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We load the integrated, clustered Seurat object :

## Loading the Seurat object
sobj <- base::readRDS(
  file = "/shared/projects/form_2022_32/SingleCellRNASeq/Normalization/Scaled_Normalized_Harmony_Clustering_Seurat_Object.RDS"
)
## A quick overview of the object
sobj
An object of class Seurat 
6941 features across 3984 samples within 1 assay 
Active assay: RNA (6941 features, 2000 variable features)
 3 dimensional reductions calculated: pca, umap, harmony

To annotate cells, we need some knowledge base.

We will use a database focused on immunological cell types called ImmGen, thanks to the celldex R package that “provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq”

Note : In the following chunk code, you may be prompted to create a directory. This is normal, and you can safely say yes. This is due to the fact that for your first call, the ImmGen package will download and deposit the database in a personal cache, which will make any future calls faster.

## Loading the ImmGen database
annotation <- celldex::ImmGenData()
## A quick description of the db
annotation
class: SummarizedExperiment 
dim: 22134 830 
metadata(0):
assays(1): logcounts
rownames(22134): Zglp1 Vmn2r65 ... Tiparp Kdm1a
rowData names(0):
colnames(830):
  GSM1136119_EA07068_260297_MOGENE-1_0-ST-V1_MF.11C-11B+.LU_1.CEL
  GSM1136120_EA07068_260298_MOGENE-1_0-ST-V1_MF.11C-11B+.LU_2.CEL ...
  GSM920654_EA07068_201214_MOGENE-1_0-ST-V1_TGD.VG4+24ALO.E17.TH_1.CEL
  GSM920655_EA07068_201215_MOGENE-1_0-ST-V1_TGD.VG4+24ALO.E17.TH_2.CEL
colData names(3): label.main label.fine label.ont

This database contains 3 levels of granularity : * A “main” level (coarse grain) * A “fine” level (self-explanatory) * The “ONT” level (data are mapped to a defined ontology)

As we are in a context of sorted cells of the same lineage, we’re going to use the fine label.

Let’s see how many cell types are described in this ImmGen database :

length(unique(annotation$label.fine))
[1] 253

The tool we will use to perform the automatic cell type annotation, SingleR works better with the normalized data. Thus, we will extract the normalized matrix from our Seurat object :

norm_exp_mat <- Seurat::GetAssayData(
  object = sobj,
  assay = "RNA",
  slot = "data"
)
dim(norm_exp_mat)
[1] 6941 3984

Single cell annotation

We are ready to start the annotation :

ann_predictions <- if (file.exists('ann_predictions.RDS')) readRDS('ann_predictions.RDS') else {
  SingleR::SingleR(
    test = norm_exp_mat,
    ref = annotation,
    labels = annotation$label.fine,
    assay.type.test = "logcounts",
    assay.type.ref = "logcounts",
    BPPARAM = BiocParallel::SerialParam()
  )
}

The resulting object is a special kind of data.frame :

is(ann_predictions)
 [1] "DFrame"            "DataFrame"         "SimpleList"       
 [4] "RectangularData"   "List"              "DataFrame_OR_NULL"
 [7] "Vector"            "list_OR_List"      "Annotated"        
[10] "vector_OR_Vector" 
head(ann_predictions, n = 3)
DataFrame with 3 rows and 5 columns
                                     scores          first.labels
                                   <matrix>           <character>
TD3A.1024 0.2468332:0.2531070:0.2672337:... T cells (T.CD4TESTCJ)
TD3A.3642 0.1841115:0.1843756:0.1871214:...      T cells (T.DPsm)
TD3A.1731 0.0862956:0.0870638:0.0932266:...      T cells (T.DPsm)
              tuning.scores           labels    pruned.labels
                <DataFrame>      <character>      <character>
TD3A.1024 0.294951:0.294104 T cells (T.8Nve) T cells (T.8Nve)
TD3A.3642 0.336526:0.270188 T cells (T.DPsm) T cells (T.DPsm)
TD3A.1731 0.465899:0.334169 T cells (T.DPsm) T cells (T.DPsm)

It contains 5 columns of information for each cell :

dim(ann_predictions)
[1] 3984    5

How many different kind of labels were identified ?

length(unique(ann_predictions$labels))
[1] 37

How many cells have been labelled for each annotation ?

sorted_idlab <- sort(table(ann_predictions$labels), decreasing = TRUE)
head(sorted_idlab, n = 10)

     T cells (T.DPsm)     T cells (T.DP69+)      T cells (T.DPbl) 
                 3457                   216                    71 
      T cells (T.ISP)  T cells (T.4SP24int)      T cells (T.DN3A) 
                   55                    45                    42 
     T cells (T.8Nve)  T cells (T.8NVE.OT1)    T cells (T.CD4.5H) 
                   28                     9                     8 
T cells (T.CD4TESTCJ) 
                    6

Besides scoring, SingleR assesses the score quality, and prunes bad results.

