Hi everyone,
Could you please help me to know the answer to below question: What does this part of your Agilent data show ''AgilentData$other$gIsWellAboveBG"
My data has three different Cell-lines, each containing a resistant group and a sensitive group with two repetitions. My goal is compare the sensitive and resistant groups, I don't want to do the contrast between cell lines. Despite this fact, what number should I use for filtering
(Filt_Data <- rowSums (AgilentData $ other $ gIsWellAboveBG> 0)> = **????**) *2, 3, ... ????*
If I want to do the contrast, would be different with this above mentioned number?
Thanks 💐
Hi,
The decision of which number to use is the analyst's choice. If your dataset has 16 samples, then you may choose a value of 8, so that only probes that have signal (above background noise) in >=50% (8 / 16 =~ 50%) samples are retained.
Kevin
NO, 12 samples ...
Cell line 1 Resistance 2 parebtal 2
Cell line 2 Resistance 2 parebtal 2
Cell line 3 Resistance 2 parebtal 2
12 samples - great. As the analyst, you can make the decision about which threshold to use.
I want to compare the resistant and sensitive groups of each cell-line, but I don't make the contrast between the cell-lines. So our analysis is divided into three separate sections (three cell-lines).
The AgilentData$other$gIsWellAboveBG matrix has 12 columns, that four of which related to a cell-line, respectively. so that only probes that have signal in >=50% (In each of the cell-lines) I used the following code for the filter ...
EProbe <- rowSums(y$other$gIsWellAboveBG[ , 1:4] > 0) >= 2 | rowSums(y$other$gIsWellAboveBG[ , 5:8] > 0) >= 2 | rowSums(y$other$gIsWellAboveBG[ , 9:12] > 0) >= 2
what is your idea?
I think that you may want to consider the logical operator of use:
Other than that, I do not see any problem.
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