Raphael Gottardo
January 28, 2014
Let's first turn on the cache for increased performance and improved styling
# Set some global knitr options
library("knitr")
opts_chunk$set(tidy=TRUE, tidy.opts=list(blank=FALSE, width.cutoff=60), cache=TRUE, messages=FALSE)- Classical approaches (t/F-test, adjusted p-values)
- Two conditions (t-test)
- Multiple testing (FWER, FDR)
- Alternative to the t-test
- Bayesian and Empirical Bayesian approaches
Let's assume that our data have been normalized and probes summarized.
| Condition | 1 | -- | 1 | 2 | -- | 2 |
|---|---|---|---|---|---|---|
| Replicate | 1 | -- | 1 | -- | ||
| Gene 1 | x | -- | x | y | -- | y |
| Gene 2 | x | -- | x | y | -- | y |
| Gene G | x | -- | x | y | -- | y |
Our goal here is to find genes that are differentially expressed between the two conditions.
Note: Here I will focus on oligo based arrays
- For each gene: Is gene g differentially expressed between the two conditions?
- Is the mean expression level under condition 1 different from the mean expression level under condition 2?
- Test an hypothesis about the equality of the means of the two distributions
- For each gene, are the mean log expression values equal?
Welch's t-test: $$t_g=(\bar{y}{1g}-\bar{y}{2g})/\sqrt{s^2_{1g}/R_1+s^2_{2g}/R_2}$$
If the means are equal,
p-value
- Fix the type I error rate (0.05)
- Minimize the type II
- This is what we do for each gene with a p-value cut off of 0.05
- Problem?
- Look at many genes!
- 1000 t-tests, all null hypothesis are true (
$\mu_1=\mu_2$ )- For one test, Pr of making an error is 0.05.
- For 1000 tests, Pr of making at least one error is 1-(1-0.05)^1000 which is 1!
- The error probability is much greater when looking at many genes!
- We look at
$G$ genes with$G$ very large! For each gene,$\alpha$ error probability - Multiple testing is used to control an overall measure of error (FWER, FDR)
Controls the probability of making at least one type I error
Example: Bonferroni multiple adjustment
If
Many other (more powerful) FWER procedures exist (Holm's step-down, Hochberg's step-up).
Proportion of false positive among the genes called DE
First procedure introduced by Benjamini and Hochberg (1995)
- Order the p-values
$p_{(1)} \le \dots \le p_{(g)} \le \dots \le p_{(G)}$
Let
FDR is controlled at
- Hypothesis need to be independent!
- Alternative approaches exist for dealing with the dependence at the cost of losing some power when the test are in fact independent.
library(ggplot2)
p <- rbeta(1000, 0.1, 0.1)
p_sorted <- sort(p)
qplot(1:1000, p_sorted, ) + geom_abline(intercept = 0, slope = 0.05/1000) +
xlim(c(0, 500)) + ylim(c(0, 0.1))## Warning: Removed 570 rows containing missing values (geom_point).
- Look at the
p.adjustfunction in R!
Microarray experiments are expensive, and as such the number of replicates is usually small. These can lead to the following issues:
- The test statistic is not normally distributed
- The variance estimates are noisy, with thousands of test, some of the estimated variances can be extremely small!
- Small variance problem:
Regularized t-test: $$t_g=(\bar{y}{1g}-\bar{y}{2g})/\sqrt{s^2_{1g}/R_1+s^2_{2g}/R_2+c}$$
where
- Small sample size and distributional assumption:
Estimate the null distribution by permutation. Under the assumption of no differential expression we can permute the columns of the data matrix.
This is the main idea behind SAM.
Tusher, V. G., Tibshirani, R., & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America, 98(9), 5116–5121. doi:10.1073/pnas.091062498
LIMMA is popular Bioconductor package for the analysis of microarray data that provides a flexible linear modeling framework for assessing differential expression.
