

expressed

part

of

the

DNA,

it

focuses

more

directly

on

processes

underlying

biological

activity.

This

filtering

is

convenient

as

the

functionality

of

most

DNA

sequences

is

irrelevant

for

the

study

at

hand.

Compared

to

proteins,

mRNA

is

much

more

measurable.

Proteins

are

3D

conglomerates

of

multiple

molecules

and

cannot

benefit

from

the

hybridising

nature

of

the

base

pairs

in

the

2D,

single

molecule,

structure

of

mRNA

and

DNA.

Furthermore,

proteins

are

very

unstable

due

to

denaturation,

and

cannot

be

preserved

even

with

very

laborious

methods

for

sample

extraction

and

storage.

When

using

microarrays

to

study

alterations

in

gene

expression,

people

normally

will

only

want

to

study

the

types

of

RNA

that

code

for

proteins

?

the

messenger

RNA

(mRNA)

.

It

is

however

important

to

keep

in

mind

that

RNAnot

only

contains

mRNA?acopyofa

section

of

the

genomic

DNA

carrying

the

information

of

how

to

build

proteins.

Besides

the

code

for

the

synthesis

of

ribosomal

RNA,

there

are

other

non-coding

genes

that,

e.g.

,

contain

information

for

the

synthesis

of

RNA

molecules.

These

RNAs

have

different

functions

that

range

from

enzymatic

activities

to

regulating

transcription

of

mRNAs

and

translation

of

mRNA

sequences

to

proteins.

The

numbers

of

these

functional

RNAs

that

are

encoded

in

the

genome

are

not

known.

Initial

studies

looking

at

the

overall

transcriptional

activity

along

the

DNA

are

predicting

that

the

number

will

most

likely

be

larger

than

the

number

of

protein-coding

genes.

People

used

to

say

that a

large

portion

of

the

genomic

information

encoded

in

the

DNA

are

useless

(“junk

DNA”)

.

Over

the

last

years

scientific

evidence

has

accumulated

that a

large

proportion

of

the

genome

is

being

transcribed

into

RNAs

of

which a

small

portion

constitutes

messenger

RNAs.

All

these

other

non-coding

RNAs

are

divided

into

two

main

groups

depending

on

their

size.

While

short

RNAs

are

defined

to

have

sizes

below

200

bases,

the

long

RNAs

are

thought

to

be

mere

precursors

for

the

generation

of

small

RNAs,

of

which

the

function

is

currently

still

unknown ?

in

contrast

to

the

known

small

RNAs

such

as

microRNAs

or

siRNAs[6]

(see

BioBox

1.3

for

an

overview

of

different

types

of

RNA)

.

Microarrays

are

also

being

made

to

study

differences

in

abundance

of

these

kinds

of

RNA.

..........

RNA.IncontrasttomRNA(messengerRNA)whichcontainstheinformationofhowtoassembleaprotein,therearealsodifferenttypesofnon-codingRNA(sometimesabbreviatedasncRNA)a.Herearethetypesthataremostrelevantinthecontextofthisbook:

miRNAinlength,whichregulategeneexpression.

longncRNA(longnon-codingRNA)arelongRNAmoleculesthatperformregulatoryroles.AnexampleisXIST,whichcanalsobeusedfordataqualitycontroltoidentifythegenderofasubject(seeBioBox3.5).

rRNA(ribosomalRNA)arelongRNAmoleculesthatmakeupthecentralcomponentoftheribosomeb.TheyareresponsiblefordecodingmRNAintoaminoacidsandareusedforRNAqualitycontrolpurposes(seeSection3.1.2.8).

siRNA(smallinterferingRNA)aresmalldouble-strandedRNAmoleculesofabout20-25nucleotidesinlengthandplayavarietyofrolesinbiology.ThemostcommonlyknownfunctionisaprocesscalledRNAinterference(RNAi).InthisprocesssiRNAsinterferewiththeex-

pressionofaspecificgene,leadingtoadownregulationofthesynthesisofnewproteinencodedbythatgenec.

