Mercurial > repos > iuc > scater_plot_tsne
comparison README.md @ 1:2b09ca1c5e41 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 154318f74839a4481c7c68993c4fb745842c4cce"
author | iuc |
---|---|
date | Thu, 09 Sep 2021 12:24:39 +0000 |
parents | a30f4bfe8f01 |
children |
comparison
equal
deleted
inserted
replaced
0:a30f4bfe8f01 | 1:2b09ca1c5e41 |
---|---|
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 ``` |