Differential Gene Expression Analysis in R
- Differential Gene Expression (DGE) between conditions is determined from count data
- Generally speaking differential expression analysis is performed in a very similar manner to DNA microarrays, once normalization and transformations have been performed.
A lot of RNA-seq analysis has been done in R and so there are many packages available to analyze and view this data. Two of the most commonly used are:
- DESeq2, developed by Simon Anders (also created htseq) in Wolfgang Huber’s group at EMBL
- edgeR and Voom (extension to Limma [microarrays] for RNA-seq), developed out of Gordon Smyth’s group from the Walter and Eliza Hall Institute of Medical Research in Australia
xhttp://bioconductor.org/packages/release/BiocViews.html#___RNASeq
Differential Expression Analysis with Limma-Voom
limma is an R package that was originally developed for differential expression (DE) analysis of gene expression microarray data.
voom is a function in the limma package that transforms RNA-Seq data for use with limma.
Together they allow fast, flexible, and powerful analyses of RNA-Seq data. Limma-voom is our tool of choice for DE analyses because it:
-
Allows for incredibly flexible model specification (you can include multiple categorical and continuous variables, allowing incorporation of almost any kind of metadata).
-
Based on simulation studies, maintains the false discovery rate at or below the nominal rate, unlike some other packages.
-
Empirical Bayes smoothing of gene-wise standard deviations provides increased power.
Basic Steps of Differential Gene Expression
- Read count data and annotation into R and preprocessing.
- Calculate normalization factors (sample-specific adjustments)
- Filter genes (uninteresting genes, e.g. unexpressed)
- Account for expression-dependent variability by transformation, weighting, or modeling
- Fitting a linear model
- Perform statistical comparisons of interest (using contrasts)
- Adjust for multiple testing, Benjamini-Hochberg (BH) or q-value
- Check results for confidence
- Attach annotation if available and write tables
1. Read in the counts table and create our DGEList
counts <- read.delim("rnaseq_workshop_counts.txt", row.names = 1)
head(counts)
## mouse_110_WT_C mouse_110_WT_NC mouse_148_WT_C
## ENSMUSG00000102693.2 0 0 0
## ENSMUSG00000064842.3 0 0 0
## ENSMUSG00000051951.6 0 0 0
## ENSMUSG00000102851.2 0 0 0
## ENSMUSG00000103377.2 0 0 0
## ENSMUSG00000104017.2 0 0 0
## mouse_148_WT_NC mouse_158_WT_C mouse_158_WT_NC
## ENSMUSG00000102693.2 0 0 0
## ENSMUSG00000064842.3 0 0 0
## ENSMUSG00000051951.6 0 0 0
## ENSMUSG00000102851.2 0 0 0
## ENSMUSG00000103377.2 0 0 0
## ENSMUSG00000104017.2 0 0 0
## mouse_183_KOMIR150_C mouse_183_KOMIR150_NC
## ENSMUSG00000102693.2 0 0
## ENSMUSG00000064842.3 0 0
## ENSMUSG00000051951.6 0 0
## ENSMUSG00000102851.2 0 0
## ENSMUSG00000103377.2 0 0
## ENSMUSG00000104017.2 0 0
## mouse_198_KOMIR150_C mouse_198_KOMIR150_NC
## ENSMUSG00000102693.2 0 0
## ENSMUSG00000064842.3 0 0
## ENSMUSG00000051951.6 0 0
## ENSMUSG00000102851.2 0 0
## ENSMUSG00000103377.2 0 0
## ENSMUSG00000104017.2 0 0
## mouse_206_KOMIR150_C mouse_206_KOMIR150_NC
## ENSMUSG00000102693.2 0 0
## ENSMUSG00000064842.3 0 0
## ENSMUSG00000051951.6 0 0
## ENSMUSG00000102851.2 0 0
## ENSMUSG00000103377.2 0 0
## ENSMUSG00000104017.2 0 0
## mouse_2670_KOTet3_C mouse_2670_KOTet3_NC
## ENSMUSG00000102693.2 0 0
## ENSMUSG00000064842.3 0 0
## ENSMUSG00000051951.6 0 0
## ENSMUSG00000102851.2 0 0
## ENSMUSG00000103377.2 0 0
## ENSMUSG00000104017.2 0 0
## mouse_7530_KOTet3_C mouse_7530_KOTet3_NC
## ENSMUSG00000102693.2 0 0
## ENSMUSG00000064842.3 0 0
## ENSMUSG00000051951.6 0 0
## ENSMUSG00000102851.2 0 0
## ENSMUSG00000103377.2 0 0
## ENSMUSG00000104017.2 0 0
## mouse_7531_KOTet3_C mouse_7532_WT_NC mouse_H510_WT_C
## ENSMUSG00000102693.2 0 0 0
## ENSMUSG00000064842.3 0 0 0
## ENSMUSG00000051951.6 0 0 0
## ENSMUSG00000102851.2 0 0 0
## ENSMUSG00000103377.2 0 0 0
## ENSMUSG00000104017.2 0 0 0
## mouse_H510_WT_NC mouse_H514_WT_C mouse_H514_WT_NC
## ENSMUSG00000102693.2 0 0 0
## ENSMUSG00000064842.3 0 0 0
## ENSMUSG00000051951.6 0 0 0
## ENSMUSG00000102851.2 0 0 0
## ENSMUSG00000103377.2 0 0 0
## ENSMUSG00000104017.2 0 0 0
Create Differential Gene Expression List Object (DGEList) object
A DGEList is an object in the package edgeR for storing count data, normalization factors, and other information
d0 <- DGEList(counts)
1a. Read in Annotation
anno <- read.delim("ensembl_mm_104.tsv",as.is=T)
dim(anno)
## [1] 55416 11
head(anno)
## Gene.stable.ID Gene.stable.ID.version Gene.name
## 1 ENSMUSG00000064336 ENSMUSG00000064336.1 mt-Tf
## 2 ENSMUSG00000064337 ENSMUSG00000064337.1 mt-Rnr1
## 3 ENSMUSG00000064338 ENSMUSG00000064338.1 mt-Tv
## 4 ENSMUSG00000064339 ENSMUSG00000064339.1 mt-Rnr2
## 5 ENSMUSG00000064340 ENSMUSG00000064340.1 mt-Tl1
## 6 ENSMUSG00000064341 ENSMUSG00000064341.1 mt-Nd1
## Gene.description
## 1 mitochondrially encoded tRNA phenylalanine [Source:MGI Symbol;Acc:MGI:102487]
## 2 mitochondrially encoded 12S rRNA [Source:MGI Symbol;Acc:MGI:102493]
## 3 mitochondrially encoded tRNA valine [Source:MGI Symbol;Acc:MGI:102472]
## 4 mitochondrially encoded 16S rRNA [Source:MGI Symbol;Acc:MGI:102492]
## 5 mitochondrially encoded tRNA leucine 1 [Source:MGI Symbol;Acc:MGI:102482]
## 6 mitochondrially encoded NADH dehydrogenase 1 [Source:MGI Symbol;Acc:MGI:101787]
## Gene.type Transcript.count Gene...GC.content Chromosome.scaffold.name
## 1 Mt_tRNA 1 30.88 MT
## 2 Mt_rRNA 1 35.81 MT
## 3 Mt_tRNA 1 39.13 MT
## 4 Mt_rRNA 1 35.40 MT
## 5 Mt_tRNA 1 44.00 MT
## 6 protein_coding 1 37.62 MT
## Gene.start..bp. Gene.end..bp. Strand
## 1 1 68 1
## 2 70 1024 1
## 3 1025 1093 1
## 4 1094 2675 1
## 5 2676 2750 1
## 6 2751 3707 1
tail(anno)
## Gene.stable.ID Gene.stable.ID.version Gene.name
## 55411 ENSMUSG00000065289 ENSMUSG00000065289.3 Gm23650
## 55412 ENSMUSG00000065272 ENSMUSG00000065272.3 Snord57
## 55413 ENSMUSG00000027406 ENSMUSG00000027406.14 Idh3b
## 55414 ENSMUSG00000027593 ENSMUSG00000027593.16 Raly
## 55415 ENSMUSG00000074656 ENSMUSG00000074656.13 Eif2s2
## 55416 ENSMUSG00000098640 ENSMUSG00000098640.3 Gm14214
## Gene.description
## 55411 predicted gene, 23650 [Source:MGI Symbol;Acc:MGI:5453427]
## 55412 small nucleolar RNA, C/D box 57 [Source:MGI Symbol;Acc:MGI:3819544]
## 55413 isocitrate dehydrogenase 3 (NAD+) beta [Source:MGI Symbol;Acc:MGI:2158650]
## 55414 hnRNP-associated with lethal yellow [Source:MGI Symbol;Acc:MGI:97850]
## 55415 eukaryotic translation initiation factor 2, subunit 2 (beta) [Source:MGI Symbol;Acc:MGI:1914454]
## 55416 predicted gene 14214 [Source:MGI Symbol;Acc:MGI:3649357]
## Gene.type Transcript.count Gene...GC.content
## 55411 snoRNA 1 41.43
## 55412 snoRNA 1 44.44
## 55413 protein_coding 5 49.25
## 55414 protein_coding 10 41.51
## 55415 protein_coding 5 38.63
## 55416 processed_pseudogene 1 51.80
## Chromosome.scaffold.name Gene.start..bp. Gene.end..bp. Strand
## 55411 2 130119617 130119686 1
## 55412 2 130119936 130120007 1
## 55413 2 130121229 130126467 -1
## 55414 2 154633016 154709181 1
## 55415 2 154713330 154734855 -1
## 55416 2 154611175 154611950 -1
any(duplicated(anno$Gene.stable.ID))
## [1] FALSE
1b. Derive experiment metadata from the sample names
Our experiment has two factors, genotype (“WT”, “KOMIR150”, or “KOTet3”) and cell type (“C” or “NC”).
