转录组常用图形解读瑞客

易生信(/Training)——最懂你的生信培训，学习生信更容易常用图形绘制和解读易生信，毕生缘；培训版权所有。1易生信学习方式2提前预习仔细听讲先运行再理解紧跟步伐，跟不上的及时在课堂提出或寻找助教老师解决课后复习，基础知识学习靠背和反复练书读百变，其义自见码敲十遍，不会也难易生信，毕生缘；培训版权所有。易生信易生信，毕生缘；培训版权所有。3测序质量评估图谱NGS基础-FASTQ格式解释和质量评估/s/tDMih7ISLJcL4F4sWBq3Vw位数、最大值；然后在图上分别给予合适的标记。易生信，毕生缘；培训版权所有。4易生信箱线图解释–反应的是数据分布规律一组数，先算出最小值、第一四分位数、中位数、第三四分易生信5箱线图可以更真实的反应原始信息SNpaittuzreer,mMetihchoadesla1,,1e(t2a),l.1B2o1x.生Plo，tR:

a

web

to训o版l

f权o所r有ge。neration

of

box

plots.易

信 毕生缘；培肿瘤样本纯度得分箱线图易生信ABSOLUTE计算的肿瘤纯度得分。肿瘤样本按纯度得分中位数排序。6https://www.易s生ci信e，n毕ce生d缘i；re培c训t版.c权o所m有/s。cience/article/pii/S2405471218300966易生信肿瘤大小散点图Tumour

volume

for

individual

animals(dots)

on

the

day

of

euthanasia

in

theconditions

indicated.

Data

are

mean±s.d.7https://易w生w信w.，n毕a生tu缘r；e培.c训o版m权/a所r有t。icles/nature23270

Nature单细胞基因表达小提琴图易生信8易生信，毕生缘；培训版权所有。易生信Jitter-plot展示差异基因分布Log2

fold

relative

RNA

probe

distribution

showing

differential

geneexpression

from

bone

marrow-derived

macrophages

(BMDMs)datatreated

invitro

with

mock

or

Aza+

ITF-2357.

Angiogenicpathway-associated

genes

are

highlighted

(microarray,

BMDMrepresentative

of

n

=

3mice).9https://www.s易c生i信en，c毕e生di缘r；e培ct训.版c权om所/有s。cience/article/pii/S0092867417312448易生信火山图是散点图的一种，类似图的解析方式都是先看横纵坐标轴代表什么含义，图例是什么含义，图形解释是什么。差异OTU火山图展示10https://blog.csd易n生.n信e，t/q毕a生zp缘l；m培1训2_版3权/a所rt有ic。le/details/76474682易生信11差异基因火山图展示Molecular

signatures

of

antibody

responses

derived

from

a

systems

biology

study

of

five

human

vaccines易生信，毕生缘；培训版权所有。https://www.sci/science/article/pii/S0092867417312448易生信自对照样品或样品两两相比散点图展示of

log2FCScatterplotReactome

G1

pathway

inof

genes

fromeach

Library

1screen.

Eachindicatedbypairwisecolor.comparison

isPearson’s12productmoment

correlation

coefficient

isindicated

(r).展示某个通路上的基因在不同样本表达的相对高低，和样品内表达的一致性。Profound

Tissue

Specificity

in

Proliferation

Control

Underlies

Cancer

Drivers

and

Aneuploidy

Patterns易生信，毕生缘；培训版权所有。基因表达散点图Dot

ploteach

cell易生信visualization

oftype

in

lungsingle-cell

data.

The

sizeof

the

dot

encodes

thepercentage

of

cells

withina

cell

type,

and

thecolorencodes

theaverage

expression

level.13易生信，毕生缘M；a培p训pi版n权g

所th有e。Mouse

Cell

Atlas

by

Microwell-Seq易生信GSEA–unsignedlog2

FoldChange

（差异基因）GSEA

analysis

of

expressed

predictedpost-transcriptional

decoy

targets

inMDA-MB-231;

