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Errors in getSnpMats, getSnpMats10X, and getCellAlleleCount: unable to find an inherited method for function ‘seqnames’ for signature ‘"DataFrame"’ #23

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koetjen opened this issue Apr 11, 2019 · 6 comments
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Errors in getSnpMats, getSnpMats10X, and getCellAlleleCount: unable to find an inherited method for function ‘seqnames’ for signature ‘"DataFrame"’ #23

koetjen opened this issue Apr 11, 2019 · 6 comments

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@koetjen
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koetjen commented Apr 11, 2019

I'm getting an unexpected error running through the prepare data tutorial on my 10X dataset. Is there any advice on where the source of the error might be and how to tackle it?

results <- getSnpMats(snps, bamFilename, indexFilename)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘seqnames’ for signature ‘"DataFrame"’
results <- getCellAlleleCount(snps, bamFilename, indexFilename, cellBarcodename)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘seqnames’ for signature ‘"DataFrame"’
results <- getSnpMats10X(snps, bamFilename, indexFilename)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘seqnames’ for signature ‘"DataFrame"’
session_info()
─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 3.5.2 (2018-12-20)
os macOS Mojave 10.14.3
system x86_64, darwin15.6.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Chicago
date 2019-04-11

─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi 1.44.0 2018-10-30 [1] Bioconductor
ape 5.3 2019-03-17 [1] CRAN (R 3.5.2)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.5.2)
backports 1.1.3 2018-12-14 [1] CRAN (R 3.5.0)
bibtex 0.4.2 2017-06-30 [1] CRAN (R 3.5.0)
Biobase * 2.42.0 2018-10-30 [1] Bioconductor
BiocGenerics * 0.28.0 2018-10-30 [1] Bioconductor
BiocManager 1.30.4 2018-11-13 [1] CRAN (R 3.5.0)
BiocParallel * 1.16.6 2019-02-17 [1] Bioconductor
biomaRt 2.38.0 2018-10-30 [1] Bioconductor
Biostrings * 2.50.2 2019-01-03 [1] Bioconductor
bit 1.1-14 2018-05-29 [1] CRAN (R 3.5.0)
bit64 0.9-7 2017-05-08 [1] CRAN (R 3.5.0)
bitops 1.0-6 2013-08-17 [1] CRAN (R 3.5.0)
blob 1.1.1 2018-03-25 [1] CRAN (R 3.5.0)
BSgenome 1.50.0 2018-10-30 [1] Bioconductor
callr 3.2.0 2019-03-15 [1] CRAN (R 3.5.2)
caTools 1.17.1.2 2019-03-06 [1] CRAN (R 3.5.2)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.5.2)
cluster 2.0.7-1 2018-04-13 [1] CRAN (R 3.5.0)
coda 0.19-2 2018-10-08 [1] CRAN (R 3.5.0)
codetools 0.2-16 2018-12-24 [1] CRAN (R 3.5.2)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.5.2)
cowplot * 0.9.4 2019-01-08 [1] CRAN (R 3.5.2)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.5.0)
data.table 1.12.0 2019-01-13 [1] CRAN (R 3.5.2)
DBI 1.0.0 2018-05-02 [1] CRAN (R 3.5.0)
DelayedArray * 0.8.0 2018-10-30 [1] Bioconductor
desc 1.2.0 2018-05-01 [1] CRAN (R 3.5.0)
devtools * 2.0.1 2018-10-26 [1] CRAN (R 3.5.2)
digest 0.6.18 2018-10-10 [1] CRAN (R 3.5.0)
dplyr * 0.8.0.1 2019-02-15 [1] CRAN (R 3.5.2)
fitdistrplus 1.0-14 2019-01-23 [1] CRAN (R 3.5.2)
fs 1.2.7 2019-03-19 [1] CRAN (R 3.5.2)
future 1.12.0 2019-03-08 [1] CRAN (R 3.5.2)
future.apply 1.2.0 2019-03-07 [1] CRAN (R 3.5.2)
gbRd 0.4-11 2012-10-01 [1] CRAN (R 3.5.0)
gdata 2.18.0 2017-06-06 [1] CRAN (R 3.5.0)
GenomeInfoDb * 1.18.2 2019-02-12 [1] Bioconductor
GenomeInfoDbData 1.2.0 2019-03-12 [1] Bioconductor
GenomicAlignments 1.18.1 2019-01-04 [1] Bioconductor
GenomicFeatures 1.34.7 2019-03-25 [1] Bioconductor
GenomicRanges * 1.34.0 2018-10-30 [1] Bioconductor
ggplot2 * 3.1.1 2019-04-07 [1] CRAN (R 3.5.2)
ggrepel 0.8.0 2018-05-09 [1] CRAN (R 3.5.0)
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glue 1.3.1 2019-03-12 [1] CRAN (R 3.5.2)
gplots 3.0.1.1 2019-01-27 [1] CRAN (R 3.5.2)