The sample names are “mouse” followed by an animal identifier, followed by the genotype, followed by the cell type.
sample_names <- colnames(counts)
metadata <- as.data.frame(strsplit2(sample_names, c("_"))[,2:4], row.names = sample_names)
colnames(metadata) <- c("mouse", "genotype", "cell_type")
Create a new variable “group” that combines genotype and cell type.
metadata$group <- interaction(metadata$genotype, metadata$cell_type)
table(metadata$group)
##
## KOMIR150.C KOTet3.C WT.C KOMIR150.NC KOTet3.NC WT.NC
## 3 3 5 3 2 6
table(metadata$mouse)
##
## 110 148 158 183 198 206 2670 7530 7531 7532 H510 H514
## 2 2 2 2 2 2 2 2 1 1 2 2
Note: you can also enter group information manually, or read it in from an external file. If you do this, it is $VERY, VERY, VERY$ important that you make sure the metadata is in the same order as the column names of the counts table.
Quiz 1
2. Preprocessing and Normalization factors
In differential expression analysis, only sample-specific effects need to be normalized, we are NOT concerned with comparisons and quantification of absolute expression.
- Sequence depth – is a sample specific effect and needs to be adjusted for.
- RNA composition - finding a set of scaling factors for the library sizes that minimize the log-fold changes between he samples for most genes (edgeR uses a trimmed mean of M-values between each pair of sample)
- GC content – is NOT sample-specific (except when it is)
- Gene Length – is NOT sample-specific (except when it is)
In edgeR/limma, you calculate normalization factors to scale the raw library sizes (number of reads) using the function calcNormFactors, which by default uses TMM (weighted trimmed means of M values to the reference). Assumes most genes are not DE.
Proposed by Robinson and Oshlack (2010).
d0 <- calcNormFactors(d0)
d0$samples
## group lib.size norm.factors
## mouse_110_WT_C 1 2353648 1.0376252
## mouse_110_WT_NC 1 2854489 0.9883546
## mouse_148_WT_C 1 2832497 1.0117285
## mouse_148_WT_NC 1 2628945 0.9817107
## mouse_158_WT_C 1 2992578 1.0016916
## mouse_158_WT_NC 1 2672377 0.9630628
## mouse_183_KOMIR150_C 1 2551617 1.0263015
## mouse_183_KOMIR150_NC 1 1872279 1.0148950
## mouse_198_KOMIR150_C 1 2858850 1.0101615
## mouse_198_KOMIR150_NC 1 2938036 0.9866486
## mouse_206_KOMIR150_C 1 1399337 0.9850666
## mouse_206_KOMIR150_NC 1 959603 0.9917348
## mouse_2670_KOTet3_C 1 2923221 0.9942767
## mouse_2670_KOTet3_NC 1 2955510 0.9782921
## mouse_7530_KOTet3_C 1 2631541 1.0170386
## mouse_7530_KOTet3_NC 1 2902186 0.9617340
## mouse_7531_KOTet3_C 1 2682161 1.0225484
## mouse_7532_WT_NC 1 2730025 1.0055693
## mouse_H510_WT_C 1 2601395 1.0198345
## mouse_H510_WT_NC 1 2851966 1.0257803
## mouse_H514_WT_C 1 2316368 0.9896873
## mouse_H514_WT_NC 1 2665353 0.9907269
Note: calcNormFactors doesn’t normalize the data, it just calculates normalization factors for use downstream.
3. Filtering genes
We filter genes based on non-experimental factors to reduce the number of genes/tests being conducted and therefor do not have to be accounted for in our transformation or multiple testing correction. Commonly we try to remove genes that are either a) unexpressed, or b) unchanging (low-variability).
Common filters include:
- to remove genes with a max value (X) of less then Y.
- to remove genes that are less than X normalized read counts (cpm) across a certain number of samples. Ex: rowSums(cpms <=1) < 3 , require at least 1 cpm in at least 3 samples to keep.
- A less used filter is for genes with minimum variance across all samples, so if a gene isn’t changing (constant expression) its inherently not interesting therefor no need to test.
Here we will filter low-expressed genes, remove any row (gene) whose max value (for the row) is less than the cutoff (2).
cutoff <- 2
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # number of genes left
## [1] 12954 22
“Low-expressed” is subjective and depends on the dataset.
Visualizing your data with a Multidimensional scaling (MDS) plot.
plotMDS(d, col = as.numeric(metadata$group), cex=1)
The MDS plot tells you A LOT about what to expect from your experiment.
3a. Extracting “normalized” expression table
RPKM vs. FPKM vs. CPM and Model Based
- RPKM - Reads per kilobase per million mapped reads
- FPKM - Fragments per kilobase per million mapped reads
- logCPM – log Counts per million [ good for producing MDS plots, estimate of normalized values in model based ]
- Model based - original read counts are not themselves transformed, but rather correction factors are used in the DE model itself.
We use the cpm function with log=TRUE to obtain log-transformed normalized expression data. On the log scale, the data has less mean-dependent variability and is more suitable for plotting.
logcpm <- cpm(d, prior.count=2, log=TRUE)
write.table(logcpm,"rnaseq_workshop_normalized_counts.txt",sep="\t",quote=F)
Quiz 2
Note: If you changed the expression cutoff to answer the last quiz question, before proceeding change it back to the original value and rerun the above code using “Run all chunks above” in the Run menu in RStudio.
4. Voom transformation and calculation of variance weights
Specify the model to be fitted. We do this before using voom since voom uses variances of the model residuals (observed - fitted)
- The model you use will change for every experiment, and this step should be given the most time and attention.*
group <- metadata$group
mouse <- metadata$mouse
mm <- model.matrix(~0 + group + mouse)
head(mm)
## groupKOMIR150.C groupKOTet3.C groupWT.C groupKOMIR150.NC groupKOTet3.NC
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 1 0 0
## 4 0 0 0 0 0
## 5 0 0 1 0 0
## 6 0 0 0 0 0
## groupWT.NC mouse148 mouse158 mouse183 mouse198 mouse206 mouse2670 mouse7530
## 1 0 0 0 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0
## 3 0 1 0 0 0 0 0 0
## 4 1 1 0 0 0 0 0 0
## 5 0 0 1 0 0 0 0 0
## 6 1 0 1 0 0 0 0 0
## mouse7531 mouse7532 mouseH510 mouseH514
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
4a. Voom
y <- voom(d, mm, plot = T)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
What is voom doing?
- Counts are transformed to log2 counts per million reads (CPM), where “per million reads” is defined based on the normalization factors we calculated earlier.
- A linear model is fitted to the log2 CPM for each gene, and the residuals are calculated.
- A smoothed curve is fitted to the sqrt(residual standard deviation) by average expression. (see red line in plot above)
- The smoothed curve is used to obtain weights for each gene and sample that are passed into limma along with the log2 CPMs.
More details at “voom: precision weights unlock linear model analysis tools for RNA-seq read counts”
If your voom plot looks like the below (performed on the raw data), you might want to filter more:
tmp <- voom(d0, mm, plot = T)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 55359 probe(s)
5. Fitting linear models in limma
lmFit fits a linear model using weighted least squares for each gene:
fit <- lmFit(y, mm)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
head(coef(fit))
## groupKOMIR150.C groupKOTet3.C groupWT.C groupKOMIR150.NC
## ENSMUSG00000098104.2 0.4720798 -0.870603 -0.4316833 0.2638469
## ENSMUSG00000033845.14 4.8638714 5.012040 4.7598766 5.1087704
## ENSMUSG00000025903.15 5.1366531 5.637192 5.5201340 5.2593887
## ENSMUSG00000033813.16 5.8881295 5.647722 5.7741947 5.9388317
## ENSMUSG00000033793.13 5.2602006 5.383638 5.4209753 5.0792648
## ENSMUSG00000025907.15 6.5237647 6.660778 6.5419298 6.3403233
## groupKOTet3.NC groupWT.NC mouse148 mouse158
## ENSMUSG00000098104.2 -1.235836 -0.7398125 0.80427646 0.39783246
## ENSMUSG00000033845.14 4.739165 4.6245885 0.23188142 0.16714923
## ENSMUSG00000025903.15 5.602049 5.3569676 -0.03870226 0.04787700
## ENSMUSG00000033813.16 5.813070 5.7816498 0.05747635 0.04162350
## ENSMUSG00000033793.13 4.859347 5.1505746 -0.16372359 -0.22602668
## ENSMUSG00000025907.15 6.752538 6.2901305 -0.10201676 -0.02431071
## mouse183 mouse198 mouse206 mouse2670 mouse7530
## ENSMUSG00000098104.2 -1.7776673 -0.420725961 NA 0.82418308 0.570412284
## ENSMUSG00000033845.14 -0.4524378 -0.101096491 NA -0.04010506 -0.009189037
## ENSMUSG00000025903.15 0.3692613 0.412042949 NA 0.06371828 -0.116273915
## ENSMUSG00000033813.16 -0.2522189 -0.008124444 NA 0.17466166 0.200066974
## ENSMUSG00000033793.13 -0.3293721 0.061917018 NA 0.13070082 0.391684706
## ENSMUSG00000025907.15 -0.1200980 0.124757191 NA -0.09364496 -0.137254436
## mouse7531 mouse7532 mouseH510 mouseH514
## ENSMUSG00000098104.2 NA -1.71711464 1.25795206 0.45053235
## ENSMUSG00000033845.14 NA 0.19460873 0.16938109 0.15717135
## ENSMUSG00000025903.15 NA 0.25756753 0.10915539 0.22798142
## ENSMUSG00000033813.16 NA 0.06063102 0.04585317 -0.03198657
## ENSMUSG00000033793.13 NA -0.29461885 -0.03879688 -0.15134462
## ENSMUSG00000025907.15 NA 0.18072028 0.01131418 -0.12993225
Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models:
6. Specify which groups to compare using contrasts:
Comparison between cell types for genotype WT.
contr <- makeContrasts(groupWT.C - groupWT.NC, levels = colnames(coef(fit)))
contr
## Contrasts
## Levels groupWT.C - groupWT.NC
## groupKOMIR150.C 0
## groupKOTet3.C 0
## groupWT.C 1
## groupKOMIR150.NC 0
## groupKOTet3.NC 0
## groupWT.NC -1
## mouse148 0
## mouse158 0
## mouse183 0
## mouse198 0
## mouse206 0
## mouse2670 0
## mouse7530 0
## mouse7531 0
## mouse7532 0
## mouseH510 0
## mouseH514 0
6a. Estimate contrast for each gene
tmp <- contrasts.fit(fit, contr)
The variance characteristics of low expressed genes are different from high expressed genes, if treated the same, the effect is to over represent low expressed genes in the DE list. This is corrected for by the log transformation and voom. However, some genes will have increased or decreased variance that is not a result of low expression, but due to other random factors. We are going to run empirical Bayes to adjust the variance of these genes.
