In [ ]:

In linux:

sudo apt-get install python-virtualenv

sudo bash Anaconda3-2019.03-Linux-x86_64.sh

ubuntu@eta190812:~/tools$ sudo sudo bash Anaconda3-2019.07-Linux-x86_64.sh

%then reopen the terminal

(base) ubuntu@eta190812:~$ sudo pip3 install louvain (base) ubuntu@eta190812:~$ sudo pip3 install umap-learn

pip install louvain

pip install umap-learn

sudo apt-get install libudunits2-dev

sudo apt install libgdal-dev

sudo apt-get install mesa-common-dev

sudo apt-get install xorg

sudo apt-get install libcgal-dev libglu1-mesa-dev libglu1-mesa-dev

sudo apt-get install libx11-dev

sudo apt-get install libglu1-mesa-dev

conda install -c r r-rgl

In sudo R:

devtools::dev_mode(path='~/tools/Seurat3/')

devtools::dev_mode(path='~/tools/Monocle3/')

source("http://bioconductor.org/biocLite.R")

biocLite()

biocLite("monocle")

install.packages("reticulate")

library(reticulate)

devtools::install_github("cole-trapnell-lab/DDRTree", ref="simple-ppt-like")

devtools::install_github("cole-trapnell-lab/L1-graph")

devtools::install_github("cole-trapnell-lab/monocle-release", ref="monocle3_alpha")

py_install('umap-learn', pip = T, pip_ignore_installed = T) #It doesn't work

py_install("louvain") #It doesn't work

In [4]:
devtools::dev_mode(path='~/tools/Seurat3/')

Dev mode: OFF
In [5]:
library(Seurat)
library(dplyr)
library(monocle)

Loading required package: ggplot2
Loading required package: cowplot

Attaching package: ‘cowplot’

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    ggsave

Loading required package: Matrix

Attaching package: ‘dplyr’

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    filter, lag

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    intersect, setdiff, setequal, union

Loading required package: DelayedArray
Loading required package: stats4
Loading required package: matrixStats

Attaching package: ‘matrixStats’

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    count

Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:dplyr’:

    combine, intersect, setdiff, union

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    colMeans, colSums, rowMeans, rowSums, which

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    IQR, mad, sd, var, xtabs

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    anyDuplicated, append, as.data.frame, basename, cbind, colMeans,
    colnames, colSums, dirname, do.call, duplicated, eval, evalq,
    Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
    lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames,
    rowSums, sapply, setdiff, sort, table, tapply, union, unique,
    unsplit, which, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

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    expand.grid

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    collapse, desc, slice

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    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

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    aperm, apply

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: ‘Biobase’

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    anyMissing, rowMedians

Loading required package: igraph

Attaching package: ‘igraph’

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Loading required package: DDRTree
Loading required package: irlba
Loading required package: L1Graph
Loading required package: lpSolveAPI

Attaching package: ‘L1Graph’

The following object is masked from ‘package:DDRTree’:

    sqdist_R

Warning message in rgl.init(initValue, onlyNULL):
“RGL: unable to open X11 display”Warning message:
“'rgl_init' failed, running with rgl.useNULL = TRUE”
In [6]:
sessionInfo()

R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] monocle_2.99.3          L1Graph_0.1.1           lpSolveAPI_5.5.2.0-17.1
 [4] DDRTree_0.1.5           irlba_2.3.3             igraph_1.2.4.1         
 [7] Biobase_2.42.0          DelayedArray_0.8.0      BiocParallel_1.16.6    
[10] IRanges_2.16.0          S4Vectors_0.20.1        BiocGenerics_0.28.0    
[13] matrixStats_0.54.0      dplyr_0.8.1             Seurat_2.3.4           
[16] Matrix_1.2-17           cowplot_0.9.4           ggplot2_3.1.1          