gridExtra 2.3 2017-09-09 [1] CRAN (R 3.5.0)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.5.2)
gtools 3.8.1 2018-06-26 [1] CRAN (R 3.5.0)
hdf5r * 1.1.1 2019-03-26 [1] CRAN (R 3.5.2)
hms 0.4.2 2018-03-10 [1] CRAN (R 3.5.0)
HoneyBADGER * 0.1 2019-04-05 [1] Github (4eb9c06)
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htmlwidgets 1.3 2018-09-30 [1] CRAN (R 3.5.0)
httr 1.4.0 2018-12-11 [1] CRAN (R 3.5.0)
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IRanges * 2.16.0 2018-10-30 [1] Bioconductor
irlba 2.3.3 2019-02-05 [1] CRAN (R 3.5.2)
jsonlite 1.6 2018-12-07 [1] CRAN (R 3.5.0)
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labeling 0.3 2014-08-23 [1] CRAN (R 3.5.0)
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lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.5.2)
listenv 0.7.0 2018-01-21 [1] CRAN (R 3.5.0)
lmtest 0.9-36 2018-04-04 [1] CRAN (R 3.5.0)
lsei 1.2-0 2017-10-23 [1] CRAN (R 3.5.0)
magrittr 1.5 2014-11-22 [1] CRAN (R 3.5.0)
MASS 7.3-51.4 2019-03-31 [1] CRAN (R 3.5.2)
Matrix * 1.2-17 2019-03-22 [1] CRAN (R 3.5.2)
matrixStats * 0.54.0 2018-07-23 [1] CRAN (R 3.5.0)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.5.0)
metap 1.1 2019-02-06 [1] CRAN (R 3.5.2)
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.5.0)
nlme 3.1-137 2018-04-07 [1] CRAN (R 3.5.2)
npsurv 0.4-0 2017-10-14 [1] CRAN (R 3.5.0)
pbapply 1.4-0 2019-02-05 [1] CRAN (R 3.5.2)
pillar 1.3.1 2018-12-15 [1] CRAN (R 3.5.0)
pkgbuild 1.0.3 2019-03-20 [1] CRAN (R 3.5.2)
pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.5.0)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.5.0)
plotly 4.8.0 2018-07-20 [1] CRAN (R 3.5.2)
plyr 1.8.4 2016-06-08 [1] CRAN (R 3.5.0)
png 0.1-7 2013-12-03 [1] CRAN (R 3.5.0)
prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.5.0)
processx 3.3.0 2019-03-10 [1] CRAN (R 3.5.2)
progress 1.2.0 2018-06-14 [1] CRAN (R 3.5.0)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.5.0)
purrr 0.3.2 2019-03-15 [1] CRAN (R 3.5.2)
R.methodsS3 1.7.1 2016-02-16 [1] CRAN (R 3.5.0)
R.oo 1.22.0 2018-04-22 [1] CRAN (R 3.5.0)
R.utils 2.8.0 2019-02-14 [1] CRAN (R 3.5.2)
R6 2.4.0 2019-02-14 [1] CRAN (R 3.5.2)
RANN 2.6.1 2019-01-08 [1] CRAN (R 3.5.2)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.5.0)
Rcpp 1.0.1 2019-03-17 [1] CRAN (R 3.5.2)
RCurl 1.95-4.12 2019-03-04 [1] CRAN (R 3.5.2)
Rdpack 0.10-1 2018-10-04 [1] CRAN (R 3.5.0)
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reshape2 * 1.4.3 2017-12-11 [1] CRAN (R 3.5.0)
reticulate 1.11.1 2019-03-06 [1] CRAN (R 3.5.2)
rjags 4-8 2018-10-19 [1] CRAN (R 3.5.0)
rlang 0.3.4 2019-04-07 [1] CRAN (R 3.5.2)
ROCR 1.0-7 2015-03-26 [1] CRAN (R 3.5.0)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.5.0)
Rsamtools * 1.34.1 2019-01-31 [1] Bioconductor
RSQLite 2.1.1 2018-05-06 [1] CRAN (R 3.5.0)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.5.2)
rsvd 1.0.0 2018-11-06 [1] CRAN (R 3.5.0)
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S4Vectors * 0.20.1 2018-11-09 [1] Bioconductor
scales 1.0.0 2018-08-09 [1] CRAN (R 3.5.0)
sctransform * 0.0.0.900 2019-04-08 [1] Github (satijalab/sctransform@4ab830c)
SDMTools 1.1-221 2014-08-05 [1] CRAN (R 3.5.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.5.0)
Seurat * 3.0.0.9000 2019-03-30 [1] Github (satijalab/seurat@6d361c0)
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.5.2)
stringr 1.4.0 2019-02-10 [1] CRAN (R 3.5.2)
SummarizedExperiment * 1.12.0 2018-10-30 [1] Bioconductor
survival 2.44-1.1 2019-04-01 [1] CRAN (R 3.5.2)
tibble 2.1.1 2019-03-16 [1] CRAN (R 3.5.2)
tidyr 0.8.3 2019-03-01 [1] CRAN (R 3.5.2)
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tsne 0.1-3 2016-07-15 [1] CRAN (R 3.5.0)
usethis * 1.4.0 2018-08-14 [1] CRAN (R 3.5.0)
VariantAnnotation * 1.28.13 2019-03-19 [1] Bioconductor
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zoo 1.8-5 2019-03-21 [1] CRAN (R 3.5.2)

[1] /Library/Frameworks/R.framework/Versions/3.5/Resources/library
@JEFworks
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JEFworks commented Apr 12, 2019 via email

@koetjen
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koetjen commented Apr 12, 2019 via email •
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@koetjen
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koetjen commented Apr 12, 2019

It looks like GenomicRanges deprecated seqnames in the most recent version

CHANGES IN VERSION 1.34.0

DEPRECATED AND DEFUNCT

o Deprecate several RangedData methods: seqinfo, seqinfo<-, seqnames, and
  findOverlaps#RangedData#GenomicRanges

@koetjen koetjen closed this as completed Apr 12, 2019
@koetjen
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koetjen commented Apr 12, 2019

I apologize, closing this issue was inadvertent.

@koetjen koetjen reopened this Apr 12, 2019
@JEFworks
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Thanks for the excellent debugging.

As you've seen, the getSnpMats, getCellAlleleCount, and getSnpMats10X functions are using the information provided in your snps object to obtain pileups in your provided bams with help from your provided index files.

As a result, the chromosome names in your snps object must match those used in your bams and subsequently your index files. By default, the chromosome names are provided in NCBI/Ensemble style (ie. without the 'chr'):

> library(HoneyBADGER)
> load(system.file("ExAC", "ExAC_chr1.RData", package = "HoneyBADGER"))
> seqlevelsStyle(snps)
[1] "NCBI"    "Ensembl"

If your snps object does not have 'chr' in its seqname but your reads are indexed with chr in their chromosome names (because you are using a different style), you will get errors as you've seen. Luckily, you can toggle between styles as follows:

> seqlevelsStyle(snps) <- "UCSC"
> seqlevels(snps)
 [1] "chr1"       "chr2"       "chr3"       "chr4"       "chr5"      
 [6] "chr6"       "chr7"       "chr8"       "chr9"       "chr10"     
[11] "chr11"      "chr12"      "chr13"      "chr14"      "chr15"     
[16] "chr16"      "chr17"      "chr18"      "chr19"      "chr20"     
[21] "chr21"      "chr22"      "chrX"       "chrY"       "chrM"      
[26] "GL000207.1" "GL000226.1" "GL000229.1" "GL000231.1" "GL000210.1"
[31] "GL000239.1" "GL000235.1" "GL000201.1" "GL000247.1" "GL000245.1"
[36] "GL000197.1" "GL000203.1" "GL000246.1" "GL000249.1" "GL000196.1"
[41] "GL000248.1" "GL000244.1" "GL000238.1" "GL000202.1" "GL000234.1"
[46] "GL000232.1" "GL000206.1" "GL000240.1" "GL000236.1" "GL000241.1"
[51] "GL000243.1" "GL000242.1" "GL000230.1" "GL000237.1" "GL000233.1"
[56] "GL000204.1" "GL000198.1" "GL000208.1" "GL000191.1" "GL000227.1"
[61] "GL000228.1" "GL000214.1" "GL000221.1" "GL000209.1" "GL000218.1"
[66] "GL000220.1" "GL000213.1" "GL000211.1" "GL000199.1" "GL000217.1"
[71] "GL000216.1" "GL000215.1" "GL000205.1" "GL000219.1" "GL000224.1"
[76] "GL000223.1" "GL000195.1" "GL000212.1" "GL000222.1" "GL000200.1"
[81] "GL000193.1" "GL000194.1" "GL000225.1" "GL000192.1" "NC_007605"

Hope that helps.

  • Jean

@koetjen
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koetjen commented Apr 22, 2019

I found that the problem arose from the initial structure of snps
In the tutorial:
snps <- rowData(vcf) # result is a data frame, with incorrect results for seqlevels() and does not contain seqnames()
Instead, it needs to be:
snps <- rowRanges(vcf) # result is a GRanges object, so seqlevels() matches the expected output and contains seqnames()
Thanks for all your help, Kary

@koetjen koetjen closed this as completed Apr 22, 2019
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