Question: Interpreting GSEA results
5
gravatar for Ron
18 months ago by
Ron950
United States
Ron950 wrote:

Hi all,

I am working with GSEA results using GSEA pre ranked. My input is list of genes with their log2foldchange as the metric . The genes with log2foldchange were calculated using Differential expression.

Now in my GSEA results. I see this pathway plot. The pathway name is RB_P130_DN.V1_DN and its description in Msigdb database is "Genes down-regulated in primary keratinocytes from RB1 and RBL2".

So this pathway is upregulated in my experiment or downregulated ? Can someone explain this result ? I see that the genes in my experiment have a positive Enrichment score and the pathway seems to be upregulated to me. But then it does not correlate with the pathway as in that the genes go down ? https://ibb.co/mVWS5R

enplot RB P130 DN V1 DN 821 Thanks, Ron

gsea rna-seq next-gen • 1.8k views
ADD COMMENTlink modified 13 months ago by pshubhamoy20 • written 18 months ago by Ron950

I've been struggling with interpreting GSEA results the past few days as well. It would be helpful to know what your experiment was, but based on my understanding of GSEA this pathway was upregulated in your experiment.

This gene set is of genes that are downregulated when RB1 and RBL2 are knocked out. If your plot was primarily negative, then that would mean your data looked similar to a RB1 & RBL2 KO. Instead, most of these genes are upregulated in your data, so your experiment is correlated to the presence of those two genes.

ADD REPLYlink written 18 months ago by abbey130

Yes , I also think the same . that in their experiment the genes are downregulated when RB1 and RB2 are knocked out, but in our data those genes are up regulated. So can we say that still our data is related to those two genes being knocked out / down ,despite of the fact that the genes regulated by RB1 and RB2 knockdown are upregulated ?

ADD REPLYlink written 18 months ago by Ron950

Hi , I have a formal class (S4) data like this

Formal class 'gseaResult' [package "DOSE"] with 10 slots ..@ result :'data.frame': 20 obs. of 11 variables: .. ..$ ID : chr [1:20] "rno05200" "rno04010" "rno04151" "rno05166" ... .. ..$ Description : chr [1:20] "Pathways in cancer" "MAPK signaling pathway" "PI3K-Akt signaling pathway" "HTLV-I infection" ... .. ..$ setSize : int [1:20] 370 224 231 203 152 133 118 108 102 136 ... .. ..$ enrichmentScore: num [1:20] -0.384 -0.401 -0.388 -0.425 -0.432 ... .. ..$ NES : num [1:20] -1.54 -1.52 -1.48 -1.59 -1.56 ... .. ..$ pvalue : num [1:20] 0.00164 0.00167 0.00169 0.00173 0.00174 ... .. ..$ p.adjust : num [1:20] 0.0145 0.0145 0.0145 0.0145 0.0145 ... .. ..$ qvalues : num [1:20] 0.00836 0.00836 0.00836 0.00836 0.00836 ... .. ..$ rank : num [1:20] 2544 1610 1928 1804 2600 ... .. ..$ leading_edge : chr [1:20] "tags=34%, list=23%, signal=27%" "tags=23%, list=15%, signal=20%" "tags=29%, list=18%, signal=24%" "tags=23%, list=17%, signal=20%" ... .. ..$ core_enrichment: chr [1:20] "171140/294962/25729/314384/29560/367072/288264/399489/116502/24426/24654/170668/501110/81685/29492/287357/11448"| __truncated__ "25266/25054/360640/59323/79114/24446/25267/29496/24674/114495/117269/25597/78965/116683/24516/292763/24329/2571"| __truncated__ "65248/361696/292406/24514/89805/78975/25513/300253/29302/25155/310553/64033/25266/25054/59323/79114/25634/11666"| __truncated__ "308995/84420/303539/414788/313050/365527/114212/64033/25266/360640/84426/299611/25267/84027/24674/24516/25289/5"| __truncated__ ... ..@ organism : chr "rno" ..@ setType : chr "KEGG" ..@ geneSets :List of 324 .. ..$ rno00010: chr [1:72] "100145871" "100364027" "100364062" "100911515" ... .. ..$ rno00020: chr [1:33] "100125384" "103690168" "103693780" "114096" ... .. ..$ rno00030: chr [1:31] "100360180" "108348261" "114508" "24189" ... .. ..$ rno00040: chr [1:34] "113992" "116463" "154516" "171408" ... .. ..$ rno00051: chr [1:39] "100364027" "100911515" "100911725" "114508" ... .. ..$ rno00052: chr [1:32] "100364027" "103690059" "114860" "116463" ... .. ..$ rno00053: chr [1:27] "113992" "154516" "24861" "24862" ... .. ..$ rno00061: chr [1:14] "113976" "114024" "116719" "117243" ... .. ..$ rno00062: chr [1:31] "100911186" "102549542" "113965" "140547" ... .. ..$ rno00071: chr [1:47] "100145871" "100911186" "113965" "113976" ... .. ..$ rno00072: chr [1:11] "100361036" "117099" "24450" "25014" ... .. ..$ rno00100: chr [1:19] "114100" "114700" "117278" "140910" ... .. ..$ rno00120: chr [1:16] "170588" "192242" "246211" "25284" ... .. ..$ rno00130: chr [1:12] "103693015" "24314" "24813" "25249" ... .. ..$ rno00140: chr [1:84] "100362350" "100910877" "108348086" "108348266" ... .. ..$ rno00190: chr [1:143] "100188937" "100361126" "100361960" "100362331" ... .. ..$ rno00220: chr [1:20] "192268" "24398" "24399" "24401" ... .. ..$ rno00230: chr [1:182] "100360582" "100361574" "100362333" "100363253" ... .. ..$ rno00232: chr [1:6] "114768" "116631" "116632" "24297" ... .. ..$ rno00240: chr [1:104] "100360582" "100361574" "100362333" "100363253" ... .. ..$ rno00250: chr [1:35] "100360621" "117544" "192268" "24240" ... .. ..$ rno00260: chr [1:40] "103691744" "114027" "114123" "171133" ... .. ..$ rno00270: chr [1:47] "100360621" "100912604" "103691744" "171347" ... .. ..$ rno00280: chr [1:56] "100360621" "100361036" "100911186" "113965" ... .. ..$ rno00290: chr [1:4] "25044" "29592" "360816" "64203" .. ..$ rno00310: chr [1:61] "100169747" "100359816" "100361710" "100362634" ... .. ..$ rno00330: chr [1:52] "100912604" "108348083" "114027" "24264" ... .. ..$ rno00340: chr [1:24] "24443" "25375" "25750" "266603" ... .. ..$ rno00350: chr [1:40] "100145871" "100360621" "103694877" "171178" ... .. ..$ rno00360: chr [1:22] "100360621" "103694877" "171179" "24311" ... .. ..$ rno00410: chr [1:33] "100911186" "100912604" "116593" "140547" ... .. ..$ rno00430: chr [1:11] "116568" "156275" "24379" "24380" ... .. ..$ rno00440: chr [1:6] "140544" "286936" "310773" "362713" ... .. ..$ rno00450: chr [1:19] "100911305" "103691744" "24962" "291314" ... .. .. [list output truncated] ..@ geneList : Named num [1:10844] 8.15 6.39 5.33 4.82 4.78 ... .. ..- attr(*, "names")= chr [1:10844] "116463" "313352" "500685" "246253" ... ..@ keytype : chr "UNKNOWN" ..@ permScores : num[0 , 0 ] ..@ params :List of 6 .. ..$ pvalueCutoff : num 0.05 .. ..$ nPerm : num 1000 .. ..$ pAdjustMethod: chr "BH" .. ..$ exponent : num 1 .. ..$ minGSSize : num 100 .. ..$ maxGSSize : num 500 ..@ gene2Symbol: chr(0) ..@ readable : logi FALSE

how do I subset this based on my desired genesets or pvalue? Any idea? thanks

ADD REPLYlink written 13 months ago by pshubhamoy20
1
gravatar for anp375
18 months ago by
anp375160
anp375160 wrote:

Normally, IPA handles this. Was the data normalized? I'd say plot all the log2foldchanges for all genes, and then highlight the genes that were assigned to that pathway.

ADD COMMENTlink written 18 months ago by anp375160

The data is normalized by DESEQ package while doing differential expression.Thanks for your comment ,however thats not what I asked. I just want to know if my results from differential expression analysis - how they relate with this specific pathway based on the enrichment plot ?

ADD REPLYlink written 18 months ago by Ron950
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