Last updated on 2020-09-25 07:50:07 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.8 | 26.35 | 276.66 | 303.01 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.1.8 | 22.08 | 206.79 | 228.87 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.1.8 | 446.25 | WARN | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.8 | 472.73 | OK | |||
| r-devel-windows-ix86+x86_64 | 0.1.8 | 69.00 | 301.00 | 370.00 | OK | |
| r-patched-linux-x86_64 | 0.1.8 | 29.20 | 261.25 | 290.45 | OK | |
| r-patched-solaris-x86 | 0.1.8 | 420.70 | WARN | |||
| r-release-linux-x86_64 | 0.1.8 | 26.02 | 259.43 | 285.45 | OK | |
| r-release-macos-x86_64 | 0.1.8 | WARN | ||||
| r-release-windows-ix86+x86_64 | 0.1.8 | 59.00 | 271.00 | 330.00 | OK | |
| r-oldrel-macos-x86_64 | 0.1.8 | WARN | ||||
| r-oldrel-windows-ix86+x86_64 | 0.1.8 | 59.00 | 247.00 | 306.00 | WARN |
Version: 0.1.8
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'SingleCellSignalR', 'dittoSeq'
Flavors: r-devel-linux-x86_64-fedora-clang, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.1.8
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘introduction.Rmd’ using knitr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Attaching package: 'magrittr'
The following object is masked from 'package:purrr':
set_names
The following object is masked from 'package:tidyr':
extract
Attaching package: 'tidyseurat'
The following object is masked from 'package:Seurat':
pbmc_small
The following object is masked from 'package:ggplot2':
ggplot
The following object is masked from 'package:magrittr':
extract
The following objects are masked from 'package:tidyr':
as_tibble, extract, nest, pivot_longer, separate, unite, unnest
The following objects are masked from 'package:dplyr':
arrange, as_tibble, bind_cols, bind_rows, count, distinct, filter,
full_join, group_by, inner_join, left_join, mutate, pull, rename,
right_join, rowwise, sample_frac, sample_n, select, slice,
summarise
The following object is masked from 'package:stats':
filter
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
tidyseurat says: A data frame is returned for independent data analysis.
Warning in vst(umi = new("dgCMatrix", i = c(1L, 5L, 8L, 11L, 22L, 30L, 33L, :
The 'show_progress' argument is deprecated as of v0.3. Use 'verbosity' instead.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 220 by 80
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 220 genes, 80 cells
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Second step: Get residuals using fitted parameters for 220 genes
Computing corrected count matrix for 220 genes
Calculating gene attributes
Wall clock passed: Time difference of 1.688021 secs
Warning in irlba(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
tidyseurat says: A data frame is returned for independent data analysis.
Calculating cluster 0
For a more efficient implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the limma package
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('limma')
--------------------------------------------
After installation of limma, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
16:20:12 UMAP embedding parameters a = 0.9922 b = 1.112
16:20:12 Read 80 rows and found 15 numeric columns
16:20:12 Using Annoy for neighbor search, n_neighbors = 30
16:20:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:20:12 Writing NN index file to temp file /tmp/RtmpFf3rwP/working_dir/RtmpY0gYaK/file2ef0a77a3c9e5d
16:20:12 Searching Annoy index using 1 thread, search_k = 3000
16:20:12 Annoy recall = 100%
16:20:12 Commencing smooth kNN distance calibration using 1 thread
16:20:13 Initializing from normalized Laplacian + noise
16:20:13 Commencing optimization for 500 epochs, with 2040 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:20:14 Optimization finished
tidyseurat says: A data frame is returned for independent data analysis.
tidyseurat says: A data frame is returned for independent data analysis.
Quitting from lines 307-320 (./../man/fragments/intro.Rmd)
Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
Error: processing vignette 'introduction.Rmd' failed with diagnostics:
Insufficient values in manual scale. 11 needed but only 4 provided.
--- failed re-building ‘introduction.Rmd’
SUMMARY: processing the following file failed:
‘introduction.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.1.8
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building ‘introduction.Rmd’ using knitr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Attaching package: 'magrittr'
The following object is masked from 'package:purrr':
set_names
The following object is masked from 'package:tidyr':
extract
Attaching package: 'tidyseurat'
The following object is masked from 'package:Seurat':
pbmc_small
The following object is masked from 'package:ggplot2':
ggplot
The following object is masked from 'package:magrittr':
extract
The following objects are masked from 'package:tidyr':
as_tibble, extract, nest, pivot_longer, separate, unite, unnest
The following objects are masked from 'package:dplyr':
arrange, as_tibble, bind_cols, bind_rows, count, distinct, filter,
full_join, group_by, inner_join, left_join, mutate, pull, rename,
right_join, rowwise, sample_frac, sample_n, select, slice,
summarise
The following object is masked from 'package:stats':
filter
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
tidyseurat says: A data frame is returned for independent data analysis.
