Pierre9344
commented
6 months ago I get the same problem with the example data:
> library(methylclock)
Loading required package: methylclockData
Loading required package: devtools
Loading required package: usethis
Loading required package: quadprog
Registered S3 methods overwritten by 'ggpp':
method from
heightDetails.titleGrob ggplot2
widthDetails.titleGrob ggplot2
Registered S3 method overwritten by 'ggpmisc':
method from
as.character.polynomial polynom
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)
Warning message:
replacing previous import ‘utils::findMatches’ by ‘S4Vectors::findMatches’ when loading ‘ExperimentHubData’
> library(tidyverse)
── Attaching core tidyverse packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package to force all conflicts to become errors
> path <- system.file("extdata", package = "methylclock")
> covariates <- read_csv(file.path(path, "SampleAnnotationExample55.csv"))
Rows: 16 Columns: 14
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (6): id, title, geo_accession, TissueDetailed, Tissue, CauseofDeath
dbl (6): OriginalOrder, diseaseStatus, Age, PostMortemInterval, individual, Female
lgl (2): Caucasian, FemaleOriginal
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
> covariates
# A tibble: 16 × 14
OriginalOrder id title geo_accession TissueDetailed Tissue diseaseStatus Age PostMortemInterval CauseofDeath individual Female Caucasian FemaleOriginal
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <lgl> <lgl>
1 3 GSM946048 Autism_occ_AN0… GSM946048 Fresh frozen … occip… 1 60 26.5 cancer 18 0 NA NA
2 4 GSM946049 Control_occ_AN… GSM946049 Fresh frozen … occip… 0 39 NA cardiac 2 0 NA NA
3 7 GSM946052 Autism_occ_AN0… GSM946052 Fresh frozen … occip… 1 28 43 cancer 3 0 NA NA
4 9 GSM946054 Autism_occ_AN0… GSM946054 Fresh frozen … occip… 1 39 14 cardiac 6 0 NA NA
5 10 GSM946055 Autism_occ_AN1… GSM946055 Fresh frozen … occip… 1 8 22.2 cancer 4 0 NA NA
6 11 GSM946056 Autism_occ_AN0… GSM946056 Fresh frozen … occip… 1 22 25 hypoxia 8 0 NA NA
7 14 GSM946059 Control_occ_UM… GSM946059 Fresh frozen … occip… 0 4 17 cardiac 28 0 NA NA
8 17 GSM946062 Control_occ_UM… GSM946062 Fresh frozen … occip… 0 28 13 other 35 0 NA NA
9 19 GSM946064 Autism_occ_AN0… GSM946064 Fresh frozen … occip… 1 5 25.5 hypoxia 21 0 NA NA
10 20 GSM946065 Autism_occ_AN0… GSM946065 Fresh frozen … occip… 1 2 4 hypoxia 14 0 NA NA
11 21 GSM946066 Autism_occ_AN1… GSM946066 Fresh frozen … occip… 1 30 16 cardiac 17 0 NA NA
12 22 GSM946067 Control_occ_BT… GSM946067 Fresh frozen … occip… 0 1 19 unknown 30 0 NA NA
13 28 GSM946073 Control_occ_AN… GSM946073 Fresh frozen … occip… 0 60 24.2 unknown 13 0 NA NA
14 29 GSM946074 Control_occ_AN… GSM946074 Fresh frozen … occip… 0 22 21.5 unknown 24 0 NA NA
15 30 GSM946075 Control_occ_UM… GSM946075 Fresh frozen … occip… 0 8 5 cardiac 33 0 NA NA
16 31 GSM946076 Control_occ_AN… GSM946076 Fresh frozen … occip… 0 30 15 hypoxia 11 0 NA NA
> age <- covariates$Age
> head(age)
[1] 60 39 28 39 8 22
> age.example55 <- DNAmAge(MethylationData, age=age, cell.count=TRUE)
Error: object 'MethylationData' not found
> library(tidyverse)
> MethylationData <- get_MethylationDataExample()
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
downloading 1 resources
retrieving 1 resource
|===================================================================================================================================================================| 100%
