├── codecov.yml
├── .gitignore
├── screenshots
├── screenshot_qc.png
├── screenshot_global.png
├── screenshot_differential_expression.png
└── screenshot_differential_adenylation.png
├── R
├── utils-pipe.R
├── nanotail-package.R
├── polya_annotate.R
├── read_polyA_data.R
├── polya_helper_functions.R
└── polya_stats.R
├── .travis.yml
├── man
├── pipe.Rd
├── plot_polyA_PCA.Rd
├── gm_mean.Rd
├── nanotail_ggplot2_theme.Rd
├── spread_multiple.Rd
├── annotate_with_annotables.Rd
├── calculate_scaling_vector_for_virutal_gel.Rd
├── remove_failed_reads.Rd
├── nanoTailApp.Rd
├── annotate_with_org_packages.Rd
├── subsample_table.Rd
├── plot_nanopolish_qc.Rd
├── get_nanopolish_processing_info.Rd
├── stat_median_line.Rd
├── plot_quantiles.Rd
├── calculate_pca.Rd
├── annotate_with_biomart.Rd
├── plot_MA.Rd
├── read_polya_single.Rd
├── getmode.Rd
├── plot_volcano.Rd
├── summarize_polya_per_transcript.Rd
├── summarize_polya.Rd
├── dot-kruskal_polya.Rd
├── normalize_counts_to_depth.Rd
├── read_polya_multiple.Rd
├── calculate_diff_exp_binom.Rd
├── dot-basic_aesthetics.Rd
├── plot_virtual_gel.Rd
├── dot-polya_stats.Rd
├── plot_annotations_comparison_boxplot.Rd
├── plot_counts_scatter.Rd
├── plot_polya_boxplot.Rd
├── kruskal_polya.Rd
├── plot_polya_violin.Rd
├── plot_polya_distribution.Rd
└── calculate_polya_stats.Rd
├── tests
├── testthat
│ ├── test-shiny_app.R
│ ├── test-helper_functions.R
│ ├── test-polya_annotate.R
│ ├── test-read_polyA_data.R
│ ├── test-polya_stats.R
│ └── test-polya_plots.R
└── testthat.R
├── inst
└── extdata
│ └── about.md
├── appveyor.yml
├── NAMESPACE
├── DESCRIPTION
├── README.md
├── LICENSE
└── LICENSE.md
/codecov.yml:
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1 | comment: false
2 |
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/.gitignore:
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1 | .Rproj.user
2 | .Rhistory
3 | .RData
4 | .Ruserdata
5 | nanotail.Rproj
6 | temp
7 | inst/libs
8 |
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/screenshots/screenshot_qc.png:
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https://raw.githubusercontent.com/smaegol/nanotail/HEAD/screenshots/screenshot_qc.png
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/screenshots/screenshot_global.png:
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https://raw.githubusercontent.com/smaegol/nanotail/HEAD/screenshots/screenshot_global.png
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/screenshots/screenshot_differential_expression.png:
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https://raw.githubusercontent.com/smaegol/nanotail/HEAD/screenshots/screenshot_differential_expression.png
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/screenshots/screenshot_differential_adenylation.png:
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https://raw.githubusercontent.com/smaegol/nanotail/HEAD/screenshots/screenshot_differential_adenylation.png
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/R/utils-pipe.R:
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1 | #' Pipe operator
2 | #'
3 | #' See \code{magrittr::\link[magrittr]{\%>\%}} for details.
4 | #'
5 | #' @name %>%
6 | #' @rdname pipe
7 | #' @keywords internal
8 | #' @export
9 | #' @importFrom magrittr %>%
10 | #' @usage lhs \%>\% rhs
11 | NULL
12 |
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/.travis.yml:
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1 | # R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
2 |
3 | language: R
4 | sudo: false
5 | cache: packages
6 |
7 | warnings_are_errors: false
8 |
9 | r_packages:
10 | - covr
11 |
12 | after_success:
13 | - Rscript -e 'library(covr); codecov()'
14 |
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/man/pipe.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils-pipe.R
3 | \name{\%>\%}
4 | \alias{\%>\%}
5 | \title{Pipe operator}
6 | \usage{
7 | lhs \%>\% rhs
8 | }
9 | \description{
10 | See \code{magrittr::\link[magrittr]{\%>\%}} for details.
11 | }
12 | \keyword{internal}
13 |
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/R/nanotail-package.R:
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1 | ## usethis namespace: start
2 | #' @importFrom tibble tibble
3 | #' @importFrom stats as.formula
4 | #' @importFrom stats glm
5 | #' @importFrom stats median
6 | #' @importFrom stats p.adjust
7 | #' @importFrom stats prcomp
8 | #' @importFrom stats sd
9 | #' @importFrom stats wilcox.test
10 | #' @importFrom utils packageVersion
11 | ## usethis namespace: end
12 | NULL
13 |
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/man/plot_polyA_PCA.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_polyA_PCA}
4 | \alias{plot_polyA_PCA}
5 | \title{PCA biplot}
6 | \usage{
7 | plot_polyA_PCA(pca_object, samples_names)
8 | }
9 | \arguments{
10 | \item{pca_object}{pca object}
11 |
12 | \item{samples_names}{names of samples to be shown on the plot}
13 | }
14 | \value{
15 | \link[ggplot2]{ggplot} object
16 | }
17 | \description{
18 | Plots PCA biplot using \link[ggbiplot]{ggbiplot}
19 | }
20 |
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/man/gm_mean.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \name{gm_mean}
4 | \alias{gm_mean}
5 | \title{Calculates geometric mean}
6 | \usage{
7 | gm_mean(x, na.rm = TRUE)
8 | }
9 | \arguments{
10 | \item{x}{input vector}
11 |
12 | \item{na.rm}{should NA values be removed?}
13 | }
14 | \value{
15 | geometric mean of values provided as an input
16 | }
17 | \description{
18 | Calculates geometric mean
19 | }
20 | \examples{
21 | a <- rnorm(100,33,5)
22 | gm_mean(a)
23 | }
24 |
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/man/nanotail_ggplot2_theme.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \docType{data}
4 | \name{nanotail_ggplot2_theme}
5 | \alias{nanotail_ggplot2_theme}
6 | \title{Default theme for ggplot2-based plots in the NanoTail package}
7 | \format{
8 | An object of class \code{theme} (inherits from \code{gg}) of length 4.
