Hello everyone, Greetings of the day. I am doing differential expression analysis of RNA seq data using DESeq2.

Here are my codes

# #loading our expression data
> hep <- read.csv("hep.csv")

#create experiment labels (two conditions)
> colData <- DataFrame(condition=factor(c("U", "I", "U", "U", "I", "U", "I", "U", "I", "U", "I", "U", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U", "I", "U")))

# create DESeq input matrix
> countDataMatrix <- as.matrix(hep[ , -1])
> rownames(countDataMatrix) <- hep[ , 1]
> dds <- DESeqDataSetFromMatrix(countDataMatrix, colData,formula(~condition))

# run DEseq
> dds <- DESeq(dds)

# visualize differentially expressed genes
> plotMA(dds)

# get differentially expressed genes
> res <- results(dds)

# order by BH adjusted p-value
> resOrdered <- res[order(res$padj),]

# top of ordered matrix
> head(resOrdered)

# how many differentially expressed genes ? FDR=10%, |fold-change|>2 (up and down)
# get differentially expressed gene matrix
> sig <- resOrdered[!is.na(resOrdered$padj) & resOrdered$padj<0.10 & abs(resOrdered$log2FoldChange)>=1,]
> head(sig)
>dim(sig)

#Here is my output table 
#                  baseMean log2FoldChange                 lfcSE                  stat            pvalue     padj
ENSG00000185559 2060.96296009909    -7.90822697617837   1.10122142389957    -7.18132321488469   6.90399166815319E-13    2.10979081387093E-08
ENSG00000167244 3949.71951799667    -5.08717606829602   0.754384957336078   -6.74347495774585   1.54642640462757E-11    2.3628622249507E-07
ENSG00000149948 321.403334257597    -6.05675672917759   0.947898431269116   -6.38966848069193   1.66245778534973E-10    1.69343491541675E-06
ENSG00000225972 1293.57298498576    3.62414564398317    0.592157910519195   6.12023512580551    9.34373799741777E-10    7.13838223657724E-06
ENSG00000002726 56.1685926119863    -3.15318299470966   0.527678364772323   -5.97557755863301   2.29276426158759E-09    1.4012916613971E-05
ENSG00000207805 10.6401492600533    -4.28119895712609   0.792715217804701   -5.40067714226831   6.63898166734393E-08    0.000338134401287
ENSG00000213759 529.226580136276    2.12463768243135    0.406108642472941   5.23169777794845    1.67960198273993E-07    0.000733242242722
ENSG00000225431 32.3231732869748    -2.30244023822726   0.459828852435173   -5.00716783219222   5.52367609453529E-07    0.002109975222161
ENSG00000146755 113.963384966465    -3.4300823355342    0.69271221959034    -4.9516700276526    7.35792915628377E-07    0.002498343967632
ENSG00000081051 2444.2873558266 -3.16862679078253   0.644789347009389   -4.91420462431492   8.91436088073928E-07    0.002724139541545
ENSG00000268916 14.4309338536539    4.36074748766037    0.941713857295316   4.63065022764433    3.64519195369733E-06    0.010126674628458
ENSG00000226674 58.3897020661263    2.28950978708294    0.49820234156553    4.59554200385427    4.31625829754784E-06    0.010991711442897
ENSG00000240280 43.9359006543918    4.00711572786826    0.881318877406284   4.54672631052812    5.44867771430736E-06    0.012361076873163
ENSG00000260468 6.7333035049915 1.78191180184629    0.39261294093126    4.53859670957274    5.66298230388024E-06    0.012361076873163
ENSG00000144407 141.648572691354    3.10707268649824    0.697153223423106   4.45680028737749    8.31920389624939E-06    0.016948436791032
ENSG00000224271 5.62601524439391    4.14894392002511    0.938786651098153   4.41947477115472    9.89410705328228E-06    0.017785530437721
ENSG00000266976 69.4694521188834    3.62763362034337    0.819229796198089   4.42810263637702    9.50656643611501E-06    0.017785530437721