Warning in vst(umi = new("dgCMatrix", i = c(1L, 5L, 8L, 11L, 22L, 30L, 33L, :
The 'show_progress' argument is deprecated as of v0.3. Use 'verbosity' instead.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 220 by 80
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 220 genes, 80 cells
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in sqrt(1/i) : NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Second step: Get residuals using fitted parameters for 220 genes
Computing corrected count matrix for 220 genes
Calculating gene attributes
Wall clock passed: Time difference of 3.546262 secs
Warning in irlba(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
tidyseurat says: A data frame is returned for independent data analysis.
Calculating cluster 0
For a more efficient implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the limma package
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('limma')
--------------------------------------------
After installation of limma, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
00:51:16 UMAP embedding parameters a = 0.9922 b = 1.112
00:51:16 Read 80 rows and found 15 numeric columns
00:51:16 Using Annoy for neighbor search, n_neighbors = 30
00:51:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:51:16 Writing NN index file to temp file /tmp/Rtmpa2aq5Y/working_dir/RtmpAEa4OX/file6347674e5467
00:51:16 Searching Annoy index using 1 thread, search_k = 3000
00:51:16 Annoy recall = 100%
00:51:17 Commencing smooth kNN distance calibration using 1 thread
00:51:18 Initializing from normalized Laplacian + noise
00:51:19 Commencing optimization for 500 epochs, with 2040 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:51:20 Optimization finished
tidyseurat says: A data frame is returned for independent data analysis.
tidyseurat says: A data frame is returned for independent data analysis.
Quitting from lines 307-320 (./../man/fragments/intro.Rmd)
Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
Error: processing vignette 'introduction.Rmd' failed with diagnostics:
Insufficient values in manual scale. 11 needed but only 4 provided.
--- failed re-building ‘introduction.Rmd’
SUMMARY: processing the following file failed:
‘introduction.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-patched-solaris-x86
Version: 0.1.8
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘introduction.Rmd’ using knitr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Attaching package: 'magrittr'
The following object is masked from 'package:purrr':
set_names
The following object is masked from 'package:tidyr':
extract
Attaching package: 'tidyseurat'
The following object is masked from 'package:Seurat':
pbmc_small
The following object is masked from 'package:ggplot2':
ggplot
The following object is masked from 'package:magrittr':
extract
The following objects are masked from 'package:tidyr':
as_tibble, extract, nest, pivot_longer, separate, unite, unnest
The following objects are masked from 'package:dplyr':
arrange, as_tibble, bind_cols, bind_rows, count, distinct, filter,
full_join, group_by, inner_join, left_join, mutate, pull, rename,
right_join, rowwise, sample_frac, sample_n, select, slice,
summarise
The following object is masked from 'package:stats':
filter
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
tidyseurat says: A data frame is returned for independent data analysis.
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Warning in irlba(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
tidyseurat says: A data frame is returned for independent data analysis.
Calculating cluster 0
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
07:10:52 UMAP embedding parameters a = 0.9922 b = 1.112
07:10:52 Read 80 rows and found 15 numeric columns
07:10:52 Using Annoy for neighbor search, n_neighbors = 30
07:10:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:10:52 Writing NN index file to temp file /Volumes/Temp/tmp/Rtmp2PYENS/filec4e13fa7acec
07:10:52 Searching Annoy index using 1 thread, search_k = 3000
07:10:52 Annoy recall = 100%
07:10:52 Commencing smooth kNN distance calibration using 1 thread
07:10:53 Initializing from normalized Laplacian + noise
07:10:53 Commencing optimization for 500 epochs, with 2058 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:10:53 Optimization finished
tidyseurat says: A data frame is returned for independent data analysis.
tidyseurat says: A data frame is returned for independent data analysis.
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Error: processing vignette 'introduction.Rmd' failed with diagnostics:
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--- failed re-building ‘introduction.Rmd’
SUMMARY: processing the following file failed:
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Error: Vignette re-building failed.
