Mercurial > repos > iuc > scater_filter

comparison README.md @ 0:e6ca62ac65c6 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
author iuc
date Thu, 18 Jul 2019 11:13:41 -0400
parents
children b7ea9f09c02f
comparison
equal deleted inserted replaced
-1:000000000000 0:e6ca62ac65c6
1 # Wrappers for Scater
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).
4 Various filter scripts are provided, along with some plotting functions for QC.
5
6
7 ## Typical workflow
8
9 1. Read in data with `scater-create-qcmetric-ready-sce`.
10 2. Visualise it.\
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`.
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.
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.
17 5. Normalise data with `scater-normalize`.
18 6. Investigate other confounding factors.\
19 Plot the data (using PCA) and display various annotated properties of the cells using `scater-plot-pca`.
20
21 ## Command-line usage
22
23 For help with any of the following scripts, run:
24 `<script-name> --help`
25
26 ---
27
28 `scater-create-qcmetric-ready-sce.R`
29 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.
30
31
32 ```
33 ./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
34 ```
35
36 ---
37
38 `scater-plot-dist-scatter.R`
39 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.
40
41 ```
42 ./scater-plot-dist-scatter.R -i test-data/scater_qcready.loom -o test-data/scater_reads_genes_dist.pdf
43 ```
44
45 ---
46
47 `scater-plot-exprs-freq.R`
48 Plots mean expression vs % of expressing cells and provides information as to the number of genes expressed in 50% and 25% of cells.
49
50 ---
51
52 `scater-pca-filter.R`
53 Takes SingleCellExperiment object (from Loom file) and automatically removes outliers from data using PCA. Save the filtered SingleCellExperiment object in Loom format.
54
55 ```
56 ./scater-pca-filter.R -i test-data/scater_qcready.loom -o test-data/scater_pca_filtered.loom
57 ```
58
59 ---
60
61 `scater-manual-filter.R`
62 Takes SingleCellExperiment object (from Loom file) and filters data using user-provided parameters. Save the filtered SingleCellExperiment object in Loom format.
63
64 ```
65 ./scater-manual-filter.R -i test-data/scater_qcready.loom -l 10000 -d 4 -m 33 -o test-data/scater_manual_filtered.loom
66 ```
67
68 ---
69
70 `scater-normalize.R`
71 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.
72
73 ```
74 ./scater-normalize.R -i test-data/scater_manual_filtered.loom -o test-data/scater_man_filtered_normalised.loom
75 ```
76
77 ---
78
79 `scater-plot-pca.R`
80 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`.
81
82 ```
83 ./scater-plot-pca.R -i test-data/scater_man_filtered_normalised.loom -c Treatment -p Mutation_Status -o test-data/scater_pca_plot.pdf
84 ```