loaded via a namespace (and not attached):
  [1] reticulate_1.11.1       R.utils_2.8.0           tidyselect_0.2.5       
  [4] htmlwidgets_1.3         grid_3.5.3              trimcluster_0.1-2.1    
  [7] docopt_0.6.1            Rtsne_0.15              devtools_2.0.1         
 [10] munsell_0.5.0           units_0.6-3             codetools_0.2-16       
 [13] ica_1.0-2               pbdZMQ_0.3-3            miniUI_0.1.1.1         
 [16] withr_2.1.2             colorspace_1.4-1        fastICA_1.2-1          
 [19] knitr_1.23              uuid_0.1-2              rstudioapi_0.10        
 [22] ROCR_1.0-7              robustbase_0.93-4       dtw_1.20-1             
 [25] pbmcapply_1.4.1         gbRd_0.4-11             Rdpack_0.10-1          
 [28] lars_1.2                slam_0.1-45             repr_0.19.2            
 [31] bit64_0.9-7             pheatmap_1.0.12         rprojroot_1.3-2        
 [34] LearnBayes_2.15.1       coda_0.19-2             xfun_0.7               
 [37] diptest_0.75-7          R6_2.4.0                doParallel_1.0.14      
 [40] VGAM_1.1-1              hdf5r_1.1.1             manipulateWidget_0.10.0
 [43] flexmix_2.3-15          bitops_1.0-6            assertthat_0.2.1       
 [46] promises_1.0.1          SDMTools_1.1-221        scales_1.0.0           
 [49] nnet_7.3-12             gtable_0.3.0            npsurv_0.4-0           
 [52] processx_3.3.1          rlang_0.3.4             splines_3.5.3          
 [55] lazyeval_0.2.2          acepack_1.4.1           checkmate_1.9.1        
 [58] rgl_0.100.19            reshape2_1.4.3          crosstalk_1.0.0        
 [61] backports_1.1.4         httpuv_1.5.1            Hmisc_4.2-0            
 [64] tools_3.5.3             usethis_1.5.0           spData_0.3.0           
 [67] gplots_3.0.1.1          RColorBrewer_1.1-2      proxy_0.4-23           
 [70] sessioninfo_1.1.1       ggridges_0.5.1          Rcpp_1.0.1             
 [73] plyr_1.8.4              base64enc_0.1-3         classInt_0.3-3         
 [76] purrr_0.3.2             densityClust_0.3        ps_1.3.0               
 [79] prettyunits_1.0.2       deldir_0.1-16           rpart_4.1-13           
 [82] viridis_0.5.1           pbapply_1.4-0           zoo_1.8-5              
 [85] ggrepel_0.8.1           cluster_2.0.7-1         fs_1.2.7               
 [88] magrittr_1.5            data.table_1.12.2       gmodels_2.18.1         
 [91] lmtest_0.9-36           RANN_2.6.1              mvtnorm_1.0-10         
 [94] fitdistrplus_1.0-14     pkgload_1.0.2           lsei_1.2-0             
 [97] mime_0.6                evaluate_0.14           xtable_1.8-4           
[100] mclust_5.4.3            sparsesvd_0.1-4         gridExtra_2.3          
[103] HSMMSingleCell_1.2.0    compiler_3.5.3          tibble_2.1.3           
[106] KernSmooth_2.23-15      crayon_1.3.4            R.oo_1.22.0            
[109] htmltools_0.3.6         spdep_1.1-2             segmented_0.5-3.0      
[112] later_0.8.0             Formula_1.2-3           snow_0.4-3             
[115] tidyr_0.8.3             expm_0.999-4            DBI_1.0.0              
[118] MASS_7.3-51.1           fpc_2.1-11.1            boot_1.3-20            
[121] sf_0.7-4                cli_1.1.0               R.methodsS3_1.7.1      
[124] gdata_2.18.0            metap_1.1               pkgconfig_2.0.2        
[127] sp_1.3-1                foreign_0.8-70          IRdisplay_0.7.0        
[130] plotly_4.9.0            foreach_1.4.4           webshot_0.5.1          
[133] bibtex_0.4.2            stringr_1.4.0           callr_3.2.0            
[136] digest_0.6.19           tsne_0.1-3              htmlTable_1.13.1       
[139] kernlab_0.9-27          shiny_1.3.2             gtools_3.8.1           
[142] modeltools_0.2-22       nlme_3.1-137            jsonlite_1.6           
[145] desc_1.2.0              viridisLite_0.3.0       limma_3.38.3           
[148] pillar_1.4.1            lattice_0.20-38         httr_1.4.0             
[151] DEoptimR_1.0-8          pkgbuild_1.0.3          survival_2.43-3        
[154] glue_1.3.1              remotes_2.0.2           qlcMatrix_0.9.7        
[157] FNN_1.1.3               png_0.1-7               prabclus_2.2-7         
[160] iterators_1.0.10        glmnet_2.0-18           bit_1.1-14             
[163] class_7.3-15            stringi_1.4.3           mixtools_1.1.0         
[166] doSNOW_1.0.16           latticeExtra_0.6-28     caTools_1.17.1.2       
[169] memoise_1.1.0           IRkernel_0.8.15.9000    e1071_1.7-2            
[172] ape_5.3
In [7]:
R.utils::sourceDirectory('~/tools/Rcodes/Monocle3-alpha-plus/',modifiedOnly=FALSE,verbose=TRUE)
In [8]:
DeepGenes <- read.table("190220DeepTree4000genes.txt",sep="_")
DeepGenes2=as.vector(unlist(DeepGenes[,2]))
In [9]:
load("190627MouseLimb10X_Monocle3.Robj")
In [10]:
MouseFull <- ReadH5AD(file = "../190714MouseLimb10Xraw.h5ad")