allwere

sorted

byexpressed

genessiOIP5-AS1

to

NTabsolute

fold

changes,

GSEA

usedweighted

enrichment

statistics

and

ratioof

classes,

with p-values

computedusing

1k

gene-set

permutations.https://w易ww生.信n，cb毕i生.缘nl；m培.训ni版h权.所go有v。/pmc/articles/PMC5906131

Cell

reports14易生信GSEA–unsignedlog2

FoldChange

（差异基因）HSC

gene

sets

are

negativelyenriched

inZNF384r.GSEA

of

all

ZNF384rpositive

enrichment

forcasesgenesupregulated

in

ETP-ALL

(a

stemcell

leukaemia),and

negativefor

genesenrichmentupregulated

in Ph-like

ALL

inother

B-ALL

cases.15https://s易ci生-信h，ub毕.生t缘w/；1培0训.1版0权3所8/有s。41586-018-0436-0

Nature

FigS6易生信GSEA的展示方式–二Common

cell-essential

genes

are

involved

in

fundamental

biologicalprocesses.

Gene

set

enrichment

analysis

was

performed

on

genesranked

by

average

CS

(crispr

gene

score).易生信，毕生缘；培训版权所有。/science/article/pii/S0092867417300612易生信Epigenetic

modification

level

and

GSEAGene

set

enrichment

plot

showing

that

genes

associated

with

high

levels

of

H3K9ac

and

H3K27ac

are

enriched

for

two

independently

defined

pluripotency

gene

sets:Muller

PluriNet

and

Wong

ESC

CoreP

values

are

calculated

based

on

1,000

permutations

by

the

GSEA

algorithm

andwere

not

adjusted

for

multiple

comparisons./articles/s41556-018-0086-3

Nature

cell

biology易生信，毕生缘；培训版权所有。易生信相关性基因的GSEA富集分析筛选与Myc/MycN表达显著正相关和负相关的基因相关性系数作为加权值进行GSEA分析901个MF

GO条目去冗余为396个MF条目，用做检测基因集(条目基因集重合小于

75%)热图每一行为一个条目，每一列为一种癌症，颜色表示

normalized

enrichment

score/CureFirstResearch/MYC

代码https://www.scienced易i生r信ec，t毕.生co缘m；/培sc训i版e权nc所e有/。article/pii/S2405471218300978

Cell易生信GSEA标准化富集得分(NES)热图分布GSEA

was

performed

using

PreRanked

weighted

mode.Log2FC

values

for

each

screen

were

computed

into

Zscores,

and

the

average

Z

score

of

allORFs

correspondingto

agiven

gene

was

calculated,

and

used

to

generate

theGSEA

rank

ordered

list.GSEA

was

run

considering

all

thecanonical

pathwaysgenesets

(c2.cp.V5.1.symbols)

except

the

minimum

gene

setsize

was

reduced

to10

(from

default

=

15),

tocompensatefor

the

smaller

size

of

thisdataset.Redundant

gene

sets

were

excluded

(including,

in

somecases,

gene

sets

that

were

non-redundant

in

name

butcontained

similar

genes)

and

additionalgene

sets

addedsothat

each

screen

had

a

total

of

ten

gene

setsrepresented.19https://www.sciencedi易r生e信ct，.毕c生om缘/；s培ci训e版n权ce所/有a。rticle/pii/S0092867418302149

Cell易生信20GSEA通路FDR和NES展示/science/article/pii/S0092867417312448

Cell易生信，毕生缘；培训版权所有。富集分析泡泡图易生信21易生信，毕生缘；培训版权所有。样本突变数目分布易生信每种癌症所有样品过滤后的突变数（深蓝点）的中位数排序。癌症中每个样本按过滤后的突变数（深蓝点）排序，浅蓝点表示未过滤的突变数。22https://www.scienc易e生d信ir，e毕c生t.缘c；o培m/训s版c权ie所n有c。e/article/pii/S2405471218300966/articles/nature09380/figures/1易生信1万个shRNA敲除细胞系对Oct4表达的影响Mediator

and

cohesin

components

were

highly

represented

in

anshRNA

screenfor

regulators

of

EScell

state.24易生信，毕生缘；培训版权所有。易生信增强子和超级增强子的ChIP结合图谱Metagene

representations

of

the

meanChIP-seq

signal

for

the

indicatedtranscriptionenhancersfactors

across

typicaland

super-enhancerdomains.

Metagenes

are

centered

onthe

enhancer

region,

and

the

length

ofthe

enhancer

reflects

the

differenceinmedian

lengths

(703bp

for

typicalbp

for

super-enhancers,

8,667enhancers).