9 | }
10 | \usage{
11 | nanotail_ggplot2_theme
12 | }
13 | \description{
14 | Default theme for ggplot2-based plots in the NanoTail package
15 | }
16 | \keyword{datasets}
17 |
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/man/spread_multiple.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \name{spread_multiple}
4 | \alias{spread_multiple}
5 | \title{Spread multiple columns}
6 | \usage{
7 | spread_multiple(df, key, value)
8 | }
9 | \arguments{
10 | \item{df}{data frame to apply spread on}
11 |
12 | \item{key}{as in \link{spread}}
13 |
14 | \item{value}{vector of columns to be taken as value for \link{spread}}
15 | }
16 | \value{
17 | \link{tibble}
18 | }
19 | \description{
20 | Spread multiple columns
21 | }
22 |
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/man/annotate_with_annotables.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_annotate.R
3 | \name{annotate_with_annotables}
4 | \alias{annotate_with_annotables}
5 | \title{Annotate polyA predictions using annotables}
6 | \usage{
7 | annotate_with_annotables(polya_data, genome)
8 | }
9 | \arguments{
10 | \item{polya_data}{polya data table to annotate}
11 |
12 | \item{genome}{valid genome from annotables to use for annotation}
13 | }
14 | \value{
15 | a \link[tibble]{tibble}
16 | }
17 | \description{
18 | Annotate polyA predictions using annotables
19 | }
20 |
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/tests/testthat/test-shiny_app.R:
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1 | context("Test shiny app")
2 |
3 | library(assertthat)
4 | library(testthat)
5 | library(assertive)
6 |
7 |
8 | test_that("valid parameters are provided for nanoTailApp()",{
9 |
10 |
11 | expect_error(nanoTailApp())
12 | expect_error(nanoTailApp(empty_polya_data_table))
13 | #expect_silent(nanoTailApp(example_valid_polya_table))
14 | expect_error(nanoTailApp(example_valid_polya_table %>% dplyr::select(-polya_length)))
15 | expect_error(nanoTailApp(example_valid_polya_table %>% dplyr::select(-transcript)))
16 | expect_error(nanoTailApp(example_valid_polya_table %>% dplyr::select(-sample_name)))
17 | })
18 |
19 |
20 |
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/tests/testthat/test-helper_functions.R:
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1 | context("Helper functions")
2 |
3 | test_that("geometric mean calculation works",{
4 |
5 | empty_vector <- vector()
6 | test_vector <- (seq(10,100))
7 | test_vector_with_NAs <- (rep(c(seq(20,40),NA),10))
8 | non_numeric_vector <- c(1,2,3,"A")
9 | expect_error(gm_mean(empty_vector))
10 | expect_error(gm_mean(non_numeric_vector))
11 | expect_error(gm_mean(test_vector.na.rm="TRUE"))
12 | expect_equal(gm_mean(5),5)
13 | expect_equal(round(gm_mean(test_vector)),round(47.29746))
14 | expect_true(is.na(gm_mean(test_vector_with_NAs,na.rm = FALSE)))
15 | expect_false(is.na(gm_mean(test_vector_with_NAs,na.rm = TRUE)))
16 | })
17 |
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/man/calculate_scaling_vector_for_virutal_gel.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \name{calculate_scaling_vector_for_virutal_gel}
4 | \alias{calculate_scaling_vector_for_virutal_gel}
5 | \title{Calculates scaling vector for virtual gel plotting}
6 | \usage{
7 | calculate_scaling_vector_for_virutal_gel(input_data, groupingFactor)
8 | }
9 | \arguments{
10 | \item{input_data}{input polyA table for calculation of scaling factor (count of reads)}
11 |
12 | \item{groupingFactor}{for which factor calculate counts}
13 | }
14 | \value{
15 | named vector
16 | }
17 | \description{
18 | Calculates scaling vector for virtual gel plotting
19 | }
20 |
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/man/remove_failed_reads.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/read_polyA_data.R
3 | \name{remove_failed_reads}
4 | \alias{remove_failed_reads}
5 | \title{Removes reads which failed during Nanopolish polya processing}
6 | \usage{
7 | remove_failed_reads(polya_data)
8 | }
9 | \arguments{
10 | \item{polya_data}{output table from \link{read_polya_single} or \link{read_polya_multiple}}
11 | }
12 | \value{
13 | a \link[tibble:tibble-package]{tibble} with only reads having qc_tag=='PASS'
14 | }
15 | \description{
16 | Convenient function to quickly remove all reads failing during nanopolish polya processing
17 | }
18 | \seealso{
19 | \link{read_polya_single}, \link{read_polya_multiple}
20 | }
21 |
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/man/nanoTailApp.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_shiny_app.R
3 | \name{nanoTailApp}
4 | \alias{nanoTailApp}
5 | \title{wrapper for NanoTail Shiny interface}
6 | \usage{
7 | nanoTailApp(
8 | polya_table,
9 | precomputed_polya_statistics = NA,
10 | precomputed_annotations = NA
11 | )
12 | }
13 | \arguments{
14 | \item{polya_table}{polyA predictions table. Can be obtained with \link{read_polya_multiple}}
15 |
16 | \item{precomputed_polya_statistics}{precomputed differential adenylation table (obtained with \link{calculate_polya_stats})}
17 |
18 | \item{precomputed_annotations}{precomputed annotations}
19 | }
20 | \description{
21 | wrapper for NanoTail Shiny interface
22 | }
23 |
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/man/annotate_with_org_packages.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_annotate.R
3 | \name{annotate_with_org_packages}
4 | \alias{annotate_with_org_packages}
5 | \title{Title}
6 | \usage{
7 | annotate_with_org_packages(
8 | polya_data,
9 | columns_of_annotation = c("GENENAME", "SYMBOL"),
10 | keytype = "ENSEMBLTRANS",
11 | organism = "mus_musculus"
12 | )
13 | }
14 | \arguments{
15 | \item{polya_data}{polya data table to annotate}
16 |
17 | \item{columns_of_annotation}{which columns to use}
18 |
19 | \item{keytype}{whic keytype to use}
20 |
21 | \item{organism}{whic organism database to use}
22 | }
23 | \value{
24 | a \link[tibble]{tibble}
25 | }
26 | \description{
27 | Title
28 | }
29 |
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/man/subsample_table.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \name{subsample_table}
4 | \alias{subsample_table}
5 | \title{Subsample a date frame}
6 | \usage{
7 | subsample_table(input_table, groupingFactor = NA, subsample = NA)
8 | }
9 | \arguments{
10 | \item{input_table}{input table for subsampling}
11 |
12 | \item{groupingFactor}{grouping factor(s)}
13 |
14 | \item{subsample}{specify absolute number of rows or fraction to subsample from the data frame (group-wise)}
15 | }
16 | \value{
17 | \link{tibble}
18 | }
19 | \description{
20 | Uses base subsetting and \link{sample} or dplyr \link[dplyr]{sample_n} or \link[dplyr]{sample_frac} to get the subset of the bigger data.frame or tibble
21 | }
22 |
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/man/plot_nanopolish_qc.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_nanopolish_qc}
4 | \alias{plot_nanopolish_qc}
5 | \title{Plot Nanopolish polya QC}
6 | \usage{
7 | plot_nanopolish_qc(nanopolish_processing_info, frequency = TRUE, ...)
8 | }
9 | \arguments{
10 | \item{nanopolish_processing_info}{output of \link{get_nanopolish_processing_info}}
11 |
12 | \item{frequency}{show frequency plot instead of counts plot}
13 |
14 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
15 | }
16 | \value{
17 | \link[ggplot2]{ggplot} object
18 | }
19 | \description{
20 | Plot Nanopolish polya QC
21 | }
22 |
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/man/get_nanopolish_processing_info.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{get_nanopolish_processing_info}
4 | \alias{get_nanopolish_processing_info}
5 | \title{Get information about nanopolish processing}
6 | \usage{
7 | get_nanopolish_processing_info(polya_data, grouping_factor = NA)
8 | }
9 | \arguments{
10 | \item{polya_data}{A data.frame or tibble containig unfiltered polya output from Nanopolish,}
11 |
12 | \item{grouping_factor}{How to group results (e.g. by sample_name)
13 | best read with \link[nanotail]{read_polya_single} or \link[nanotail]{read_polya_multiple}}
14 | }
15 | \value{
16 | A \link[tibble]{tibble} with counts for each processing state
17 | }
18 | \description{
19 | Process the information returned by \code{nanopolish polya} in the \code{qc_tag} column
20 | }
21 |
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/man/stat_median_line.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \name{stat_median_line}
4 | \alias{stat_median_line}
5 | \title{Helper function for calculating median stat for violin/boxpolot ggplot plots}
6 | \usage{
7 | stat_median_line(
8 | mapping = NULL,
9 | data = NULL,
10 | geom = "hline",
11 | position = "identity",
12 | na.rm = FALSE,
13 | show.legend = NA,
14 | inherit.aes = TRUE,
15 | ...
16 | )
17 | }
18 | \arguments{
19 | \item{mapping}{}
20 |
21 | \item{data}{}
22 |
23 | \item{geom}{}
24 |
25 | \item{position}{}
26 |
27 | \item{na.rm}{}
28 |
29 | \item{show.legend}{}
30 |
31 | \item{inherit.aes}{}
32 |
33 | \item{...}{}
34 | }
35 | \value{
36 |
37 | }
38 | \description{
39 | Helper function for calculating median stat for violin/boxpolot ggplot plots
40 | }
41 |
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/man/plot_quantiles.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_quantiles}
4 | \alias{plot_quantiles}
5 | \title{Plot quantiles}
6 | \usage{
7 | plot_quantiles(
8 | summarized_data,
9 | transcript_id,
10 | transcript_id_column = "transcript",
11 | groupBy
12 | )
13 | }
14 | \arguments{
15 | \item{summarized_data}{\itemize{
16 | \item summarized data table (output of summarize_polya_per_transcript, with quantiles calculated)
17 | }}
18 |
19 | \item{transcript_id}{\itemize{
20 | \item id of transcript to show
21 | }}
22 |
23 | \item{transcript_id_column}{\itemize{
24 | \item column with transcript ids
25 | }}
26 |
27 | \item{groupBy}{\itemize{
28 | \item which column use for grouping (e.g. with timepoints)
29 | }}
30 | }
31 | \value{
32 | ggplot object
33 | }
34 | \description{
35 | Plot quantiles
36 | }
37 |
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/man/calculate_pca.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{calculate_pca}
4 | \alias{calculate_pca}
5 | \title{Calculates PCA using polya predictions or counts}
6 | \usage{
7 | calculate_pca(
8 | polya_data_summarized,
9 | parameter = "polya_median",
10 | transcript_id_column = "transcript"
11 | )
12 | }
13 | \arguments{
14 | \item{polya_data_summarized}{summarized polyA predictions. Generate use \link{summarize_polya}}
15 |
16 | \item{parameter}{\itemize{
17 | \item parameter used for PCA calculation. One of: polya_median,polya_mean,polya_gm_mean,counts
18 | }}
19 |
20 | \item{transcript_id_column}{column which respresnrt transcript id}
21 | }
22 | \value{
23 | pca object
24 | }
25 | \description{
26 | Needs polyA predictions table summarized by \link{summarize_polya} function, using "sample_name" as summary_factors
27 | }
28 |
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/man/annotate_with_biomart.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_annotate.R
3 | \name{annotate_with_biomart}
4 | \alias{annotate_with_biomart}
5 | \title{Title}
6 | \usage{
7 | annotate_with_biomart(
8 | polya_data,
9 | attributes_to_get = c("ensembl_transcript_id", "external_gene_name", "description",
10 | "transcript_biotype"),
11 | filters = "ensembl_transcript_id",
12 | mart_to_use = NA
13 | )
14 | }
15 | \arguments{
16 | \item{polya_data}{polya data table to annotate}
17 |
18 | \item{attributes_to_get}{what annotations should be retrieved. Default = c('external_gene_name','description','transcript_biotype')}
19 |
20 | \item{filters}{which column should be matched in the target mart}
21 |
22 | \item{mart_to_use}{mart object created with \link[biomaRt]{useMart} or \link[biomaRt]{useEnsembl}}
23 | }
24 | \value{
25 | a \link[tibble]{tibble}
26 | }
27 | \description{
28 | Title
29 | }
30 |
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/tests/testthat/test-polya_annotate.R:
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1 | context("annotation of polya predictions table")
2 |
3 | test_that("annotation with annotables works",{
4 |
5 | expect_error(annotate_with_annotables())
6 | expect_error(annotate_with_annotables(empty_polya_data_table))
7 |
8 | expect_error(annotate_with_annotables(example_valid_polya_table))
9 | expect_error(annotate_with_annotables(empty_polya_data_table,"grcm38"))
10 |
11 | })
12 |
13 |
14 | test_that("annotation with biomart works",{
15 |
16 |
17 |
18 | number_of_reads_per_sample=20000
19 | number_of_transcripts_per_sample=50
20 | #mouse_genes_ensembl_mart <- biomaRt::useMart("ensembl",dataset="mmusculus_gene_ensembl")
21 |
22 |
23 |
24 | expect_error(annotate_with_biomart())
25 | expect_error(annotate_with_biomart(empty_polya_data_table))
26 |
27 | expect_error(annotate_with_biomart(example_valid_polya_table))
28 | expect_error(annotate_with_biomart(empty_polya_data_table))
29 |
30 |
31 | })
32 |
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/man/plot_MA.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_MA}
4 | \alias{plot_MA}
5 | \title{Plots MA plot of differential expression analysis}
6 | \usage{
7 | plot_MA(input_data, transcript_id_column, labels = FALSE, nlabels = 10, ...)