Smyth, G. K. (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), Article3. doi:10.2202/1544-6115.1027
Let
Smyth (2004) assumes that the mean expression value of
where
It is futher assume that
where
The goal here will be to test if some of these contrats are equal to zero.
As an example of the difference between the design matrix
If we are interested in the difference between
It is assumed that the linear model is fitted for each gene to obtain an estimator
where
Note: So far no distributional assumptions are made, and the fitting is not necessarily done by least-squares. However the contrast estimator will be assumed to be approximately normal with mean
Let
$$ \hat{\beta}{gj} | \beta{gj} , \sigma_g^2 \sim \mathrm{N}(\beta_{gj} , v_{gj} \sigma_g^2)$$
and
where
$$ t_{gj}=\frac{\hat{\beta}{gj}}{s_g\sqrt{v{gj}}}$$
follows an approximate t-distribution on
This approach still suffers from the small sample size problem mentioned previously. One solution is to use a hierarchical model to borrow strength across genes. In particular, we will place a prior distribution on the inverse variances as follows:
where
with
Under the above hierarchical model, the posterior mean of
and we can define the following moderated t-statistics:
$$ \tilde{t}{gj}=\frac{\hat{\beta}{gj}}{\tilde{s}g\sqrt{v{gj}}}$$
The moderated t-statistics $\tilde{t}{gj}$ and the residual sample variances $s^2$
are shown to be distributed independently. The moderated t is shown to follow a t-
distribution under the null hypothesis $H_0 : \beta{gj}$ = 0 with degrees of freedom
All parameters except
This is typical of frequentist inference where the alternative does not matter.
Here are a few other Bayesian approaches that are available for the analysis of gene expression microarray data:
-
Kendziorski, C. M., Newton, M. A., Lan, H., & Gould, M. N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine, 22(24), 3899–3914. doi:10.1002/sim.1548
-
Gottardo, R., Raftery, A. E., Yeung, K. Y., & Bumgarner, R. E. (2006). Bayesian robust inference for differential gene expression in microarrays with multiple samples. Biometrics, 62(1), 10–18. doi:10.1111/j.1541-0420.2005.00397.x
-
Lewin, A., Bochkina, N., & Richardson, S. (2007). Fully Bayesian mixture model for differential gene expression: simulations and model checks. Statistical Applications in Genetics and Molecular Biology, 6(1), Article36. doi:10.2202/1544-6115.1314
However, in my opinion, LIMMA provides the best user experience in terms of analysis in R and Bioconductor.
Let's first install Limma:
source("http://bioconductor.org/biocLite.R")## Bioconductor version 2.14 (BiocInstaller 1.14.3), ?biocLite for
## help
## A newer version of Bioconductor is available for this version of
## R, ?BiocUpgrade for help
biocLite("limma")## BioC_mirror: http://bioconductor.org
## Using Bioconductor version 2.14 (BiocInstaller 1.14.3), R version
## 3.1.2.
## Installing package(s) 'limma'
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## The downloaded binary packages are in
## /var/folders/_r/hy8xh8lx3xx0jqslfghgw7jr0000gn/T//RtmpmrOGBh/downloaded_packages
## Old packages: 'codetools', 'devtools', 'dplyr', 'Formula',
## 'knitr', 'Matrix', 'matrixStats', 'robustbase', 'shiny'
Now we're ready to start using Limma
library(limma)
library(Biobase)## Loading required package: BiocGenerics
## Loading required package: parallel
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## Attaching package: 'BiocGenerics'
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## The following object is masked from 'package:limma':
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## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rep.int, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unlist
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## 'citation("Biobase")', and for packages 'citation("pkgname")'.
library(data.table)but we need some data!