Error in ReadH5AD(file = "../190714MouseLimb10Xraw.h5ad"): could not find function "ReadH5AD"
Traceback:
In [ ]:
pData(updated_MouseLimb10X_Monocle)$bh.pval.more.than.0.1 <- !MouseFull@meta.data$bh.pval.less.than.0.1
In [10]:
head(pData(updated_MouseLimb10X_Monocle))
nGenenUMIorig.identpercent.mitores.0.6res.0.7res.1res.0.9res.0.8res.0.5res.0.3res.1.5res.0.1batchstageSize_FactorTotal_mRNAsnum_genes_expressedlouvain_componentbh.pval.more.than.0.1
limb12_13_0AAACCTGAGATCGATA_13404 11426 3 0.016366183 3 2 3 2 4 3 3 0 limb12 13.0 1.5223071 11426 3404 1 FALSE
limb12_13_0AAACCTGAGATGAGAG_12322 6474 2 0.013283900 1 10 1 0 3 2 6 0 limb12 13.0 0.8625430 6474 2322 1 TRUE
limb12_13_0AAACCTGAGCAGATCG_12162 8269 5 0.0116096312 11 12 11 11 8 5 16 3 limb12 13.0 1.1016942 8269 2162 5 TRUE
limb12_13_0AAACCTGAGCGATCCC_13771 14966 6 0.0110918111 10 11 10 10 9 6 10 0 limb12 13.0 1.9939479 14966 3771 1 TRUE
limb12_13_0AAACCTGAGTGTACCT_12517 7649 1 0.0180415711 0 1 0 0 0 1 20 0 limb12 13.0 1.0190904 7649 2517 1 TRUE
limb12_13_0AAACCTGAGTTGTAGA_11871 4317 1 0.022006021 0 1 0 0 0 1 20 0 limb12 13.0 0.5751619 4317 1871 1 TRUE
In [13]:
cell_type_color <- c('0'='#000000',
           '1'='#0118FA',
           '2'='#C0C0C0',
           '3'='#00FFFF',
           '4'='#FF8380',
           '5'='#8B8A1D',
           '6'='#CAC379',
           '7'='#00FF00',
           '8'='#FFFA2C',
           '9'='#C39CFB',
           '10'='#7762F0',
           '11'='#717E8D',
           '12'='#A71206',
           '13'='#35B5E2',
           '14'='#9834E7',
           '15'='#995432',
           '16'='#483F84',
           '17'='#ff0000',
           '18'='#216407',
           '19'='#ff42ef',
           '20'='#FBC83C',
           '21'='#FF9A21',
           '22'='#B4246E',
           '23'='#09658A',
           '24'='#016edb')
In [ ]:
DelayedArray:::set_verbose_block_processing(TRUE)
In [11]:
#updated_MouseLimb10X_Monocle <- updated_MouseLimb10X_Monocle[,
#                        pData(updated_MouseLimb10X_Monocle)$bh.pval.more.than.0.1]
In [ ]:
options(DelayedArray.block.size=1000e6)
In [ ]:
options(future.globals.maxSize= 8096 * 1024^2)
library(future)
plan(strategy = "multicore", workers = 30)
In [15]:
updated_MouseLimb10X_Monocle <- estimateSizeFactors(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- estimateDispersions(updated_MouseLimb10X_Monocle)