Empirical Bayes smoothing of standard errors (shifts standard errors that are much larger or smaller than those from other genes towards the average standard error) (see “Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments”
6b. Apply EBayes
tmp <- eBayes(tmp)
7. Multiple Testing Adjustment
The TopTable. Adjust for multiple testing using method of Benjamini & Hochberg (BH), or its ‘alias’ fdr. “Controlling the false discovery rate: a practical and powerful approach to multiple testing.
here n=Inf says to produce the topTable for all genes.
top.table <- topTable(tmp, adjust.method = "BH", sort.by = "P", n = Inf)
Multiple Testing Correction
Simply a must! Best choices are:
The FDR (or qvalue) is a statement about the list and is no longer about the gene (pvalue). So a FDR of 0.05, says you expect 5% false positives among the list of genes with an FDR of 0.05 or less.
The statement “Statistically significantly different” means FDR of 0.05 or less.
7a. How many DE genes are there (false discovery rate corrected)?
length(which(top.table$adj.P.Val < 0.05))
## [1] 5725
8. Check your results for confidence.
You’ve conducted an experiment, you’ve seen a phenotype. Now check which genes are most differentially expressed (show the top 50)? Look up these top genes, their description and ensure they relate to your experiment/phenotype.
head(top.table, 50)
## logFC AveExpr t P.Value adj.P.Val
## ENSMUSG00000020608.8 -2.487303 7.859413 -44.96194 1.162922e-19 1.506450e-15
## ENSMUSG00000049103.15 2.163439 9.880300 40.37803 7.630203e-19 3.576675e-15
## ENSMUSG00000052212.7 4.552604 6.191089 40.18876 8.283175e-19 3.576675e-15
## ENSMUSG00000030203.18 -4.120822 6.994223 -34.50726 1.182848e-17 3.830654e-14
## ENSMUSG00000021990.17 -2.675552 8.357254 -33.50752 1.973093e-17 4.488344e-14
## ENSMUSG00000026193.16 4.806490 10.144216 33.15139 2.376095e-17 4.488344e-14
## ENSMUSG00000027508.16 -1.899075 8.113147 -33.06551 2.485730e-17 4.488344e-14
## ENSMUSG00000024164.16 1.800409 9.861851 32.84297 2.795434e-17 4.488344e-14
## ENSMUSG00000037820.16 -4.166020 7.117346 -32.63710 3.118349e-17 4.488344e-14
## ENSMUSG00000048498.8 -5.796772 6.490853 -30.30744 1.128039e-16 1.408799e-13
## ENSMUSG00000038807.20 -1.561911 9.003764 -30.20493 1.196294e-16 1.408799e-13
## ENSMUSG00000030342.9 -3.675930 6.038229 -29.76804 1.539986e-16 1.500863e-13
## ENSMUSG00000021614.17 6.016099 5.427761 29.74002 1.565324e-16 1.500863e-13
## ENSMUSG00000030413.8 -2.606984 6.640424 -29.67898 1.622054e-16 1.500863e-13
## ENSMUSG00000028885.9 -2.352048 7.043370 -29.51912 1.781152e-16 1.538203e-13
## ENSMUSG00000020437.13 -1.226612 10.305956 -29.30716 2.017951e-16 1.633784e-13
## ENSMUSG00000027215.14 -2.570108 6.890304 -28.99455 2.429700e-16 1.746171e-13
## ENSMUSG00000018168.9 -3.877947 5.456427 -28.97496 2.458286e-16 1.746171e-13
## ENSMUSG00000029254.17 -2.453377 6.686820 -28.76516 2.787785e-16 1.746171e-13
## ENSMUSG00000039959.14 -1.483315 8.932358 -28.72046 2.863830e-16 1.746171e-13
## ENSMUSG00000051177.17 3.186486 4.986113 28.68998 2.916932e-16 1.746171e-13
## ENSMUSG00000037185.10 -1.536981 9.480073 -28.66259 2.965553e-16 1.746171e-13
## ENSMUSG00000022584.15 4.737613 6.736124 28.13190 4.097269e-16 2.298400e-13
## ENSMUSG00000020108.5 -2.051064 6.943477 -28.06926 4.258267e-16 2.298400e-13
## ENSMUSG00000020272.9 -1.298731 10.443857 -27.85991 4.846543e-16 2.511285e-13
## ENSMUSG00000020212.15 -2.163118 6.774248 -27.78875 5.065564e-16 2.523820e-13
## ENSMUSG00000038147.15 1.689977 7.138663 27.66749 5.463224e-16 2.621134e-13
## ENSMUSG00000023827.9 -2.143574 6.406302 -27.42669 6.353764e-16 2.921590e-13
## ENSMUSG00000018001.19 -2.613093 7.171597 -27.38072 6.540536e-16 2.921590e-13
## ENSMUSG00000020387.16 -4.942657 4.326890 -27.18977 7.380655e-16 3.186967e-13
## ENSMUSG00000021728.9 1.661634 8.387251 27.00981 8.277121e-16 3.354909e-13
## ENSMUSG00000023809.11 -3.193245 4.824177 -27.00784 8.287563e-16 3.354909e-13
## ENSMUSG00000008496.20 -1.485223 9.417190 -26.84548 9.196454e-16 3.610026e-13
## ENSMUSG00000039109.18 4.739066 8.315608 26.77182 9.642921e-16 3.673953e-13
## ENSMUSG00000035493.11 1.922015 9.747580 26.70411 1.007353e-15 3.728358e-13
## ENSMUSG00000051457.8 -2.261944 9.813548 -26.28658 1.321924e-15 4.756724e-13
## ENSMUSG00000026923.16 2.014614 6.624214 25.78622 1.840857e-15 6.444991e-13
## ENSMUSG00000029287.15 -3.784250 5.404697 -25.51756 2.204635e-15 7.515484e-13
## ENSMUSG00000043263.14 1.784599 7.846194 25.46720 2.280897e-15 7.576086e-13
## ENSMUSG00000042700.17 -1.813755 6.084302 -25.10440 2.919582e-15 9.383199e-13
## ENSMUSG00000044783.17 -1.728460 7.013402 -25.07950 2.969825e-15 9.383199e-13
## ENSMUSG00000025701.13 -2.754499 5.339541 -24.95884 3.226637e-15 9.951869e-13
## ENSMUSG00000027435.9 3.036282 6.718479 24.91343 3.329268e-15 9.953293e-13
## ENSMUSG00000050335.18 1.111991 8.961475 24.89119 3.380770e-15 9.953293e-13
## ENSMUSG00000033705.18 1.697787 7.149056 24.77195 3.671663e-15 1.056949e-12
## ENSMUSG00000016496.8 -3.558726 6.399878 -24.73266 3.773227e-15 1.062573e-12
## ENSMUSG00000005800.4 5.748643 4.125134 24.61749 4.088308e-15 1.118450e-12
## ENSMUSG00000020340.17 -2.252345 8.645285 -24.59800 4.144326e-15 1.118450e-12
## ENSMUSG00000022818.14 -1.748114 6.777333 -24.47904 4.504253e-15 1.190777e-12
## ENSMUSG00000040809.11 3.884905 7.155146 24.42513 4.678089e-15 1.211999e-12
## B
## ENSMUSG00000020608.8 35.28095
## ENSMUSG00000049103.15 33.50073
## ENSMUSG00000052212.7 32.85197
## ENSMUSG00000030203.18 30.67902
## ENSMUSG00000021990.17 30.25682
## ENSMUSG00000026193.16 29.99183
## ENSMUSG00000027508.16 30.02660
## ENSMUSG00000024164.16 29.85071
## ENSMUSG00000037820.16 29.70891
## ENSMUSG00000048498.8 28.11766
## ENSMUSG00000038807.20 28.40817
## ENSMUSG00000030342.9 28.04034
## ENSMUSG00000021614.17 26.92463
## ENSMUSG00000030413.8 28.14937
## ENSMUSG00000028885.9 28.06012
## ENSMUSG00000020437.13 27.77878
## ENSMUSG00000027215.14 27.74709
## ENSMUSG00000018168.9 27.53382
## ENSMUSG00000029254.17 27.61018
## ENSMUSG00000039959.14 27.51272
## ENSMUSG00000051177.17 27.14811
## ENSMUSG00000037185.10 27.44954
## ENSMUSG00000022584.15 27.20102
## ENSMUSG00000020108.5 27.17760
## ENSMUSG00000020272.9 26.86194
## ENSMUSG00000020212.15 27.02030
## ENSMUSG00000038147.15 26.93753
## ENSMUSG00000023827.9 26.78743
## ENSMUSG00000018001.19 26.75808
## ENSMUSG00000020387.16 25.19657
## ENSMUSG00000021728.9 26.46737
## ENSMUSG00000023809.11 26.14980
## ENSMUSG00000008496.20 26.28405
## ENSMUSG00000039109.18 26.32662
## ENSMUSG00000035493.11 26.17735
## ENSMUSG00000051457.8 25.87927
## ENSMUSG00000026923.16 25.72519
## ENSMUSG00000029287.15 25.44939
## ENSMUSG00000043263.14 25.45149
## ENSMUSG00000042700.17 25.27035
## ENSMUSG00000044783.17 25.23768
## ENSMUSG00000025701.13 25.08077
## ENSMUSG00000027435.9 25.14253
## ENSMUSG00000050335.18 24.98141
## ENSMUSG00000033705.18 25.00700
## ENSMUSG00000016496.8 25.00045
## ENSMUSG00000005800.4 23.06283
## ENSMUSG00000020340.17 24.80052
## ENSMUSG00000022818.14 24.82373
## ENSMUSG00000040809.11 24.78761
Columns are
- logFC: log2 fold change of WT.C/WT.NC
- AveExpr: Average expression across all samples, in log2 CPM
- t: logFC divided by its standard error
- P.Value: Raw p-value (based on t) from test that logFC differs from 0
- adj.P.Val: Benjamini-Hochberg false discovery rate adjusted p-value
- B: log-odds that gene is DE (arguably less useful than the other columns)
ENSMUSG00000030203.18 has higher expression at WT NC than at WT C (logFC is negative). ENSMUSG00000026193.16 has higher expression at WT C than at WT NC (logFC is positive).