We're going to look at the dataset used in:
Nakaya, H. I., Wrammert, J., Lee, E. K., Racioppi, L., Marie-Kunze, S., Haining, W. N., et al. (2011). Systems biology of vaccination for seasonal influenza in humans. Nature Immunology, 12(8), 786–795. doi:10.1038/ni.2067
library(GEOquery)
# Download the mapping information and processed data main
# serie #gds[[1]] = LAIV/TIV 0809, gds[[2]] = FACS, gds[[3]]
# = TIV 0708
gds <- getGEO("GSE29619", destdir = "Data/GEO/")## ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29619/matrix/
## Found 3 file(s)
## GSE29619-GPL13158_series_matrix.txt.gz
## Using locally cached version: Data/GEO//GSE29619-GPL13158_series_matrix.txt.gz
## Using locally cached version of GPL13158 found here:
## Data/GEO//GPL13158.soft
## GSE29619-GPL3921_series_matrix.txt.gz
## Using locally cached version: Data/GEO//GSE29619-GPL3921_series_matrix.txt.gz
## Using locally cached version of GPL3921 found here:
## Data/GEO//GPL3921.soft
## GSE29619-GPL570_series_matrix.txt.gz
## Using locally cached version: Data/GEO//GSE29619-GPL570_series_matrix.txt.gz
## Using locally cached version of GPL570 found here:
## Data/GEO//GPL570.soft
but before we can use this, we need to clean up the pData a bit (see code in .Rmd file by clicking on the pencil icon above, which will bring you to this slide in the .Rmd file).
### Sanitize data and metadata
gds_new <- gds
sanitize_pdata <- function(pd) {
keepCols <- c("characteristics_ch1.1", "characteristics_ch1.2",
"description", "supplementary_file")
pd <- pd[, keepCols]
colnames(pd) <- c("ptid", "time", "description", "filename")
pd$ptid <- gsub(".*: ", "", pd$ptid)
pd$time <- gsub(".*: ", "", pd$time)
pd$time <- gsub("Day", "D", pd$time)
pd$description <- gsub("(-\\w*){2}$", "", pd$description)
pd$filename <- basename(as.character(pd$filename))
pd$filename <- gsub(".CEL.gz", "", pd$filename)
pd
}
pData(gds_new[[1]]) <- sanitize_pdata(pData(gds_new[[1]]))
pData(gds_new[[2]]) <- sanitize_pdata(pData(gds_new[[2]]))
pData(gds_new[[3]]) <- sanitize_pdata(pData(gds_new[[3]]))Let's create seperate ExpressionSets for the datasets of interests.
TIV_08 <- gds_new[[1]][, grepl("2008-TIV", pData(gds_new[[1]])$description)]
LAIV_08 <- gds_new[[1]][, grepl("2008-LAIV", pData(gds_new[[1]])$description)]
TIV_07 <- gds_new[[3]][, grepl("2007-TIV", pData(gds_new[[3]])$description)]TIV_08, LAIV_08 and TIV_07 are expression sets containing data from three time points (variable name is "time", with values D0, D3 and D7), for several probes (i.e., of form GSMXXXX) and patients (variable name "ptid").
We then use the limma R package to identify genes that are differentially expressed at D3 and D7 compared to baseline for each study.
mm_TIV_08 <- model.matrix(~ptid + time, TIV_08) # design matrix
fit_TIV_08 <- lmFit(TIV_08, mm_TIV_08) #Fit linear model for each gene given a series of arrays
ebay_TIV_08 <- eBayes(fit_TIV_08) # compute moderated t-statistics, moderated F-statistic, and log-odds of differential expressionLet's first look at the estimated coefficients
colnames(fit_TIV_08$coef)## [1] "(Intercept)" "ptid2" "ptid29" "ptid3" "ptid32"
## [6] "ptid35" "ptid38" "ptid39" "ptid4" "ptid42"
## [11] "ptid43" "ptid44" "ptid46" "ptid47" "ptid48"
## [16] "ptid51" "ptid53" "ptid63" "ptid65" "ptid68"
## [21] "ptid70" "ptid72" "ptid73" "ptid74" "ptid78"
## [26] "ptid80" "ptid83" "ptid85" "timeD3" "timeD7"
In this case, the design matrix contains 1's and 0's, indicating which patient and time point matches up to a given measurement in the vector,
Now we can test specific hypotheses.
