Comments (9)
When this happens, usually it is because the replicates are not averaged, or not averaged properly. Can you make sure every gene only has 4 observations (bud2, 3, 6, 7)? If you have more than 1 observations per time point (?), it leads to "jumpy" time points. Please review the code chunk where you average to reps. Try removing library
or sample name
from the group_by() step.
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thanks for the super quick reply. I will check my code. thank you very much. while you are here, sorry. more questions.. i have plot the edge table but it looked different from yours. is this ok?
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It seems all your genes are either positively correlated with each other, or anit-correlated with each other. How many genes did you put into the correlation? This is an indicator of not having enough genes correlation. I think this is fine... If you are getting results that make biological sense, I wouldn't be too worried about it.
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thanks again. I used the high var (top 40%) and high F (F>2.5) genes which are about 8600 genes from a dataset that has >30k genes.
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That seems fine. I never seen patterns like this, but I think it should be okay.
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cool, thanks
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When this happens, usually it is because the replicates are not averaged, or not averaged properly. Can you make sure every gene only has 4 observations (bud2, 3, 6, 7)? If you have more than 1 observations per time point (?), it leads to "jumpy" time points. Please review the code chunk where you average to reps. Try removing
library
orsample name
from the group_by() step.
Hi Dr Li, me again. I should mention that I have 3 factors in this exp, position (x4, bud 2,3,6,7), timepoint (x2, 3h and 24h), and treatment (x2, high and low). so maybe that's why I have this issue. I group_by(gene, position, time_point, treatment). but i graph using just two factors.
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thank you. fixed it. 👍
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