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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 154318f74839a4481c7c68993c4fb745842c4cce"
author iuc
date Thu, 09 Sep 2021 12:23:55 +0000
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1 # Wrappers for Scater 1 # Wrappers for Scater
2 2
3 This code wraps a number of [scater](https://bioconductor.org/packages/release/bioc/html/scater.html) functions as Galaxy wrappers. Briefly, the `scater-create-qcmetric-ready-sce` tool takes a sample gene expression matrix (usually read-counts) and a cell annotation file, creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics` function (using other supplied files such as ERCC's and mitochondrial gene features). 3 This code wraps a number of [scater](https://bioconductor.org/packages/release/bioc/html/scater.html) and [scuttle](https://bioconductor.org/packages/3.13/bioc/html/scuttle.html) functions as Galaxy wrappers. Briefly, the `scater-create-qcmetric-ready-sce` tool takes a sample gene expression matrix (usually read-counts) and a cell annotation file, creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics` function (using other supplied files such as ERCC's and mitochondrial gene features).
4 Various filter scripts are provided, along with some plotting functions for QC. 4 Various filter scripts are provided, along with some plotting functions for QC.
5 5
6 6
7 ## Typical workflow 7 ## Typical workflow
8 8
9 1. Read in data with `scater-create-qcmetric-ready-sce`. 9 1. Read in data with `scater-create-qcmetric-ready-sce`.
10 2. Visualise it.\ 10 2. Visualise it.
11 Take a look at the distribution of library sizes, expressed features and mitochondrial genes with `scater-plot-dist-scatter`. 11 Take a look at the distribution of library sizes, expressed features and mitochondrial genes with `scater-plot-dist-scatter`.
12 Then look at the distibution of genes across cells with `scater-plot-exprs-freq`. 12
13 3. Guided by the plots, filter the data with `scater-filter`.\ 13 3. Guided by the plots, filter the data with `scater-filter`.\
14 You can either manually filter with user-defined parameters or use PCA to automatically removes outliers. 14 You can either manually filter with user-defined parameters or use PCA to automatically removes outliers.
15 4. Visualise data again to see how the filtering performed using `scater-plot-dist-scatter`.\ 15 4. Visualise data again to see how the filtering performed using `scater-plot-dist-scatter`.\
16 Decide if you're happy with the data. If not, try increasing or decreasing the filtering parameters. 16 Decide if you're happy with the data. If not, try increasing or decreasing the filtering parameters.
17 5. Normalise data with `scater-normalize`. 17
18 6. Investigate other confounding factors.\ 18 6. Investigate other confounding factors.\
19 Plot the data (using PCA) and display various annotated properties of the cells using `scater-plot-pca`. 19 Plot the data (using PCA) and display various annotated properties of the cells using `scater-plot-pca`.
20 20
21 ## Command-line usage 21 ## Command-line usage
22 22
49 ./scater-plot-dist-scatter.R -i test-data/scater_qcready.loom -o test-data/scater_reads_genes_dist.pdf 49 ./scater-plot-dist-scatter.R -i test-data/scater_qcready.loom -o test-data/scater_reads_genes_dist.pdf
50 ``` 50 ```
51 51
52 --- 52 ---
53 53
54 `scater-plot-exprs-freq.R`
55 Plots mean expression vs % of expressing cells and provides information as to the number of genes expressed in 50% and 25% of cells.
56
57 ---
58 54
59 `scater-pca-filter.R` 55 `scater-pca-filter.R`
60 Takes SingleCellExperiment object (from Loom file) and automatically removes outliers from data using PCA. Save the filtered SingleCellExperiment object in Loom format. 56 Takes SingleCellExperiment object (from Loom file) and automatically removes outliers from data using PCA. Save the filtered SingleCellExperiment object in Loom format.
61 57
62 ``` 58 ```
72 ./scater-manual-filter.R -i test-data/scater_qcready.loom -l 10000 -d 4 -m 33 -o test-data/scater_manual_filtered.loom 68 ./scater-manual-filter.R -i test-data/scater_qcready.loom -l 10000 -d 4 -m 33 -o test-data/scater_manual_filtered.loom
73 ``` 69 ```
74 70
75 --- 71 ---
76 72
77 `scater-normalize.R` 73 `scater-plot-pca.R`
78 Compute log-normalized expression values from count data in a SingleCellExperiment object, using the size factors stored in the object. Save the normalised SingleCellExperiment object in Loom format. 74 PCA plot of a SingleCellExperiment object. The options `-c`, `-p`, and `-s` all refer to cell annotation features. These are the column headers of the `-c` option used in `scater-create-qcmetric-ready-sce.R`.
79 75
80 ``` 76 ```
81 ./scater-normalize.R -i test-data/scater_manual_filtered.loom -o test-data/scater_man_filtered_normalised.loom 77 ./scater-plot-pca.R -i test-data/scater_qcready.loom -c Treatment -p Mutation_Status -o test-data/scater_pca_plot.pdf
82 ``` 78 ```
83 79
84 --- 80 ---
85 81
86 `scater-plot-pca.R` 82 `scater-plot-tsne.R`
87 PCA plot of a normalised SingleCellExperiment object (produced with `scater-normalize.R`). The options `-c`, `-p`, and `-s` all refer to cell annotation features. These are the column headers of the `-c` option used in `scater-create-qcmetric-ready-sce.R`. 83 t-SNE plot of a SingleCellExperiment object. The options `-c`, `-p`, and `-s` all refer to cell annotation features. These are the column headers of the `-c` option used in `scater-create-qcmetric-ready-sce.R`.
88 84
89 ``` 85 ```
90 ./scater-plot-pca.R -i test-data/scater_man_filtered_normalised.loom -c Treatment -p Mutation_Status -o test-data/scater_pca_plot.pdf 86 ./scater-plot-tsne.R -i test-data/scater_qcready.loom -c Treatment -p Mutation_Status -o test-data/scater_tsne_plot.pdf
91 ``` 87 ```