Here we look for genes differentially expressed at day 3 and day 7 wrt baseline:
# Test t3=t0
topT3 <- topTable(ebay_TIV_08, coef = "timeD3", number = Inf,
sort.by = "none")
# Test t7=t0
topT7 <- topTable(ebay_TIV_08, coef = "timeD7", number = Inf,
sort.by = "none")topTable() extracts a table of the top-ranked genes from a linear model fit and outputs a data.frame with the following columns:
colnames(topT7)## [1] "ID" "GB_ACC"
## [3] "SPOT_ID" "Species.Scientific.Name"
## [5] "Annotation.Date" "Sequence.Type"
## [7] "Sequence.Source" "Target.Description"
## [9] "Representative.Public.ID" "Gene.Title"
## [11] "Gene.Symbol" "ENTREZ_GENE_ID"
## [13] "RefSeq.Transcript.ID" "Gene.Ontology.Biological.Process"
## [15] "Gene.Ontology.Cellular.Component" "Gene.Ontology.Molecular.Function"
## [17] "logFC" "AveExpr"
## [19] "t" "P.Value"
## [21] "adj.P.Val" "B"
as you can see it contains information about the probes contained in the ExpressionSet as well as values calculated by LIMMA.
lm7 <- rowMeans(exprs(TIV_08)[, grepl("D7", pData(TIV_08)$time)])
lm0 <- rowMeans(exprs(TIV_08)[, grepl("D0", pData(TIV_08)$time)])
M <- lm7 - lm0
A <- (lm7 + lm0)/2dt <- data.table(A, M, abs_t = abs(topT7$t), p = topT7$adj.P.Val)
ggplot(dt, aes(x = A, y = M, color = abs_t, shape = p < 0.01)) +
geom_point() + geom_point(data = dt[p < 0.01], aes(x = A,
y = M), color = "red")
Let's compare to ordinary t-statistics
# Ordinary t-statistic
ordinary_t <- fit_TIV_08$coef/fit_TIV_08$stdev.unscaled/fit_TIV_08$sigma
ordinary_t <- ordinary_t[, "timeD7"]
# p-values based on normal approx with BH fdr adjustment
ordinary_p <- p.adjust(2 * pnorm(abs(ordinary_t), lower.tail = FALSE),
method = "BH")dt <- data.table(A, M, abs_t = abs(ordinary_t), p = ordinary_p)
ggplot(dt[is.finite(abs_t)], aes(x = A, y = M, color = abs_t,
shape = p < 0.01)) + geom_point() + geom_point(data = dt[p <
0.01], aes(x = A, y = M), color = "red")
Suppose you want to look at the difference between timeD7 and timeD3. We need to create a contrast matrix that will get this information from the design matrix. This can easily be done using the makeContrats function as follows,
cont_matrix <- makeContrasts(timeD7 - timeD3, levels = mm_TIV_08)## Warning in makeContrasts(timeD7 - timeD3, levels = mm_TIV_08): Renaming
## (Intercept) to Intercept
fit2 <- contrasts.fit(fit_TIV_08, cont_matrix)## Warning in contrasts.fit(fit_TIV_08, cont_matrix): row names of contrasts
## don't match col names of coefficients
fit2 <- eBayes(fit2)
topTable(fit2, adjust = "fdr")## ID GB_ACC SPOT_ID Species.Scientific.Name
## 211430_PM_s_at 211430_PM_s_at M87789 Homo sapiens
## 215946_PM_x_at 215946_PM_x_at AL022324 Homo sapiens
## 214669_PM_x_at 214669_PM_x_at BG485135 Homo sapiens
## 213502_PM_x_at 213502_PM_x_at AA398569 Homo sapiens
## 215379_PM_x_at 215379_PM_x_at AV698647 Homo sapiens
## 215121_PM_x_at 215121_PM_x_at AA680302 Homo sapiens
## 214677_PM_x_at 214677_PM_x_at X57812 Homo sapiens
## 213182_PM_x_at 213182_PM_x_at R78668 Homo sapiens
## 216576_PM_x_at 216576_PM_x_at AF103529 Homo sapiens
## 209138_PM_x_at 209138_PM_x_at M87790 Homo sapiens
## Annotation.Date Sequence.Type Sequence.Source
## 211430_PM_s_at Aug 20, 2010 Exemplar sequence GenBank
## 215946_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 214669_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 213502_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 215379_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 215121_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 214677_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 213182_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 216576_PM_x_at Aug 20, 2010 Consensus sequence GenBank
## 209138_PM_x_at Aug 20, 2010 Exemplar sequence GenBank
## Target.Description
## 211430_PM_s_at gb:M87789.1 /DB_XREF=gi:185361 /FEA=FLmRNA /CNT=1 /TID=Hs.300697.0 /TIER=FL /STK=0 /UG=Hs.300697 /LL=3502 /UG_GENE=IGHG3 /DEF=Human (hybridoma H210) anti-hepatitis A IgG variable region, constant region, complementarity-determining regions mRNA, complete cds. /PROD=IgG /FL=gb:M87789.1
## 215946_PM_x_at gb:AL022324 /DB_XREF=gi:3702433 /FEA=DNA /CNT=2 /TID=Hs.296552.1 /TIER=ConsEnd /STK=0 /UG=Hs.296552 /LL=3545 /UG_GENE=IGLL3 /UG_TITLE=immunoglobulin lambda-like polypeptide 3 /DEF=Human DNA sequence from clone CTA-246H3 on chromosome 22 Contains the gene for IGLL1 (immunoglobulin lambda-like polypeptide 1, pre-B-cell specific), a pseudogene similar to LRP5 (Lipoprotein Receptor Related Protein.), ESTs, Genomic markers (D22S...
## 214669_PM_x_at gb:BG485135 /DB_XREF=gi:13417414 /DB_XREF=602503756F1 /CLONE=IMAGE:4617445 /FEA=mRNA /CNT=101 /TID=Hs.325722.1 /TIER=ConsEnd /STK=0 /UG=Hs.325722 /LL=28875 /UG_GENE=IGKV3D-15 /UG_TITLE=immunoglobulin kappa variable 3D-15
## 213502_PM_x_at gb:AA398569 /DB_XREF=gi:2051678 /DB_XREF=zt73g04.s1 /CLONE=IMAGE:728022 /FEA=DNA /CNT=47 /TID=Hs.296552.0 /TIER=Stack /STK=35 /UG=Hs.296552 /LL=3545 /UG_GENE=IGLL3 /UG_TITLE=immunoglobulin lambda-like polypeptide 3
## 215379_PM_x_at gb:AV698647 /DB_XREF=gi:10300618 /DB_XREF=AV698647 /CLONE=GKCBJC12 /FEA=mRNA /CNT=4 /TID=Hs.289110.4 /TIER=ConsEnd /STK=0 /UG=Hs.289110 /LL=28831 /UG_GENE=IGLJ3 /UG_TITLE=immunoglobulin lambda joining 3
## 215121_PM_x_at gb:AA680302 /DB_XREF=gi:2656270 /DB_XREF=ac83d05.s1 /CLONE=IMAGE:869193 /FEA=mRNA /CNT=18 /TID=Hs.181125.2 /TIER=ConsEnd /STK=1 /UG=Hs.181125 /LL=3535 /UG_GENE=IGL@ /UG_TITLE=immunoglobulin lambda locus
## 214677_PM_x_at gb:X57812.1 /DB_XREF=gi:33723 /GEN=immunoglobulin lambda light chain /FEA=mRNA /CNT=199 /TID=Hs.289110.2 /TIER=ConsEnd /STK=0 /UG=Hs.289110 /LL=28831 /UG_TITLE=immunoglobulin lambda joining 3 /DEF=Human rearranged immunoglobulin lambda light chain mRNA.
