| title | author | date | output |
|---|---|---|---|
Statistical Analysis of differential gene expression in NeuroLINCS |
Jenny Wu |
July 31, 2017 |
word_document |
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The DESeq2 package provides functions to test for differential expression using count data. This vignette is based on the summarized data from the transcriptomic experiment (RNA-seq) that uses Human iPSCs and iMN cells. This vignette loads one study of 6 ALS samples and 6 controls samples from level 3 (3 biological replicates per group, two growth replicates for each biological replicate), performs statistical tests and generates level 4 data,i.e.differentially expressed gene list.
For details of how DESeq2 works and its parameter setting, please refer to
DESeq2 manual: https://bioconductor.org/packages/release/bioc/manuals/DESeq2/man/DESeq2.pdf
DESeq2 vignette: https://www.bioconductor.org/packages/3.3/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf
We first load the count file with read.delim. For input, the DESeq2 package expects count data as obtained in the form of a matrix of integer values. The value in the i-th row and the j-th column of the matrix represents how many reads have been mapped to gene i in sample j. These count values must be raw counts of sequencing reads.
countsTable <- read.csv("https://hpc.oit.uci.edu/~jiew5/dcic.csv", header=T, stringsAsFactors=FALSE,check.names=F)We specify that the first line of our file is the header and disable treating the strings in the file as factors.
head(countsTable)## gene 29iALS-n1 29iALS-n2 28iALS-n1 28iALS-n2 30iALS-n1 30iALS-n2
## 1 MTVR2 0 1 0 0 1 0
## 2 CHMP1B 1713 1870 1781 1859 1715 1762
## 3 LOC441204 41 41 39 82 80 76
## 4 TCOF1 5733 6559 6446 6405 5478 6940
## 5 NSRP1 975 1223 1096 1136 1101 1186
## 6 SPPL3 1510 1617 1612 1354 1569 1399
## 00iCTR_n1 00iCTR_n2 25iCTR_n1 25iCTR_n2 83iCTR_n1 83iCTR_n2
## 1 2 0 1 0 1 0
## 2 1808 1758 1785 1503 1603 1470
## 3 70 48 53 32 41 38
## 4 5861 5518 6986 5810 6512 5690
## 5 1073 1086 1127 954 1097 980
## 6 1715 1775 1845 2056 1897 1780
The first column of the file is the gene names so we use them as the row names.
rownames(countsTable) <- countsTable$geneAfter setting the row names we remove the first column from the table so that the countsTable is the count matrix now.
countsTable <- countsTable[,-1]Use head to examine the matrix.
head(countsTable)## 29iALS-n1 29iALS-n2 28iALS-n1 28iALS-n2 30iALS-n1 30iALS-n2
## MTVR2 0 1 0 0 1 0
## CHMP1B 1713 1870 1781 1859 1715 1762
## LOC441204 41 41 39 82 80 76
## TCOF1 5733 6559 6446 6405 5478 6940
## NSRP1 975 1223 1096 1136 1101 1186
## SPPL3 1510 1617 1612 1354 1569 1399
## 00iCTR_n1 00iCTR_n2 25iCTR_n1 25iCTR_n2 83iCTR_n1 83iCTR_n2
## MTVR2 2 0 1 0 1 0
## CHMP1B 1808 1758 1785 1503 1603 1470
## LOC441204 70 48 53 32 41 38
## TCOF1 5861 5518 6986 5810 6512 5690
## NSRP1 1073 1086 1127 954 1097 980
## SPPL3 1715 1775 1845 2056 1897 1780
dim(countsTable)## [1] 23708 12
So we have read counts for the 23708 genes from 12 samples.
Now that we have the count matrix, we set up the experiment in DEseq2. First load the package,
library(DESeq2)Then we set up the metadata table for 12 samples. Note that the length of this factor vector should be the same as the number of columns in the data matrix.
