From 0ec1eb76a8b9dbd2426f6b196cd1430df8d9bc6a Mon Sep 17 00:00:00 2001 From: Pavankumar Videm Date: Wed, 22 Nov 2023 15:16:22 +0100 Subject: [PATCH] correct the scanpy tool version in the throughout the tutorial --- .../tutorials/scrna-scanpy-pbmc3k/tutorial.md | 72 +++++++++---------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md index 51770838d87cda..44cabbda8b5bfe 100644 --- a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md +++ b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md @@ -301,7 +301,7 @@ Genes that appear in less than a few cells can be considered noise and thus remo > Remove genes found in less than 3 cells > -> 1. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `Input 3k PBMC` > - *"Method used for filtering"*: `Filter genes based on number of cells or counts, using 'pp.filter_genes'` > - *"Filter"*: `Minimum number of cells expressed` @@ -485,7 +485,7 @@ We can now compute QC metrics on the `AnnData` object. > Compute QC metrics > -> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with mito annotation` > - *"Method used for inspecting"*: `Calculate quality control metrics, using 'pp.calculate_qc_metrics'` > - *"Name of kind of values in X"*: `counts` @@ -559,7 +559,7 @@ We would like to visualize 3 of the more informative QC metrics: > Visualize QC metrics > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with mito annotation and qc metrics` > - *"Method used for plotting"*: `Generic: Violin plot, using 'pl.violin'` > - *"Keys for accessing variables"*: `Subset of variables in 'adata.var_names' or fields of '.obs'` @@ -588,7 +588,7 @@ We would like to visualize 3 of the more informative QC metrics: > > > {: .question} > -> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with mito annotation and qc metrics` > - *"Method used for plotting"*: `Generic: Scatter plot along observations or variables axes, using 'pl.scatter'` > - *"Plotting tool that computed coordinates"*: `Using coordinates` @@ -612,7 +612,7 @@ We would like to visualize 3 of the more informative QC metrics: > > > {: .question} > -> 5. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 5. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with mito annotation and qc metrics` > - *"Method used for plotting"*: `Generic: Scatter plot along observations or variables axes, using 'pl.scatter'` > - *"Plotting tool that computed coordinates"*: `Using coordinates` @@ -652,7 +652,7 @@ Based on the previous plot, we would like to remove cells that have: > Remove low-quality cells > -> 1. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with mito annotation and qc metrics` > - *"Method used for filtering"*: `Filter cell outliers based on counts and numbers of genes expressed, using 'pp.filter_cells'` > - *"Filter"*: `Minimum number of genes expressed` @@ -677,7 +677,7 @@ Based on the previous plot, we would like to remove cells that have: > > > {: .question} > -> 3. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: output of **Filter** {% icon tool %} > - *"Method used for filtering"*: `Filter cell outliers based on counts and numbers of genes expressed, using 'pp.filter_cells'` > - *"Filter"*: `Maximum number of genes expressed` @@ -747,7 +747,7 @@ Here we would to normalize our count table such that each cell have 10,000 reads > Normalize for cell size > -> 1. {% tool [Normalize with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_normalize/scanpy_normalize/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Normalize with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_normalize/scanpy_normalize/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC after QC filtering` > - *"Method used for normalization"*: `Normalize counts per cell, using 'pp.normalize_total'` > - *"Target sum"*: `10000.0` @@ -763,7 +763,7 @@ With log-transformation, the differences in the log-values represent log-fold ch > Log-transform the counts > -> 1. {% tool [Inspect and Manipulate](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Inspect and Manipulate](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: output of **Normalize** {% icon tool %} > - *"Method used for inspecting"*: `Logarithmize the data matrix, using 'pp.log1p'` {: .hands_on} @@ -793,7 +793,7 @@ Once the per-gene variation has been quantified, we need to select the subset of > Identify the highly variable genes > -> 1. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Filter with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC after QC filtering and normalization` > - *"Method used for filtering"*: `Annotate (and filter) highly variable genes, using 'pp.highly_variable_genes'` > - *"Flavor for computing normalized dispersion"*: `seurat` @@ -802,7 +802,7 @@ Once the per-gene variation has been quantified, we need to select the subset of > - *"Minimal normalized dispersion cutoff"*: `0.5` > - *"Inplace subset to highly-variable genes?"*: `No` > -> 2. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 2. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: output of the last **Filter** {% icon tool %} > - *"Method used for plotting"*: `Preprocessing: Plot dispersions versus means for genes, using 'pl.highly_variable_genes'` > @@ -904,12 +904,12 @@ Prior to any downstream analysis like dimensional reduction, we need to apply a > Scale the data > -> 1. {% tool [Remove confounders with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_remove_confounders/scanpy_remove_confounders/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Remove confounders with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_remove_confounders/scanpy_remove_confounders/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG` > - *"Method used for plotting"*: `Regress out unwanted sources of variation, using 'pp.regress_out'` > - *"Keys for observation annotation on which to regress on"*: `total_counts, pct_counts_mito` > -> 2. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} +> 2. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} > with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: output of **Remove confounders** {% icon tool %} > - *"Method used for inspecting"*: `Scale data to unit variance and zero mean, using 'pp.scale'` @@ -945,7 +945,7 @@ Here we perform the PCA on the log-normalized expression values and compute the > Perform the PCA > -> 1. {% tool [Cluster, infer trajectories and embed with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/0.9.6+galaxy1) %} +> 1. {% tool [Cluster, infer trajectories and embed with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/1.9.6+galaxy1) %} > with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling` > - *"Method used for plotting"*: `Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using 'tl.pca'` @@ -1078,7 +1078,7 @@ Scanpy provides several useful ways of visualizing both cells and genes that def > Plot the top 2 PCs the PCA > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling and PCA` > - *"Method used for plotting"*: `PCA: Plot PCA results, using 'pl.pca_overview'` > - In *"Plot attributes"* @@ -1100,7 +1100,7 @@ On these plots we see the different cells projected onto the first 3 PCs. We can > Visualize the top genes associated with PCs > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling and PCA` > - *"Method used for plotting"*: `PCA: Rank genes according to contributions to PCs, using 'pl.pca_loadings'` > - *"List of comma-separated components"*: `1,2,3` @@ -1118,7 +1118,7 @@ On these plots we see the different cells projected onto the first 3 PCs. We can > > {: .solution} > {: .question} > -> 2. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 2. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling and PCA` > - *"Method used for plotting"*: `PCA: Plot PCA results, using 'pl.pca_overview'` > - *"Keys for annotations of observations/cells or variables/genes"*: `CST3, NKG7, PPBP` @@ -1160,7 +1160,7 @@ A simple heuristic for choosing the number of PCs generates an "Elbow plot": a r > Generate an Elbow plot > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling and PCA` > - *"Method used for plotting"*: `PCA: Scatter plot in PCA coordinates, using 'pl.pca_variance_ratio'` > - *"Use the log of the values?"*: `Yes` @@ -1211,7 +1211,7 @@ Here, to reproduce original results, we choose 10 neighbors for a KNN graph, the > Compute the neighborhood graph > -> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling and PCA` > - *"Method used for inspecting"*: `Compute a neighborhood graph of observations, using 'pp.neighbors'` > - *"The size of local neighborhood (in terms of number of neighboring data points) used for manifold approximation"*: `10` @@ -1254,7 +1254,7 @@ Here, we will reduce the neighborhood to 2 UMAP components and then we will chec > Embed and plot the neighborhood graph > -> 1. {% tool [Cluster, infer trajectories and embed with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Cluster, infer trajectories and embed with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA and KNN graph` > - *"Method used for plotting"*: `Embed the neighborhood graph using UMAP, using 'tl.umap'` > @@ -1280,7 +1280,7 @@ Here, we will reduce the neighborhood to 2 UMAP components and then we will chec > > > {: .question} > -> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP` > - *"Method used for plotting"*: `Embeddings: Scatter plot in UMAP basis, using 'pl.umap'` > - *"Keys for annotations of observations/cells or variables/genes"*: `CST3, NKG7, PPBP` @@ -1312,7 +1312,7 @@ Currently, the Louvain graph-clustering method (community detection based on opt > Cluster the neighborhood graph > -> 1. {% tool [Cluster, infer trajectories and embed with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Cluster, infer trajectories and embed with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP` > - *"Method used for plotting"*: `Cluster cells into subgroups, using 'tl.louvain'` > - *"Flavor for the clustering"*: `vtraag (much more powerful)` @@ -1344,7 +1344,7 @@ The cells in the same clusters should be co-localized in the UMAP coordinate plo > Plot the neighborhood graph and the clusters > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering` > - *"Method used for plotting"*: `Embeddings: Scatter plot in UMAP basis, using 'pl.umap'` > - *"Keys for annotations of observations/cells or variables/genes"*: `louvain, CST3, NKG7, PPBP` @@ -1384,7 +1384,7 @@ The simplest and fastest method is the Welch *t*-test. It has good statistical p > Rank the highly differential genes using t-test > -> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering` > - *"Method used for inspecting"*: `Rank genes for characterizing groups, using 'tl.rank_genes_groups'` > - *"The key of the observations grouping to consider"*: `louvain` @@ -1422,7 +1422,7 @@ The simplest and fastest method is the Welch *t*-test. It has good statistical p > > {: .solution} > {: .question} > -> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with t-test` > - *"Method used for plotting"*: `Marker genes: Plot ranking of genes using dotplot plot, using 'pl.rank_genes_groups'` > - *"Number of genes to show"*: `20` @@ -1469,7 +1469,7 @@ Another widely used method for pairwise comparisons between groups of observatio > Rank the highly differential genes using Wilcoxon rank sum > -> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering` > > **Note:** *Please pay attention to the dataset name.