8 | }
9 | \arguments{
10 | \item{input_data}{a table with output from \link{calculate_diff_exp_binom}}
11 |
12 | \item{transcript_id_column}{column used for transcript id}
13 |
14 | \item{labels}{show point labels using ggrepel}
15 |
16 | \item{nlabels}{number of labels to show}
17 |
18 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
19 | }
20 | \value{
21 | \link[ggplot2]{ggplot} object
22 | }
23 | \description{
24 | Crates simple MA plot, with log10(mean expression) on the X-axis and log2(fold_change) on the Y-axis
25 | }
26 |
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/man/read_polya_single.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/read_polyA_data.R
3 | \name{read_polya_single}
4 | \alias{read_polya_single}
5 | \title{Read Single Nanopolish polyA preditions from file}
6 | \usage{
7 | read_polya_single(polya_path, gencode = TRUE, sample_name = NA)
8 | }
9 | \arguments{
10 | \item{polya_path}{path to nanopolish output file}
11 |
12 | \item{gencode}{are contig names GENCODE-compliant.
13 | Can get transcript names and ensembl_transcript IDs if reads were mapped for example to Gencode reference transcriptome}
14 |
15 | \item{sample_name}{sample name (optional), provided as a string.
16 | If specified will be included as an additional column sample_name.}
17 | }
18 | \value{
19 | a \link[tibble:tibble-package]{tibble} with polya predictions
20 | }
21 | \description{
22 | This is the basic function used to import output from \code{nanopolish polya} to R
23 | }
24 | \seealso{
25 | \link{read_polya_multiple}
26 | }
27 |
--------------------------------------------------------------------------------
/man/getmode.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_helper_functions.R
3 | \name{getmode}
4 | \alias{getmode}
5 | \title{Function calculating statistical mode of given vector.}
6 | \usage{
7 | getmode(x, method = "density", na.rm = FALSE)
8 | }
9 | \arguments{
10 | \item{x}{\link{character} data for which the most frequent value is to be calculated
11 | (polya_data column with the lengths of the poly(A) tails)}
12 |
13 | \item{method}{\link{character} "density"/"value"; density mode is computed
14 | by default.}
15 |
16 | \item{na.rm}{\link{boolean} parameter defining whether to remove missing values or
17 | not. By a default set to false}
18 | }
19 | \value{
20 | statistical mode of given vector.
21 | }
22 | \description{
23 | Function calculating statistical mode of given vector.
24 | }
25 | \examples{
26 | \dontrun{
27 |
28 | getmode(x = polya_data$tail_length,
29 | method = "density",
30 | na.rm = FALSE)
31 |
32 | }
33 |
34 | }
35 |
--------------------------------------------------------------------------------
/inst/extdata/about.md:
--------------------------------------------------------------------------------
1 | ## NanoTail
2 |
3 | The goal of **NanoTail** is to provide a set of functions to manipulate and analyze data coming from polyA lengths estimations done using Oxford Nanopore Direct RNA sequencing and Nanopolish software. The software is still in thee development phase so all suggestions are welcome. Please also expect the code to be changed frequently, so use it with caution.
4 |
5 | ## Usage
6 |
7 | Please use navigation options in the sidebar (located on the left) to access analysis functions and plots. NanoTail currently supports differential adenylation analysis using Wilcoxon and Kolmogorov-Smirnov tests and differential expression analysis using binomTest from EdgeR. More analysis options are in the development and are expected to be available soon.
8 |
9 | ## Citation
10 |
11 | Please cite NanoTail as:
12 | Krawczyk PS et al., NanoTail - R package for exploratory analysis of Nanopore Direct RNA based polyA lengths estimations
13 |
14 |
15 | ---
16 |
17 | (C) Pawel Krawczyk 2019
18 |
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/man/plot_volcano.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_volcano}
4 | \alias{plot_volcano}
5 | \title{Plots volcano plot of differential expression analysis}
6 | \usage{
7 | plot_volcano(
8 | input_data,
9 | transcript_id_column,
10 | labels = FALSE,
11 | nlabels = 10,
12 | ...
13 | )
14 | }
15 | \arguments{
16 | \item{input_data}{a table with output from \link{calculate_diff_exp_binom} or \link{calculate_polya_stats}}
17 |
18 | \item{transcript_id_column}{column used for transcript id}
19 |
20 | \item{labels}{show point labels using ggrepel}
21 |
22 | \item{nlabels}{number of labels to show
23 | #'}
24 |
25 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
26 | }
27 | \value{
28 | \link[ggplot2]{ggplot} object
29 | }
30 | \description{
31 | Plots volcano plot of differential expression analysis
32 | }
33 |
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/man/summarize_polya_per_transcript.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{summarize_polya_per_transcript}
4 | \alias{summarize_polya_per_transcript}
5 | \title{Summarize poly(A) data per transcript}
6 | \usage{
7 | summarize_polya_per_transcript(
8 | polya_data,
9 | groupBy = NULL,
10 | transcript_id_column = transcript,
11 | summary_functions = list("median", "mean"),
12 | quantiles = NA
13 | )
14 | }
15 | \arguments{
16 | \item{polya_data}{input table with poly(A) data.}
17 |
18 | \item{transcript_id_column}{column with transcript identifier. Default to "transcript"}
19 |
20 | \item{summary_functions}{list of summary functions. Set to NA to get only counts per transcript}
21 |
22 | \item{quantiles}{vector with quantile values (optional)}
23 |
24 | \item{summary_factors}{vector of grouping columns. Set to NULL to omit grouping}
25 | }
26 | \value{
27 | a tibble with summarized poly(A) length data
28 | }
29 | \description{
30 | Summarize poly(A) data per transcript
31 | }
32 |
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/man/summarize_polya.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{summarize_polya}
4 | \alias{summarize_polya}
5 | \title{Summarizes input polya table}
6 | \usage{
7 | summarize_polya(
8 | polya_data,
9 | summary_factors = c("group"),
10 | transcript_id_column = c("transcript")
11 | )
12 | }
13 | \arguments{
14 | \item{polya_data}{input table with polyA predictions}
15 |
16 | \item{summary_factors}{specifies column used for grouping (default: group)}
17 |
18 | \item{transcript_id_column}{specifies which column use as transcript identifier (default: transcript). Set to \code{NULL} to omit per-transcript stats}
19 | }
20 | \value{
21 | long-format \link[tibble]{tibble} with per-transcript statistics for each sample
22 | }
23 | \description{
24 | Summarizes input table with polyA predictions, calculating medians, mean, geometric means and standard deviation values for each transcript (default).