How many cells got a poor quality annotation ?

summary(is.na(ann_predictions$pruned.labels))
   Mode   FALSE    TRUE 
logical    3973      11

Annotation diagnostic

SingleR allows to visualize some control plots :

  • We can visualize the score of each cell, split by cell type label, as a heatmap :
SingleR::plotScoreHeatmap(ann_predictions)

  • We can also visualize the “Delta”, ie. the gap between the scores of cells to their best label to the scores of any other label :
SingleR::plotDeltaDistribution(results = ann_predictions, ncol = 4)

Add annotation

We add the annotation to our Seurat object.

all.equal(rownames(ann_predictions), colnames(sobj))
[1] TRUE
sobj$singler_cells_labels = ann_predictions$labels

We can visualize cells annotation the the 2D projection (uMAP, here) :

seeable_palette = setNames(
  c(RColorBrewer::brewer.pal(name = "Dark2", n = 8),
    c(1:(length(unique(ann_predictions$labels)) - 8))),
  nm = names(sort(table(ann_predictions$labels), decreasing = TRUE)))

ann_plot = Seurat::DimPlot(
  object = sobj, 
  reduction = "umap", 
  group.by = "singler_cells_labels",
  pt.size = 2,
  cols = seeable_palette
) + ggplot2::theme(legend.position = "bottom")

clust_plot = Seurat::DimPlot(
  object = sobj, 
  reduction = "umap", 
  group.by = "RNA_snn_res.0.8",
  pt.size = 2,
  label = TRUE,
  repel = TRUE
)

print(ann_plot + clust_plot)

Maybe the annotation is not perfectly suited for our dataset. Some cell populations in the annotation are closely related, and this leads to annotation competition for our cells.

It is possible to run the annotation at the cluster level : it will be cleaner than the single cell level annotation. But, be sure that the clustering is not merging several cell populations.

We can check the number of cell types attributed to each cluster :

table(sobj$singler_cells_labels,
      sobj$RNA_snn_res.0.8)
                                        
                                           0   1   2   3   4   5   6   7   8
  DC (DC.8-4-11B-)                         0   0   0   0   0   0   0   0   0
  DC (DC.PDC.8+)                           0   0   0   0   0   0   0   0   1
  Macrophages (MF.II+480LO)                0   0   0   0   0   0   0   0   0
  Macrophages (MF.MEDL)                    0   0   0   0   0   0   0   0   0
  Monocytes (MO.6C+II-)                    0   0   0   0   0   0   0   0   0
  T cells (T.4FP3+25+)                     0   0   0   0   0   0   0   0   1
  T cells (T.4Nve)                         0   0   0   0   0   0   1   0   4
  T cells (T.4SP24int)                     0   0   0   0  41   2   1   0   1
  T cells (T.8EFF.OT1.12HR.LISOVA)         0   0   0   0   3   0   0   0   2
  T cells (T.8EFF.OT1.24HR.LISOVA)         0   0   0   0   0   0   1   0   1
  T cells (T.8EFF.OT1.48HR.LISOVA)         0   0   0   0   0   0   0   0   1
  T cells (T.8MEM.OT1.D45.LISOVA)          0   0   0   0   0   0   0   0   1
  T cells (T.8Mem)                         0   0   0   0   0   0   0   0   1
  T cells (T.8MEM)                         0   0   0   0   0   0   0   0   2
  T cells (T.8MEMKLRG1-CD127+.D8.LISOVA)   0   0   0   0   0   0   0   0   3
  T cells (T.8NVE.OT1)                     0   0   0   0   1   0   0   0   8
  T cells (T.8Nve)                         0   0   0   0   2   0   0   0  26
  T cells (T.8SP24-)                       0   0   0   0   0   0   0   1   1
  T cells (T.8SP24int)                     0   0   0   0   0   0   0   0   1
  T cells (T.CD4.5H)                       0   0   0   0   8   0   0   0   0
  T cells (T.CD4.CTR)                      0   0   0   0   0   0   0   0   1
  T cells (T.CD4TESTCJ)                    0   0   0   0   3   0   1   0   2
  T cells (T.CD8.1H)                       0   0   0   0   0   0   0   0   1
  T cells (T.CD8.5H)                       0   0   0   0   3   0   0   0   0
  T cells (T.CD8.CTR)                      0   0   0   0   0   0   0   0   1
  T cells (T.DN2A)                         0   0   0   0   0   0   0   0   0
  T cells (T.DN3-4)                        0   0   0   0   1   0   0   0   0
  T cells (T.DN3A)                         0   0   0   0   0   0   0   0   0
  T cells (T.DP69+)                        0   0   1   0 201   3  10   0   0
  T cells (T.DPbl)                         0   0   0   0   0   0   0  71   0
  T cells (T.DPsm)                       877 771 531 529  79 316 284  22   0
  T cells (T.ISP)                          0   0   1   0   0   0   0  54   0
  T cells (T.Tregs)                        0   0   0   0   0   0   0   0   2
  Tgd (Tgd.imm.VG1+VD6+)                   0   0   0   0   2   0   0   0   0
  Tgd (Tgd.imm.vg2)                        0   0   0   0   0   0   0   0   1
  Tgd (Tgd.VG2+)                           0   0   0   0   0   0   0   0   1
  Tgd (Tgd)                                0   0   0   0   0   0   0   0   1
                                        
                                           9  10
  DC (DC.8-4-11B-)                         1   0
  DC (DC.PDC.8+)                           1   0
  Macrophages (MF.II+480LO)                1   0
  Macrophages (MF.MEDL)                    1   0
  Monocytes (MO.6C+II-)                    1   0
  T cells (T.4FP3+25+)                     0   0
  T cells (T.4Nve)                         0   0
  T cells (T.4SP24int)                     0   0
  T cells (T.8EFF.OT1.12HR.LISOVA)         0   0
  T cells (T.8EFF.OT1.24HR.LISOVA)         0   0
  T cells (T.8EFF.OT1.48HR.LISOVA)         0   0
  T cells (T.8MEM.OT1.D45.LISOVA)          0   0
  T cells (T.8Mem)                         0   0
  T cells (T.8MEM)                         0   0
  T cells (T.8MEMKLRG1-CD127+.D8.LISOVA)   0   0
  T cells (T.8NVE.OT1)                     0   0
  T cells (T.8Nve)                         0   0
  T cells (T.8SP24-)                       0   0
  T cells (T.8SP24int)                     0   0
  T cells (T.CD4.5H)                       0   0
  T cells (T.CD4.CTR)                      0   0
  T cells (T.CD4TESTCJ)                    0   0
  T cells (T.CD8.1H)                       0   0
  T cells (T.CD8.5H)                       0   0
  T cells (T.CD8.CTR)                      0   0
  T cells (T.DN2A)                         0   2
  T cells (T.DN3-4)                        0   1
  T cells (T.DN3A)                         0  42
  T cells (T.DP69+)                        1   0
  T cells (T.DPbl)                         0   0
  T cells (T.DPsm)                        46   2
  T cells (T.ISP)                          0   0
  T cells (T.Tregs)                        0   0
  Tgd (Tgd.imm.VG1+VD6+)                   0   0
  Tgd (Tgd.imm.vg2)                        0   0
  Tgd (Tgd.VG2+)                           0   0
  Tgd (Tgd)                                0   0

We can eventually check if some clusters contain multiple cell types. We compute the proportion of each cell type in each cluster. If a cluster is composed of two cell types (or more) representing more than 30 % of cells, maybe this cluster is too large, thus underclustered ?

pop_by_cluster = prop.table(table(sobj$singler_cells_labels,
                                  sobj$RNA_snn_res.0.8),
                            margin = 2)
colSums(pop_by_cluster > 0.3)
 0  1  2  3  4  5  6  7  8  9 10 
 1  1  1  1  1  1  1  2  1  1  1

Clusters : 7 maybe contain several cell types : be careful !

Cluster-level annotation

We will now run the annotation at the cluster level (SingleR will summarize the expression profiles of all cells from the same cluster, and then assess the resulting aggregation) :

clust_col <- 'RNA_snn_res.0.8'
clust_ann_predictions  <- if (file.exists('clust_ann_predictions.RDS')) readRDS('clust_ann_predictions.RDS') else {
    SingleR::SingleR(
    test = norm_exp_mat,
    clusters = sobj[[clust_col]],
    ref = annotation,
    labels = annotation$label.fine,
    assay.type.test = "logcounts",
    assay.type.ref = "logcounts",
    BPPARAM = BiocParallel::SerialParam()
  )
}

How many labels were affected to our clusters ?

length(unique(clust_ann_predictions$labels))
[1] 5

How many clusters have been labelled for each annotation label ?

head(sort(table(clust_ann_predictions$labels), decreasing = TRUE), n = 10)

 T cells (T.DPsm)  T cells (T.8Nve)  T cells (T.DN3A) T cells (T.DP69+) 
                7                 1                 1                 1 
 T cells (T.DPbl) 
                1

For how many clusters was the annotation of poor quality ?

summary(is.na(clust_ann_predictions$pruned.labels))
   Mode   FALSE 
logical      11

Annotation diagnostic

We can visualize the scores for each cell type, to each cell, as a heatmap :

SingleR::plotScoreHeatmap(clust_ann_predictions)

Add annotation

We add the annotation to our Seurat object.

clust_labels_col <- paste0(clust_col, '_labels')
sobj@meta.data[[clust_labels_col]] <- sobj@meta.data[[clust_col]]
levels(sobj@meta.data[[clust_labels_col]]) = clust_ann_predictions$labels

We can visualize cells annotation the the 2D projection :

ann_plot = Seurat::DimPlot(
  object = sobj, 
  reduction = "umap", 
  group.by = clust_labels_col,
  pt.size = 2,
  label = TRUE
) + ggplot2::theme(legend.position = "bottom")

clust_plot = Seurat::DimPlot(
  object = sobj, 
  reduction = "umap", 
  group.by = clust_col,
  pt.size = 2,
  label = TRUE,
  repel = TRUE
)

ann_plot + clust_plot

Save

We save the annotated Seurat object :

path <- "/shared/projects/form_2022_32/SingleCellRNASeq/Normalization/"
base::saveRDS(
  object = sobj,
  file = paste0(path, "/Scaled_Normalized_Harmony_Clustering_Annotated_Seurat_Object.RDS")
)

Session

sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.2.1/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] celldex_1.6.0               SummarizedExperiment_1.26.1
 [3] Biobase_2.56.0              GenomicRanges_1.48.0       
 [5] GenomeInfoDb_1.32.4         IRanges_2.30.1             
 [7] S4Vectors_0.34.0            BiocGenerics_0.42.0        
 [9] MatrixGenerics_1.8.1        matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] AnnotationHub_3.4.0           BiocFileCache_2.4.0          
  [3] plyr_1.8.8                    igraph_1.3.5                 
  [5] lazyeval_0.2.2                sp_1.5-1                     
  [7] splines_4.2.1                 BiocParallel_1.30.4          
  [9] listenv_0.8.0                 scattermore_0.8              
 [11] ggplot2_3.4.0                 digest_0.6.30                
 [13] htmltools_0.5.3               viridis_0.6.2                
 [15] fansi_1.0.3                   magrittr_2.0.3               
 [17] memoise_2.0.1                 ScaledMatrix_1.4.1           
 [19] tensor_1.5                    cluster_2.1.4                
 [21] ROCR_1.0-11                   globals_0.16.1               
 [23] Biostrings_2.64.1             spatstat.sparse_3.0-0        
 [25] colorspace_2.0-3              rappdirs_0.3.3               
 [27] blob_1.2.3                    ggrepel_0.9.2                
 [29] xfun_0.34                     dplyr_1.0.10                 
 [31] crayon_1.5.2                  RCurl_1.98-1.9               
 [33] jsonlite_1.8.3                progressr_0.11.0             
 [35] spatstat.data_3.0-0           survival_3.4-0               
 [37] zoo_1.8-11                    glue_1.6.2                   
 [39] polyclip_1.10-4               gtable_0.3.1                 
 [41] zlibbioc_1.42.0               XVector_0.36.0               
 [43] leiden_0.4.3                  DelayedArray_0.22.0          
 [45] BiocSingular_1.12.0           future.apply_1.10.0          
 [47] abind_1.4-5                   scales_1.2.1                 
 [49] pheatmap_1.0.12               DBI_1.1.3                    
 [51] spatstat.random_3.0-1         miniUI_0.1.1.1               
 [53] Rcpp_1.0.9                    viridisLite_0.4.1            
 [55] xtable_1.8-4                  reticulate_1.26              
 [57] rsvd_1.0.5                    bit_4.0.4                    
 [59] htmlwidgets_1.5.4             httr_1.4.4                   
 [61] RColorBrewer_1.1-3            ellipsis_0.3.2               
 [63] Seurat_4.2.1                  ica_1.0-3                    
 [65] farver_2.1.1                  pkgconfig_2.0.3              
 [67] dbplyr_2.2.1                  sass_0.4.2                   
 [69] uwot_0.1.14                   deldir_1.0-6                 
 [71] utf8_1.2.2                    labeling_0.4.2               
 [73] tidyselect_1.2.0              rlang_1.0.6                  
 [75] reshape2_1.4.4                later_1.3.0                  
 [77] AnnotationDbi_1.58.0          BiocVersion_3.15.2           
 [79] munsell_0.5.0                 tools_4.2.1                  
 [81] cachem_1.0.6                  cli_3.4.1                    
 [83] ExperimentHub_2.4.0           generics_0.1.3               
 [85] RSQLite_2.2.18                ggridges_0.5.4               
 [87] evaluate_0.18                 stringr_1.4.1                
 [89] fastmap_1.1.0                 yaml_2.3.6                   
 [91] goftest_1.2-3                 knitr_1.40                   
 [93] bit64_4.0.5                   fitdistrplus_1.1-8           
 [95] purrr_0.3.5                   RANN_2.6.1                   
 [97] KEGGREST_1.36.3               sparseMatrixStats_1.8.0      
 [99] pbapply_1.5-0                 future_1.29.0                
[101] nlme_3.1-160                  mime_0.12                    
[103] compiler_4.2.1                rstudioapi_0.14              
[105] interactiveDisplayBase_1.34.0 filelock_1.0.2               
[107] curl_4.3.3                    plotly_4.10.1                
[109] png_0.1-7                     spatstat.utils_3.0-1         
[111] tibble_3.1.8                  bslib_0.4.1                  
[113] stringi_1.7.8                 highr_0.9                    
[115] lattice_0.20-45               Matrix_1.5-3                 
[117] vctrs_0.5.0                   pillar_1.8.1                 
[119] lifecycle_1.0.3               BiocManager_1.30.19          
[121] spatstat.geom_3.0-3           lmtest_0.9-40                
[123] jquerylib_0.1.4               BiocNeighbors_1.14.0         
[125] RcppAnnoy_0.0.20              data.table_1.14.4            
[127] cowplot_1.1.1                 bitops_1.0-7                 
[129] irlba_2.3.5.1                 httpuv_1.6.6                 
[131] patchwork_1.1.2               R6_2.5.1                     
[133] promises_1.2.0.1              KernSmooth_2.23-20           
[135] gridExtra_2.3                 parallelly_1.32.1            
[137] SingleR_1.10.0                codetools_0.2-18             
[139] MASS_7.3-58.1                 assertthat_0.2.1             
[141] withr_2.5.0                   SeuratObject_4.1.3           
[143] sctransform_0.3.5             GenomeInfoDbData_1.2.8       
[145] parallel_4.2.1                beachmat_2.12.0              
[147] grid_4.2.1                    tidyr_1.2.1                  
[149] DelayedMatrixStats_1.18.2     rmarkdown_2.18               
[151] Rtsne_0.16                    spatstat.explore_3.0-5       
[153] shiny_1.7.3
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Double-clic de souris : mettre en pause/reprendre le téléchargement
Clic milieu de souris : annuler le téléchargement
Double-clic de souris : ouvrir le fichier
Double-clic de souris + Ctrl : ouvrir le répertoire
Clic milieu de souris : retirer de la liste
Clic milieu de souris + Ctrl : supprimer du système
Appui sur un bouton de la souris + Ctrl : glisser & déposer
SingleCell_IntroR_RStudio.html
100%
00:01
1.25 Mio
2.69 Mio/s
SingleCell_IntroR_RStudio.html
Source:
De :
Vers :
/home/job/Documents/SingleCell_IntroR_RStudio.html
État :
1.25 Mio / 3.18 Mio ( 2.69 Mio/s )
Taille du fichier :
5.00 Mio
Analyse de l'antivirus :
Horaire de démarrage :
2022.11.13 - 23:34:30
Durée du téléchargement :
00:18
Temps restant :
00:01
Progression :
100%
Vitesse moyenne :
281 Kio/s
Double-clic de souris : mettre en pause/reprendre le téléchargement
Clic milieu de souris : annuler le téléchargement
Double-clic de souris : ouvrir le fichier
Double-clic de souris + Ctrl : ouvrir le répertoire
Clic milieu de souris : retirer de la liste
Clic milieu de souris + Ctrl : supprimer du système
Appui sur un bouton de la souris + Ctrl : glisser & déposer
Normalization.Rmd
100%
00:00
2 Kio
28 Kio/s
Normalization.Rmd
Source:
De :
Vers :
/home/job/WORKSPACE/ENCADREMENT/2022/EBAII_n1_2022/ATELIER_SC/TD/MD/Normalization/Normalization.Rmd
État :
2 Kio / 2 Kio ( 28 Kio/s )
Taille du fichier :
4 Kio
Analyse de l'antivirus :
Horaire de démarrage :
2022.11.16 - 08:41:28
Durée du téléchargement :
<00:01
Temps restant :
00:00
Progression :
100%
Vitesse moyenne :
55 Kio/s
Double-clic de souris : mettre en pause/reprendre le téléchargement
Clic milieu de souris : annuler le téléchargement
Double-clic de souris : ouvrir le fichier
Double-clic de souris + Ctrl : ouvrir le répertoire
Clic milieu de souris : retirer de la liste
Clic milieu de souris + Ctrl : supprimer du système
Appui sur un bouton de la souris + Ctrl : glisser & déposer
TD_SC_TDCT_dimRed_Clust.html
100%
--:--
5.47 Mio
-.--
TD_SC_TDCT_dimRed_Clust.html
Source:
De :
Vers :
/home/job/WORKSPACE/ENCADREMENT/2022/EBAII_n1_2022/ATELIER_SC/TD/MD/DimRedVisu/TD_SC_TDCT_dimRed_Clust.html
État :
5.47 Mio / 5.47 Mio ( -.-- )
Taille du fichier :
5.47 Mio
Analyse de l'antivirus :
Horaire de démarrage :
2022.11.16 - 15:10:09
Durée du téléchargement :
<00:01
Temps restant :
--:--
Progression :
100%
Vitesse moyenne :
24.11 Mio/s
Double-clic de souris : mettre en pause/reprendre le téléchargement
Clic milieu de souris : annuler le téléchargement
Double-clic de souris : ouvrir le fichier
Double-clic de souris + Ctrl : ouvrir le répertoire
Clic milieu de souris : retirer de la liste
Clic milieu de souris + Ctrl : supprimer du système
Appui sur un bouton de la souris + Ctrl : glisser & déposer
Cell Type Annotation Using SingleR.html
Inc.
Inc.
-.--
-.--
Cell Type Annotation Using SingleR.html
Source:
De :
Vers :
/home/job/WORKSPACE/ENCADREMENT/2022/EBAII_n1_2022/ATELIER_SC/TD/MD/Annotation/Cell Type Annotation Using SingleR.html
État :
-.-- / Valeur inconnue ( -.-- )
Taille du fichier :
Fichier introuvable
Analyse de l'antivirus :
Horaire de démarrage :
2022.11.17 - 10:08:27
Durée du téléchargement :
Valeur inconnue
Temps restant :
Valeur inconnue
Progression :
Valeur inconnue
Vitesse moyenne :
Valeur inconnue
Double-clic de souris : mettre en pause/reprendre le téléchargement
Clic milieu de souris : annuler le téléchargement
Double-clic de souris : ouvrir le fichier
Double-clic de souris + Ctrl : ouvrir le répertoire
Clic milieu de souris : retirer de la liste
Clic milieu de souris + Ctrl : supprimer du système
Appui sur un bouton de la souris + Ctrl : glisser & déposer