loading from cache
> MethylationData
# A tibble: 27,578 × 17
ProbeID GSM946048 GSM946049 GSM946052 GSM946054 GSM946055 GSM946056 GSM946059 GSM946062 GSM946064 GSM946065 GSM946066 GSM946067 GSM946073 GSM946074 GSM946075 GSM946076
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 cg00000292 0.706 0.730 0.705 0.751 0.715 0.634 0.682 0.635 0.728 0.777 0.601 0.752 0.671 0.768 0.749 0.665
2 cg00002426 0.272 0.274 0.311 0.279 0.178 0.269 0.330 0.501 0.197 0.282 0.203 0.221 0.238 0.188 0.232 0.179
3 cg00003994 0.0370 0.0147 0.0171 0.0290 0.0163 0.0243 0.0127 0.0206 0.0151 0.0105 0.0290 0.0145 0.0330 0.0251 0.0219 0.0213
4 cg00005847 0.133 0.120 0.121 0.107 0.110 0.129 0.102 0.124 0.104 0.108 0.122 0.125 0.132 0.0910 0.0990 0.0955
5 cg00006414 0.0309 0.0192 0.0217 0.0132 0.0181 0.0243 0.0199 0.0143 0.0184 0.0173 0.0179 0.0183 0.0181 0.0136 0.0185 0.0177
6 cg00007981 0.0700 0.0715 0.0655 0.0719 0.0914 0.0508 0.0294 0.0564 0.0458 0.0377 0.0413 0.0579 0.0354 0.0415 0.0342 0.0469
7 cg00008493 0.993 0.993 0.993 0.994 0.991 0.994 0.993 0.996 0.992 0.994 0.994 0.993 0.995 0.993 0.991 0.991
8 cg00008713 0.0215 0.0202 0.0187 0.0169 0.0162 0.0143 0.0172 0.0189 0.0194 0.0188 0.0153 0.0199 0.0171 0.0180 0.0178 0.0167
9 cg00009407 0.0105 0.00518 0.00410 0.00671 0.00758 0.00518 0.00543 0.00624 0.00642 0.00680 0.00712 0.00769 0.00662 0.00475 0.00632 0.00712
10 cg00010193 0.634 0.635 0.621 0.639 0.599 0.591 0.594 0.583 0.610 0.631 0.618 0.617 0.617 0.593 0.581 0.623
# ℹ 27,568 more rows
# ℹ Use `print(n = ...)` to see more rows
> age.example55 <- DNAmAge(MethylationData, age=age, cell.count=TRUE)
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
rows : 353 cols : 16
snapshotDate(): 2023-10-24
see ?methylclockData and browseVignettes('methylclockData') for documentation
loading from cache
Error in DNAmAge(MethylationData, age = age, cell.count = TRUE) :
cell counts cannot be estimated since
meffilEstimateCellCountsFromBetas function is giving an error.
Probably your data do not have any of the required CpGs for that
reference panel.
In addition: Warning messages:
1: In predAge(cpgs.imp, coefHannum, intercept = FALSE, min.perc) :
The number of missing CpGs forHannumclock exceeds 80%.
---> This DNAm clock will be NA.
2: In predAge(cpgs.imp, coefSkin, intercept = TRUE, min.perc) :
The number of missing CpGs forSkinclock exceeds 80%.
---> This DNAm clock will be NA.
3: In predAge(cpgs.imp, coefPedBE, intercept = TRUE, min.perc) :
The number of missing CpGs forPedBEclock exceeds 80%.
---> This DNAm clock will be NA.
4: In predAge(cpgs.imp, coefTL, intercept = TRUE, min.perc) :
The number of missing CpGs forTLclock exceeds 80%.
---> This DNAm clock will be NA.
5: In predAge(cpgs.imp, coefBLUP, intercept = TRUE, min.perc) :
The number of missing CpGs forBLUPclock exceeds 80%.
---> This DNAm clock will be NA.
6: In predAge(cpgs.imp, coefEN, intercept = TRUE, min.perc) :
The number of missing CpGs forENclock exceeds 80%.
---> This DNAm clock will be NA.
Hello,
When I add a vector of numeric value to the age argument, the DNAmAge function fail with the error message next:
What is weird is that if I remove the age argument, the function work even when I don't specify a specific clock.
Any idea of what can cause that? I'm using the latest version (on bioconductor) with R 4.3.3.