[R] RNA Seq Analysis in R

[Rasmus Liland]

On 2020-08-01 15:52 -0400, Matthew McCormack wrote:
| On 8/1/20 1:13 PM, Jeff Newmiller wrote:
| | On August 1, 2020 4:01:08 AM PDT, Anas Jamshed wrote:
| | | I performed this in GEO2R and find 
| | | R script there and Runs R script 

Anas, how did you come up with this 
script at all by reading the article?

How can you be sure that 
limma::lmFit/limma::eBayes procedure was 
the one Jia et al. used in their 
article?

The three author emails are listed on 
page 1 of the article.

| | | After running this no genes are 
| | | found plz help me
| |
| | https://www.bioconductor.org/help/
| 
| As with the previous post, I agree 
| that Bioconductor will be a better 
| place to ask this question.
| 
| As a quick thought you also might try 
| to adjust the p-value in the last 
| line:
 
This is the "distribution" of 
possible log2 Fold Change in tT:

	> tab <- table(signif(tT$logFC, 1))
	> tab[as.character(sort(
	+   as.numeric(names(tab)),
	+   decreasing=F))]
	 -0.5  -0.4  -0.3  -0.2  -0.1 -0.09   0.1
	    1    25   158   376   185     7    49
	  0.2   0.3   0.4   0.5   0.6   0.7
	  250   140    42    11     4     2

... knowing full well “regulated” is 
supposed to be abs(logFC)>1, we can 
instead select above .5 there to get 
the few up-regulated ones ...

	> rownames(tT) <- NULL
	> subset(x=tT,
	+   subset=
	+     adj.P.Val < .01 &
	+     abs(logFC) > .5,
	+   select=adj.P.Val:Gene.symbol)
	       adj.P.Val      P.Value        t
	4   7.457501e-05 5.894525e-09 7.075753
	5   7.457501e-05 7.877860e-09 6.993182
	9   1.170092e-04 1.926078e-08 6.738920
	29  2.565179e-04 1.432599e-07 6.168230
	42  3.202947e-04 2.511181e-07 6.008168
	60  4.039665e-04 4.433103e-07 5.845695
	343 1.043185e-03 6.555444e-06 5.066127
	475 1.391091e-03 1.208538e-05 4.885755
	            B     logFC       Gene.symbol
	4   10.264385 0.6225559             REG1A
	5    9.996103 0.6630585              TNMD
	9    9.168329 0.5138611            NKAIN4
	29   7.306904 0.5538644              C1QB
	42   6.785641 0.5530439             ISG20
	60   6.257651 0.5082288              GZMH
	343  3.755608 0.5543619 MIR155///MIR155HG
	475  3.188253 0.7264114            CXCL13

... none of which are in the network of 
important proteins in figure 5 on page 6.

Best,
Rasmus

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