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Removing 38 outliers
In [16]:
plot_pc_variance_explained(updated_MouseLimb10X_Monocle)

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In [ ]:
In [21]:
updated_MouseLimb10X_Monocle <- preprocessCDS(updated_MouseLimb10X_Monocle, num_dim = 50)
updated_MouseLimb10X_Monocle <- reduceDimension(updated_MouseLimb10X_Monocle, reduction_method = 'UMAP')
updated_MouseLimb10X_Monocle <- partitionCells(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- learnGraph(updated_MouseLimb10X_Monocle,  RGE_method = 'SimplePPT')
plot_cell_trajectory(updated_MouseLimb10X_Monocle,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)

Warning message in if (method == "PCA") {:
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In [22]:
updated_MouseLimb10X_Monocle <- preprocessCDS(updated_MouseLimb10X_Monocle, num_dim = 12)
updated_MouseLimb10X_Monocle <- reduceDimension(updated_MouseLimb10X_Monocle, reduction_method = 'UMAP')
updated_MouseLimb10X_Monocle <- partitionCells(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- learnGraph(updated_MouseLimb10X_Monocle,  RGE_method = 'SimplePPT')
plot_cell_trajectory(updated_MouseLimb10X_Monocle,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)

Warning message in if (method == "PCA") {:
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Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”Warning message:
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In [17]:
pData(updated_MouseLimb10X_Monocle)$bh.pval <- MouseFull@meta.data[row.names(pData(updated_MouseLimb10X_Monocle)),
                                                                   'bh.pval']
In [18]:
pData(updated_MouseLimb10X_Monocle)$doublet.scores <- MouseFull@meta.data[row.names(pData(updated_MouseLimb10X_Monocle)),
                                                                   'doublet.scores']
In [27]:
pData(updated_MouseLimb10X_Monocle)$doublet.scores <- MouseFull@meta.data[row.names(pData(updated_MouseLimb10X_Monocle)),
                                                                         'doublet.scores']

Muscle cells

In [ ]:
R.utils::sourceDirectory('~/tools/Rcodes/Monocle3-alpha-plus/',modifiedOnly=FALSE,verbose=TRUE)
In [ ]:
cds = ClusterSubsetPCA_3alpha(updated_MouseLimb10X_Monocle,idents=c("4","7","12","17","22"),genes = DeepGenes2)
In [ ]:
plot_pc_variance_explained(cds)
In [219]:
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 20)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”Processing block 1/1 ... OK
Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”
In [220]:
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [221]:
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
In [222]:
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
                       "Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [172]:
cell_ids = 'limb7_10_5AAATGCCAGCGAGAAA_1'
In [111]:
WhichCells(object = MouseFull, expression = Pax3 > 20)
  1. 'limb7_10_5AAATGCCAGCGAGAAA_1'
  2. 'limb7_10_5ACATGGTTCCGCTGTT_1'
  3. 'limb7_10_5CCGTTCACATTACGAC_1'
  4. 'limb7_10_5CTCGGGATCCTCATTA_1'
  5. 'limb7_10_5TCGCGAGTCCGCGCAA_1'
In [223]:
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 9)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
                       "Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2") 
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”Processing block 1/1 ... OK
Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [224]:
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 7)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
                       "Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2") 
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”Processing block 1/1 ... OK
Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [28]:
plot_cell_trajectory(updated_MouseLimb10X_Monocle_muscle,cell_size=0.1,
                     color_by = "bh.pval") + scale_color_distiller(palette = "RdYlGn")

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Clean Muscle

In [11]:
cds = ClusterSubsetPCA_3alpha(updated_MouseLimb10X_Monocle,idents=c("4","7","12","17"),genes = DeepGenes2)
plot_pc_variance_explained(cds)

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”
In [14]:
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 20)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
                       "Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2") 
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [216]:
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 9)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
                       "Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2") 
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”Processing block 1/1 ... OK
Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [213]:
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 7)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "orig.ident") +
                     scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
                     color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
                       "Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Warning message in if (method == "PCA") {:
“the condition has length > 1 and only the first element will be used”Processing block 1/1 ... OK
Warning message:
“Deprecated, use tibble::rownames_to_column() instead.”Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [215]:
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
                     markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red", 
                na.value = "snow2")

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
In [ ]: