Mercurial > repos > iuc > scater_filter
annotate README.md @ 1:b7ea9f09c02f draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
| author | iuc |
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| date | Tue, 03 Sep 2019 14:27:39 -0400 |
| parents | e6ca62ac65c6 |
| children | 7a365ec81b52 |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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1 # Wrappers for Scater |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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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). |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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4 Various filter scripts are provided, along with some plotting functions for QC. |
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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7 ## Typical workflow |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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9 1. Read in data with `scater-create-qcmetric-ready-sce`. |
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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10 2. Visualise it.\ |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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11 Take a look at the distribution of library sizes, expressed features and mitochondrial genes with `scater-plot-dist-scatter`. |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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12 Then look at the distibution of genes across cells with `scater-plot-exprs-freq`. |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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13 3. Guided by the plots, filter the data with `scater-filter`.\ |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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14 You can either manually filter with user-defined parameters or use PCA to automatically removes outliers. |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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15 4. Visualise data again to see how the filtering performed using `scater-plot-dist-scatter`.\ |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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16 Decide if you're happy with the data. If not, try increasing or decreasing the filtering parameters. |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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17 5. Normalise data with `scater-normalize`. |
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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18 6. Investigate other confounding factors.\ |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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19 Plot the data (using PCA) and display various annotated properties of the cells using `scater-plot-pca`. |
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e6ca62ac65c6
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21 ## Command-line usage |
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b7ea9f09c02f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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23 The scripts require the installation of scater and few other R/BioConductor packages. An easy way to install them is to create a [conda](https://conda.io/) environment using the `environment.yml` file distributed together with these wrappers: |
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b7ea9f09c02f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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25 ``` |
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b7ea9f09c02f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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26 conda env create -f environment.yml |
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b7ea9f09c02f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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27 conda activate scater |
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b7ea9f09c02f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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28 ``` |
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b7ea9f09c02f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 61f3899168453092fd25691cf31871a3a350fd3b"
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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30 For help with any of the following scripts, run: |
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31 `<script-name> --help` |
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33 --- |
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35 `scater-create-qcmetric-ready-sce.R` |
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e6ca62ac65c6
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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36 Takes an expression matrix (usually read-counts) of samples (columns) and gene/transcript features (rows), along with other annotation information, such as cell metadata, control genes (mitochondrail genes, ERCC's), creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics`. Save the resulting SingleCellExperiment object in Loom format. |
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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39 ``` |
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40 ./scater-create-qcmetric-ready-sce.R -a test-data/counts.txt -c test-data/annotation.txt -f test-data/mt_controls.txt -o test-data/scater_qcready.loom |
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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41 ``` |
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43 --- |
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45 `scater-plot-dist-scatter.R` |
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46 Takes SingleCellExperiment object (from Loom file) and plots a panel of read and feature graphs, including the distribution of library sizes, distribution of feature counts, a scatterplot of reads vs features, and % of mitochondrial genes in library. |
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48 ``` |
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49 ./scater-plot-dist-scatter.R -i test-data/scater_qcready.loom -o test-data/scater_reads_genes_dist.pdf |
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50 ``` |
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52 --- |
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54 `scater-plot-exprs-freq.R` |
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55 Plots mean expression vs % of expressing cells and provides information as to the number of genes expressed in 50% and 25% of cells. |
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59 `scater-pca-filter.R` |
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60 Takes SingleCellExperiment object (from Loom file) and automatically removes outliers from data using PCA. Save the filtered SingleCellExperiment object in Loom format. |
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62 ``` |
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63 ./scater-pca-filter.R -i test-data/scater_qcready.loom -o test-data/scater_pca_filtered.loom |
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64 ``` |
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68 `scater-manual-filter.R` |
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69 Takes SingleCellExperiment object (from Loom file) and filters data using user-provided parameters. Save the filtered SingleCellExperiment object in Loom format. |
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71 ``` |
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72 ./scater-manual-filter.R -i test-data/scater_qcready.loom -l 10000 -d 4 -m 33 -o test-data/scater_manual_filtered.loom |
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73 ``` |
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77 `scater-normalize.R` |
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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. |
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79 |
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80 ``` |
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81 ./scater-normalize.R -i test-data/scater_manual_filtered.loom -o test-data/scater_man_filtered_normalised.loom |
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82 ``` |
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83 |
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84 --- |
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85 |
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86 `scater-plot-pca.R` |
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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`. |
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88 |
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89 ``` |
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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 |
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91 ``` |