conds<-factor(c(rep("ALS",6),rep("CTR",6)))
summary(conds)## ALS CTR
## 6 6
id=substr(colnames(countsTable),0,3)
colData=data.frame(condition=conds,subject=id)
colData## condition subject
## 1 ALS 29i
## 2 ALS 29i
## 3 ALS 28i
## 4 ALS 28i
## 5 ALS 30i
## 6 ALS 30i
## 7 CTR 00i
## 8 CTR 00i
## 9 CTR 25i
## 10 CTR 25i
## 11 CTR 83i
## 12 CTR 83i
We then construct the data object from the matrix of counts and the metadata table
(dds<-DESeqDataSetFromMatrix(countsTable,colData,design=~ condition))## class: DESeqDataSet
## dim: 23708 12
## metadata(1): version
## assays(1): counts
## rownames(23708): MTVR2 CHMP1B ... NFIX SELP
## rowData names(0):
## colnames(12): 29iALS-n1 29iALS-n2 ... 83iCTR_n1 83iCTR_n2
## colData names(2): condition subject
Since we have two growth replicates for each subject, we need to collapse them. In order to do so, we use
ddsCollapsed <- collapseReplicates(dds,groupby = dds$subject,renameCols = F)
dds<-ddsCollapsedWe first examine the relationship between variance
mu=rowMeans(countsTable)
sigma2=apply(countsTable,1,var)
plot(log(mu),log(sigma2),xlim=c(0,log(max(mu))), ylim=c(0,log(max(mu))),pch=16, cex=0.3)
abline(0,1,col="red")
We see that the variance grows with the mean, i.e. over dispersion. The red line shows where the variance equals the mean (as in Poisson distribution). To correct for this heteroskedasticity, we use regularized log transfor to transform the data before doing the analysis.
rld<-rlog(dds)
head(assay(rld))## 00iCTR_n1 25iCTR_n1 28iALS-n1 29iALS-n1 30iALS-n1
## MTVR2 0.01617359 0.002124626 -0.01021703 0.002220984 0.002910098
## CHMP1B 11.80084840 11.663947056 11.76670037 11.742827756 11.763472808
## LOC441204 6.79122735 6.578936113 6.77161218 6.565824623 6.947360372
## TCOF1 13.54360691 13.575263933 13.59617611 13.547441277 13.602879494
## NSRP1 11.10236213 11.004512382 11.07947364 11.056349661 11.135531943
## SPPL3 11.76627188 11.792862138 11.57849258 11.612001099 11.613758351
## 83iCTR_n1
## MTVR2 0.003500129
## CHMP1B 11.697045603
## LOC441204 6.596101355
## TCOF1 13.626740569
## NSRP1 11.092225501
## SPPL3 11.834231295
colnames(rld)=colnames(assay(ddsCollapsed))The function rlog returns a SummarizedExperiment object which contains the rlog-transformed values in its assay slot.
After transforming the data, we use PCA to visualize sample-to-sample distances. In this method, the data points (i.e., here, the samples that have 23710-D) are projected onto the 2-D plane such that they spread out in the two directions which explain most of the differences in the data. The x-axis is the direction (or principal component) which separates the data points the most. The amount of the total variance that is explained by the component is printed in the axis label.
plotPCA(rld, intgroup = "condition")
We use the function plotPCA that comes with DESeq2 package. The term "condition" specified by intgroup is the group for labeling the samples; they tell the function to use them to choose colors. In this example, the control samples are well separated from the treated samples so we expect to find differentially expressed genes.
Finally we are ready to run the differential expression pipeline. With the data object dds prepared, the DESeq2 analysis can now be run with a single call to the function DESeq:
dds<-DESeq(dds)## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
This will print out a list of messages for the various steps it performs. For more details, please refer to DESeq2 manual, which can be accessed by typing ?DESeq. Briefly these are: first estimate the size factors (normalization), then estimate the dispersion for each gene (
DESeq function returns a DESeqDataSet object that contains all the fitted information within it. Calling the results function without any arguments will extract the estimated log2 fold changes and p values for the last variable in the design formula design=~ condition, which is condition : TRT vs. CTRL in this case.
(res <- results(dds))## log2 fold change (MAP): condition CTR vs ALS
## Wald test p-value: condition CTR vs ALS
## DataFrame with 23708 rows and 6 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## MTVR2 1.006682 7.074320e-02 0.13780771 0.5133471509