ENSG00000124333 75.3122612551427    1.86859729730161    0.42444319890528    4.40246728448254    1.07026732667891E-05    0.018170166242212
ENSG00000108849 11.5714376765048    -6.53424383502879   1.50322134616343    -4.34682746603201   1.38120780494038E-05    0.022214910163775
ENSG00000137434 18.1272824429854    -1.56573178834447   0.363351519148957   -4.30913786190224   1.63892185405838E-05    0.024548070212095
ENSG00000214814 79.102503812347 -3.53012101909442   0.820434185566485   -4.30274735158309   1.68693175317906E-05    0.024548070212095
ENSG00000116017 799.692245375309    -1.74222460326634   0.406662384483919   -4.28420397297708   1.83394607706386E-05    0.02547434462227
ENSG00000176566 13.2968817707014    4.95489705054512    1.16245918134296    4.26242669856233    2.02218841381916E-05    0.026867850320826
ENSG00000184368 162.58416359746 -2.81013518467859   0.667606961944708   -4.20926584781681   2.56201797362355E-05    0.032621961356651
ENSG00000112164 20.8835046336506    -3.38561722888854   0.817704276653758   -4.14039320271541   3.46711000502653E-05    0.041270008145417
ENSG00000171004 27.4773686946661    -2.64659962606986   0.64275932734876    -4.11755926901363   3.82906073410181E-05    0.041270008145417
ENSG00000187922 7.34933234064925    2.04931419571231    0.498331216458142   4.11235364759543    3.91645746332371E-05    0.041270008145417
ENSG00000272620 27.9589191748043    2.52755230744927    0.612831220135527   4.12438567814855    3.71725566875677E-05    0.041270008145417
ENSG00000144045 84.7602950281065    -2.88426440799247   0.715357797805104   -4.03191859631937   5.53233512132309E-05    0.056354209657504
ENSG00000236254 1.78090497100626    3.37973217752836    0.850391083903516   3.97432692028533    7.05785644901908E-05    0.069574527492121
ENSG00000251775 12.8515566707548    1.18084328772373    0.298992607613174   3.94940629853786    7.83452718012966E-05    0.074817286280495
ENSG00000251199 4.25714234412063    4.35370844102631    1.11319774138175    3.91099288040443    9.19174820054295E-05    0.08511837371527
ENSG00000073792 1032.69179338924    -2.00886422868782   0.515737834607477   -3.89512673666214   9.81473976860241E-05    0.088214303702565
ENSG00000275325 5.92655429825542    -2.9449307887884    0.758921164537715   -3.88041726386991   0.000104277413332   0.091046099257144
ENSG00000158639 34.5094755666945    3.13929027696926    0.8155897399841 3.84910466999065    0.000118550345532   0.099822574573307
ENSG00000233797 7.30499491728945    -2.17425214494496   0.566532412943642   -3.83782480096377   0.000124128990929   0.099822574573307
ENSG00000268460 8.57078380261895    2.48504144410798    0.647351546931695   3.83878196613036    0.000123646166067   0.099822574573307

Now, I want to determine in this table, which genes upregulated genes and which genes are downregulated. Any help would be appreciated.

Hi,

We would usually qualify 'up- / down-regulated' in the context of statistical significance. If you literally just want anything that has a positive or negative log2 fold-change, then one can just do:

subset(sig, log2FoldChange > 0) # up-regulated
subset(sig, log2FoldChange < 0) # down-regulated

However, we can add an extra filter for statistical significance (adjusted p-value < 0.05):

subset(sig, log2FoldChange > 0 & padj < 0.05) # statistically significantly up-regulated
subset(sig, log2FoldChange < 0 & padj < 0.05) # statistically significantly down-regulated

Or, perhaps we want statistically significantly differentially expressed genes that have positive or negative log2 fold-change greater or less than 1:

subset(sig, abs(log2FoldChange) > 1 & padj < 0.05)

Notice the use of abs() function, to compute an absolute value or values.

Kevin