Project B - Tom Emberson¶

Contents¶

Outline
Background
Pipeline
Low level analysis
High level analysis
Functional and pathway analysis
References

Outline¶

This study is to explain the effect of the transcription factor SP1 in colon cells. To elucidate this effect a colon cell line was used and a silencing of the transcription factor SP1 was obtained using RNAi techniques in vitro. A gene expression profile of the cells with SP1 silencing and without silencing was done after 72hrs in culture.

The expression profiles were quantified using Affymetrix GeneChip HGU133 PLUS 2. The files containing the data are as follow:

M72-1.CEL control at 72hrs in culture - sample 1
M72-2.CEL control at 72hrs in culture - sample 2
S72-1.CEL SP1 silenced at 72hrs in culture - sample 1
S72-2.CEL SP1 silenced at 72hrs in culture - sample 2

After estimating gene expression, visualise the data and describe the findings. Identify which genes are changing between conditions and define any potential pathway that the silencing of SP1 might have altered.

Background¶

SP1 (specificity protein 1) is one of the first transcription factors to be isolated and characterised in mammalian cells, originally found to bind to the simian virus 40 promoter through three tandem Cys2His2 zinc-finger motifs (Dynan and Tjian 1983). This subsequently lead to the discovery of other highly similar transcription factors, such as the Krüppel (a Drosophila gap gene) (Kadonaga et al. 1987). This formed the Sp1-like/Krüppel-like factor (Sp1/KLF) protein family.

Initially SP1 was thought of a general transcription factor of ‘housekeeping’ genes, involved in numerous cell processes (e.g. proliferation, growth, death and metabolism) (Black et al. 2001). It has since been shown not to only maintain basal transcription, but to the transcriptional regulation of a large number of genes (Beishline and Azizkhan-Clifford 2015); estimates suggesting that there are a minimum of 12,000 SP binding sites within the human genome (Oleaga et al. 2012). As well as being involved in essential cellular functions, SP1 contributes to the regulation of a number genes that contribute to the ‘hallmarks of cancer’, by both activating and suppressing tumour suppressers and essential oncogenes (Beishline and Azizkhan-Clifford 2015). SP1 also involved in inflammation, where the use of non-steroidal inflammatory drugs to decrease inflammation has been associated with decreases in SP1 levels (Abdelrahim and Safe 2005). Moreover, SP1 has been implicated in genetic instability, regulating centriole function and chromosomal stability (Atrinidis et al. 2010).

SP1 therefore, has a wide ranging regulatory affect on multiple genes throughout the human genome. Through RNAi silencing techniques and micro-array analysis, this study aims to highlight and define any potential pathways in colon cells where SP1 may be acting.

Pipeline¶

Low level analysis

Step1: Load packages with data from Bioconductor and/or access it from file in the data directory
Step 2: Arrange the data in an affybatch using Bioconductor commands. Annotate the PhenoData

High level analysis

Step 3: Analysis and diagnostic plotting of gene expression data using different methods and normalisation techniques. Inlcuding mas5, RMA and puma.
Step 4: Basic use of limma and puma for Differential Expression Analysis
Step 5: Visualisation of the data with PCA
Step 6: Hierarchical clustering of DE (Differentially Expressed) genes

Functional and pathway analysis

Step 7: Functional analysis of DE targets using PANTHER or DAVID
Step 8: Pathway analysis using PANTHER

Low level analysis¶

Step1: Load packages with data from Bioconductor and/or access it from file in the data directory

options(jupyter.plot_mimetypes ='image/png')

setwd("~/Autumn2016/ProjectB/data_projectB")
getwd()

Step 2: Arrange the data in an affybatch using Bioconductor commands. Annotate the PhenoData

library(affy)
sp1_filenames <- c("M72-1.CEL", "M72-2.CEL", "S72-1.CEL", "S72-2.CEL")
affybatch.sp1 <- ReadAffy(filenames=sp1_filenames)

Attaching package: ‘affy’

The following objects are masked from ‘package:oligo’:

    intensity, MAplot, mm, mm<-, mmindex, pm, pm<-, pmindex,
    probeNames, rma

The following object is masked from ‘package:oligoClasses’:

    list.celfiles

phenoData(affybatch.sp1)

pData(affybatch.sp1) <- data.frame(
"sample"=c("control","control","silenced","silenced"), row.names=rownames(pData(affybatch.sp1)))
pData(affybatch.sp1)

An object of class 'AnnotatedDataFrame'
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: sample
  varMetadata: labelDescription

High Level Analysis¶

Step 3: Analysis and diagnostic plotting of gene expression data using different methods and normalisation techniques. Inlcuding mas5, RMA and puma.