25 | To get overall summary for each sample or group, specify \code{transcript_id_column=NULL}
26 | }
27 |
--------------------------------------------------------------------------------
/man/dot-kruskal_polya.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{.kruskal_polya}
4 | \alias{.kruskal_polya}
5 | \title{Compute Kruskal-Wallis test on single poly(A) data}
6 | \usage{
7 | .kruskal_polya(
8 | input_data,
9 | grouping_factor = "sample_name",
10 | verbose = F,
11 | verbosity_level = 1
12 | )
13 |
14 | .kruskal_polya(
15 | input_data,
16 | grouping_factor = "sample_name",
17 | verbose = F,
18 | verbosity_level = 1
19 | )
20 | }
21 | \arguments{
22 | \item{input_data}{input data.frame (for single transcript)}
23 |
24 | \item{grouping_factor}{which column contains group information}
25 |
26 | \item{verbose}{verbose output}
27 |
28 | \item{verbosity_level}{how verbose the output should be (levels 1 - little verbosity, or 2 - very verbose)}
29 | }
30 | \value{
31 | data.frame with test statistics
32 |
33 | data.frame with test statistics
34 | }
35 | \description{
36 | Compute Kruskal-Wallis test on single poly(A) data
37 |
38 | Compute Kruskal-Wallis test on single poly(A) data
39 | }
40 |
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/man/normalize_counts_to_depth.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{normalize_counts_to_depth}
4 | \alias{normalize_counts_to_depth}
5 | \title{Normalize counts to sequencingdepth}
6 | \usage{
7 | normalize_counts_to_depth(
8 | summarized_data,
9 | raw_data,
10 | spike_in_data = NULL,
11 | groupBy,
12 | force = F
13 | )
14 | }
15 | \arguments{
16 | \item{summarized_data}{\itemize{
17 | \item output of summarize_polya_per_transcript()
18 | }}
19 |
20 | \item{raw_data}{\itemize{
21 | \item raw polyA data (loaded with read_polya_single() or read_polya_multiple())
22 | }}
23 |
24 | \item{spike_in_data}{\itemize{
25 | \item spike-in data for normalization (optional) (raw polya data loaded with read_polya_single() or read_polya_multiple(), with the same metadata as raw data)
26 | }}
27 |
28 | \item{groupBy}{\itemize{
29 | \item grouping variable
30 | }}
31 |
32 | \item{force}{\itemize{
33 | \item force recalculation
34 | }}
35 | }
36 | \value{
37 | data.frame (tibble) with normalized data
38 | }
39 | \description{
40 | Normalize counts to sequencingdepth
41 | }
42 |
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/man/read_polya_multiple.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/read_polyA_data.R
3 | \name{read_polya_multiple}
4 | \alias{read_polya_multiple}
5 | \title{Reads multiple nanopolish polyA predictions at once}
6 | \usage{
7 | read_polya_multiple(samples_table, ...)
8 | }
9 | \arguments{
10 | \item{samples_table}{data.frame or tibble containing samples metadata and paths to files.
11 | Should have at least two columns: \itemize{
12 | \item polya_path - containing path to the polya predictions file
13 | \item sample_name - unique name of the sample
14 | }
15 | Additional columns can provide metadata which will be included in the final table}
16 |
17 | \item{...}{\itemize{
18 | \item additional parameters to pass to read_polya_single(), like gencode=(TRUE/FALSE)
19 | }}
20 | }
21 | \value{
22 | a \link[tibble:tibble-package]{tibble} containing polyA predictions for all specified samples, with metadata provided in samples_table
23 | stored as separate columns
24 | }
25 | \description{
26 | This function can be used to load any number of files with polyA predictions with single invocation,
27 | allowing for metadata specification.
28 | }
29 | \seealso{
30 | \link{read_polya_single}
31 | }
32 |
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/man/calculate_diff_exp_binom.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{calculate_diff_exp_binom}
4 | \alias{calculate_diff_exp_binom}
5 | \title{Performs differential expression analysis}
6 | \usage{
7 | calculate_diff_exp_binom(
8 | polya_data,
9 | grouping_factor = NA,
10 | condition1 = NA,
11 | condition2 = NA,
12 | alpha = 0.05,
13 | summarized_input = FALSE
14 | )
15 | }
16 | \arguments{
17 | \item{polya_data}{polya_data tibble}
18 |
19 | \item{grouping_factor}{name of column containing factor with groups for comparison}
20 |
21 | \item{condition1}{first condition to compare}
22 |
23 | \item{condition2}{second condition to compare}
24 |
25 | \item{alpha}{threshold for a pvalue, to treat the result as significant (default = 0.05)}
26 |
27 | \item{summarized_input}{is input table already summarized?}
28 | }
29 | \value{
30 | a tibble with differential expression results
31 | }
32 | \description{
33 | Uses counts for each identified transcript to calculate differential expression between specified groups.
34 | This function is a wrapper for \code{\link[edgeR]{binomTest}} from \code{edgeR} package
35 | }
36 | \seealso{
37 | \link[edgeR]{binomTest}
38 | }
39 |
--------------------------------------------------------------------------------
/man/dot-basic_aesthetics.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{.basic_aesthetics}
4 | \alias{.basic_aesthetics}
5 | \title{Title}
6 | \usage{
7 | .basic_aesthetics(
8 | ggplot_object,
9 | scale_x_limit_low = NA,
10 | scale_x_limit_high = NA,
11 | scale_y_limit_low = NA,
12 | scale_y_limit_high = NA,
13 | color_palette = "Set1",
14 | plot_title = NA,
15 | color_mode = "color",
16 | axis_titles_size = 16
17 | )
18 | }
19 | \arguments{
20 | \item{ggplot_object}{ggplot2 object to manipulate asesthetics}
21 |
22 | \item{scale_x_limit_low}{lower limit of x continuous scale}
23 |
24 | \item{scale_x_limit_high}{upper limit of x continuous scale}
25 |
26 | \item{scale_y_limit_low}{lower limit of y continuous scale}
27 |
28 | \item{scale_y_limit_high}{upper limit of y continuous scale}
29 |
30 | \item{color_palette}{color palette (one from RColorBrewer of ggsci packages)}
31 |
32 | \item{plot_title}{Title of the plot}
33 |
34 | \item{color_mode}{if using color, fill or both, when specifying color_palette}
35 |
36 | \item{axis_titles_size}{size of axis titles}
37 | }
38 | \value{
39 | \link[ggplot2]{ggplot} object
40 | }
41 | \description{
42 | Title
43 | }
44 |
--------------------------------------------------------------------------------
/man/plot_virtual_gel.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_virtual_gel}
4 | \alias{plot_virtual_gel}
5 | \title{Title}
6 | \usage{
7 | plot_virtual_gel(
8 | input_data,
9 | groupingFactor,
10 | valuesColumn,
11 | density_bw = 0.6,
12 | kernel = "gaussian",
13 | shift_bands = 0,
14 | kernel_from = 0,
15 | kernel_to = NA,
16 | scale_by_size = T,
17 | scaling_vector = NA
18 | )
19 | }
20 | \arguments{
21 | \item{input_data}{data frame (or tibble) with input values}
22 |
23 | \item{groupingFactor}{factor to group input table by}
24 |
25 | \item{valuesColumn}{column with numeric values to calculate density on}
26 |
27 | \item{density_bw}{bandwidth of density (bw argument to \link{density} function, default=1 )}
28 |
29 | \item{kernel}{kenrel to use}
30 |
31 | \item{shift_bands}{shift bands by specified value}
32 |
33 | \item{kernel_from}{from which value start density estimation}
34 |
35 | \item{kernel_to}{to which value perform density estimation}
36 |
37 | \item{scale_by_size}{should densities be scaled by sample size}
38 |
39 | \item{scaling_vector}{vector containing scaling factors}
40 | }
41 | \value{
42 | list containing plot and density estimates (data.frame)
43 | }
44 | \description{
45 | Title
46 | }
47 |
--------------------------------------------------------------------------------
/appveyor.yml:
--------------------------------------------------------------------------------
1 | # DO NOT CHANGE the "init" and "install" sections below
2 |
3 | # Download script file from GitHub
4 | init:
5 | ps: |
6 | $ErrorActionPreference = "Stop"
7 | Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1"
8 | Import-Module '..\appveyor-tool.ps1'
9 |
10 | install:
11 | ps: Bootstrap
12 |
13 | cache:
14 | - C:\RLibrary
15 |
16 | environment:
17 | NOT_CRAN: true
18 | # env vars that may need to be set, at least temporarily, from time to time
19 | # see https://github.com/krlmlr/r-appveyor#readme for details
20 | # USE_RTOOLS: true
21 | # R_REMOTES_STANDALONE: true
22 |
23 | # Adapt as necessary starting from here
24 |
25 | build_script:
26 | - travis-tool.sh install_deps
27 |
28 | test_script:
29 | - travis-tool.sh run_tests
30 |
31 | on_failure:
32 | - 7z a failure.zip *.Rcheck\*
33 | - appveyor PushArtifact failure.zip
34 |
35 | artifacts:
36 | - path: '*.Rcheck\**\*.log'
37 | name: Logs
38 |
39 | - path: '*.Rcheck\**\*.out'
40 | name: Logs
41 |
42 | - path: '*.Rcheck\**\*.fail'
43 | name: Logs
44 |
45 | - path: '*.Rcheck\**\*.Rout'
46 | name: Logs
47 |
48 | - path: '\*_*.tar.gz'
49 | name: Bits
50 |
51 | - path: '\*_*.zip'
52 | name: Bits
53 |
--------------------------------------------------------------------------------
/man/dot-polya_stats.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{.polya_stats}
4 | \alias{.polya_stats}
5 | \title{Calculates polyA statistics for single group of reads (for single transcript)}
6 | \usage{
7 | .polya_stats(
8 | polya_data,
9 | stat_test,
10 | grouping_factor,
11 | min_reads = 0,
12 | use_dwell_time = FALSE,
13 | custom_glm_formula = NA
14 | )
15 | }
16 | \arguments{
17 | \item{polya_data}{\itemize{
18 | \item input data frame with polyA predictions
19 | }}
20 |
21 | \item{stat_test}{\itemize{
22 | \item statistical test to use. One of : Wilcoxon, KS (Kolmogorov-Smirnov) or glm (Generalized Linear Model). All tests use log2(polya_length) as a response variable
23 | }}
24 |
25 | \item{grouping_factor}{\itemize{
26 | \item factor defining groups (Need to have 2 levels)
27 | }}
28 |
29 | \item{min_reads}{\itemize{
30 | \item minimum reads per group to include in the statistics calculation
31 | }}
32 |
33 | \item{use_dwell_time}{\itemize{
34 | \item use dwell time instead of calculated polya length for statistics
35 | }}
36 |
37 | \item{custom_glm_formula}{\itemize{
38 | \item custom glm formula (when using glm for statistics)
39 | }}
40 | }
41 | \value{
42 | data frame
43 | }
44 | \description{
45 | Calculates polyA statistics for single group of reads (for single transcript)
46 | }
47 |
--------------------------------------------------------------------------------
/NAMESPACE:
--------------------------------------------------------------------------------
1 | # Generated by roxygen2: do not edit by hand
2 |
3 | export("%>%")
4 | export(annotate_with_annotables)
5 | export(annotate_with_biomart)
6 | export(annotate_with_org_packages)
7 | export(calculate_diff_exp_binom)
8 | export(calculate_pca)
9 | export(calculate_polya_stats)
10 | export(calculate_scaling_vector_for_virutal_gel)
11 | export(get_nanopolish_processing_info)
12 | export(getmode)
13 | export(gm_mean)
14 | export(kruskal_polya)
15 | export(nanoTailApp)
16 | export(nanotail_ggplot2_theme)
17 | export(normalize_counts_to_depth)
18 | export(plot_MA)
19 | export(plot_annotations_comparison_boxplot)
20 | export(plot_counts_scatter)
21 | export(plot_nanopolish_qc)
22 | export(plot_polyA_PCA)
23 | export(plot_polya_boxplot)
24 | export(plot_polya_distribution)
25 | export(plot_polya_violin)
26 | export(plot_quantiles)
27 | export(plot_virtual_gel)
28 | export(plot_volcano)
29 | export(read_polya_multiple)
30 | export(read_polya_single)
31 | export(remove_failed_reads)
32 | export(spread_multiple)
33 | export(stat_median_line)
34 | export(subsample_table)
35 | export(summarize_polya)
36 | export(summarize_polya_per_transcript)
37 | importFrom(magrittr,"%>%")
38 | importFrom(stats,as.formula)
39 | importFrom(stats,glm)
40 | importFrom(stats,median)
41 | importFrom(stats,p.adjust)
42 | importFrom(stats,prcomp)
43 | importFrom(stats,sd)
44 | importFrom(stats,wilcox.test)
45 | importFrom(tibble,tibble)
46 | importFrom(utils,packageVersion)
47 |
--------------------------------------------------------------------------------
/man/plot_annotations_comparison_boxplot.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_annotations_comparison_boxplot}
4 | \alias{plot_annotations_comparison_boxplot}
5 | \title{Title}
6 | \usage{
7 | plot_annotations_comparison_boxplot(
8 | annotated_polya_data,
9 | annotation_factor = NA,
10 | grouping_factor = NA,
11 | condition1 = NA,
12 | condition2 = NA,
13 | annotation_levels = c(),
14 | violin = FALSE,
15 | ...
16 | )
17 | }
18 | \arguments{
19 | \item{annotated_polya_data}{data frame(or tibble) with polyA predictions and associated annotations}
20 |
21 | \item{annotation_factor}{column specifying factor grouping transcripts by annotation}
22 |
23 | \item{grouping_factor}{column in polya_data_table specifing factor grouping samples}
24 |
25 | \item{condition1}{if only 2 conditions to show, choose which one is first}
26 |
27 | \item{condition2}{if only 2 conditions to show, choose which one is second}
28 |
29 | \item{annotation_levels}{vector specifying selected annotation levels from annotation_factor}
30 |
31 | \item{violin}{plot violin instead of boxplot?}
32 |
33 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
34 | }
35 | \value{
36 | \link[ggplot2]{ggplot} object
37 | }
38 | \description{
39 | Title
40 | }
41 |
--------------------------------------------------------------------------------
/man/plot_counts_scatter.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_counts_scatter}
4 | \alias{plot_counts_scatter}
5 | \title{Title}
6 | \usage{
7 | plot_counts_scatter(
8 | polya_data_summarized,
9 | groupingFactor = NA,
10 | condition1 = NA,
11 | condition2 = NA,
12 | min_counts = 0,
13 | max_counts = 0,
14 | points_coloring_factor = NA,
15 | repel_elements = NA,
16 | repel_group = NA,
17 | transcript_id_column = "transcript",
18 | ...
19 | )
20 | }
21 | \arguments{
22 | \item{polya_data_summarized}{polyA predictions table, summarized using \link{summarize_polya}}
23 |
24 | \item{groupingFactor}{name of column used for grouping}
25 |
26 | \item{condition1}{first condition to use for plotting}
27 |
28 | \item{condition2}{second condition to use for plotting}
29 |
30 | \item{min_counts}{minimum number of counts to be shown}
31 |
32 | \item{max_counts}{maximum number of counts to be shown}
33 |
34 | \item{points_coloring_factor}{factor specifying how to color points}
35 |
36 | \item{repel_elements}{TBD}
37 |
38 | \item{repel_group}{TBD}
39 |
40 | \item{transcript_id_column}{TBD
41 | #'}
42 |
43 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
44 | }
45 | \value{
46 | \link[ggplot2]{ggplot} object
47 | }
48 | \description{
49 | Title
50 | }
51 |
--------------------------------------------------------------------------------
/man/plot_polya_boxplot.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_polya_boxplot}
4 | \alias{plot_polya_boxplot}
5 | \title{Plots boxplot of estimated polya lengths}
6 | \usage{
7 | plot_polya_boxplot(
8 | polya_data,
9 | groupingFactor,
10 | additional_grouping_factor = NA,
11 | condition1 = NA,
12 | condition2 = NA,
13 | violin = FALSE,
14 | add_points = FALSE,
15 | max_points = 500,
16 | auto_scale = T,
17 | ...
18 | )
19 | }
20 | \arguments{
21 | \item{polya_data}{input table with polyA predictions}
22 |
23 | \item{groupingFactor}{which factor to use for grouping}
24 |
25 | \item{additional_grouping_factor}{additional coloring grouping factor}
26 |
27 | \item{condition1}{First condition to include on the plot}
28 |
29 | \item{condition2}{Second condition to include on the plot}
30 |
31 | \item{violin}{Should violin plot be plotted instead of boxplot?}
32 |
33 | \item{add_points}{should individual points be plotted (only if less than max_points). Represented as \link[ggforce]{geom_sina}}
34 |
35 | \item{max_points}{maximum number of points to be plotted if add_points is specified}
36 |
37 | \item{auto_scale}{automatically adjust axis scales (default=TRUE)}
38 |
39 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
40 | }
41 | \value{
42 | \link[ggplot2]{ggplot} object
43 | }
44 | \description{
45 | Plots boxplot of estimated polya lengths
46 | }
47 |
--------------------------------------------------------------------------------
/man/kruskal_polya.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{kruskal_polya}
4 | \alias{kruskal_polya}
5 | \title{Compute Kruskal-Wallis test on poly(A) data}
6 | \usage{
7 | kruskal_polya(
8 | input_data,
9 | grouping_factor = "sample_name",
10 | transcript_id = "transcript",
11 | verbose = F,
12 | verbosity_level = 1
13 | )
14 |
15 | kruskal_polya(
16 | input_data,
17 | grouping_factor = "sample_name",
18 | transcript_id = "transcript",
19 | verbose = F,
20 | verbosity_level = 1
21 | )
22 | }
23 | \arguments{
24 | \item{input_data}{\itemize{
25 | \item input tibble/data.frame with nanopolish output.