Execution halted
Flavor: r-release-macos-x86_64
Version: 0.1.8
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'BiocStyle', 'SingleCellSignalR', 'dittoSeq'
Flavor: r-oldrel-macos-x86_64
Version: 0.1.8
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘introduction.Rmd’ using knitr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Attaching package: 'magrittr'
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set_names
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extract
Attaching package: 'tidyseurat'
The following object is masked from 'package:Seurat':
pbmc_small
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ggplot
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extract
The following objects are masked from 'package:tidyr':
as_tibble, extract, nest, pivot_longer, separate, unite, unnest
The following objects are masked from 'package:dplyr':
arrange, as_tibble, bind_cols, bind_rows, count, distinct, filter,
full_join, group_by, inner_join, left_join, mutate, pull, rename,
right_join, rowwise, sample_frac, sample_n, select, slice,
summarise
The following object is masked from 'package:stats':
filter
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
tidyseurat says: A data frame is returned for independent data analysis.
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Warning in irlba(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
tidyseurat says: A data frame is returned for independent data analysis.
Calculating cluster 0
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
11:51:51 UMAP embedding parameters a = 0.9922 b = 1.112
11:51:51 Read 80 rows and found 15 numeric columns
11:51:51 Using Annoy for neighbor search, n_neighbors = 30
11:51:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:51:51 Writing NN index file to temp file /tmp/RtmpGekY7q/file17bea31c05baa
11:51:51 Searching Annoy index using 1 thread, search_k = 3000
11:51:51 Annoy recall = 100%
11:51:51 Commencing smooth kNN distance calibration using 1 thread
11:51:52 Initializing from normalized Laplacian + noise
11:51:52 Commencing optimization for 500 epochs, with 2058 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:51:53 Optimization finished
tidyseurat says: A data frame is returned for independent data analysis.
tidyseurat says: A data frame is returned for independent data analysis.
Quitting from lines 307-320 (./../man/fragments/intro.Rmd)
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Error: processing vignette 'introduction.Rmd' failed with diagnostics:
Insufficient values in manual scale. 11 needed but only 4 provided.
--- failed re-building ‘introduction.Rmd’
SUMMARY: processing the following file failed:
‘introduction.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-macos-x86_64
Version: 0.1.8
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building 'introduction.Rmd' using knitr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Attaching package: 'magrittr'
The following object is masked from 'package:purrr':
set_names
The following object is masked from 'package:tidyr':
extract
Attaching package: 'tidyseurat'
The following object is masked from 'package:Seurat':
pbmc_small
The following object is masked from 'package:ggplot2':
ggplot
The following object is masked from 'package:magrittr':
extract
The following objects are masked from 'package:tidyr':
as_tibble, extract, nest, pivot_longer, separate, unite, unnest
The following objects are masked from 'package:dplyr':
arrange, as_tibble, bind_cols, bind_rows, count, distinct, filter,
full_join, group_by, inner_join, left_join, mutate, pull, rename,
right_join, rowwise, sample_frac, sample_n, select, slice,
summarise
The following object is masked from 'package:stats':
filter
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
tidyseurat says: A data frame is returned for independent data analysis.
Warning in vst(umi = new("dgCMatrix", i = c(1L, 5L, 8L, 11L, 22L, 30L, 33L, :
The 'show_progress' argument is deprecated as of v0.3. Use 'verbosity' instead.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 220 by 80
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 220 genes, 80 cells
Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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Second step: Get residuals using fitted parameters for 220 genes
Computing corrected count matrix for 220 genes
Calculating gene attributes
Wall clock passed: Time difference of 1.598 secs
Warning in irlba(A = t(x = object), nv = npcs, ...) :
You're computing too large a percentage of total singular values, use a standard svd instead.
tidyseurat says: A data frame is returned for independent data analysis.
Calculating cluster 0
For a more efficient implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the limma package
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('limma')
--------------------------------------------
After installation of limma, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
10:26:03 UMAP embedding parameters a = 0.9922 b = 1.112
10:26:03 Read 80 rows and found 15 numeric columns
10:26:03 Using Annoy for neighbor search, n_neighbors = 30
10:26:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:26:03 Writing NN index file to temp file D:\temp\RtmpQBGfoq\file2782c1860cba
10:26:03 Searching Annoy index using 1 thread, search_k = 3000
10:26:03 Annoy recall = 100%
10:26:03 Commencing smooth kNN distance calibration using 1 thread
10:26:04 Initializing from normalized Laplacian + noise
10:26:04 Commencing optimization for 500 epochs, with 2256 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:26:05 Optimization finished
tidyseurat says: A data frame is returned for independent data analysis.
tidyseurat says: A data frame is returned for independent data analysis.
Quitting from lines 307-320 (./../man/fragments/intro.Rmd)
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Error: processing vignette 'introduction.Rmd' failed with diagnostics:
Insufficient values in manual scale. 11 needed but only 4 provided.
--- failed re-building 'introduction.Rmd'
SUMMARY: processing the following file failed:
'introduction.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64