With single point statistics:

MAS 5.0 expression measure.
RMA (robust multi-array avergae expression measure)

Building the raw values into an AffyBatch to create an expression set (eset) with both Mas5 and RMA techniques.

eset_rma<-affy::rma(affybatch.sp1)
show(eset_rma)

Warning message:
“replacing previous import ‘AnnotationDbi::tail’ by ‘utils::tail’ when loading ‘hgu133plus2cdf’”Warning message:
“replacing previous import ‘AnnotationDbi::head’ by ‘utils::head’ when loading ‘hgu133plus2cdf’”

Background correcting
Normalizing
Calculating Expression
ExpressionSet (storageMode: lockedEnvironment)
assayData: 54675 features, 4 samples 
  element names: exprs 
protocolData
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: ScanDate
  varMetadata: labelDescription
phenoData
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: sample
  varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu133plus2

eset_mas5<-mas5(affybatch.sp1)
show(eset_mas5)

background correction: mas 
PM/MM correction : mas 
expression values: mas 
background correcting...done.
54675 ids to be processed
|                    |
|####################|
ExpressionSet (storageMode: lockedEnvironment)
assayData: 54675 features, 4 samples 
  element names: exprs, se.exprs 
protocolData
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: ScanDate
  varMetadata: labelDescription
phenoData
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: sample
  varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu133plus2

Extracting the expression sets and then checking that the two different methods of normalisation (Mas5 and RMA) have returned different values

e_rma<-exprs(eset_rma) 
e_mas5<-exprs(eset_mas5)

identical(eset_mas5,eset_rma)
identical(e_mas5,e_rma)

ls()

First step of visualising the processed data through density estimates

density(e_rma)
density(e_mas5)

Call:
	density.default(x = e_rma)

Data: e_rma (218700 obs.);	Bandwidth 'bw' = 0.1574

       x                y            
 Min.   : 1.953   Min.   :0.0000003  
 1st Qu.: 5.047   1st Qu.:0.0076393  
 Median : 8.140   Median :0.0546753  
 Mean   : 8.140   Mean   :0.0807386  
 3rd Qu.:11.234   3rd Qu.:0.1463547  
 Max.   :14.327   Max.   :0.2239915

Call:
	density.default(x = e_mas5)

Data: e_mas5 (218700 obs.);	Bandwidth 'bw' = 23.54

       x                  y            
 Min.   :  -70.51   Min.   :0.000e+00  
 1st Qu.:12249.81   1st Qu.:1.840e-07  
 Median :24570.13   Median :5.890e-07  
 Mean   :24570.13   Mean   :3.324e-05  
 3rd Qu.:36890.45   3rd Qu.:1.862e-06  
 Max.   :49210.77   Max.   :6.346e-03

boxplot(e_mas5, ylab="Gene Expression levels", xlab="Sample Names")

Due to the large value of outliers in the Mas5 plot, the Mas5 processed data needs to be normalised. RMA has this normalisation step built in, so does not require it.

e_mas5<-log2(e_mas5)
density(e_mas5)

Call:
	density.default(x = e_mas5)

Data: e_mas5 (218700 obs.);	Bandwidth 'bw' = 0.2155

       x                y            
 Min.   :-3.760   Min.   :1.100e-07  
 1st Qu.: 1.238   1st Qu.:3.401e-03  
 Median : 6.236   Median :3.115e-02  
 Mean   : 6.236   Mean   :4.997e-02  
 3rd Qu.:11.233   3rd Qu.:9.059e-02  
 Max.   :16.231   Max.   :1.566e-01

Visualisation and comparison between both single point statistical methods, through a boxplot and histogram

library(limma)
par(mfrow=c(2,2))
boxplot(e_rma, main="RMA analysis", ylab="Gene Expression levels", xlab="Sample Names")
boxplot(e_mas5, main="Mas5 analysis", ylab="Gene Expression levels", xlab="Sample Names")
plotMA(e_rma, main="RMA analysis")
abline(h=0, col='red')
plotMA(e_mas5, main="Mas5 analysis")
abline(h=0, col='red')

A comparison between the two single point statistical methods indicates that:

Boxplots - Mas5 boxplot shows that medians are aligned and the data looks normalised, there are negative outliers though. Whereas the RMA box plot shows a similar trend, with medians being aligned and normalised data, yet there are no negative outliers.
MA-plots - An underlying assumption of gene expression analysis is that most of the genes would not see any change in their expression. This expression is shown on the Y-axis, and since Log(1) is 0, you would expect that the majority of points are located at or near to 0. A visual analysis of each of the MA-plots shows that this is more the case with RMA analysis, where more of the genes do not show any change in their expression, in comparison with the Mas5 MA-plot. The RMA MA-plot also shows fewer, but some outliers, indicating that some genes are differentially expressed so that the data has not been normalised too much. Therefore is still sensitive to gene expression.

Between these two models, RMA is the better normalisation model

With a probabilistic approach:

PUMA(Propagating Uncertainty in Microarray Analysis)

library(puma)
eset_puma<-mmgmos(affybatch.sp1)

Model optimising ..............................................................................................................
Expression values calculating ..............................................................................................................
Done.

Visualisation between RMA and PUMA to determine which method of normalisation to take further to differential analysis

save(eset_puma, file="eset_puma.rda")
e_puma<-exprs(eset_puma) 
Norm_eset_puma<-pumaNormalize(eset_puma)
Norm_e_puma<-exprs(Norm_eset_puma)

par(mfrow=c(3,2))
boxplot(Norm_e_puma, main="PUMA Boxplot", xlab="Sample Names", ylab="Expression")
boxplot(e_rma, main="RMA Boxplot", ylab="Gene Expression levels", xlab="Sample Names")
plotMA(Norm_e_puma, main="PUMA MA-Plot")
abline(h=0, col='red')
plotMA(e_rma, main="RMA MA-Plot")
abline(h=0, col='red')
plot(density(Norm_e_puma), main="PUMA Density Plot", xlab="bins", ylab= "Density" ) 
plot(density(e_rma), main="RMA Density Plot", xlab="bins", ylab="Density")

Between these 2 models, RMA again seems the better model due to the lack of negative outliers and MA-plot distrobution, showing more gene expression nearer to Y=0.

Step 4: Basic use of limma and puma for Differential Expression Analysis

As, in previous analysis between both the PUMA and RMA contained replicates, before a decision can be made on which to take further replicates must be combined.

eset_puma_comb <- pumaComb(eset_puma)

Calculating expected completion time
pumaComb expected completion time is 117 minutes 
.......20%.......40%.......60%.......80%......100%
..................................................

show(eset_puma_comb)
pData(eset_puma_comb)
dim(eset_puma_comb)

ExpressionSet (storageMode: lockedEnvironment)
assayData: 54675 features, 2 samples 
  element names: exprs, se.exprs 
protocolData: none
phenoData
  sampleNames: control silenced
  varLabels: sample
  varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation:

pumaDERes <- pumaDE(eset_puma_comb)
write.reslts(pumaDERes, file="pumaDERes")

puma_comb<-exprs(eset_puma_comb)


for (i in 1:2) {
temp<-puma_comb[,i]
temp[temp<0] <- 0
puma_comb[,i] <- temp
}

fc<- puma_comb[,1] - puma_comb[,2]

Combining replicates using RMA

comb_rma<-matrix(0,nrow=dim(e_rma)[1], ncol=2)
colnames(comb_rma)<-c("Control","Silenced")
comb_rma[,1]<- (e_rma[,1]+e_rma[,2])/2
comb_rma[,2]<- (e_rma[,3]+e_rma[,4])/2
fc_rma<- comb_rma[,1] - comb_rma[,2]

par(mfrow=c(1,2))
plotMA(eset_puma_comb, main="PUMA Combined MA-plot")
abline(h=0, col="red")
plotMA(comb_rma, main="RMA Combined MA-plot")
abline(h=0, col="red")

With combined replicates I have chosen the RMA expression set to analyse further with the Limma package.

show(eset_rma)

ExpressionSet (storageMode: lockedEnvironment)
assayData: 54675 features, 4 samples 
  element names: exprs 
protocolData
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: ScanDate
  varMetadata: labelDescription
phenoData
  sampleNames: M72-1.CEL M72-2.CEL S72-1.CEL S72-2.CEL
  varLabels: sample
  varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu133plus2

*Using Limma to identify the top 100 differentially expressed genes. By building a design contrast matrix, fitting a linear model and then calculating p-values with an empirical Bayes test.