## 213182_PM_x_at gb:R78668 /DB_XREF=gi:854949 /DB_XREF=yi74c04.r1 /CLONE=IMAGE:144966 /FEA=EST /CNT=286 /TID=Hs.106070.2 /TIER=ConsEnd /STK=0 /UG=Hs.106070 /LL=1028 /UG_GENE=CDKN1C /UG_TITLE=cyclin-dependent kinase inhibitor 1C (p57, Kip2)
## 216576_PM_x_at gb:AF103529.1 /DB_XREF=gi:4378387 /FEA=mRNA /CNT=1 /TID=Hs.247910.0 /TIER=ConsEnd /STK=0 /UG=Hs.247910 /DEF=Homo sapiens isolate donor N clone N88K immunoglobulin kappa light chain variable region mRNA, partial cds. /PROD=immunoglobulin kappa light chain variableregion
## 209138_PM_x_at gb:M87790.1 /DB_XREF=gi:185363 /FEA=FLmRNA /CNT=660 /TID=Hs.181125.0 /TIER=FL+Stack /STK=584 /UG=Hs.181125 /LL=3535 /UG_GENE=IGL@ /DEF=Human (hybridoma H210) anti-hepatitis A immunoglobulin lambda chain variable region, constant region, complementarity-determining regions mRNA, complete cds. /PROD=immunoglobulin lambda-chain /FL=gb:M87790.1
## Representative.Public.ID
## 211430_PM_s_at M87789
## 215946_PM_x_at AL022324
## 214669_PM_x_at BG485135
## 213502_PM_x_at AA398569
## 215379_PM_x_at AV698647
## 215121_PM_x_at AA680302
## 214677_PM_x_at X57812
## 213182_PM_x_at R78668
## 216576_PM_x_at AF103529
## 209138_PM_x_at M87790
## Gene.Title
## 211430_PM_s_at immunoglobulin heavy locus /// immunoglobulin heavy constant gamma 1 (G1m marker) /// immunoglobulin heavy constant mu /// immunoglobulin heavy variable 4-31 /// hypothetical protein LOC100290146
## 215946_PM_x_at immunoglobulin lambda-like polypeptide 1 /// immunoglobulin lambda-like polypeptide 3 /// glucuronidase, beta/immunoglobulin lambda-like polypeptide 1 pseudogene
## 214669_PM_x_at immunoglobulin kappa locus /// immunoglobulin kappa constant /// immunoglobulin kappa variable 3-20 /// similar to hCG1686089
## 213502_PM_x_at glucuronidase, beta/immunoglobulin lambda-like polypeptide 1 pseudogene
## 215379_PM_x_at immunoglobulin lambda variable 1-44
## 215121_PM_x_at cyclosporin A transporter 1 /// immunoglobulin lambda variable 1-44
## 214677_PM_x_at Immunoglobulin lambda locus
## 213182_PM_x_at cyclin-dependent kinase inhibitor 1C (p57, Kip2)
## 216576_PM_x_at immunoglobulin kappa locus /// immunoglobulin kappa constant /// similar to Ig kappa chain V-I region HK102 precursor /// similar to Ig kappa chain V-I region HK102 precursor
## 209138_PM_x_at Immunoglobulin lambda locus
## Gene.Symbol
## 211430_PM_s_at IGH@ /// IGHG1 /// IGHM /// IGHV4-31 /// LOC100290146
## 215946_PM_x_at IGLL1 /// IGLL3 /// LOC91316
## 214669_PM_x_at IGK@ /// IGKC /// IGKV3-20 /// LOC100291682
## 213502_PM_x_at LOC91316
## 215379_PM_x_at IGLV1-44
## 215121_PM_x_at CYAT1 /// IGLV1-44
## 214677_PM_x_at IGL@
## 213182_PM_x_at CDKN1C
## 216576_PM_x_at IGK@ /// IGKC /// LOC652493 /// LOC652694
## 209138_PM_x_at IGL@
## ENTREZ_GENE_ID
## 211430_PM_s_at 100290146 /// 28396 /// 3492 /// 3500 /// 3507
## 215946_PM_x_at 3543 /// 91316 /// 91353
## 214669_PM_x_at 100291682 /// 28912 /// 3514 /// 50802
## 213502_PM_x_at 91316
## 215379_PM_x_at 28823
## 215121_PM_x_at 100290481 /// 28823
## 214677_PM_x_at 3535
## 213182_PM_x_at 1028
## 216576_PM_x_at 3514 /// 50802 /// 652493 /// 652694
## 209138_PM_x_at 3535
## RefSeq.Transcript.ID
## 211430_PM_s_at XM_001718220 /// XM_002347483
## 215946_PM_x_at NM_001013618 /// NM_020070 /// NM_152855 /// NR_024448 /// NR_029395
## 214669_PM_x_at XM_002345544
## 213502_PM_x_at NR_024448
## 215379_PM_x_at
## 215121_PM_x_at