In the paper, the authors specify that NC cells were identified by low expression of Ly6C (which is now called Ly6c1 or ENSMUSG00000079018.11). Is this gene differentially expressed?
top.table["ENSMUSG00000079018.11",]
## logFC AveExpr t P.Value adj.P.Val B
## NA NA NA NA NA NA NA
d0$counts["ENSMUSG00000079018.11",]
## mouse_110_WT_C mouse_110_WT_NC mouse_148_WT_C
## 2 0 2
## mouse_148_WT_NC mouse_158_WT_C mouse_158_WT_NC
## 0 2 0
## mouse_183_KOMIR150_C mouse_183_KOMIR150_NC mouse_198_KOMIR150_C
## 1 0 1
## mouse_198_KOMIR150_NC mouse_206_KOMIR150_C mouse_206_KOMIR150_NC
## 0 1 0
## mouse_2670_KOTet3_C mouse_2670_KOTet3_NC mouse_7530_KOTet3_C
## 2 0 2
## mouse_7530_KOTet3_NC mouse_7531_KOTet3_C mouse_7532_WT_NC
## 0 1 0
## mouse_H510_WT_C mouse_H510_WT_NC mouse_H514_WT_C
## 1 0 2
## mouse_H514_WT_NC
## 0
Ly6c1 was removed from our data by the filtering step, because the maximum counts for the gene did not exceed 2.
9. Write top.table to a file, adding in cpms and annotation
top.table$Gene <- rownames(top.table)
top.table <- top.table[,c("Gene", names(top.table)[1:6])]
top.table <- data.frame(top.table,anno[match(top.table$Gene,anno$Gene.stable.ID.version),],logcpm[match(top.table$Gene,rownames(logcpm)),])
head(top.table)
## Gene logFC AveExpr t
## ENSMUSG00000020608.8 ENSMUSG00000020608.8 -2.487303 7.859413 -44.96194
## ENSMUSG00000049103.15 ENSMUSG00000049103.15 2.163439 9.880300 40.37803
## ENSMUSG00000052212.7 ENSMUSG00000052212.7 4.552604 6.191089 40.18876
## ENSMUSG00000030203.18 ENSMUSG00000030203.18 -4.120822 6.994223 -34.50726
## ENSMUSG00000021990.17 ENSMUSG00000021990.17 -2.675552 8.357254 -33.50752
## ENSMUSG00000026193.16 ENSMUSG00000026193.16 4.806490 10.144216 33.15139
## P.Value adj.P.Val B Gene.stable.ID
## ENSMUSG00000020608.8 1.162922e-19 1.506450e-15 35.28095 ENSMUSG00000020608
## ENSMUSG00000049103.15 7.630203e-19 3.576675e-15 33.50073 ENSMUSG00000049103
## ENSMUSG00000052212.7 8.283175e-19 3.576675e-15 32.85197 ENSMUSG00000052212
## ENSMUSG00000030203.18 1.182848e-17 3.830654e-14 30.67902 ENSMUSG00000030203
## ENSMUSG00000021990.17 1.973093e-17 4.488344e-14 30.25682 ENSMUSG00000021990
## ENSMUSG00000026193.16 2.376095e-17 4.488344e-14 29.99183 ENSMUSG00000026193
## Gene.stable.ID.version Gene.name
## ENSMUSG00000020608.8 ENSMUSG00000020608.8 Smc6
## ENSMUSG00000049103.15 ENSMUSG00000049103.15 Ccr2
## ENSMUSG00000052212.7 ENSMUSG00000052212.7 Cd177
## ENSMUSG00000030203.18 ENSMUSG00000030203.18 Dusp16
## ENSMUSG00000021990.17 ENSMUSG00000021990.17 Spata13
## ENSMUSG00000026193.16 ENSMUSG00000026193.16 Fn1
## Gene.description
## ENSMUSG00000020608.8 structural maintenance of chromosomes 6 [Source:MGI Symbol;Acc:MGI:1914491]
## ENSMUSG00000049103.15 chemokine (C-C motif) receptor 2 [Source:MGI Symbol;Acc:MGI:106185]
## ENSMUSG00000052212.7 CD177 antigen [Source:MGI Symbol;Acc:MGI:1916141]
## ENSMUSG00000030203.18 dual specificity phosphatase 16 [Source:MGI Symbol;Acc:MGI:1917936]
## ENSMUSG00000021990.17 spermatogenesis associated 13 [Source:MGI Symbol;Acc:MGI:104838]
## ENSMUSG00000026193.16 fibronectin 1 [Source:MGI Symbol;Acc:MGI:95566]
## Gene.type Transcript.count Gene...GC.content
## ENSMUSG00000020608.8 protein_coding 12 38.40
## ENSMUSG00000049103.15 protein_coding 4 38.86
## ENSMUSG00000052212.7 protein_coding 2 52.26
## ENSMUSG00000030203.18 protein_coding 7 41.74
## ENSMUSG00000021990.17 protein_coding 9 47.38
## ENSMUSG00000026193.16 protein_coding 17 43.66
## Chromosome.scaffold.name Gene.start..bp. Gene.end..bp.
## ENSMUSG00000020608.8 12 11315887 11369786
## ENSMUSG00000049103.15 9 123901987 123913594
## ENSMUSG00000052212.7 7 24443408 24459736
## ENSMUSG00000030203.18 6 134692431 134769588
## ENSMUSG00000021990.17 14 60871450 61002005
## ENSMUSG00000026193.16 1 71624679 71692359
## Strand mouse_110_WT_C mouse_110_WT_NC mouse_148_WT_C
## ENSMUSG00000020608.8 1 6.624180 9.067061 7.044276
## ENSMUSG00000049103.15 1 10.898210 8.821314 11.259023
## ENSMUSG00000052212.7 -1 8.612549 4.153554 8.373256
## ENSMUSG00000030203.18 -1 4.995600 8.975553 5.198700
## ENSMUSG00000021990.17 1 6.968790 9.548885 7.355297
## ENSMUSG00000026193.16 -1 12.915767 7.928271 12.391111
## mouse_148_WT_NC mouse_158_WT_C mouse_158_WT_NC
## ENSMUSG00000020608.8 9.420064 6.775251 9.189817
## ENSMUSG00000049103.15 8.960843 11.075129 8.726306
## ENSMUSG00000052212.7 3.633102 8.118945 3.391894
## ENSMUSG00000030203.18 9.080282 5.464359 9.205093
## ENSMUSG00000021990.17 9.779998 7.039495 9.734873
## ENSMUSG00000026193.16 7.987542 12.510277 7.394448
## mouse_183_KOMIR150_C mouse_183_KOMIR150_NC
## ENSMUSG00000020608.8 6.796259 9.271688
## ENSMUSG00000049103.15 11.032684 8.698646
## ENSMUSG00000052212.7 8.719722 3.499342
## ENSMUSG00000030203.18 5.226591 8.877177
## ENSMUSG00000021990.17 7.412196 9.545027
## ENSMUSG00000026193.16 12.829169 6.909847
## mouse_198_KOMIR150_C mouse_198_KOMIR150_NC
## ENSMUSG00000020608.8 6.504840 8.970617
## ENSMUSG00000049103.15 10.721537 8.509301
## ENSMUSG00000052212.7 8.651454 3.759903
## ENSMUSG00000030203.18 4.814481 9.188429
## ENSMUSG00000021990.17 6.548190 9.308671
## ENSMUSG00000026193.16 12.639374 7.689191
## mouse_206_KOMIR150_C mouse_206_KOMIR150_NC
## ENSMUSG00000020608.8 6.444808 8.900377
## ENSMUSG00000049103.15 10.797927 8.505028
## ENSMUSG00000052212.7 8.756322 3.625476
## ENSMUSG00000030203.18 5.239606 9.469661
## ENSMUSG00000021990.17 6.593106 9.147597
## ENSMUSG00000026193.16 12.839469 8.805309
## mouse_2670_KOTet3_C mouse_2670_KOTet3_NC
## ENSMUSG00000020608.8 6.591425 9.506668
## ENSMUSG00000049103.15 11.453884 7.521540
## ENSMUSG00000052212.7 7.821076 4.119719
## ENSMUSG00000030203.18 4.940924 9.878845
## ENSMUSG00000021990.17 7.796727 10.621256
## ENSMUSG00000026193.16 12.170910 6.458439
## mouse_7530_KOTet3_C mouse_7530_KOTet3_NC
## ENSMUSG00000020608.8 6.425808 9.338677
## ENSMUSG00000049103.15 11.185680 7.209795
## ENSMUSG00000052212.7 8.236312 3.283936
## ENSMUSG00000030203.18 4.042293 9.447064
## ENSMUSG00000021990.17 7.348218 10.446254
## ENSMUSG00000026193.16 13.002444 6.275277
## mouse_7531_KOTet3_C mouse_7532_WT_NC mouse_H510_WT_C
## ENSMUSG00000020608.8 6.259694 8.832110 6.444612
## ENSMUSG00000049103.15 11.317396 9.572462 11.429035
## ENSMUSG00000052212.7 9.009130 4.523782 8.938977
## ENSMUSG00000030203.18 4.072628 8.995467 4.149351
## ENSMUSG00000021990.17 6.907025 9.512566 6.517651
## ENSMUSG00000026193.16 13.034729 8.118894 12.935082
## mouse_H510_WT_NC mouse_H514_WT_C mouse_H514_WT_NC
## ENSMUSG00000020608.8 8.973205 6.481128 9.157598
## ENSMUSG00000049103.15 9.500685 11.194546 9.005389
## ENSMUSG00000052212.7 4.565919 8.737952 4.328884
## ENSMUSG00000030203.18 8.994633 4.820908 9.165231
## ENSMUSG00000021990.17 9.429523 6.796284 9.585713
## ENSMUSG00000026193.16 8.312858 12.498512 7.582299
write.table(top.table, file = "WT.C_v_WT.NC.txt", row.names = F, sep = "\t", quote = F)
Quiz 3
Note: If you changed the expression cutoff to answer quiz questions, before proceeding change it back to the original value and rerun the above code using “Run all chunks above” in the Run menu in RStudio.