Additional

3

kbsurrounding

each

enhancer

region

isalso

shown.25https://www.sciencedi易r生ec信t，.毕co生m缘/；sc培i训e版nc权e所/有ar。ticle/pii/S0092867413012270#!Meta-gene

profile易生信(TFBS)

和Sequence-转

录

因

子lncRNA(LongHorn,based不同的预测算法)在启动子区的结合位点分布。Random把启动子区随机打乱10次，分布再做一次预测。26https易:生/信/w，w毕w生.n缘c；b培i.训n版l权m.所n有i。h.gov/pmc/articles/PMC5906131/

NG生存曲线折线图易生信27https://www.scienced易i生re信c，t毕.c生o缘m；/s培c训i版en权c所e有/a。rticle/pii/S0092867418303076

Cell易生信转录因子结合Motifbinding

motifs

enriched

atsuper-enhancer

regions

and28Table

depicting

transcription

factorconstituent

enhancers

withinassociated

p

values./s/W346MWTZv6N_iuSPzBZn6w

R绘制Motif/s/PTaA1IR5yhIpK9EFAk5iVQ

在线绘制motif

Logohttps://www.scienced易ir生e信c，t.毕c生o缘m/；s培c训ie版n权c所e/有a。rticle/pii/S0092867413012270#!易生信基因组浏览器展示多组学信息30Binding

profiles

for

ES

cell

transcription

factors

(Oct4,

Nanog

and

Sox2),mediator

(Med1

and

Med12),

cohesin

(Smc1a,

Smc3

and

Nipbl),

CTCF andcomponents

of

the

transcription

apparatus

(Pol2

and

TBP)

at

the

Oct4

andNanog

loci.

ChIP-Seq

data

are

shown

in

reads

per

million

with

the

y

axis

floorset

to 0.5

reads

per

million.

Oct4/Sox2,

CTCF

and TBP

(TATA

box)

sequencemotifs

areindicated./articles/nature09380/figu

res/2易生信，毕生缘；培训版权所有。易生信Sashimi

plot展示Junction

reads和可变剪接Sashimi

plots

around

an

activated

exonwithin

the

3′UTR

of

the HNRNPDL

gene(inclusion

of

which

creates

an

NMD-substrate

transcript)

in

either

theabsenceor

presence

of

MAGOHB

knockdown

ineither

ChagoK1

cellsreconstituted

ChagoK1orcells.MAGOH-V5Numbersreflect

junction

spanning

reads

averagedover

three

replicates

for

each

condition.31https://www.易n生at信u，r毕e.生c缘o；m/培a训r版ti权c所l有es。/s41588-018-0155-3

NG易生信TAD三维互作ChIP-seq

binding

profiles

forOSN

and

Med1

at

the

Klf4

locusin

ESCs.

PreviouslyHi-C

interactiondescribedfrequency(normalized

interaction

counts)and

genomic

coordinatescomprising

a

portion

of

atopological

domain

(Dixon

et

al.,2012)

are

indicated

above

the

binding

profiles.32https://www.sciencedi易r生ec信t，.毕co生m缘/；sc培i训e版nc权e所/有ar。ticle/pii/S0092867413003929易生信易生信，毕生缘；培训版权所有。34Venn图和ChIP-seq信号热图Venn

diagram

showing

the

overlap

of

high-confidence

(P

<

10−9)

cohesin

(Smc1a)occupied

sites

with

those

bound

by

CTCF,

mediator

(Med12)

and

Nipbl.

c,

Region

mapshowing

that

Smc1a,

Nipbl

and

Med12

co-occupied

sites

generally

occur

in

closeproximity

to

Pol2

and

in

the

absence

of

CTCF.

For

each

Smc1a

occupied

region,

theoccupancy

of

Med12,

Nipbl,

Pol2

and

CTCF

is

indicated

within

a

10-kb

window

centredon

the

Smc1a

region./articles/nature09380/figures/2易生信易生信，毕生缘；培训版权所有。35样品相关性热图与PCA聚类相似的是相关系数层级聚类，也可以展示样品之间的相关性。同时可以增加样品的属性信息借以查看不同的属性信息与分组的关系，从而确定有没有哪些意料外的信息影响到了样品分组。易生信http易s生:信//，w毕w生w.缘n；a培tu训r版e权.c所o有m。/articles/nature10110

Nature37相关性热图加样本属性标记小鼠不同株野生型和KLA处理组基因表达谱相似性热图。Clustered

Spearman

correlation

matrixfor

different

RNA-seq

replicates

for

notreatment

and

KLA1h.易生信OTU与表型相关性热图糖尿病前期病人差异

OTU所注释到的最小分类单元(每一列)与临床指标(每一行)之间的相关性热图。蓝色是负相关，红色是正相关。中间的文字代表P-value。图例和图注的重要性。38Aberrant

intestinal

microbiota

in

individuals

with

prediabetes易生信，毕生缘；培训版权所有。易生信差异OTU丰度热图水稻生育期相关的微生物标志OTU随时间变化热图。纵轴为采样时间点，也就是样品分组；横轴是代表性OTU。每个框的颜色代表OTU的相对丰度。

注意Legend是-4到4和Z-score标记。Z-score是数据标准化方式，展示相对丰度。39Root

microbiota

shift

in

rice

correlates

with

resident

time

in

the

field

anddevelopmenta易l

生st信ag，e毕生缘；培训版权所有。https://www.ncbi.nlm.nih.gorvi/gphmtc./articles/PMC5906131/

NGlncRNAs调节的转录因子和RNA结合蛋白分布易生信interactions

whereHigh-confidencelncRNAs,

including

OIP5-AS1,NEAT1,

and

TUG1,

were

predicted

tomodulate

regulators

of

TFs

andRBPs

and

were

correlated

with

theexpression

of

the

targets

of

theseTFs

and

RBPs.

The

40lncRNAs

withthe

most

TF

and

RBP

targets

and

thefrequent

target

TFs

and40

mostRBPsnumberwere

included;

the

totalof

predicted

interactionsacross

the

14tumor

types

is

given

at40易生信，毕生缘；培训版权所有。易生信热图绘制热图绘制需要的数据与最后呈现的热图一般是一致的，数据中每一行对应于热图中每一行，数据中每一列对应于热图每一列。如果做了聚类分析，顺序可能会变。如下OTU表就可以直接去绘制每个样品中的OTU丰度热图。或稍加处理，绘制样品的OTU丰度相关性。41http://www.ehbio易.c生o信m，/I毕m生a缘g；eG培P训/版in权d所ex有.p。hp/Home/Index/PHeatmap.html易生信每组样品共有和特有Gene维恩图(venn)Venn图如右所示，这里每个圈代表一个样品组的所有基因。重叠的部分表示样品组之间都表达的基因。每个圈特有的部分表示样品特异表达的基因。中间的219表示3个样品组中都表达的基因。注：表达的基因OTU是个伪概念，只能说声高于某个表达水平的基因或筛选出的特异高表达的基因。42http://www.ehb易io生.c信o，m毕/I生m缘a；ge培G训P版/i权nd所e有x。.php/Home/Index/VennDiagram.html易生信每组样品共有和特有基因UpSetView图43蓝色的柱子表示每个分组总的基因数目。黑色的柱子表示不同的组合之间共有或特有的基因数目。单个点表示单个样品组特有。两个点相连表示连起的两个样品组共有的基因。三个点相连表示三个样品共有的基因。http://www.ehb易io生.信co，m毕/生Im缘a；g培eG训P版/权in所d有ex。.php/Home/Index/UpsetView.html易生信哈扎人肠道菌群在不同季节的变化–桑基图拟杆菌门、厚壁菌门OTUs在不同季节中物种动态变化；随着时间变化，哪些OTUs保留或消失，哪些新来的，以及在每个阶段中的变化。44Seasonal

cycling

in

the易生信g，u毕t

生m缘ic；ro培b训io版m权e所o有f。

the

Hadza

hunter-gatherers

of

Tanzania.

易生信miRNA-靶基因和通路的关系图https://www.science易di生r信e，ct毕.生c缘om；/培s训ci版e权n所ce有/。article/pii/S0169260716306964?via%3Dihub易生信46诺贝儿化学奖获得者属性分布桑基图/s/K1ZvvNMy5Gb9FpkwNuDVmg易生信，毕生缘；培训版权所有。易生信维恩，UpSetView，桑基图的绘制输入数据都可以是如下格式，第一列是OTU的名字，第二列是OTU从属于哪个样品分组，如果一个OTU从属于多个样品分组，分不同的行分别录入。47直接拷贝到ImageGP网站每个对应的图的Datamatrix中即可绘制。http://www.eh易bi生o信.c，om毕/生Im缘；ag培e训G版P权/in所d有e。x.php/Home/Index/Sankey.html易生信易生信，毕生缘；培训版权所有。51样本聚类PCA分析主成分分析