library(limma)

group<-factor(c("Control", "Silenced"))
design<-createDesignMatrix(eset_rma)
colnames(design)<-c("Control", 'Silenced')

contrastmatrix<-makeContrasts(Control-Silenced,levels=design)

fit<-lmFit(eset_rma,design)
fit2<-contrasts.fit(fit,contrasts=contrastmatrix)
fit3<-eBayes(fit2)

topDEGenes<-topTable(fit3, coef=1, adjust="BH",n=100)
topDEGenes

Warning message:
“Zero sample variances detected, have been offset”

Histogram for the contrast of interest: Control vs Silenced to ensure histogram is distributed as expected

hist(fit3$p.value[,1], main="Histogram of p-vales for Control vs Silenced", xlab="p-value")

Annotating the top differentially expressed genes

library(hgu133plus2.db)
library(annotate)

geneProbes<-as.character(rownames(topDEGenes))
annotated_list<-select(hgu133plus2.db, geneProbes,c("SYMBOL","GENENAME"))
annotated_list

'select()' returned 1:many mapping between keys and columns

Step 5: Visualisation of the data with principle component analysis (PCA)

pca_rma <- prcomp(t(e_rma))


plot(pca_rma$x, xlab="Component 1", ylab="Component 2", 
     pch=unclass(as.factor(pData(eset_rma)[,1])), 
     col=unclass(as.factor(pData(eset_rma)[,1])),
     main="Standard PCA")

groups<-paste(eset_rma$sample, sep =" ")

legend(40, 0, groups,pch=unclass(as.factor(pData(eset_rma)[,1]))
, col=unclass(as.factor(pData(eset_rma)[,1])))

PCA is an algorithm which reduces the dimensionality of the data, while retaining variation. It achieves this by identifiying directions which have the maximal variation in the data, known as principle components. Therefore the sample can be represented by a few numbers instead of the 54,675 genes per sample. By plotting these samples it is possible to visually assess similarities and differences between samples and determine if they should be grouped.

Step 6: Hierarchical clustering of DE (Differentially Expressed) genes

library(gplots)

geneProbes<-rownames(topDEGenes)
annotated_list<-select(hgu133plus2.db, geneProbes,"SYMBOL")
indx<-match(annotated_list$PROBEID,rownames(topDEGenes))

tID<-rownames(topDEGenes)[indx]
ind<-1
j<-1
for (i in 1: length(tID)) {
	ind[j]<-which(rownames(eset_rma)==tID[i],arr.ind=TRUE)
	j<-j+1
}

# ind is the vector with all the indexes
topExpr<-e_rma[ind,]
rownames(topExpr)<-annotated_list$SYMBOL

heatmap.2(topExpr, col=redgreen(75), scale="row",
key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.4, cexCol=0.8)