Linear models and contrasts
Let’s say we want to compare genotypes for cell type C. The only thing we have to change is the call to makeContrasts:
contr <- makeContrasts(groupWT.C - groupKOMIR150.C, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
head(top.table, 20)
## logFC AveExpr t P.Value adj.P.Val
## ENSMUSG00000030703.9 -2.9797670 4.8096334 -15.320007 1.227322e-11 1.589873e-07
## ENSMUSG00000044229.10 -3.2497085 6.8295076 -11.553745 1.159548e-09 7.510393e-06
## ENSMUSG00000032012.10 -5.2346131 5.0045873 -8.974519 5.394136e-08 1.290840e-04
## ENSMUSG00000030748.10 1.7326877 7.0660459 8.961804 5.507239e-08 1.290840e-04
## ENSMUSG00000040152.9 -2.2564160 6.4434805 -8.915612 5.939444e-08 1.290840e-04
## ENSMUSG00000066687.6 -2.0804192 4.9245631 -8.911574 5.978878e-08 1.290840e-04
## ENSMUSG00000067017.6 4.9881763 3.1242577 8.125642 2.252247e-07 4.167944e-04
## ENSMUSG00000008348.10 -1.1946393 6.3051297 -7.873947 3.501860e-07 5.670387e-04
## ENSMUSG00000096780.8 -5.6777310 2.3856596 -7.574784 5.981805e-07 8.609812e-04
## ENSMUSG00000020893.18 -1.2308836 7.5344588 -7.221692 1.142776e-06 1.480352e-03
## ENSMUSG00000028028.12 0.8592901 7.2921677 7.152000 1.301107e-06 1.509347e-03
## ENSMUSG00000039146.6 7.4351099 0.1201065 7.113499 1.398191e-06 1.509347e-03
## ENSMUSG00000028037.14 5.6377499 2.3456220 7.019584 1.667896e-06 1.661994e-03
## ENSMUSG00000030365.12 1.0085592 6.6857808 6.911812 2.045107e-06 1.892309e-03
## ENSMUSG00000055435.7 -1.3874349 4.9739950 -6.744988 2.812774e-06 2.429111e-03
## ENSMUSG00000028619.16 3.0833446 4.6811807 6.579713 3.871575e-06 3.134524e-03
## ENSMUSG00000024772.10 -1.2968233 6.3307475 -6.463771 4.854945e-06 3.699468e-03
## ENSMUSG00000051495.9 -0.8710340 7.1412361 -6.362568 5.924188e-06 4.263441e-03
## ENSMUSG00000042105.19 -0.7272502 7.4676681 -6.274772 7.048657e-06 4.805700e-03
## ENSMUSG00000054008.10 -0.9751131 6.5631010 -5.978097 1.277750e-05 7.935676e-03
## B
## ENSMUSG00000030703.9 16.553755
## ENSMUSG00000044229.10 12.462720
## ENSMUSG00000032012.10 7.865419
## ENSMUSG00000030748.10 8.678626
## ENSMUSG00000040152.9 8.449051
## ENSMUSG00000066687.6 8.537970
## ENSMUSG00000067017.6 5.734503
## ENSMUSG00000008348.10 6.738268
## ENSMUSG00000096780.8 3.704435
## ENSMUSG00000020893.18 5.399071
## ENSMUSG00000028028.12 5.316104
## ENSMUSG00000039146.6 2.354527
## ENSMUSG00000028037.14 4.027195
## ENSMUSG00000030365.12 5.026454
## ENSMUSG00000055435.7 4.766223
## ENSMUSG00000028619.16 4.198940
## ENSMUSG00000024772.10 4.100269
## ENSMUSG00000051495.9 3.775662
## ENSMUSG00000042105.19 3.556041
## ENSMUSG00000054008.10 3.062152
length(which(top.table$adj.P.Val < 0.05)) # number of DE genes
## [1] 43
top.table$Gene <- rownames(top.table)
top.table <- top.table[,c("Gene", names(top.table)[1:6])]
top.table <- data.frame(top.table,anno[match(top.table$Gene,anno$Gene.stable.ID),],logcpm[match(top.table$Gene,rownames(logcpm)),])
write.table(top.table, file = "WT.C_v_KOMIR150.C.txt", row.names = F, sep = "\t", quote = F)
What if we refit our model as a two-factor model (rather than using the group variable)?
Create new model matrix:
genotype <- factor(metadata$genotype, levels = c("WT", "KOMIR150", "KOTet3"))
cell_type <- factor(metadata$cell_type, levels = c("C", "NC"))
mouse <- factor(metadata$mouse, levels = c("110", "148", "158", "183", "198", "206", "2670", "7530", "7531", "7532", "H510", "H514"))
mm <- model.matrix(~genotype*cell_type + mouse)
We are specifying that model includes effects for genotype, cell type, and the genotype-cell type interaction (which allows the differences between genotypes to differ across cell types).
colnames(mm)
## [1] "(Intercept)" "genotypeKOMIR150"
## [3] "genotypeKOTet3" "cell_typeNC"
## [5] "mouse148" "mouse158"
## [7] "mouse183" "mouse198"
## [9] "mouse206" "mouse2670"
## [11] "mouse7530" "mouse7531"
## [13] "mouse7532" "mouseH510"
## [15] "mouseH514" "genotypeKOMIR150:cell_typeNC"
## [17] "genotypeKOTet3:cell_typeNC"
y <- voom(d, mm, plot = F)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
fit <- lmFit(y, mm)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
head(coef(fit))
## (Intercept) genotypeKOMIR150 genotypeKOTet3 cell_typeNC
## ENSMUSG00000098104.2 -0.4316833 0.90376316 -0.43891963 -0.308129211
## ENSMUSG00000033845.14 4.7598766 0.10399479 0.25216348 -0.135288112
## ENSMUSG00000025903.15 5.5201340 -0.38348090 0.11705772 -0.163166304
## ENSMUSG00000033813.16 5.7741947 0.11393482 -0.12647239 0.007455091
## ENSMUSG00000033793.13 5.4209753 -0.16077468 -0.03733697 -0.270400666
## ENSMUSG00000025907.15 6.5419298 -0.01816504 0.11884872 -0.251799272
## mouse148 mouse158 mouse183 mouse198 mouse206
## ENSMUSG00000098104.2 0.80427646 0.39783246 -1.7776673 -0.420725961 NA
## ENSMUSG00000033845.14 0.23188142 0.16714923 -0.4524378 -0.101096491 NA
## ENSMUSG00000025903.15 -0.03870226 0.04787700 0.3692613 0.412042949 NA
## ENSMUSG00000033813.16 0.05747635 0.04162350 -0.2522189 -0.008124444 NA
## ENSMUSG00000033793.13 -0.16372359 -0.22602668 -0.3293721 0.061917018 NA
## ENSMUSG00000025907.15 -0.10201676 -0.02431071 -0.1200980 0.124757191 NA
## mouse2670 mouse7530 mouse7531 mouse7532
## ENSMUSG00000098104.2 0.82418308 0.570412284 NA -1.71711464
## ENSMUSG00000033845.14 -0.04010506 -0.009189037 NA 0.19460873
## ENSMUSG00000025903.15 0.06371828 -0.116273915 NA 0.25756753
## ENSMUSG00000033813.16 0.17466166 0.200066974 NA 0.06063102
## ENSMUSG00000033793.13 0.13070082 0.391684706 NA -0.29461885
## ENSMUSG00000025907.15 -0.09364496 -0.137254436 NA 0.18072028
## mouseH510 mouseH514 genotypeKOMIR150:cell_typeNC
## ENSMUSG00000098104.2 1.25795206 0.45053235 0.09989628
## ENSMUSG00000033845.14 0.16938109 0.15717135 0.38018710
## ENSMUSG00000025903.15 0.10915539 0.22798142 0.28590194
## ENSMUSG00000033813.16 0.04585317 -0.03198657 0.04324708
## ENSMUSG00000033793.13 -0.03879688 -0.15134462 0.08946483
## ENSMUSG00000025907.15 0.01131418 -0.12993225 0.06835786
## genotypeKOTet3:cell_typeNC
## ENSMUSG00000098104.2 -0.05710341
## ENSMUSG00000033845.14 -0.13758697
## ENSMUSG00000025903.15 0.12802409
## ENSMUSG00000033813.16 0.15789279
## ENSMUSG00000033793.13 -0.25389106
## ENSMUSG00000025907.15 0.34355915
colnames(coef(fit))
## [1] "(Intercept)" "genotypeKOMIR150"
## [3] "genotypeKOTet3" "cell_typeNC"
## [5] "mouse148" "mouse158"
## [7] "mouse183" "mouse198"
## [9] "mouse206" "mouse2670"
## [11] "mouse7530" "mouse7531"
## [13] "mouse7532" "mouseH510"
## [15] "mouseH514" "genotypeKOMIR150:cell_typeNC"
## [17] "genotypeKOTet3:cell_typeNC"
- The coefficient genotypeKOMIR150 represents the difference in mean expression between KOMIR150 and the reference genotype (WT), for cell type C (the reference level for cell type)
- The coefficient cell_typeNC represents the difference in mean expression between cell type NC and cell type C, for genotype WT
- The coefficient genotypeKOMIR150:cell_typeNC is the difference between cell types NC and C of the differences between genotypes KOMIR150 and WT (the interaction effect).
Let’s estimate the difference between genotypes WT and KOMIR150 in cell type C.
tmp <- contrasts.fit(fit, coef = 2) # Directly test second coefficient
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
head(top.table, 20)
## logFC AveExpr t P.Value adj.P.Val
## ENSMUSG00000030703.9 2.9797670 4.8096334 15.320007 1.227322e-11 1.589873e-07
## ENSMUSG00000044229.10 3.2497085 6.8295076 11.553745 1.159548e-09 7.510393e-06
## ENSMUSG00000032012.10 5.2346131 5.0045873 8.974519 5.394136e-08 1.290840e-04
## ENSMUSG00000030748.10 -1.7326877 7.0660459 -8.961804 5.507239e-08 1.290840e-04
## ENSMUSG00000040152.9 2.2564160 6.4434805 8.915612 5.939444e-08 1.290840e-04
## ENSMUSG00000066687.6 2.0804192 4.9245631 8.911574 5.978878e-08 1.290840e-04
## ENSMUSG00000067017.6 -4.9881763 3.1242577 -8.125642 2.252247e-07 4.167944e-04
## ENSMUSG00000008348.10 1.1946393 6.3051297 7.873947 3.501860e-07 5.670387e-04
## ENSMUSG00000096780.8 5.6777310 2.3856596 7.574784 5.981805e-07 8.609812e-04
## ENSMUSG00000020893.18 1.2308836 7.5344588 7.221692 1.142776e-06 1.480352e-03
## ENSMUSG00000028028.12 -0.8592901 7.2921677 -7.152000 1.301107e-06 1.509347e-03
## ENSMUSG00000039146.6 -7.4351099 0.1201065 -7.113499 1.398191e-06 1.509347e-03
## ENSMUSG00000028037.14 -5.6377499 2.3456220 -7.019584 1.667896e-06 1.661994e-03
## ENSMUSG00000030365.12 -1.0085592 6.6857808 -6.911812 2.045107e-06 1.892309e-03
## ENSMUSG00000055435.7 1.3874349 4.9739950 6.744988 2.812774e-06 2.429111e-03
## ENSMUSG00000028619.16 -3.0833446 4.6811807 -6.579713 3.871575e-06 3.134524e-03
## ENSMUSG00000024772.10 1.2968233 6.3307475 6.463771 4.854945e-06 3.699468e-03
## ENSMUSG00000051495.9 0.8710340 7.1412361 6.362568 5.924188e-06 4.263441e-03
## ENSMUSG00000042105.19 0.7272502 7.4676681 6.274772 7.048657e-06 4.805700e-03
## ENSMUSG00000054008.10 0.9751131 6.5631010 5.978097 1.277750e-05 7.935676e-03
## B
## ENSMUSG00000030703.9 16.553755
## ENSMUSG00000044229.10 12.462720
## ENSMUSG00000032012.10 7.865419
## ENSMUSG00000030748.10 8.678626
## ENSMUSG00000040152.9 8.449051
## ENSMUSG00000066687.6 8.537970
## ENSMUSG00000067017.6 5.734503
## ENSMUSG00000008348.10 6.738268
## ENSMUSG00000096780.8 3.704435
## ENSMUSG00000020893.18 5.399071
## ENSMUSG00000028028.12 5.316104
## ENSMUSG00000039146.6 2.354527
## ENSMUSG00000028037.14 4.027195
## ENSMUSG00000030365.12 5.026454
## ENSMUSG00000055435.7 4.766223
## ENSMUSG00000028619.16 4.198940
## ENSMUSG00000024772.10 4.100269
## ENSMUSG00000051495.9 3.775662
## ENSMUSG00000042105.19 3.556041
## ENSMUSG00000054008.10 3.062152
length(which(top.table$adj.P.Val < 0.05)) # number of DE genes
## [1] 43
We get the same results as with the model where each coefficient corresponded to a group mean. In essence, these are the same model, so use whichever is most convenient for what you are estimating.
The interaction effects genotypeKOMIR150:cell_typeNC are easier to estimate and test in this setup.
head(coef(fit))
## (Intercept) genotypeKOMIR150 genotypeKOTet3 cell_typeNC
## ENSMUSG00000098104.2 -0.4316833 0.90376316 -0.43891963 -0.308129211
## ENSMUSG00000033845.14 4.7598766 0.10399479 0.25216348 -0.135288112
## ENSMUSG00000025903.15 5.5201340 -0.38348090 0.11705772 -0.163166304
## ENSMUSG00000033813.16 5.7741947 0.11393482 -0.12647239 0.007455091
## ENSMUSG00000033793.13 5.4209753 -0.16077468 -0.03733697 -0.270400666
## ENSMUSG00000025907.15 6.5419298 -0.01816504 0.11884872 -0.251799272
## mouse148 mouse158 mouse183 mouse198 mouse206
## ENSMUSG00000098104.2 0.80427646 0.39783246 -1.7776673 -0.420725961 NA
## ENSMUSG00000033845.14 0.23188142 0.16714923 -0.4524378 -0.101096491 NA
## ENSMUSG00000025903.15 -0.03870226 0.04787700 0.3692613 0.412042949 NA
## ENSMUSG00000033813.16 0.05747635 0.04162350 -0.2522189 -0.008124444 NA
## ENSMUSG00000033793.13 -0.16372359 -0.22602668 -0.3293721 0.061917018 NA
## ENSMUSG00000025907.15 -0.10201676 -0.02431071 -0.1200980 0.124757191 NA
## mouse2670 mouse7530 mouse7531 mouse7532
## ENSMUSG00000098104.2 0.82418308 0.570412284 NA -1.71711464
## ENSMUSG00000033845.14 -0.04010506 -0.009189037 NA 0.19460873
## ENSMUSG00000025903.15 0.06371828 -0.116273915 NA 0.25756753
## ENSMUSG00000033813.16 0.17466166 0.200066974 NA 0.06063102
## ENSMUSG00000033793.13 0.13070082 0.391684706 NA -0.29461885
## ENSMUSG00000025907.15 -0.09364496 -0.137254436 NA 0.18072028
## mouseH510 mouseH514 genotypeKOMIR150:cell_typeNC
## ENSMUSG00000098104.2 1.25795206 0.45053235 0.09989628
## ENSMUSG00000033845.14 0.16938109 0.15717135 0.38018710
## ENSMUSG00000025903.15 0.10915539 0.22798142 0.28590194
## ENSMUSG00000033813.16 0.04585317 -0.03198657 0.04324708
## ENSMUSG00000033793.13 -0.03879688 -0.15134462 0.08946483
## ENSMUSG00000025907.15 0.01131418 -0.12993225 0.06835786
## genotypeKOTet3:cell_typeNC
## ENSMUSG00000098104.2 -0.05710341
## ENSMUSG00000033845.14 -0.13758697
## ENSMUSG00000025903.15 0.12802409
## ENSMUSG00000033813.16 0.15789279
## ENSMUSG00000033793.13 -0.25389106
## ENSMUSG00000025907.15 0.34355915
colnames(coef(fit))
## [1] "(Intercept)" "genotypeKOMIR150"
## [3] "genotypeKOTet3" "cell_typeNC"
## [5] "mouse148" "mouse158"
## [7] "mouse183" "mouse198"
## [9] "mouse206" "mouse2670"
## [11] "mouse7530" "mouse7531"
## [13] "mouse7532" "mouseH510"
## [15] "mouseH514" "genotypeKOMIR150:cell_typeNC"
## [17] "genotypeKOTet3:cell_typeNC"
tmp <- contrasts.fit(fit, coef = 16) # Test genotypeKOMIR150:cell_typeNC
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
head(top.table, 20)
## logFC AveExpr t P.Value adj.P.Val
## ENSMUSG00000030748.10 0.7177323 7.0660459 4.557231 0.0002554872 0.752253
## ENSMUSG00000076609.3 -4.4637783 3.4662592 -4.518670 0.0002778949 0.752253
## ENSMUSG00000033004.17 -0.3905878 8.7783261 -4.497904 0.0002907781 0.752253
## ENSMUSG00000029004.16 -0.3452175 8.5118706 -4.272042 0.0004768723 0.752253
## ENSMUSG00000019761.11 3.7126802 -0.8639142 4.147051 0.0006277713 0.752253
## ENSMUSG00000015501.11 -0.8405195 5.4935666 -4.119791 0.0006666220 0.752253
## ENSMUSG00000004952.14 -0.4538479 7.8927502 -4.105685 0.0006876671 0.752253
## ENSMUSG00000054387.14 -0.3568046 7.9949874 -4.097867 0.0006996178 0.752253
## ENSMUSG00000049313.9 0.3097374 9.7730340 4.028431 0.0008153758 0.752253
## ENSMUSG00000070305.11 1.6486709 3.4358103 4.015474 0.0008390193 0.752253
## ENSMUSG00000004110.18 -3.5113056 0.6676078 -3.973949 0.0009195433 0.752253
## ENSMUSG00000030724.8 -2.8044757 1.0347984 -3.955979 0.0009567535 0.752253
## ENSMUSG00000026357.4 0.9338323 4.3874897 3.932417 0.0010078447 0.752253
## ENSMUSG00000032026.8 -0.6472912 5.6061466 -3.911615 0.0010552201 0.752253
## ENSMUSG00000110218.2 -1.8838622 2.5483876 -3.845545 0.0012210127 0.752253
## ENSMUSG00000029647.16 -0.3429068 7.5827393 -3.836288 0.0012462392 0.752253
## ENSMUSG00000024772.10 -0.6795287 6.3307475 -3.831189 0.0012603560 0.752253
## ENSMUSG00000039115.14 -3.6008455 -0.5458855 -3.825240 0.0012770273 0.752253
## ENSMUSG00000005533.11 -0.8340858 5.6300063 -3.794652 0.0013662994 0.752253
## ENSMUSG00000037020.17 -0.9150202 3.9982178 -3.761799 0.0014691453 0.752253
## B
## ENSMUSG00000030748.10 0.49889404
## ENSMUSG00000076609.3 -1.09189870
## ENSMUSG00000033004.17 0.49295200
## ENSMUSG00000029004.16 0.03530396
## ENSMUSG00000019761.11 -3.83546078
## ENSMUSG00000015501.11 -0.42926939
## ENSMUSG00000004952.14 -0.27908591
## ENSMUSG00000054387.14 -0.30212565
## ENSMUSG00000049313.9 -0.53079814
## ENSMUSG00000070305.11 -1.94698557
## ENSMUSG00000004110.18 -3.10923798
## ENSMUSG00000030724.8 -2.84358549
## ENSMUSG00000026357.4 -1.10816474
## ENSMUSG00000032026.8 -0.75365191
## ENSMUSG00000110218.2 -2.50493951
## ENSMUSG00000029647.16 -0.81763098
## ENSMUSG00000024772.10 -0.80967362
## ENSMUSG00000039115.14 -3.89839390
## ENSMUSG00000005533.11 -0.93199028
## ENSMUSG00000037020.17 -1.60813286
length(which(top.table$adj.P.Val < 0.05))
## [1] 0
The log fold change here is the difference between genotypes KOMIR150 and WT in the log fold changes between cell types NC and C.
A gene for which this interaction effect is significant is one for which the effect of cell type differs between genotypes, and for which the effect of genotypes differs between cell types.
More complicated models
Specifying a different model is simply a matter of changing the calls to model.matrix (and possibly to contrasts.fit).
What if we want to adjust for a continuous variable like some health score? (We are making this data up here, but it would typically be a variable in your metadata.)
# Generate example health data
set.seed(99)
HScore <- rnorm(n = 22, mean = 7.5, sd = 1)
HScore
## [1] 7.713963 7.979658 7.587829 7.943859 7.137162 7.622674 6.636155 7.989624
## [9] 7.135883 6.205758 6.754231 8.421550 8.250054 4.991446 4.459066 7.500266
## [17] 7.105981 5.754972 7.998631 7.770954 8.598922 8.252513
Model adjusting for HScore score:
mm <- model.matrix(~0 + group + mouse + HScore)
y <- voom(d, mm, plot = F)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
fit <- lmFit(y, mm)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
contr <- makeContrasts(groupKOMIR150.NC - groupWT.NC,
levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
head(top.table, 20)
## logFC AveExpr t P.Value adj.P.Val
## ENSMUSG00000044229.10 3.1987995 6.829508 20.779123 8.166437e-14 1.057880e-09
## ENSMUSG00000032012.10 5.5010286 5.004587 15.028445 1.810899e-11 1.172919e-07
## ENSMUSG00000030703.9 3.2463615 4.809633 14.648972 2.750293e-11 1.187576e-07
## ENSMUSG00000096780.8 5.6399635 2.385660 11.472603 1.367125e-09 4.427434e-06
## ENSMUSG00000040152.9 3.0295977 6.443481 10.074846 1.006610e-08 2.607926e-05
## ENSMUSG00000008348.10 1.3189988 6.305130 9.385412 2.905956e-08 5.942725e-05
## ENSMUSG00000028619.16 -2.8352838 4.681181 -9.322188 3.211292e-08 5.942725e-05
## ENSMUSG00000100801.2 -2.5294894 5.601294 -8.638082 9.757368e-08 1.579962e-04
## ENSMUSG00000070372.12 0.9118442 7.401338 8.506451 1.216297e-07 1.582863e-04
## ENSMUSG00000028173.11 -1.7975063 6.802695 -8.503714 1.221910e-07 1.582863e-04
## ENSMUSG00000020893.18 1.1002785 7.534459 8.400447 1.454781e-07 1.713203e-04
## ENSMUSG00000042396.11 -1.0170672 6.740141 -8.327979 1.645544e-07 1.776365e-04
## ENSMUSG00000030365.12 -1.0476802 6.685781 -7.966362 3.073945e-07 3.063068e-04
## ENSMUSG00000030748.10 -1.0082768 7.066046 -7.782130 4.253964e-07 3.936133e-04
## ENSMUSG00000067017.6 -3.9105723 3.124258 -7.469609 7.457612e-07 6.440394e-04
## ENSMUSG00000066687.6 1.8445862 4.924563 7.389341 8.632439e-07 6.989039e-04
## ENSMUSG00000035212.15 0.8121425 7.115871 7.224844 1.168188e-06 8.901590e-04
## ENSMUSG00000028037.14 -5.8022207 2.345622 -6.988465 1.815880e-06 1.306828e-03
## ENSMUSG00000028028.12 -0.8012194 7.292168 -6.806644 2.562649e-06 1.665698e-03
## ENSMUSG00000094344.2 -3.5178028 2.290910 -6.804793 2.571711e-06 1.665698e-03
## B
## ENSMUSG00000044229.10 21.352239
## ENSMUSG00000032012.10 14.578435
## ENSMUSG00000030703.9 14.937279
## ENSMUSG00000096780.8 7.847477
## ENSMUSG00000040152.9 10.282960
## ENSMUSG00000008348.10 9.299900
## ENSMUSG00000028619.16 8.407363
## ENSMUSG00000100801.2 8.040057
## ENSMUSG00000070372.12 7.796628
## ENSMUSG00000028173.11 7.889217
## ENSMUSG00000020893.18 7.618497
## ENSMUSG00000042396.11 7.573348
## ENSMUSG00000030365.12 6.994763
## ENSMUSG00000030748.10 6.604236
## ENSMUSG00000067017.6 4.114893
## ENSMUSG00000066687.6 5.869839
## ENSMUSG00000035212.15 5.573721
## ENSMUSG00000028037.14 2.659460
## ENSMUSG00000028028.12 4.789225
## ENSMUSG00000094344.2 3.648720
length(which(top.table$adj.P.Val < 0.05))
## [1] 105
What if we want to look at the correlation of gene expression with a continuous variable like pH?
# Generate example pH data
set.seed(99)
pH <- rnorm(n = 22, mean = 8, sd = 1.5)
pH
## [1] 8.320944 8.719487 8.131743 8.665788 7.455743 8.184011 6.704232 8.734436
## [9] 7.453825 6.058637 6.881346 9.382326 9.125082 4.237169 3.438599 8.000399
## [17] 7.408972 5.382459 8.747947 8.406431 9.648382 9.128770
Specify model matrix:
mm <- model.matrix(~pH)
head(mm)
## (Intercept) pH
## 1 1 8.320944
## 2 1 8.719487
## 3 1 8.131743
## 4 1 8.665788
## 5 1 7.455743
## 6 1 8.184011
y <- voom(d, mm, plot = F)
fit <- lmFit(y, mm)
tmp <- contrasts.fit(fit, coef = 2) # test "pH" coefficient
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
head(top.table, 20)
## logFC AveExpr t P.Value adj.P.Val
## ENSMUSG00000056054.10 -1.14842087 1.05687300 -4.975042 4.898963e-05 0.6346116
## ENSMUSG00000026822.15 -1.12095682 1.26099406 -4.517840 1.532675e-04 0.9116602
## ENSMUSG00000094497.2 -0.93628174 -0.63040953 -4.389724 2.111302e-04 0.9116602
## ENSMUSG00000027111.17 -0.51907470 2.39218496 -4.095370 4.403412e-04 0.9997747
## ENSMUSG00000069049.12 -1.17601721 1.57125414 -4.072303 4.663983e-04 0.9997747
## ENSMUSG00000069045.12 -1.19631391 2.07697676 -3.742943 1.056204e-03 0.9997747
## ENSMUSG00000016356.19 0.26469693 1.69006497 3.719233 1.119854e-03 0.9997747
## ENSMUSG00000031169.14 0.39704088 0.44264683 3.662833 1.286808e-03 0.9997747
## ENSMUSG00000085355.3 0.46191526 -0.04205788 3.640092 1.360846e-03 0.9997747
## ENSMUSG00000056071.13 -0.96130484 0.89681065 -3.638298 1.366859e-03 0.9997747
## ENSMUSG00000036764.13 -0.32437971 0.29216063 -3.563286 1.643128e-03 0.9997747
## ENSMUSG00000091537.3 -0.09567479 5.41424839 -3.529924 1.782924e-03 0.9997747
## ENSMUSG00000046032.17 -0.07883002 5.16379061 -3.525376 1.802862e-03 0.9997747
## ENSMUSG00000015312.10 -0.13294451 3.28311160 -3.447404 2.180589e-03 0.9997747
## ENSMUSG00000040521.12 -0.17418858 2.87291831 -3.446616 2.184778e-03 0.9997747
## ENSMUSG00000027132.4 -0.15617432 3.35964531 -3.446501 2.185388e-03 0.9997747
## ENSMUSG00000035877.18 -0.16472058 2.74347447 -3.435552 2.244395e-03 0.9997747
## ENSMUSG00000090946.4 -0.10221759 5.80558585 -3.416833 2.348885e-03 0.9997747
## ENSMUSG00000037316.10 -0.10360521 4.19965492 -3.397744 2.460326e-03 0.9997747
## ENSMUSG00000056673.15 -1.06863653 1.12453223 -3.397444 2.462117e-03 0.9997747
## B
## ENSMUSG00000056054.10 0.1448894
## ENSMUSG00000026822.15 -0.4176793
## ENSMUSG00000094497.2 -1.8376937
## ENSMUSG00000027111.17 -0.6056622
## ENSMUSG00000069049.12 -0.7201317
## ENSMUSG00000069045.12 -1.1349454
## ENSMUSG00000016356.19 -2.4108641
## ENSMUSG00000031169.14 -3.0994289
## ENSMUSG00000085355.3 -3.3028052
## ENSMUSG00000056071.13 -1.9188648
## ENSMUSG00000036764.13 -2.6714599
## ENSMUSG00000091537.3 -1.1717994
## ENSMUSG00000046032.17 -1.1882332
## ENSMUSG00000015312.10 -1.5873959
## ENSMUSG00000040521.12 -1.7137923
## ENSMUSG00000027132.4 -1.5672199
## ENSMUSG00000035877.18 -1.7730156
## ENSMUSG00000090946.4 -1.4054647
## ENSMUSG00000037316.10 -1.5070077
## ENSMUSG00000056673.15 -2.0472196
length(which(top.table$adj.P.Val < 0.05))
## [1] 0
In this case, limma is fitting a linear regression model, which here is a straight line fit, with the slope and intercept defined by the model coefficients:
ENSMUSG00000056054 <- y$E["ENSMUSG00000056054.10",]
plot(ENSMUSG00000056054 ~ pH, ylim = c(0, 3.5))
intercept <- coef(fit)["ENSMUSG00000056054.10", "(Intercept)"]
slope <- coef(fit)["ENSMUSG00000056054.10", "pH"]
abline(a = intercept, b = slope)
slope
## [1] -1.148421
In this example, the log fold change logFC is the slope of the line, or the change in gene expression (on the log2 CPM scale) for each unit increase in pH.
Here, a logFC of 0.20 means a 0.20 log2 CPM increase in gene expression for each unit increase in pH, or a 15% increase on the CPM scale (2^0.20 = 1.15).
A bit more on linear models
Limma fits a linear model to each gene.
Linear models include analysis of variance (ANOVA) models, linear regression, and any model of the form
Y = β0 + β1X1 + β2X2 + … + βpXp + ε
The covariates X can be:
- a continuous variable (pH, HScore score, age, weight, temperature, etc.)
- Dummy variables coding a categorical covariate (like cell type, genotype, and group)
The β’s are unknown parameters to be estimated.
In limma, the β’s are the log fold changes.
The error (residual) term ε is assumed to be normally distributed with a variance that is constant across the range of the data.
Normally distributed means the residuals come from a distribution that looks like this:
The log2 transformation that voom applies to the counts makes the data “normal enough”, but doesn’t completely stabilize the variance:
mm <- model.matrix(~0 + group + mouse)
tmp <- voom(d, mm, plot = T)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
The log2 counts per million are more variable at lower expression levels. The variance weights calculated by voom address this situation.
Both edgeR and limma have VERY comprehensive user manuals
The limma users’ guide has great details on model specification.
Quiz 4
Simple plotting
mm <- model.matrix(~genotype*cell_type + mouse)
colnames(mm) <- make.names(colnames(mm))
y <- voom(d, mm, plot = F)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
fit <- lmFit(y, mm)
## Coefficients not estimable: mouse206 mouse7531
## Warning: Partial NA coefficients for 12954 probe(s)
contrast.matrix <- makeContrasts(genotypeKOMIR150, cell_typeNC, levels=colnames(coef(fit)))
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
top.table <- topTable(fit2, coef = 1, sort.by = "P", n = 40)
Volcano plot
volcanoplot(fit2, coef=1, highlight=8, names=rownames(fit2), main="genotype")
head(anno[match(rownames(fit2), anno$Gene.stable.ID.version),
c("Gene.stable.ID", "Gene.name") ])
## Gene.stable.ID Gene.name
## 48443 ENSMUSG00000098104 Gm6085
## 48521 ENSMUSG00000033845 Mrpl15
## 48766 ENSMUSG00000025903 Lypla1
## 48915 ENSMUSG00000033813 Tcea1
## 49347 ENSMUSG00000033793 Atp6v1h
## 49551 ENSMUSG00000025907 Rb1cc1
identical(anno[match(rownames(fit2), anno$Gene.stable.ID,version),
c("Gene.stable.ID.version")], rownames(fit2))
## [1] FALSE
volcanoplot(fit2, coef=1, highlight=8, names=anno[match(rownames(fit2), anno$Gene.stable.ID.version), "Gene.name"], main="genotype")
Heatmap
#using a red and blue color scheme without traces and scaling each row
heatmap.2(logcpm[rownames(top.table),],col=brewer.pal(11,"RdBu"),scale="row", trace="none")
anno[match(rownames(top.table), anno$Gene.stable.ID.version),
c("Gene.stable.ID", "Gene.name")]
## Gene.stable.ID Gene.name
## 15527 ENSMUSG00000030703 Gdpd3
## 43284 ENSMUSG00000044229 Nxpe4
## 25910 ENSMUSG00000032012 Nectin1
## 52793 ENSMUSG00000030748 Il4ra
## 54841 ENSMUSG00000040152 Thbs1
## 4616 ENSMUSG00000066687 Zbtb16
## 47835 ENSMUSG00000067017 Capza1-ps1
## 39290 ENSMUSG00000008348 Ubc
## 27827 ENSMUSG00000096780 Tmem181b-ps
## 44187 ENSMUSG00000020893 Per1
## 21589 ENSMUSG00000028028 Alpk1
## 3187 ENSMUSG00000039146 Ifi44l
## 3155 ENSMUSG00000028037 Ifi44
## 27579 ENSMUSG00000030365 Clec2i
## 19396 ENSMUSG00000055435 Maf
## 7979 ENSMUSG00000028619 Tceanc2
## 10870 ENSMUSG00000024772 Ehd1
## 36587 ENSMUSG00000051495 Irf2bp2
## 40438 ENSMUSG00000042105 Inpp5f
## 2310 ENSMUSG00000054008 Ndst1
## 845 ENSMUSG00000096768 Gm47283
## 16280 ENSMUSG00000076937 Iglc2
## 24548 ENSMUSG00000055994 Nod2
## 38695 ENSMUSG00000070372 Capza1
## 13097 ENSMUSG00000100801 Gm15459
## 1995 ENSMUSG00000033863 Klf9
## 9425 ENSMUSG00000051439 Cd14
## 41587 ENSMUSG00000035212 Leprot
## 29618 ENSMUSG00000003545 Fosb
## 20260 ENSMUSG00000028173 Wls
## 29012 ENSMUSG00000034342 Cbl
## 24843 ENSMUSG00000031431 Tsc22d3
## 51174 ENSMUSG00000040139 9430038I01Rik
## 38153 ENSMUSG00000048534 Jaml
## 27991 ENSMUSG00000020108 Ddit4
## 49768 ENSMUSG00000030577 Cd22
## 43260 ENSMUSG00000035385 Ccl2
## 27704 ENSMUSG00000045382 Cxcr4
## 54994 ENSMUSG00000027435 Cd93
## 4482 ENSMUSG00000042396 Rbm7
identical(anno[match(rownames(top.table), anno$Gene.stable.ID.version), "Gene.stable.ID.version"], rownames(top.table))
## [1] TRUE
heatmap.2(logcpm[rownames(top.table),],col=brewer.pal(11,"RdBu"),scale="row", trace="none", labRow = anno[match(rownames(top.table), anno$Gene.stable.ID.version), "Gene.name"])
2 factor venn diagram
results <- decideTests(fit2)
vennDiagram(results)
Download the Enrichment Analysis R Markdown document
download.file("https://raw.githubusercontent.com/ucdavis-bioinformatics-training/2020-mRNA_Seq_Workshop/master/data_analysis/enrichment_mm.Rmd", "enrichment_mm.Rmd")
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] gplots_3.1.1 RColorBrewer_1.1-2 edgeR_3.34.0 limma_3.48.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 locfit_1.5-9.4 lattice_0.20-44 gtools_3.9.2
## [5] digest_0.6.27 bitops_1.0-7 grid_4.1.0 magrittr_2.0.1
## [9] evaluate_0.14 highr_0.9 KernSmooth_2.23-20 rlang_0.4.11
## [13] stringi_1.6.2 rmarkdown_2.8 tools_4.1.0 stringr_1.4.0
## [17] xfun_0.23 yaml_2.2.1 compiler_4.1.0 caTools_1.18.2
## [21] htmltools_0.5.1.1 knitr_1.33