26 | }}
27 |
28 | \item{grouping_factor}{\itemize{
29 | \item which column contains group information
30 | }}
31 |
32 | \item{transcript_id}{column which transcript ids}
33 |
34 | \item{verbose}{verbose output}
35 |
36 | \item{verbosity_level}{how verbose the output should be (levels 1 - little verbosity, or 2 - very verbose)}
37 | }
38 | \value{
39 | data.frame with statistis
40 |
41 | data.frame with statistis
42 | }
43 | \description{
44 | Compute Kruskal-Wallis test on poly(A) data
45 |
46 | Compute Kruskal-Wallis test on poly(A) data
47 | }
48 | \examples{
49 | \dontrun{
50 | polya_table <- nanotail::read_polya_single("nanopolish.tsv")
51 | kruskal_polya(polya_table,grouping_factor="group",transcript_id="transcript",verbose=T)
52 | }
53 | \dontrun{
54 | polya_table <- nanotail::read_polya_single("nanopolish.tsv")
55 | kruskal_polya(polya_table,grouping_factor="group",transcript_id="transcript",verbose=T)
56 | }
57 | }
58 |
--------------------------------------------------------------------------------
/man/plot_polya_violin.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_polya_violin}
4 | \alias{plot_polya_violin}
5 | \title{Plots violin plot of estimated polya lengths, other version}
6 | \usage{
7 | plot_polya_violin(
8 | polya_data,
9 | groupingFactor,
10 | additional_grouping_factor = NA,
11 | condition1 = NA,
12 | condition2 = NA,
13 | violin = FALSE,
14 | add_points = FALSE,
15 | max_points = 500,
16 | add_boxplot = TRUE,
17 | fill_by = NA,
18 | auto_scale = T,
19 | transcript_id,
20 | transcript_id_column = "transcript",
21 | ...
22 | )
23 | }
24 | \arguments{
25 | \item{polya_data}{input table with polyA predictions}
26 |
27 | \item{groupingFactor}{which factor to use for grouping}
28 |
29 | \item{additional_grouping_factor}{additional coloring grouping factor}
30 |
31 | \item{condition1}{First condition to include on the plot}
32 |
33 | \item{condition2}{Second condition to include on the plot}
34 |
35 | \item{violin}{Should violin plot be plotted instead of boxplot?}
36 |
37 | \item{add_points}{should individual points be plotted (only if less than max_points). Represented as \link[ggforce]{geom_sina}}
38 |
39 | \item{max_points}{maximum number of points to be plotted if add_points is specified}
40 |
41 | \item{add_boxplot}{Add boxplot inside violin?}
42 |
43 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
44 | }
45 | \value{
46 | \link[ggplot2]{ggplot} object
47 | }
48 | \description{
49 | Plots violin plot of estimated polya lengths, other version
50 | }
51 |
--------------------------------------------------------------------------------
/DESCRIPTION:
--------------------------------------------------------------------------------
1 | Package: nanotail
2 | Title: Analysis and visualisation of Nanopolish-based polyA lengths estimations
3 | Version: 0.1.0
4 | Authors@R:
5 | person(given = "Pawel",
6 | family = "Krawczyk",
7 | role = c("aut", "cre"),
8 | email = "p.krawczyk@ibb.waw.pl",
9 | comment = c(ORCID = "0000-0001-9531-1298"))
10 | Maintainer:
11 | Description: Provides functions for visualization and exploratory analysis of Oxford Nanopore direct RNA seq and Nanopolish based polyA lengths predictions.
12 | License: GPL-3 + file LICENSE
13 | Encoding: UTF-8
14 | LazyData: true
15 | Roxygen: list(markdown = TRUE)
16 | RoxygenNote: 7.2.1
17 | biocViews:
18 | Depends:
19 | R (>= 3.5)
20 | Suggests:
21 | annotables (>= 0.1.91),
22 | ggbiplot (>= 0.55),
23 | biomaRt (>= 2.38.0)
24 | Imports:
25 | magrittr (>= 1.5),
26 | tidyr (>= 0.8.2),
27 | dplyr (>= 0.7.8),
28 | data.table (>= 1.11.8),
29 | shiny (>= 1.3.1),
30 | assertthat (>= 0.2.1),
31 | assertive (>= 0.3.5),
32 | purrr (>= 0.2.5),
33 | FSA (>= 0.8.21),
34 | tibble (>= 1.4.2),
35 | shinydashboard (>= 0.7.1),
36 | shinyWidgets (>= 0.4.8),
37 | RColorBrewer (>= 1.1.2),
38 | edgeR (>= 3.24.3),
39 | forcats (>= 0.4.0),
40 | multcomp (>= 1.4.10),
41 | ggplot2 (>= 3.1.1),
42 | plotly (>= 4.8.0),
43 | testthat (>= 2.0.1),
44 | shinycssloaders (>= 0.2.0),
45 | DT (>= 0.5),
46 | stringr (>= 1.4.0),
47 | rlang (>= 0.3.0.1),
48 | ggsci (>= 2.9),
49 | AnnotationDbi,
50 | ggforce,
51 | ggrepel,
52 | modeest (>= 2.3.3),
53 | effsize
54 | Remotes:
55 | stephenturner/annotables,
56 | vqv/ggbiplot
57 |
--------------------------------------------------------------------------------
/man/plot_polya_distribution.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_plots.R
3 | \name{plot_polya_distribution}
4 | \alias{plot_polya_distribution}
5 | \title{Plots polya distribution as ndensity plot}
6 | \usage{
7 | plot_polya_distribution(
8 | polya_data,
9 | groupingFactor = NA,
10 | parameter_to_plot = "polya_length",
11 | condition1 = NA,
12 | condition2 = NA,
13 | show_center_values = "none",
14 | subsample = NA,
15 | ndensity = TRUE,
16 | mode_method = "density",
17 | auto_scale = T,
18 | transcript_id = NULL,
19 | transcript_id_column = "transcript",
20 | ...
21 | )
22 | }
23 | \arguments{
24 | \item{polya_data}{input data with polyA predictions}
25 |
26 | \item{groupingFactor}{how to group}
27 |
28 | \item{parameter_to_plot}{what to plot on x scale (defaults to polya_length)}
29 |
30 | \item{condition1}{First condition to include on the plot}
31 |
32 | \item{condition2}{Second condition to include on the plot}
33 |
34 | \item{show_center_values}{Show center values as vertical line. Possible values: "none","median","mean"}
35 |
36 | \item{subsample}{Subsample input table, provide either absolute number or fraction}
37 |
38 | \item{ndensity}{Should ndensity (scaled density) be plotted instead of normal denisty (default = TRUE)}
39 |
40 | \item{mode_method}{method used for the mode calculation (argument to modeest::mlv method parameter)}
41 |
42 | \item{auto_scale}{automatically adjust axis scales (default=TRUE)}
43 |
44 | \item{...}{parameters passed to .basic_aesthetics function (scale_x_limit_low = NA, scale_x_limit_high = NA, scale_y_limit_low = NA, scale_y_limit_high = NA, color_palette = "Set1",plot_title=NA)}
45 | }
46 | \value{
47 | \link[ggplot2]{ggplot} object
48 | }
49 | \description{
50 | Plots polya distribution as ndensity plot
51 | }
52 |
--------------------------------------------------------------------------------
/man/calculate_polya_stats.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/polya_stats.R
3 | \name{calculate_polya_stats}
4 | \alias{calculate_polya_stats}
5 | \title{Calculates basic statistics for polya lengths}
6 | \usage{
7 | calculate_polya_stats(
8 | polya_data,
9 | transcript_id_column = "transcript",
10 | min_reads = 0,
11 | grouping_factor = "sample_name",
12 | condition1 = NA,
13 | condition2 = NA,
14 | stat_test = "Wilcoxon",
15 | alpha = 0.05,
16 | add_summary = TRUE,
17 | length_summary_to_show = "gm_mean",
18 | ...
19 | )
20 | }
21 | \arguments{
22 | \item{polya_data}{input table with polyA predictions}
23 |
24 | \item{transcript_id_column}{\itemize{
25 | \item name of the column with transcript ids (default = "transcript")
26 | }}
27 |
28 | \item{min_reads}{minimum number of reads to include transcript in the analysis}
29 |
30 | \item{grouping_factor}{which column defines groups (default: sample_name)}
31 |
32 | \item{condition1}{if \code{grouping_factor} has more than 2 levels, which level use for comparison}
33 |
34 | \item{condition2}{if \code{grouping_factor} has more than 2 levels, which level use for comparison}
35 |
36 | \item{stat_test}{what statistical test to use for testing, currently supports "Wilcoxon" (for \link{wilcox.test}), "KS" (for \link[FSA]{ksTest} from FSA package) or "glm" (for \link{glm})}
37 |
38 | \item{alpha}{\itemize{
39 | \item alpha value to consider a hit significant (default - 0.05)
40 | }}
41 |
42 | \item{add_summary}{\itemize{
43 | \item add summary (mean polya lengths, counts) to statistics results?
