ANOTA: anotaPerformQc results in quantile error
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Entering edit mode
@kesarwanianil-8573
Last seen 2.1 years ago
United States

I am getting this error when I run "anotaPerformQc" with default parameters. I have no any zero value in the matrix (log2 transformed normalized FPKM). Could some body please help me out.

Error in quantile.default(as.numeric(x), c(0.25, 0.75), na.rm = na.rm,

missing values and NaN's not allowed if 'na.rm' is FALSE

1: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

2: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

3: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

4: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

5: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

6: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

7: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

8: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

9: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

10: In anova.lm(tmpLm) :

ANOVA F-tests on an essentially perfect fit are unreliable

anota • 1.1k views
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Entering edit mode

You will need to give the results from traceback() right after you get the error, as well as sessionInfo().

That said, R isn't complaining about zeros - it says there are either missing or NaN values. In addition, it also tells you that you cannot use na.rm = FALSE. Have you tried adding a na.rm = TRUE to your function call? I haven't looked at the code to see if there is an ellipsis argument to pass arbitrary arguments to helper functions, but if there is such an argument, that might help.

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Entering edit mode
p.ihmor • 0
@pihmor-11060
Last seen 5.3 years ago

I had the same problem, and I could solve it by double checking the input data sets. "ANOVA F-tests on an essentially perfect fit are unreliable" is indicating that both the transcription and translation data sets are downright identical. The linear models of the transcript data fitts 100% perfectly to the translation data.

For you, or rather for anyone else coming across the same problem, I'd suggest to double check that you have not mixed up dataT with dataP. It is  just one letter, but it did cost me one day of bug hunting.

Happy hunting!

Phillip