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专业背景:计算机科学 研究方向与兴趣: JavaEE-Web软件开发, 生物信息学, 数据挖掘与机器学习, 智能信息系统 目前工作: 基因组, 转录组, NGS高通量数据分析, 生物数据挖掘, 植物系统发育和比较进化基因组学

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R 绘制 heatmap  

2010-10-20 09:02:15|  分类: R&Bioconductor |  标签: |举报 |字号 订阅

  下载LOFTER 我的照片书  |

http://www.bjt.name/tag/heatmap/

 

R 绘制 heatmap NBA联盟50位顶级球员的指标表现

介绍如何使用 R 绘制 heatmap 的文章。

今天无意间在Flowingdata看到一篇关于如何使用 R 来做 heatmap 的文章(请移步到这里)。虽然 heatmap 只是 R 中一个很普通的图形函数,但这个例子使用了2008-2009赛季 NBA 50个顶级球员数据做了一个极佳的演示,效果非常不错。对 R 大致了解的童鞋可以直接在 R console 上敲

?heatmap

直接查看帮助即可。

没有接触过 R 的童鞋继续围观,下面会仔细介绍如何使用 R 实现 NBA 50位顶级球员指标表现热图:

关于 heatmap,中文一般翻译为“热图”,其统计意义wiki上解释的很清楚:


A heat map is a graphical representation of data where the values taken by a variable in a two-dimensional map are represented as colors.Heat maps originated in 2D displays of the values in a data matrix. Larger values were represented by small dark gray or black squares (pixels) and smaller values by lighter squares.


下面这个图即是Flowingdata用一些 R 函数对2008-2009 赛季NBA 50名顶级球员指标做的一个热图(点击参看大图):

先解释一下数据:

这里共列举了50位球员,估计爱好篮球的童鞋对上图右边的每个名字都会耳熟能详。这些球员每个人会有19个指标,包括打了几场球(G)、上场几分钟(MIN)、得分(PTS)……这样就行成了一个50行×19列的矩阵。但问题是,数据有些多,需要使用一种比较好的办法来展示,So it comes, heatmap!

简单的说明:

比如从上面的热图上观察得分前3名(Wade、James、Bryant)PTS、FGM、FGA比较高,但Bryant的FTM、FTA和前两者就差一些;Wade在这三人中STL是佼佼者;而James的DRB和TRB又比其他两人好一些……

姚明的3PP(3 Points Percentage)这条数据很有意思,非常出色!仔细查了一下这个数值,居然是100%。仔细回想一下,似乎那个赛季姚明好像投过一个3分,并且中了,然后再也没有3p。这样本可真够小的!

最后是如何做这个热图(做了些许修改):

Step 0. Download R

R 官网:http://www.r-project.org,它是免费的。官网上面提供了Windows,Mac,Linux版本(或源代码)的R程序。

Step 1. Load the data

R 可以支持网络路径,使用读取csv文件的函数read.csv。

读取数据就这么简单:


nba<- read.csv("http://datasets.flowingdata.com/ppg2008.csv", sep=",") 

Step 2. Sort data

按照球员得分,将球员从小到大排序:


nba <- nba[order(nba$PTS),]


当然也可以选择MIN,BLK,STL之类指标

Step 3. Prepare data

把行号换成行名(球员名称):


row.names(nba) <- nba$Name


去掉第一列行号:


nba <- nba[,2:20] # or nba <- nba[,-1]

Step 4. Prepare data, again

把 data frame 转化为我们需要的矩阵格式:


nba_matrix <- data.matrix(nba)

Step 5. Make a heatmap

# R 的默认还会在图的左边和上边绘制 dendrogram,使用Rowv=NA, Colv=NA去掉


heatmap(nba_matrix, Rowv=NA, Colv=NA, col=cm.colors(256), revC=FALSE, scale='column')


