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$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$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$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$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$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$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$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$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$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$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$fitted) : iteration limit reached
Warning in theta.ml(y = y, mu = fit$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
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
12:35:52 UMAP embedding parameters a = 0.9922 b = 1.112
12:35:52 Read 80 rows and found 15 numeric columns
12:35:52 Using Annoy for neighbor search, n_neighbors = 30
12:35:52 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:35:52 Writing NN index file to temp file D:\temp\RtmpAVHKfY\file86305cbd6122
12:35:52 Searching Annoy index using 1 thread, search_k = 3000
12:35:52 Annoy recall = 100%
12:35:52 Commencing smooth kNN distance calibration using 1 thread
12:35:52 Initializing from normalized Laplacian + noise
12:35:52 Commencing optimization for 500 epochs, with 2142 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:35:53 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) 
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