* @@ -1483,7 +1483,7 @@ Another widely used method for pairwise comparisons between groups of observatio > > 2. Rename the generated output `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > -> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > - *"Method used for plotting"*: `Marker genes: Plot ranking of genes using dotplot plot, using 'pl.rank_genes_groups'` > - *"Number of genes to show"*: `20` @@ -1531,7 +1531,7 @@ CD3D | FCN1 | MS4A1 | HLA-C | FTH1 | CST7 | HLA-DQA1 | GPX1 > Compare differential expression for CST3, NKG7 and PPBP in the different clusters > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > - *"Method used for plotting"*: `Generic: Violin plot, using 'pl.violin'` > - *"Keys for accessing variables"*: `Subset of variables in 'adata.var_names' or fields in '.obs'` @@ -1565,7 +1565,7 @@ The assumption should be even more true for the top marker genes. The first way > Plot expression probability distributions across clusters of top marker genes > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > - *"Method used for plotting"*: `Generic: Stacked violin plot, using 'pl.stacked_violin'` > - *"Variables to plot (columns of the heatmaps)"*: `Subset of variables in 'adata.var_names'` @@ -1600,7 +1600,7 @@ Another approach consists of displaying the mean expression of the marker genes > Plot top marker gene expression on an UMAP plot > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > - *"Method used for plotting"*: `Embeddings: Scatter plot in UMAP basis, using 'pl.umap'` > - *"Keys for annotations of observations/cells or variables/genes"*: `louvain, LDHB, LYZ, CD74, CCL5, LST1, NKG7, HLA-DPA1, PF4` @@ -1628,7 +1628,7 @@ We would like now to have a look at the expression of the top 20 marker genes in > Plot heatmap of the gene expression in cells > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > - *"Method used for plotting"*: `Marker genes: Plot ranking of genes as heatmap plot, using 'pl.rank_genes_groups_heatmap'` > - *"Number of genes to show"*: `20` @@ -1661,7 +1661,7 @@ In some cases, it may also be interesting to find marker genes distinguishing on > Identify the marker genes distinguishing cluster 0 from cluster 1 using Wilcoxon rank sum > -> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Inspect and Manipulate with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test` > - *"Method used for inspecting"*: `Rank genes for characterizing groups, using 'tl.rank_genes_groups'` > - *"The key of the observations grouping to consider"*: `louvain` @@ -1675,7 +1675,7 @@ In some cases, it may also be interesting to find marker genes distinguishing on > > 2. Rename the generated output `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes for 0 vs 1 with Wilcoxon test` > -> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes for 0 vs 1 with Wilcoxon test` > - *"Method used for plotting"*: `Marker genes: Plot ranking of genes using dotplot plot, using 'pl.rank_genes_groups'` > - *"Number of genes to show"*: `20` @@ -1699,7 +1699,7 @@ In some cases, it may also be interesting to find marker genes distinguishing on The marker genes distinguishing cluster 0 from cluster 1 are extracted based on their differences in expression, which can be easily visualized. > Plot expression difference for the marker genes distinguishing cluster 0 from cluster 1 -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes for 0 vs 1 with Wilcoxon test` > - *"Method used for plotting"*: `Marker genes: Plot ranking of genes as violin plot, using 'pl.rank_genes_groups_violin'` > - *"Which genes to plot?"*: `A number of genes` @@ -1819,7 +1819,7 @@ Cluster | Cell type > > {: .solution} > {: .question} > -> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 3. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test, annotation` > - *"Method used for plotting"*: `Embeddings: Scatter plot in UMAP basis, using 'pl.umap'` > - *"Keys for annotations of observations/cells or variables/genes"*: `louvain` @@ -1846,7 +1846,7 @@ With the annotated cell types, we can also visualize the expression of their can > Plot expression of canonical marker genes for the annotated cell types > -> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/0.9.6+galaxy1) %} with the following parameters: +> 1. {% tool [Plot with scanpy](toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.9.6+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Annotated data matrix"*: `3k PBMC with only HVG, after scaling, PCA, KNN graph, UMAP, clustering, marker genes with Wilcoxon test, annotation` > - *"Method used for plotting"*: `Generic: Makes a dot plot of the expression values, using 'pl.dotplot'` > - *"Variables to plot (columns of the heatmaps)"*: `Subset of variables in 'adata.var_names'`