tRNA(transferRNA)aresmallsingle-strandedRNAmoleculesofabout74-95nucleotidesinlenghts,whichtransferasingleaminoacidtoagrowingpolypeptidechainattheribosomalsiteofproteinsynthesis.EachtypeoftRNAmoleculecanbeattachedtoonlyonetypeofaminoacid.

aNon-codingRNAreferstoRNAmoleculesthataretranscribedfromDNAbutnottranslatedintoprotein.

bRibosomescanbeseenastheproteinmanufacturingmachineryofalllivingcells.

cThereare,however,alsoprocessesknownassmallRNA-inducedgeneactivationwherebydouble-strandedRNAstargetgenepromoterstoinducetranscriptionalactivationofassociatedgenes.

BioBox

1.3:

siRNA

..........................................

In

this

book

we

will

focus

on

studying

mRNA.

However,

most

likely

many

remarks

given

on

the

experimental

design

and

the

data

analysis

will

apply

to

the

study

of

small

RNA

as

well.

1.2

Research

question

The

key

to

optimal

data

analysis

lies

in a

clear

formulation

of

the

research

question.

Being

aware

of

having

to

define

what

one

considers

to

be a

“relevant”

finding

in

the

data

analysis

step

will

help

in

asking

the

right

question

and

in

designing

the

experiment

properly

so

that

the

question

can

really

be

answered. A

well-thought-out

and

focused

research

question

leads

directly

into

hypotheses,

which

are

both

testable

and

measurable

by

proposed

experiments.

Furthermore, a

well-formulated

hypothesis

helps

to

choose

the

most

appropriate

test

statistic

out

of

the

plethora

of

available

statistical

procedures

and

helps

to

set

up

the

design

of

the

study

in a

carefully

considered

manner.

To

formulate

the

right

question,

one

needs

to

disentangle

the

research

topic

into

testable

hypotheses

and

to

put

it

in a

wider

framework

to

reflect

on

potentially

confounding

factors.

Some

of

the

most

commonly

used

study

designs

in

microarray

research

will

be

introduced

here

by

means

of

real-life

examples.

For

each

type

of

study,

research

questions

are

formulated

and

example

datasets

described.

These

datasets

will

be

used

troughout

the

book

to

illustrate

some

technical

and

statistical

issues.

1.2.1

Correlational

vs.

experimental

research

Microarray

research

can

either

be

correlational

or

experimental.

In

correlational

research,

scientists

generally

do

not

apply a

treatment

or

stimulus

to

provoke

an

effect

on,

e.g.

,

gene

expression

(influence

variables)

,

but

measure

them

and

look

for

correlations

with

mRNA

(see

StatsBox

1.1)

. A

typical

example

are

cohort

studies,

where

individuals

of

populations

with

specific

characteristics

(like

diseased

patients

and

healthy

controls)

are

sampled

and

analysed.

In

experimental

research,

scientists

manipulate

certain

variables

(e.g.

,

apply a

compound

to a

cell

line)

and

then

measure

the

effects

of

this

manipulation

on

mRNA.

Experiments

are

designed

studies

where

individuals

are

assigned

to

specifically

chosen

conditions,

and

mRNA

is

afterwards

collected

and

compared.

It

is

important

to

comprehend

that

only

experimental

data

can

conclusively

demonstrate

causal

relations

between

variables.

For

example,

if

we

found

that a

certain

treatment A

affects

the

expression

levels

of

gene

X,

then

we

can

conclude

that

treatment A

influences

the

expression

of

gene

X.

Data

from

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《基于Affymetrix芯片的基因表达研究(导读版)》内容简介：Affymetrix GeneChip系统是目前应用最广泛的生物芯片平台。但是由于Aflymetrix芯片含有超大量的信息，很多Affymetrix芯片用户趋向于使用默认的分析设置，得到的常常不是最优化的结论。分子生物学家和生物统计学家根据十余年的基因表达谱实验研究和数据分析的实践经验编写了《基于Affymetrix芯片的基因表达研究》，从理论概念到实验结果，解释了使用Affymetrix芯片进行基因表达研究的全部过程，拆除了分子生物学、生物信息学和生物统计学之间无处不在的语言障碍。

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