44 | }}
45 |
46 | \item{length_summary_to_show}{\itemize{
47 | \item which length summary to show ("median"/"mean"/"gm_mean")
48 | }}
49 |
50 | \item{...}{\itemize{
51 | \item additional parameters to pass to .polya_stats (custom_glm_formula,use_dwell_time)
52 | }}
53 | }
54 | \value{
55 | summary table with pvalues and median/mean values associated to each transcript
56 | }
57 | \description{
58 | Takes polyA predictions table as input and checks if there is significant difference in polyA lengths between chosen conditions for each transcript.
59 | By default, Wilcoxon Rank Sum (\link{wilcox.test}) test is used.
60 | }
61 |
--------------------------------------------------------------------------------
/tests/testthat/test-read_polyA_data.R:
--------------------------------------------------------------------------------
1 | context("test functions for data import and filtering")
2 |
3 | library(assertthat)
4 | library(testthat)
5 | library(assertive)
6 |
7 | empty_sample_table = data.frame()
8 |
9 | sample_table_with_correct_columns_wrong_data = data.frame(sample_name = c("iorem","ipsum"),polya_path=c("iorem","ipsum"))
10 | sample_table_with_wrong_columns = data.frame(sample_name_wrong = c("iorem","ipsum"),polya_path_wrong=c("iorem","ipsum"))
11 |
12 |
13 | test_that("read_polya_single correctly parses provided parameters",{
14 | expect_error(read_polya_single(""))
15 | expect_error(read_polya_single("/foo/bar"))
16 | expect_error(read_polya_single(" ",gencode="TRUE"))
17 | expect_error(read_polya_single(" ",gencode="FALSE"))
18 | expect_error(read_polya_single(" ",gencode=FALSE,sample_name="sample1"))
19 | expect_error(read_polya_single(sample_tempfile1,gencode="FALSE",sample_name="sample1"))
20 | })
21 |
22 | test_that("polya data are correctly read from files",{
23 | empty_tempfile = tempfile()
24 | expect_error(read_polya_single(empty_tempfile))
25 | expect_message(read_polya_single(sample_tempfile1),"Loading data from",fixed=TRUE)
26 | expect_message(read_polya_single(sample_tempfile1,gencode=TRUE),"Loading data from",fixed=TRUE)
27 | expect_warning(read_polya_single(sample_tempfile1,gencode=TRUE,sample_name="sample1"),"sample_name was provided in the input file",fixed=TRUE,all=FALSE)
28 | expect_message(read_polya_multiple(example_sample_table),"Loading data from",fixed=TRUE)
29 | expect_length(rownames(read_polya_multiple(example_sample_table)),number_of_reads_per_sample*2)
30 | })
31 |
32 |
33 |
34 | test_that("Sample table is correctly provided",{
35 | expect_error(read_polya_multiple())
36 | expect_error(read_polya_multiple(samples_table = empty_sample_table))
37 | expect_error(read_polya_multiple(samples_table = sample_table_with_wrong_columns))
38 | expect_error(read_polya_multiple(samples_table = sample_table_with_correct_columns_wrong_data))
39 | })
40 |
41 | test_that("Filtering works",{
42 | example_polya_table_read_from_disk <- read_polya_multiple(samples_table = example_sample_table)
43 | expect_error(remove_failed_reads())
44 | expect_error(remove_failed_reads(empty_sample_table))
45 | expect_silent(remove_failed_reads(example_valid_polya_table))
46 | })
47 |
--------------------------------------------------------------------------------
/tests/testthat/test-polya_stats.R:
--------------------------------------------------------------------------------
1 | context("test functions for statistics calculation")
2 |
3 |
4 | library(assertthat)
5 | library(testthat)
6 | library(assertive)
7 |
8 |
9 |
10 | test_that("Valid input parameters provided for calculate_polya_stats()",{
11 | expect_error(calculate_polya_stats())
12 | expect_error(calculate_polya_stats(empty_polya_data_table))
13 |
14 | #expect_silent(calculate_polya_stats(example_valid_polya_table,grouping_factor="group"))
15 | #expect_silent(calculate_polya_stats(example_valid_polya_table,grouping_factor="group"))
16 | #expect_silent(calculate_polya_stats(example_valid_polya_table,grouping_factor="group"))
17 | #expect_silent(calculate_polya_stats(example_valid_polya_table,grouping_factor="group"))
18 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",stat_test = "ttest"),"Please provide one of available statistical tests")
19 |
20 | #expect_silent(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",stat_test="glm",custom_glm_formula = "polya_length ~ group"))
21 | #expect_silent(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",stat_test="glm",custom_glm_formula = polya_length ~ group))
22 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",stat_test="glm",custom_glm_formula = polya_length ~ group + group2))
23 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",stat_test="glm",custom_glm_formula = polya_length ~ NULL))
24 |
25 | #expect_silent(calculate_polya_stats(example_valid_polya_table_3levels,grouping_factor="group",condition1 = "group1",condition2 = "group2"))
26 | expect_error(calculate_polya_stats(example_valid_polya_table_3levels,grouping_factor="group",condition1 = "group1",condition2 = "nonexistent_group"))
27 | expect_error(calculate_polya_stats(example_valid_polya_table_3levels,grouping_factor="group",condition2 = "group2",condition1 = "nonexistent_group"))
28 | expect_error(calculate_polya_stats(example_valid_polya_table_3levels,grouping_factor="group"))
29 | expect_error(calculate_polya_stats(example_polya_table_sample1,grouping_factor="group"))
30 |
31 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="non-existent-group"))
32 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",use_dwell_time = "TRUE"))
33 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",min_reads = "2"))
34 | expect_error(calculate_polya_stats(example_valid_polya_table,grouping_factor="group",min_reads = NULL))
35 | })
36 |
37 |
38 | test_that("Valid input parameters provided for calculate_pca()",{
39 |
40 | summarized_example_valid_polya_table <- summarize_polya(example_valid_polya_table,summary_factors = "sample_name")
41 |
42 | expect_error(calculate_pca())
43 | expect_error(calculate_pca(empty_polya_data_table))
44 | expect_error(calculate_pca(example_valid_polya_table))
45 | expect_error(calculate_pca(example_valid_polya_table,parameter = "polya_imagined_summary"))
46 | expect_named(calculate_pca(summarized_example_valid_polya_table),c("pca","sample_names"),ignore.order = TRUE)
47 | expect_error(calculate_pca(summarized_example_valid_polya_table %>% dplyr::select(-sample_name)))
48 | expect_error(calculate_pca(summarized_example_valid_polya_table %>% dplyr::ungroup() %>% dplyr::select(-transcript)))
49 | })
50 |
51 |
52 | test_that("summarize polyA is working",{
53 | expect_error(summarize_polya())
54 | expect_error(summarize_polya(empty_polya_data_table))
55 | expect_error(summarize_polya(example_valid_polya_table,summary_factors = 1))
56 | expect_error(summarize_polya(example_valid_polya_table_3levels,summary_factors = "non-existent-factor"))
57 | #expect_silent(summarize_polya(example_valid_polya_table_3levels))
58 | })
59 |
60 |
61 | test_that("Valid input parameters provided for calculate_diff_exp_binom()",{
62 | expect_error(calculate_diff_exp_binom())
63 | expect_error(calculate_diff_exp_binom(empty_polya_data_table))
64 |
65 | expect_error(calculate_diff_exp_binom(example_valid_polya_table))
66 | expect_error(calculate_diff_exp_binom(example_valid_polya_table,grouping_factor = "group"))
67 |
68 | expect_error(calculate_diff_exp_binom(example_valid_polya_table,grouping_factor = "group",condition1 = "group1", condition2 = "non-existent-factor-level"))
69 | expect_error(calculate_diff_exp_binom(example_valid_polya_table,grouping_factor = "group",condition2 = "group1", condition1 = "non-existent-factor-level"))
70 |
71 | #expect_length(rownames(calculate_diff_exp_binom(example_valid_polya_table,grouping_factor = "group",condition1 = "group1",condition2="group2")),50)
72 | })
73 |
74 |
--------------------------------------------------------------------------------
/R/polya_annotate.R:
--------------------------------------------------------------------------------
1 | #' Annotate polyA predictions using annotables
2 | #'
3 | #' @param polya_data polya data table to annotate
4 | #' @param genome valid genome from annotables to use for annotation
5 | #'