这样就得到了上面的那张热图。

Step 6. Color selection

或者想把热图中的颜色换一下:


heatmap(nba_matrix, Rowv=NA, Colv=NA, col=heat.colors(256), revC=FALSE, scale="column", margins=c(5,10))
 
Bioinformatics and Computational Biology Solutions Using R and Bioconductor 第10章的
例子:
Heatmaps, or false color images have a reasonably long history, as has the
notion of rearranging the columns and rows to show structure in the data.
They were applied to microarray data by Eisen et al. (1998) and have
become a standard visualization method for this type of data.
A heatmap is a two-dimensional, rectangular, colored grid. It displays
data that themselves come in the form of a rectangular matrix. The color
of each rectangle is determined by the value of the corresponding entry
in the matrix. The rows and columns of the matrix can be rearranged
independently. Usually they are reordered so that similar rows are placed
next to each other, and the same for columns. Among the orderings that
are widely used are those derived from a hierarchical clustering, but many
other orderings are possible. If hierarchical clustering is used, then it is
customary that the dendrograms are provided as well. In many cases the
resulting image has rectangular regions that are relatively homogeneous
and hence the graphic can aid in determining which rows (generally the
genes) have similar expression values within which subgroups of samples
(generally the columns).
The function heatmap is an implementation with many options. In particular,
users can control the ordering of rows and columns independently
from each other. They can use row and column labels of their own choosing
or select their own color scheme.
 

> library("ALL")
> data("ALL")
> selSamples <- ALL$mol.biol %in% c("ALL1/AF4",
+ "E2A/PBX1")
> ALLs <- ALL[, selSamples]
> ALLs$mol.biol <- factor(ALLs$mol.biol)
> colnames(exprs(ALLs)) <- paste(ALLs$mol.biol,
+ colnames(exprs(ALLs)))

>library("genefilter")
> meanThr <- log2(100)
> g <- ALLs$mol.biol
> s1 <- rowMeans(exprs(ALLs)[, g == levels(g)[1]]) >
+ meanThr
> s2 <- rowMeans(exprs(ALLs)[, g == levels(g)[2]]) >
+ meanThr
> s3 <- rowttests(ALLs, g)$p.value < 2e-04
> selProbes <- (s1 | s2) & s3
> ALLhm <- ALLs[selProbes, ]

>library(RColorBrewer)

> hmcol <- colorRampPalette(brewer.pal(10, "RdBu"))(256)
> spcol <- ifelse(ALLhm$mol.biol == "ALL1/AF4",
+ "goldenrod", "skyblue")
> heatmap(exprs(ALLhm), col = hmcol, ColSideColors = spcol)

 

最后,可以

>help(heatmap) 查找帮助,看看帮助给提供的例子

也可以看这的例子:

http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/r/heatmap/

 


Using R to draw a Heatmap from Microarray Data


[c]

The first section of this page uses R to analyse an Acute lymphocytic leukemia (ALL) microarray dataset, producing a heatmap (with dendrograms) of genes differentially expressed between two types of leukemia.

There is a follow on page dealing with how to do this from Python using RPy.

The original citation for the raw data is "Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival" by Chiaretti et al. Blood 2004. (PMID: 14684422)

The analysis is a "step by step" recipe based on this paper, Bioconductor: open software development for computational biology and bioinformatics, Gentleman et al. 2004. Their Figure 2 Heatmap, which we recreate, is reproduced here:
[Published Heatmap, Gentleman et al. 2004]


Heatmaps from R

Assuming you have a recent version of R (from The R Project) and BioConductor (see Windows XP installation instructions), the example dataset can be downloaded as the BioConductor ALL package.

You should be able to install this from within R as follows:

> source("http://www.bioconductor.org/biocLite.R")
> biocLite("ALL")

Running bioCLite version 0.1 with R version 2.1.1
...

Alternatively, you can download the package by hand from here or here.

If you are using Windows, download ALL_1.0.2.zip (or similar) and save it. Then from within the R program, use the menu option "Packages", "Install package(s) from local zip files..." and select the ZIP file.

On Linux, download ALL_1.0.2.tar.gz (or similar) and use sudo R CMD INSTALL ALL_1.0.2.tar.gz at the command prompt.

With that out of the way, you should be able to start R and load this package with the library and data commands:
> library("ALL")
Loading required package: Biobase
Loading required package: tools
Welcome to Bioconductor
Vignettes contain introductory material. To view,
simply type: openVignette()
For details on reading vignettes, see
the openVignette help page.
> data("ALL")

If you inspect the resulting ALL variable, it contains 128 samples with 12625 genes, and associated phenotypic data.

> ALL
Expression Set (exprSet) with
12625 genes
128 samples
phenoData object with 21 variables and 128 cases
varLabels
cod: Patient ID
diagnosis: Date of diagnosis
sex: Gender of the patient
age: Age of the patient at entry
BT: does the patient have B-cell or T-cell ALL
remission: Complete remission(CR), refractory(REF) or NA. Derived from CR
CR: Original remisson data
date.cr: Date complete remission if achieved
t(4;11): did the patient have t(4;11) translocation. Derived from citog
t(9;22): did the patient have t(9;22) translocation. Derived from citog
cyto.normal: Was cytogenetic test normal? Derived from citog
citog: original citogenetics data, deletions or t(4;11), t(9;22) status
mol.biol: molecular biology
fusion protein: which of p190, p210 or p190/210 for bcr/able
mdr: multi-drug resistant
kinet: ploidy: either diploid or hyperd.
ccr: Continuous complete remission? Derived from f.u
relapse: Relapse? Derived from f.u
transplant: did the patient receive a bone marrow transplant? Derived from f.u
f.u: follow up data available
date last seen: date patient was last seen

We can looks at the results of molecular biology testing for the 128 samples:

> ALL$mol.biol
[1] BCR/ABL NEG BCR/ABL ALL1/AF4 NEG NEG NEG NEG NEG
[10] BCR/ABL BCR/ABL NEG E2A/PBX1 NEG BCR/ABL NEG BCR/ABL BCR/ABL
[19] BCR/ABL BCR/ABL NEG BCR/ABL BCR/ABL NEG ALL1/AF4 BCR/ABL ALL1/AF4
...
[127] NEG NEG
Levels: ALL1/AF4 BCR/ABL E2A/PBX1 NEG NUP-98 p15/p16

Ignoring the samples which came back negative on this test (NEG), most have been classified as having a translocation between chromosomes 9 and 22 (BCR/ABL), or a translocation between chromosomes 4 and 11 (ALL1/AF4).

For the purposes of this example, we are only interested in these two subgroups, so we will create a filtered version of the dataset using this as a selection criteria:

> eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")] 

The resulting variable, eset, contains just 47 samples - each with the full 12,625 gene expression levels.

This is far too much data to draw a heatmap with, but we can do one for the first 100 genes as follows:

> heatmap(exprs(eset[1:100,])) 

According to the BioConductor paper we are following, the next step in the analysis was to use the lmFit function (from the limma package) to look for genes differentially expressed between the two groups. The fitted model object is further processed by the eBayes function to produce empirical Bayes test statistics for each gene, including moderated t-statistics, p-values and log-odds of differential expression.

> library(limma)
> f <- factor(as.character(eset$mol.biol))
> design <- model.matrix(~f)
> fit <- eBayes(lmFit(eset,design))

If the limma package isn't installed, you'll need to install it first:

> source("http://www.bioconductor.org/biocLite.R")
> biocLite("limma")

Running bioCLite version 0.1 with R version 2.1.1
...

We can now reproduce Figure 1 from the paper.

> topTable(fit, coef=2)
ID M A t P.Value B
1016 1914_at -3.076231 4.611284 -27.49860 5.887581e-27 56.32653
7884 37809_at -3.971906 4.864721 -19.75478 1.304570e-20 44.23832
6939 36873_at -3.391662 4.284529 -19.61497 1.768670e-20 43.97298
10865 40763_at -3.086992 3.474092 -17.00739 7.188381e-18 38.64615
4250 34210_at 3.618194 8.438482 15.45655 3.545401e-16 35.10692
11556 41448_at -2.500488 3.733012 -14.83924 1.802456e-15 33.61391
3389 33358_at -2.269730 5.191015 -12.96398 3.329289e-13 28.76471
8054 37978_at -1.036051 6.937965 -10.48777 6.468996e-10 21.60216
10579 40480_s_at 1.844998 7.826900 10.38214 9.092033e-10 21.27732
330 1307_at 1.583904 4.638885 10.25731 1.361875e-09 20.89145

The leftmost numbers are row indices, ID is the Affymetrix HGU95av2 accession number, M is the log ratio of expression, A is the log average expression, t the moderated t-statistic, and B is the log odds of differential expression.

Next, we select those genes that have adjusted p-values below 0.05, using a very stringent Holm method to select a small number (165) of genes.

> selected  <- p.adjust(fit$p.value[, 2]) <0.05
> esetSel <- eset [selected, ]

The variable esetSel has data on (only) 165 genes for all 47 samples . We can easily produce a heatmap as follows (in R-2.1.1 this defaults to a yellow/red "heat" colour scheme):

> heatmap(exprs(esetSel))

[Heatmap picture, default colours]

If you have the topographical colours installed (yellow-green-blue), you can do this:
> heatmap(exprs(esetSel), col=topo.colors(100)) 

[Heatmap figure]

This is getting very close to Gentleman et al.'s Figure 2, except they have added a red/blue banner across the top to really emphasize how the hierarchical clustering has correctly split the data into the two groups (10 and 37 patients).

To do that, we can use the heatmap function's optional argument of ColSideColors. I created a small function to map the eselSet$mol.biol values to red (#FF0000) and blue (#0000FF), which we can apply to each of the molecular biology results to get a matching list of colours for our columns:

> color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") "#FF0000" else "#0000FF" }
> patientcolors <- unlist(lapply(esetSel$mol.bio, color.map))
> heatmap(exprs(esetSel), col=topo.colors(100), ColSideColors=patientcolors)

[Heatmap figure]

Looks pretty close now, doesn't it:
[Heatmap figure]

To recap, this is "all" we needed to type into R to achieve this:

library("ALL")
data("ALL")
eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")]
library("limma")
f <- factor(as.character(eset$mol.biol))
design <- model.matrix(~f)
fit <- eBayes(lmFit(eset,design))
selected <- p.adjust(fit$p.value[, 2]) <0.05
esetSel <- eset [selected, ]
color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") "#FF0000" else "#0000FF" }
patientcolors <- unlist(lapply(esetSel$mol.bio, color.map))
heatmap(exprs(esetSel), col=topo.colors(100), ColSideColors=patientcolors)

Heatmaps in R - More Options

One subtle point in the previous examples is that the heatmap function has automatically scaled the colours for each row (i.e. each gene has been individually normalised across patients). This can be disabled using scale="none", which you might want to do if you have already done your own normalisation (or this may not be appropriate for your data):

heatmap(exprs(esetSel), col=topo.colors(75), scale="none", ColSideColors=patientcolors, cexRow=0.5)

[Heatmap figure]

You might also have noticed in the above snippet, that I have shrunk the row captions which were so big they overlapped each other. The relevant options are cexRow and cexCol.

So far so good - but what if you wanted a key to the colours shown? The heatmap function doesn't offer this, but the good news is that heatmap.2 from the gplots library does. In fact, it offers a lot of other features, many of which I deliberately turn off in the following example:

library("gplots")
heatmap.2(exprs(esetSel), col=topo.colors(75), scale="none", ColSideColors=patientcolors,
key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)

[Heatmap picture, topographical colours WITHOUT scaling, with patient type colour bar and color key]

By default, heatmap.2 will also show a trace on each data point (removed this with trace="none"). If you ask for a key (using key=TRUE) this function will actually give you a combined "color key and histogram", but that can be overridden (with density.info="none").

Don't like the colour scheme? Try using the functions bluered/redblue for a red-white-blue spread, or redgreen/greenred for the red-black-green colour scheme often used with two-colour microarrays:

library("gplots")
heatmap.2(exprs(esetSel), col=redgreen(75), scale="row", ColSideColors=patientcolors,
key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)

[Heatmap figure]


Heatmaps from Python

So, how can we do that from within Python? One way is using RPy (R from Python), and this is discussed on this page.

P.S. If you want to use heatmap.2 from within python using RPy, use the syntax heatmap_2 due to the differences in how R and Python handle full stops and underscores.


What about other microarray data?

Well, I have also documented how you can load NCBI GEO SOFT files into R as a BioConductor expression set object. As long as you can get your data into R as a matrix or data frame, converting it into an exprSet shouldn't be too hard.

Details

If either
Rowv
or
Colv
are dendrograms they are honored (and not reordered). Otherwise, dendrograms are computed as
dd <- as.dendrogram(hclustfun(distfun(X)))
where
X
is either
x
or
t(x)
.

If either is a vector (of “weights”) then the appropriate dendrogram is reordered according to the supplied values subject to the constraints imposed by the dendrogram, by
reorder(dd, Rowv)
, in the row case. If either is missing, as by default, then the ordering of the corresponding dendrogram is by the mean value of the rows/columns, i.e., in the case of rows,
Rowv <- rowMeans(x, na.rm=na.rm)
. If either is
NULL
, no reordering will be done for the corresponding side.

If
scale="row"
the rows are scaled to have mean zero and standard deviation one. There is some empirical evidence from genomic plotting that this is useful.

The default colors range from red to white (
heat.colors
) and are not pretty. Consider using enhancements such as the RColorBrewer package, http://cran.r-project.org/src/contrib/PACKAGES.html#RColorBrewer to select better colors.

By default four components will be displayed in the plot. At the top left is the color key, top right is the column dendogram, bottom left is the row dendogram, bottom right is the image plot. When RowSideColor or ColSideColor are provided, an additional row or column is inserted in the appropriate location. This layout can be overriden by specifiying appropriate values for
lmat
,
lwid
, and
lhei
.
lmat
controls the relative postition of each element, while
lwid
controls the column width, and
lhei
controls the row height. See the help page for
layout
for details on how to use these arguments.


Value

Invisibly, a list with components


rowInd

row index permutation vector as returned by
order.dendrogram
.


colInd

column index permutation vector.


call

the matched call


rowMeans, rowSDs

mean and standard deviation of each row: only present if
scale="row"


colMeans, colSDs

mean and standard deviation of each column: only present if
scale="column"


carpet

reordered and scaled 'x' values used generate the main 'carpet'


rowDendrogram

row dendrogram, if present


colDendrogram

column dendrogram, if present


breaks

values used for color break points


col

colors used


vline

center-line value used for column trace, present only if
trace="both"
or
trace="column"


hline

center-line value used for row trace, present only if
trace="both"
or
trace="row"


colorTable

A three-column data frame providing the lower and upper bound and color for each bin


Note

The original rows and columns are reordered in any case to match the dendrogram, e.g., the rows by
order.dendrogram(Rowv)
where
Rowv
is the (possibly
reorder()
ed) row dendrogram.


heatmap.2()
uses
layout
and draws the
image
in the lower right corner of a 2x2 layout. Consequentially, it can not be used in a multi column/row layout, i.e., when
par(mfrow= *)
or
(mfcol= *)
has been called.


Author(s)

Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber, G. Warnes, revisions.


See Also


image
,
hclust


Examples
 library(gplots)
 data(mtcars)
 x  <- as.matrix(mtcars)
 rc <- rainbow(nrow(x), start=0, end=.3)
 cc <- rainbow(ncol(x), start=0, end=.3)

 ##
 ## demonstrate the effect of row and column dendogram options
 ##
 heatmap.2(x)  ## default - dendrogram plotted and reordering done.
 heatmap.2(x, dendrogram="none") ##  no dendrogram plotted, but reordering done.
 heatmap.2(x, dendrogram="row") ## row dendrogram plotted and row reordering done.
 heatmap.2(x, dendrogram="col") ## col dendrogram plotted and col reordering done.

 heatmap.2(x, keysize=2)  ## default - dendrogram plotted and reordering done.

 heatmap.2(x, Rowv=FALSE, dendrogram="both") ## generate warning!
 heatmap.2(x, Rowv=NULL, dendrogram="both")  ## generate warning!
 heatmap.2(x, Colv=FALSE, dendrogram="both") ## generate warning!

 ## Show effect of row and column label rotation
 heatmap.2(x, srtCol=NULL)
 heatmap.2(x, srtCol=0,   adjCol = c(0.5,1) )
 heatmap.2(x, srtCol=45,  adjCol = c(1,1)   )
 heatmap.2(x, srtCol=135, adjCol = c(1,0)   )
 heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )
 heatmap.2(x, srtCol=225, adjCol = c(0,0)   ) ## not very useful
 heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )
 heatmap.2(x, srtCol=315, adjCol = c(0,1)   )
 heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )

 heatmap.2(x, srtRow=45, adjRow=c(0, 1) )
 heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )
 heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )

 ## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is
 ## not also present)
 heatmap.2(x, offsetRow=0, offsetCol=0)
 heatmap.2(x, offsetRow=1, offsetCol=1)
 heatmap.2(x, offsetRow=2, offsetCol=2)
 heatmap.2(x, offsetRow=-1, offsetCol=-1)

 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)
 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)
 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)
 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)

 ## Show how to use 'extrafun' to replace the 'key' with a scatterplot
 lmat <- rbind( c(5,3,4), c(2,1,4) )
 lhei <- c(1.5, 4)
 lwid <- c(1.5, 4, 0.75)

 myplot <- function() {
             oldpar <- par("mar")
             par(mar=c(5.1, 4.1, 0.5, 0.5))
             plot(mpg ~ hp, data=x)
           }

 heatmap.2(x, lmat=lmat, lhei=lhei, lwid=lwid, key=FALSE, extrafun=myplot)


 ##
 ## Show effect of z-score scaling within columns, blue-red color scale
 ##
 hv <- heatmap.2(x, col=bluered, scale="column", tracecol="#303030")

 ###
 ## Look at the return values
 ###
 names(hv)

 ## Show the mapping of z-score values to color bins
 hv$colorTable

 ## Extract the range associated with white
 hv$colorTable[hv$colorTable[,"color"]=="#FFFFFF",]

 ## Determine the original data values that map to white
 whiteBin <- unlist(hv$colorTable[hv$colorTable[,"color"]=="#FFFFFF",1:2])
 rbind(whiteBin[1] * hv$colSDs + hv$colMeans,
       whiteBin[2] * hv$colSDs + hv$colMeans )
 ##
 ## A more decorative heatmap, with z-score scaling along columns
 ##
 hv <- heatmap.2(x, col=cm.colors(255), scale="column",
	       RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
	       xlab="specification variables", ylab= "Car Models",
	       main="heatmap(<Mtcars data>, ..., scale=\"column\")",
               tracecol="green", density="density")
 ## Note that the breakpoints are now symmetric about 0




library("gplots")
x<-read.table("f:/33.txt",head=T)
data_matrix<-data.matrix(x)
heatmap.2(data_matrix,col=redgreen(75),cexCol=0.9,key=T,symkey=F,density.info="none",trace="none")
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