'select()' returned 1:many mapping between keys and columns

	logFC	AveExpr	t	P.Value	adj.P.Val	B
226258_at	-1.942002	7.313580	-18.72097	6.557700e-06	0.1222632	3.012662
201467_s_at	2.139865	8.843529	18.22541	7.509345e-06	0.1222632	2.964737
210519_s_at	1.867496	10.215530	17.54132	9.108621e-06	0.1222632	2.893344
213208_at	1.782435	6.100312	17.46136	9.320966e-06	0.1222632	2.884574
217967_s_at	2.052130	6.365426	16.73597	1.154354e-05	0.1222632	2.800612
213338_at	-1.626308	8.585961	-16.07445	1.414183e-05	0.1222632	2.716523
217028_at	-1.823895	5.500121	-15.75313	1.565327e-05	0.1222632	2.672829
209301_at	1.945263	8.759054	14.10205	2.727934e-05	0.1601248	2.413975
201468_s_at	1.713713	9.582006	14.02486	2.803917e-05	0.1601248	2.400285
230195_at	1.493897	5.364139	13.67736	3.178804e-05	0.1601248	2.336674
226723_at	-1.527071	8.009045	-13.64088	3.221532e-05	0.1601248	2.329801
230720_at	-1.364208	7.630830	-12.78820	4.445919e-05	0.1762727	2.157855
219175_s_at	-1.197733	7.887768	-12.57038	4.843047e-05	0.1762727	2.110198
230264_s_at	-1.256816	8.616979	-12.29306	5.411508e-05	0.1762727	2.047129
206027_at	-1.173978	6.185573	-12.05160	5.972117e-05	0.1762727	1.989930
201397_at	1.328398	9.055163	11.94880	6.231621e-05	0.1762727	1.964905
217225_x_at	1.253934	9.916543	11.84057	6.519447e-05	0.1762727	1.938113
1558930_at	-1.157106	7.726814	-11.31535	8.162581e-05	0.1762727	1.801273
203031_s_at	1.309068	7.278333	11.31072	8.179107e-05	0.1762727	1.800016
203299_s_at	-1.136211	6.931924	-11.27863	8.294895e-05	0.1762727	1.791263
202839_s_at	2.339297	7.330519	11.21023	8.548169e-05	0.1762727	1.772460
219641_at	-1.077283	6.228653	-11.12501	8.876736e-05	0.1762727	1.748735
223095_at	1.094504	7.815418	11.11967	8.897815e-05	0.1762727	1.747238
226281_at	-1.507434	5.946018	-11.08185	9.048870e-05	0.1762727	1.736592
202305_s_at	1.128694	6.491732	11.01440	9.325869e-05	0.1762727	1.717444
206204_at	1.316707	5.530978	10.95615	9.573232e-05	0.1762727	1.700735
225556_at	1.768642	8.761699	10.94025	9.642101e-05	0.1762727	1.696147
242005_at	-1.186479	5.130984	-10.93852	9.649601e-05	0.1762727	1.695649
224760_at	2.345907	7.660555	10.83227	1.012568e-04	0.1762727	1.664666
38892_at	2.211509	6.017416	10.82630	1.015324e-04	0.1762727	1.662909
⋮	⋮	⋮	⋮	⋮	⋮	⋮
71	1.5535106	7.830416	8.986053	0.0002527294	0.1865253	1.0274080
72	0.9142418	9.922481	8.985152	0.0002528524	0.1865253	1.0270450
73	0.9395649	9.274893	8.947936	0.0002579965	0.1865253	1.0119973
74	1.2396798	5.111373	8.930520	0.0002604464	0.1865253	1.0049215
75	1.3067467	5.035421	8.882880	0.0002672899	0.1865253	0.9854552
76	-1.4691290	6.460288	-8.823529	0.0002761161	0.1865253	0.9609738
77	1.0755932	7.456638	8.808274	0.0002784402	0.1865253	0.9546398
78	1.5147335	7.573312	8.724744	0.0002915846	0.1865253	0.9196536
79	-0.9466420	7.117004	-8.654411	0.0003032253	0.1865253	0.8897910
80	-1.1060382	6.296930	-8.600670	0.0003124920	0.1865253	0.8667209
81	-0.9357985	7.984127	-8.579091	0.0003163069	0.1865253	0.8573953
82	1.0386010	8.341667	8.546118	0.0003222434	0.1865253	0.8430764
83	-1.3692382	6.169334	-8.516713	0.0003276491	0.1865253	0.8302355
84	1.0456659	8.937607	8.513430	0.0003282592	0.1865253	0.8287978
85	0.9120852	7.087588	8.444925	0.0003413035	0.1865253	0.7986028
86	2.4939348	7.413611	8.439367	0.0003423884	0.1865253	0.7961370
87	-0.8621251	8.344828	-8.391888	0.0003518251	0.1865253	0.7749713
88	-0.8131880	9.090789	-8.337040	0.0003631123	0.1865253	0.7502955
89	0.9262487	5.518531	8.315612	0.0003676381	0.1865253	0.7405892
90	-0.8408450	9.638550	-8.298443	0.0003713125	0.1865253	0.7327851
91	2.3158683	8.490485	8.281028	0.0003750842	0.1865253	0.7248446
92	-0.8425898	9.785481	-8.249997	0.0003819176	0.1865253	0.7106340
93	-1.0590385	8.622003	-8.186791	0.0003962973	0.1865253	0.6814432
94	-0.7779508	8.680400	-8.167198	0.0004008842	0.1865253	0.6723269
95	0.7990407	7.371832	8.165250	0.0004013436	0.1865253	0.6714189
96	0.8844053	5.813451	8.152342	0.0004044040	0.1865253	0.6653933
97	-0.8498385	7.334997	-8.139900	0.0004073800	0.1865253	0.6595721
98	-1.1761256	7.346127	-8.125531	0.0004108494	0.1865253	0.6528326
99	-0.7856796	5.827899	-8.120763	0.0004120082	0.1865253	0.6505927
100	1.9190023	6.878257	8.120762	0.0004120084	0.1865253	0.6505923

