Open yudada2020 opened 4 months ago
@yudada2020 Hi, please check the input expression matrix if there are proteins with missing values. DEqMS requires at least two values for each group.
Yafeng
Hello, Dr yafeng, I have used the code "df.LFQ.filter = df.LFQ[df.LFQ$na_count_H<2 & df.LFQ$na_count_L<2,1:34]" as per your suggestion to perform missing value removal for the protein expression matrix but the same error is reported. Interesting to be able to output seemingly normal results. Attached is my input protein expression matrix.
Thanks, Naixiang
Hi Yafeng, it is such a wonderful script, thanks for sharing with us.
I did encounter a problem when I run fit4 = spectraCounteBayes(fit3)
The error came out as Error in model.frame.default(formula = logVAR ~ x) : variable lengths differ in DeqMS (found for 'x')
Do you have any ides?
Thank you very much in advance! L
@yudada2020 Hi, i checked your input data frame, you should use
df.LFQ.filter = df.LFQ[df.LFQ$na_count_H<2 & df.LFQ$na_count_L<2,c(1,4:37)]
(don't keep column 2, 3 )
and don't forget to do log2 transformation.
protein.matrix = log2(as.matrix(df.LFQ.filter))
if you still encounter error, check the peptide count table, it should contains peptide count per protein.
Yafeng
@longchung90
Hi, i suspect there are missing values in fit3$count, which stores the peptide count per protein.
Try min(fit3$count)
to see if all proteins have valid peptide count. If it returns NA, then one of your proteins contains NA for peptide count.
Yafeng
@longchung90
Hi, i suspect there are missing values in fit3$count, which stores the peptide count per protein.
Try min(fit3$count)
to see if all proteins have valid peptide count. If it returns NA, then one of your proteins contains NA for peptide count.
Yafeng
Hello, I have tried to analyse my label-free data by referring to the tutorial "DEqMS analysis using MaxQuant outputs (label-free data)", but when I run the code fit4 = spectraCounteBayes(fit3), R pops up a warning message. How can I fix it?
Thanks, Nai xiang Yu