## CHMP1B 3422.560951 -5.775468e-02 0.10817870 -0.5338821389
## LOC441204 106.633767 -2.204349e-01 0.24250063 -0.9090077530
## TCOF1 12269.127086 -2.886299e-05 0.09426573 -0.0003061875
## NSRP1 2164.466345 -3.911645e-02 0.10821254 -0.3614780150
## ... ... ... ... ...
## GNGT1 2.005041 -0.0427856 0.16502618 -0.2592655
## SELT 4158.378149 0.4985766 0.09866758 5.0530945
## SELV 193.571330 0.1761501 0.18091082 0.9736846
## NFIX 12.786581 -0.3610697 0.26643180 -1.3552049
## SELP 0.000000 NA NA NA
## pvalue padj
## <numeric> <numeric>
## MTVR2 0.6077085 NA
## CHMP1B 0.5934231 0.8079122
## LOC441204 0.3633460 0.6383926
## TCOF1 0.9997557 0.9998143
## NSRP1 0.7177421 0.8793437
## ... ... ...
## GNGT1 7.954304e-01 NA
## SELT 4.347088e-07 0.000066765
## SELV 3.302132e-01 0.611301327
## NFIX 1.753523e-01 0.448050924
## SELP NA NA
To get the meaning of the columns,
mcols(res, use.names=TRUE)## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized counts for all samples
## log2FoldChange results log2 fold change (MAP): condition CTR vs ALS
## lfcSE results standard error: condition CTR vs ALS
## stat results Wald statistic: condition CTR vs ALS
## pvalue results Wald test p-value: condition CTR vs ALS
## padj results BH adjusted p-values
DESeq2 performs for each gene a hypothesis test to see whether the observeed data give enough evidence to decide against the null hypothesis, that is there is no effect of the treatment on the gene and that the observed difference between treatment and control was merely caused by experimental variability. The result of this test is reported as a p value, and it is found in the column pvalue. A p value indicates the probability that a fold change as strong as the observed one, or even stronger, would be seen under the situation described by the null hypothesis. By default, this p value is adjusted using Benjamini-Hochberg method (See class slides).
The column log2FoldChange (
We can also summarize the results using function summary
summary(res)##
## out of 21117 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 982, 4.7%
## LFC < 0 (down) : 1009, 4.8%
## outliers [1] : 1, 0.0047%
## low counts [2] : 4068, 19%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
We can sort the results by the adjusted p value in ascending order.
res<-res[order(res$padj),]We can also subset the results so it only contains genes with BH adjusted p value < 0.05.
deg=subset(res,padj<0.05)We can export the results as spreadsheet using write.csv()
write.csv(as.data.frame(res),file="results.csv")It is good practice to always include the session information that reports the version numbers of R and all the packages used in this session.
sessionInfo()## R version 3.3.0 (2016-05-03)
## Platform: x86_64-pc-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 methods stats graphics grDevices utils
## [8] datasets base
##
## other attached packages:
## [1] DESeq2_1.12.4 SummarizedExperiment_1.2.3
## [3] Biobase_2.32.0 GenomicRanges_1.24.3
## [5] GenomeInfoDb_1.8.7 IRanges_2.6.1
## [7] S4Vectors_0.10.3 BiocGenerics_0.18.0
## [9] RCurl_1.95-4.8 bitops_1.0-6
## [11] markdown_0.7.7 knitr_1.15.1
##
## loaded via a namespace (and not attached):
## [1] genefilter_1.54.2 locfit_1.5-9.1 splines_3.3.0
## [4] lattice_0.20-34 colorspace_1.3-2 htmltools_0.3.5
## [7] base64enc_0.1-3 survival_2.40-1 XML_3.98-1.5
## [10] foreign_0.8-67 DBI_0.5-1 BiocParallel_1.6.6
## [13] RColorBrewer_1.1-2 plyr_1.8.4 stringr_1.1.0
## [16] zlibbioc_1.18.0 munsell_0.4.3 gtable_0.2.0
## [19] evaluate_0.10 labeling_0.3 latticeExtra_0.6-28
## [22] geneplotter_1.50.0 AnnotationDbi_1.34.4 htmlTable_1.8
## [25] highr_0.6 Rcpp_0.12.8 acepack_1.4.1
## [28] xtable_1.8-2 scales_0.4.1 backports_1.0.4
## [31] checkmate_1.8.2 Hmisc_4.0-2 annotate_1.50.1
## [34] XVector_0.12.1 gridExtra_2.2.1 ggplot2_2.2.1
## [37] digest_0.6.11 stringi_1.1.2 grid_3.3.0
## [40] tools_3.3.0 magrittr_1.5 lazyeval_0.2.0
## [43] tibble_1.2 RSQLite_1.0.0 Formula_1.2-1
## [46] cluster_2.0.5 Matrix_1.2-7.1 data.table_1.10.0
## [49] assertthat_0.1 rpart_4.1-10 nnet_7.3-12