PROBEID	SYMBOL	GENENAME
226258_at	METTL20	methyltransferase like 20
226258_at	AMN1	antagonist of mitotic exit network 1 homolog
201467_s_at	NQO1	NAD(P)H dehydrogenase, quinone 1
210519_s_at	NQO1	NAD(P)H dehydrogenase, quinone 1
213208_at	GLTSCR1L	GLTSCR1-like
217967_s_at	FAM129A	family with sequence similarity 129, member A
213338_at	TMEM158	transmembrane protein 158 (gene/pseudogene)
217028_at	CXCR4	chemokine (C-X-C motif) receptor 4
209301_at	CA2	carbonic anhydrase II
201468_s_at	NQO1	NAD(P)H dehydrogenase, quinone 1
230195_at	LINC01405	long intergenic non-protein coding RNA 1405
226723_at	SVBP	small vasohibin binding protein
230720_at	RNF182	ring finger protein 182
219175_s_at	SLC41A3	solute carrier family 41, member 3
230264_s_at	AP1S2	adaptor-related protein complex 1, sigma 2 subunit
206027_at	S100A3	S100 calcium binding protein A3
201397_at	PHGDH	phosphoglycerate dehydrogenase
217225_x_at	NOMO2	NODAL modulator 2
217225_x_at	NOMO1	NODAL modulator 1
217225_x_at	NOMO3	NODAL modulator 3
217225_x_at	LOC101060373	uncharacterized LOC101060373
1558930_at	LINC00460	long intergenic non-protein coding RNA 460
203031_s_at	UROS	uroporphyrinogen III synthase
203299_s_at	AP1S2	adaptor-related protein complex 1, sigma 2 subunit
202839_s_at	NDUFB7	NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 7, 18kDa
219641_at	DET1	de-etiolated homolog 1 (Arabidopsis)
223095_at	MARVELD1	MARVEL domain containing 1
226281_at	DNER	delta/notch-like EGF repeat containing
202305_s_at	FEZ2	fasciculation and elongation protein zeta 2 (zygin II)
206204_at	GRB14	growth factor receptor-bound protein 14
⋮	⋮	⋮
224931_at	SLC41A3	solute carrier family 41, member 3
229091_s_at	CCNJ	cyclin J
226181_at	TUBE1	tubulin, epsilon 1
219987_at	ERVMER34-1	endogenous retrovirus group MER34, member 1
235223_at	RRP36	ribosomal RNA processing 36
204971_at	CSTA	cystatin A (stefin A)
224407_s_at	STK26	serine/threonine protein kinase 26
217731_s_at	ITM2B	integral membrane protein 2B
228355_s_at	NDUFAF2	NADH dehydrogenase (ubiquinone) complex I, assembly factor 2
200028_s_at	STARD7	StAR-related lipid transfer (START) domain containing 7
234915_s_at	DENR	density-regulated protein
222738_at	WWC2	WW and C2 domain containing 2
222738_at	CLDN22	claudin 22
201141_at	GPNMB	glycoprotein (transmembrane) nmb
235148_at	KRTCAP3	keratinocyte associated protein 3
225553_at	CNIH1	cornichon family AMPA receptor auxiliary protein 1
238057_at	USP45	ubiquitin specific peptidase 45
204224_s_at	GCH1	GTP cyclohydrolase 1
222453_at	CYBRD1	cytochrome b reductase 1
219347_at	NUDT15	nudix (nucleoside diphosphate linked moiety X)-type motif 15
205187_at	SMAD5	SMAD family member 5
1566557_at	BAIAP2-AS1	BAIAP2 antisense RNA 1 (head to head)
203372_s_at	SOCS2	suppressor of cytokine signaling 2
223073_at	HIATL1	hippocampus abundant transcript-like 1
206385_s_at	ANK3	ankyrin 3, node of Ranvier (ankyrin G)
236265_at	SP4	Sp4 transcription factor
219470_x_at	CCNJ	cyclin J
218499_at	STK26	serine/threonine protein kinase 26
219545_at	KCTD14	potassium channel tetramerization domain containing 14
219545_at	NDUFC2-KCTD14	NDUFC2-KCTD14 readthrough

	sample
M72-1.CEL	control
M72-2.CEL	control
S72-1.CEL	silenced
S72-2.CEL	silenced