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I am following the Yang2013data tutorial and when running df_filt <- runQTLseqAnalysis(df_filt, windowSize = 1e6, popStruc = "F2", bulkSize = c(385, 430), replications = 10000, intervals = c(95, 99) )
I get the warning:
Counting SNPs in each window...
Calculating tricube smoothed delta SNP index...
Returning the following two sided confidence intervals: 95, 99
Variable 'depth' not defined, using min and max depth from data: 40-197
Assuming bulks selected from F2 population, with 385 and 430 individuals per bulk.
Simulating 10000 SNPs with reads at each depth: 40-197
Keeping SNPs with >= 0.3 SNP-index in both simulated bulks
Joining with by = join_by(tricubeDP, CI_95, CI_99)
Warning message:
There were 5398 warnings in dplyr::mutate().
The first warning was:
ℹ In argument: nSNPs = countSNPs_cpp(POS = POS, windowSize = windowSize).
ℹ In group 1: CHROM = Chr1.
Caused by warning in countSNPs_cpp():
! subscript out of bounds (index 67784 >= vector size 67784)
ℹ Run dplyr::last_dplyr_warnings() to see the 5397 remaining warnings.
I am following the Yang2013data tutorial and when running
df_filt <- runQTLseqAnalysis(df_filt, windowSize = 1e6, popStruc = "F2", bulkSize = c(385, 430), replications = 10000, intervals = c(95, 99) )
I get the warning:
Counting SNPs in each window...
Calculating tricube smoothed delta SNP index...
Returning the following two sided confidence intervals: 95, 99
Variable 'depth' not defined, using min and max depth from data: 40-197
Assuming bulks selected from F2 population, with 385 and 430 individuals per bulk.
Simulating 10000 SNPs with reads at each depth: 40-197
Keeping SNPs with >= 0.3 SNP-index in both simulated bulks
Joining with
by = join_by(tricubeDP, CI_95, CI_99)
Warning message:
There were 5398 warnings in
dplyr::mutate()
.The first warning was:
ℹ In argument:
nSNPs = countSNPs_cpp(POS = POS, windowSize = windowSize)
.ℹ In group 1:
CHROM = Chr1
.Caused by warning in
countSNPs_cpp()
:! subscript out of bounds (index 67784 >= vector size 67784)
ℹ Run dplyr::last_dplyr_warnings() to see the 5397 remaining warnings.
Backtrace:
▆
... with 5393 more warnings.
ℹ Run dplyr::last_dplyr_warnings(n = 10) to show more.
Is this anything that could cause problems?
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