(PCA,

principal

component analysis)是一种数学降维方法,

利用正交变换把一系列可能线性相关的变量转换为一组线性不相关的新变量，也称为主成分，从而利用新变量在更小的维度下展示数据的特征。主成分是原有变量的线性组合，其数目不多于原始变量。组合之后，相当于获得了一批新的观测数据，这些数据的含义不同于原有数据，但包含了之前数据的大部分特征，并且有着较低的维度，便于进一步的分析。在空间上，PCA可以理解为把原始数据投射到一个新的坐标系统，第一主成分为第一坐标轴，它的含义代表了原始数据中多个变量经过某种变换得到的新变量的变化区间；第二成分为第二坐标轴，代表了原始数据中多个变量经过某种变换得到的第二个新变量的变化区间。这样我们把利用原始数据解释样品的差异转变为利用新变量解释样品的差异。易生信易生信，毕生缘；培训版权所有。52主成分分析的意义简化运算，降低冗余去除数据噪音，变化幅度小的噪音变化方便实现数据可视化在表达谱分析中，假如有1个基因，可以在线性层面对样本分类；如果有2个基因，可以在一个平面对样本分类；如果有3个基因，可以在一个立体空间对样本分类；如果有更多的基因，比如说n个，那么每个样品就是n维空间的一个点，则很难在图形上展示样品的分类关系。利用PCA分析，我们可以选取贡献最大的2个或3个主成分作为数据代表用以可视化。这比直接选取三个表达变化最大的基因更能反映样品之间的差异。发现隐性相关变量易生信PCA实现原理PCA分析不是简单地选取2个或3个变化最大的基因，而是先把原始的变量做一个评估，计算各个变量各自的变异度(方差)和两两变量的相关度（协方差），得到一个协方差矩阵。在这个协方差矩阵中，对角线的值为每一个变量的方差，其它值为每两个变量的协方差。随后对原变量的协方差矩阵对角化处理，即求解其特征值和特征向量。原变量与特征向量的乘积（对原始变量的线性组合）即为新变量（回顾下线性代数中的矩阵乘法）；新变量的协方差矩阵为对角协方差矩阵且对角线上的方差由大到小排列；然后从新变量中选择信息最丰富也就是方差最大的的前2个或前3个新变量也就是主成分用以可视化。53http://blog.genes易in生o.信c，om毕/生2缘01；6培/1训0版/P权C所A有/。为什么要进行中心化54易生信http://blog.genes易in生o.信c，om毕/生2缘01；6培/1训0版/P权C所A有/。主成分分析类比示例易生信，毕生缘；培训版权所有。55易生信PCA结果展示易生信，毕生缘；培训版权所有。56易生信不同预处理对PCA结果的影响易生信不同标准化的本质在于研究者认为是数值的量度本身重要还是数值的变化重要。数值的量度重要则选择原始数据或log转换。数值的变化重要则选择scale。57易生信，毕生缘；培训版权所有。选取一个主轴展示细胞反而更清晰易生信，毕生缘；培训版权所有。易生信58易生PCA绘制的输入和输出59标准化后的OTU丰度表，样品分组信息http://www.ehb易io生.c信o，m毕/I生m缘a；g培eG训P版/权in所d有ex。.php/Home/Index/PCAplot.htmltSNE聚类结果易生信t-Distributed

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Microwell-Seq/video/av19592239?from=search&seid=13296190996786271372易生信tSNE的原理和特征PCA线性降维算法尽量保证数据的差异，把不相似的数据点放在低维空tSNE把点的高维空间的距离转换成点的相似度的概率，维持高维空间和低维空间中一对点之间的条件概率差值总和最小。同时使用t-分布的长尾性解决高维数据映射到低维时的重叠问题。t-SNE算法定义了数据的局部和全局结构之间的软边界，既可以使点在局部分散，又在全局聚集，同时照顾近距离和远距离的点。其性能优于任何非线性降维算法。间时，数据点相距甚远，在某种程度上压缩了局部分布。/s/alBfj3Y08qCnZoz5JwVdaw易生信，毕生缘；培训版权所有。易生信t-SNE展示特定基因表达图谱stochastic(t-SNE)neighboranalysis

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