XM_002348112
## 214677_PM_x_at
## 213182_PM_x_at NM_000076 /// NM_001122630 /// NM_001122631
## 216576_PM_x_at XM_001724425 /// XM_942302
## 209138_PM_x_at
## Gene.Ontology.Biological.Process
## 211430_PM_s_at 0006955 // immune response // non-traceable author statement /// 0018298 // protein-chromophore linkage // inferred from electronic annotation
## 215946_PM_x_at 0005975 // carbohydrate metabolic process // inferred from electronic annotation /// 0006955 // immune response // non-traceable author statement
## 214669_PM_x_at 0006955 // immune response // non-traceable author statement
## 213502_PM_x_at 0005975 // carbohydrate metabolic process // inferred from electronic annotation
## 215379_PM_x_at 0006955 // immune response // non-traceable author statement
## 215121_PM_x_at 0006955 // immune response // non-traceable author statement
## 214677_PM_x_at 0006955 // immune response // non-traceable author statement
## 213182_PM_x_at 0000079 // regulation of cyclin-dependent protein kinase activity // traceable author statement /// 0000080 // G1 phase of mitotic cell cycle // traceable author statement /// 0000122 // negative regulation of transcription from RNA polymerase II promoter // inferred from electronic annotation /// 0007049 // cell cycle // inferred from electronic annotation /// 0007050 // cell cycle arrest // inferred from electronic annotation /// 0007050 // cell cycle arrest // traceable author statement /// 0008285 // negative regulation of cell proliferation // traceable author statement /// 0030511 // positive regulation of transforming growth factor beta receptor signaling pathway // inferred from mutant phenotype /// 0032582 // negative regulation of gene-specific transcription // inferred from direct assay /// 0033673 // negative regulation of kinase activity // inferred from direct assay /// 0042326 // negative regulation of phosphorylation // inferred from direct assay /// 0042551 // neuron maturation // inferred from electronic annotation /// 0050680 // negative regulation of epithelial cell proliferation // inferred from mutant phenotype
## 216576_PM_x_at 0006955 // immune response // non-traceable author statement
## 209138_PM_x_at
## Gene.Ontology.Cellular.Component
## 211430_PM_s_at 0005576 // extracellular region // inferred from electronic annotation /// 0005576 // extracellular region // non-traceable author statement /// 0005624 // membrane fraction // non-traceable author statement /// 0005886 // plasma membrane // inferred from electronic annotation /// 0005887 // integral to plasma membrane // non-traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral to membrane // inferred from electronic annotation
## 215946_PM_x_at 0005576 // extracellular region // inferred from electronic annotation /// 0016020 // membrane // non-traceable author statement
## 214669_PM_x_at 0005576 // extracellular region // not recorded /// 0005576 // extracellular region // non-traceable author statement
## 213502_PM_x_at
## 215379_PM_x_at 0005576 // extracellular region // non-traceable author statement
## 215121_PM_x_at 0005576 // extracellular region // non-traceable author statement
## 214677_PM_x_at 0005576 // extracellular region // non-traceable author statement
## 213182_PM_x_at 0005634 // nucleus // inferred from direct assay /// 0005634 // nucleus // inferred from electronic annotation /// 0005730 // nucleolus // inferred from direct assay /// 0005737 // cytoplasm // inferred from direct assay
## 216576_PM_x_at 0005576 // extracellular region // not recorded /// 0005576 // extracellular region // non-traceable author statement
## 209138_PM_x_at
## Gene.Ontology.Molecular.Function
## 211430_PM_s_at 0003823 // antigen binding // traceable author statement /// 0003823 // antigen binding // inferred from electronic annotation /// 0003823 // antigen binding // non-traceable author statement /// 0004872 // receptor activity // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0008270 // zinc ion binding // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation
## 215946_PM_x_at 0004553 // hydrolase activity, hydrolyzing O-glycosyl compounds // inferred from electronic annotation
## 214669_PM_x_at 0003823 // antigen binding // non-traceable author statement /// 0003823 // antigen binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction
## 213502_PM_x_at 0004553 // hydrolase activity, hydrolyzing O-glycosyl compounds // inferred from electronic annotation
## 215379_PM_x_at 0003823 // antigen binding // inferred from electronic annotation /// 0003823 // antigen binding // non-traceable author statement
## 215121_PM_x_at 0003823 // antigen binding // inferred from electronic annotation /// 0003823 // antigen binding // non-traceable author statement
## 214677_PM_x_at 0003823 // antigen binding // inferred from electronic annotation /// 0003823 // antigen binding // non-traceable author statement
## 213182_PM_x_at 0004860 // protein kinase inhibitor activity // inferred from electronic annotation /// 0004861 // cyclin-dependent protein kinase inhibitor activity // inferred from electronic annotation /// 0004861 // cyclin-dependent protein kinase inhibitor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005515 // protein binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016563 // transcription activator activity // inferred from genetic interaction /// 0016564 // transcription repressor activity // inferred from mutant phenotype
## 216576_PM_x_at 0003823 // antigen binding // non-traceable author statement /// 0003823 // antigen binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction
## 209138_PM_x_at
## logFC AveExpr t P.Value adj.P.Val
## 211430_PM_s_at 2.7674612 8.427690 8.970877 2.624795e-12 1.436157e-07
## 215946_PM_x_at 1.0410176 6.039067 6.587058 1.881578e-08 3.468005e-04
## 214669_PM_x_at 0.8635100 11.327501 6.533285 2.300599e-08 3.468005e-04
## 213502_PM_x_at 1.0441970 9.142830 6.410658 3.637000e-08 3.468005e-04
## 215379_PM_x_at 1.1873632 9.664309 6.401504 3.763385e-08 3.468005e-04
## 215121_PM_x_at 1.0060274 10.416652 6.387251 3.968904e-08 3.468005e-04
## 214677_PM_x_at 1.0212244 11.183803 6.340074 4.732293e-08 3.468005e-04
## 213182_PM_x_at -0.7084388 5.203281 -6.321544 5.070646e-08 3.468005e-04
## 216576_PM_x_at 1.2227860 6.012631 6.282835 5.857118e-08 3.560803e-04
## 209138_PM_x_at 1.1115976 10.681856 6.241708 6.826052e-08 3.734874e-04
## B
## 211430_PM_s_at 16.626413
## 215946_PM_x_at 8.873759
## 214669_PM_x_at 8.695609
## 213502_PM_x_at 8.289495
## 215379_PM_x_at 8.259188
## 215121_PM_x_at 8.212009
## 214677_PM_x_at 8.055878
## 213182_PM_x_at 7.994573
## 216576_PM_x_at 7.866542
## 209138_PM_x_at 7.730570
Ok, let's try to repeat what we've done with the TIV07 cohort.