6 | #' @return a \link[tibble]{tibble}
7 | #' @export
8 | #'
9 | annotate_with_annotables <- function(polya_data,genome) {
10 |
11 | if ( !requireNamespace('annotables',quietly = TRUE) ) {
12 | stop("NanoTail requires 'annotables'. Please install it using
13 | install.packages('devtools')
14 | devtools::install_github('stephenturner/annotables')")
15 | }
16 | require(annotables)
17 |
18 |
19 | if (missing(polya_data)) {
20 | stop("Please provide data.frame with polyA predictions as an input.",
21 | call. = FALSE)
22 | }
23 |
24 | if (missing(genome)) {
25 | stop("Please provide valid genome from annotables package to use for annotation.",
26 | call. = FALSE)
27 | }
28 |
29 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data frame provided as an input (polya_data). Please provide valid input")
30 |
31 | tx_to_gene_table = paste0(genome,"_tx2gene")
32 | # Annotatio join, with last step to deduplicate annotations - remove duplicates which occurs due to e.g multiple entrez ids for each transcript
33 | polya_data_annotated <- polya_data %>% dplyr::left_join( eval(as.symbol(tx_to_gene_table)),by=c("ensembl_transcript_id_short" = "enstxp")) %>% dplyr::left_join(eval(as.name(genome)) %>% dplyr::group_by(ensgene) %>% dplyr::slice(1) %>% dplyr::ungroup())
34 |
35 | #Explictly convert selected columns to factors (required for proper visualization)
36 | polya_data_annotated$biotype <- as.factor(polya_data_annotated$biotype)
37 | polya_data_annotated$strand <- as.factor(polya_data_annotated$strand)
38 | polya_data_annotated$chr <- as.factor(polya_data_annotated$chr)
39 |
40 |
41 |
42 |
43 | return(polya_data_annotated)
44 | }
45 |
46 |
47 | #' Title
48 | #'
49 | #' @param polya_data polya data table to annotate
50 | #' @param attributes_to_get what annotations should be retrieved. Default = c('external_gene_name','description','transcript_biotype')
51 | #' @param filters which column should be matched in the target mart
52 | #' @param mart_to_use mart object created with \link[biomaRt]{useMart} or \link[biomaRt]{useEnsembl}
53 | #'
54 | #' @return a \link[tibble]{tibble}
55 | #' @export
56 | annotate_with_biomart <- function(polya_data,attributes_to_get=c('ensembl_transcript_id','external_gene_name','description','transcript_biotype'),filters='ensembl_transcript_id',mart_to_use=NA) {
57 |
58 | if (missing(polya_data)) {
59 | stop("Please provide data.frame with polyA predictions as an input.",
60 | call. = FALSE)
61 | }
62 |
63 | if (missing(mart_to_use)) {
64 | stop("Please provide valid mart object",
65 | call. = FALSE)
66 | }
67 |
68 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data frame provided as an input (polya_data). Please provide valid input")
69 | assertthat::assert_that(class(mart_to_use)=="Mart",msg="Please provide valid mart object")
70 | assertthat::assert_that(length(attributes)>0,msg="please provide attributes")
71 |
72 | ensembl_ids = unique(polya_data$ensembl_transcript_id_short)
73 | ensembl_ids <- ensembl_ids[!is.na(ensembl_ids)]
74 |
75 | polya_data <- polya_data %>% dplyr::rename(ensembl_transcript_id = ensembl_transcript_id_short)
76 |
77 | # if using biomaRt version older than from Bioconductor 3.9, it cannot process more than 500 values at once
78 | if (packageVersion("biomaRt")<"2.40.0") {
79 | number_of_items <- length(ensembl_ids)
80 | annotation_data=data.frame()
81 | for (z in seq(1,number_of_items,by = 500)) {
82 |
83 | annotation_data_temp<-getBM(attributes=attributes_to_get, filters =filters, values = ensembl_ids[z:(z+499)], mart = mart_to_use)
84 | #print(annotation_data_temp)
85 | annotation_data<-rbind(annotation_data,annotation_data_temp)
86 | }
87 | }
88 | # since biomaRt 2.40 batch submission is possible
89 | else {
90 | annotation_data<-biomaRt::getBM(attributes=attributes_to_get, filters = filters, values = ensembl_ids, mart = mart_to_use)
91 | }
92 | polya_data_annotated <- polya_data %>% dplyr::left_join(annotation_data)
93 |
94 | return(polya_data_annotated)
95 | }
96 |
97 |
98 | #' Title
99 | #'
100 | #' @param columns_of_annotation which columns to use
101 | #' @param keytype whic keytype to use
102 | #' @param organism whic organism database to use
103 | #' @param polya_data polya data table to annotate
104 | #'
105 | #' @return a \link[tibble]{tibble}
106 | #' @export
107 | annotate_with_org_packages <- function(polya_data,columns_of_annotation=c("GENENAME","SYMBOL"),keytype='ENSEMBLTRANS',organism="mus_musculus") {
108 |
109 | if (missing(polya_data)) {
110 | stop("Please provide data.frame with polyA predictions as an input.",
111 | call. = FALSE)
112 | }
113 |
114 |
115 | # currently thos supported
116 | valid_org_packages = list("homo_sapiens" = "org.Hs.eg.db", "mus_musculus" = "org.Mm.eg.db","rattus_norvegicus" = "org.rn.eg.db","saccharomyces_cerevisiae" = "org.Sc.sgd.db","caenorhabditis_elegans" = "org.Ce.eg.db")
117 |
118 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data frame provided as an input (polya_data). Please provide valid input")
119 | assertthat::assert_that(length(columns_of_annotation)>0,msg="please provide columns of annotation")
120 |
121 | ensembl_ids = unique(polya_data$ensembl_transcript_id_short)
122 | ensembl_ids <- ensembl_ids[!is.na(ensembl_ids)]
123 |
124 |
125 |
126 | polya_data <- polya_data %>% dplyr::rename(!! rlang::sym(keytype) := ensembl_transcript_id_short)
127 |
128 |
129 |
130 |
131 | annotation_data<-AnnotationDbi::select(eval(parse(text = valid_org_packages[[organism]])),columns = columns_of_annotation,keytype = keytype,keys = ensembl_ids)
132 |
133 | polya_data_annotated <- polya_data %>% dplyr::left_join(annotation_data)
134 |
135 | return(polya_data_annotated)
136 | }
137 |
138 |
--------------------------------------------------------------------------------
/R/read_polyA_data.R:
--------------------------------------------------------------------------------
1 | #' Read Single Nanopolish polyA preditions from file
2 | #'
3 | #' This is the basic function used to import output from \code{nanopolish polya} to R
4 | #'
5 | #' @param polya_path path to nanopolish output file
6 | #' @param sample_name sample name (optional), provided as a string.
7 | #' If specified will be included as an additional column sample_name.
8 | #' @param gencode are contig names GENCODE-compliant.
9 | #' Can get transcript names and ensembl_transcript IDs if reads were mapped for example to Gencode reference transcriptome
10 | #'
11 | #' @seealso \link{read_polya_multiple}
12 | #'
13 | #' @export
14 | #'
15 | #' @return a [tibble][tibble::tibble-package] with polya predictions
16 | #'
17 | read_polya_single <- function(polya_path, gencode = TRUE, sample_name = NA, dorado = FALSE) {
18 | # required asserts
19 |
20 | #check if parameters are provided
21 | if (missing(polya_path)) {
22 | stop("The path to polyA predictions (argument polya_path) is missing",
23 | call. = FALSE)
24 | }
25 | assertthat::assert_that(assertive::is_a_non_missing_nor_empty_string(polya_path),msg = "Empty string provided as an input. Please provide a polya_path as a string")
26 | assertthat::assert_that(assertive::is_existing_file(polya_path),msg=paste("File ",polya_path," not exists",sep=""))
27 | assertthat::assert_that(assertive::is_non_empty_file(polya_path),msg=paste("File ",polya_path," is empty",sep=""))
28 | assertthat::assert_that(assertive::is_a_bool(gencode),msg="Please provide TRUE/FALSE values for gencode parameter")
29 |
30 | message(paste0("Loading data from ",polya_path))
31 |
32 | #integer64 set to "numeric" to avoid inconsistences when called from read_polya_multiple
33 |
34 | file_header <- read.table(polya_path,nrows=1)
35 | if (sum(grepl("pt",file_header))>0) {
36 | message("Seems like output from dorado. ")
37 | dorado = TRUE
38 | }
39 |
40 | polya_data <- data.table::fread(polya_path, integer64 = "numeric", data.table = F,header=TRUE,stringsAsFactors = FALSE,check.names = TRUE,showProgress = FALSE) %>% dplyr::as_tibble()
41 | if (!dorado) {
42 | polya_data <- polya_data %>% dplyr::mutate(polya_length = round(polya_length),dwell_time=transcript_start-polya_start)
43 | }
44 | else {
45 | polya_data <- polya_data %>% dplyr::mutate(polya_length = round(pt),dwell_time=NA) %>% dplyr::rename(contig=reference)
46 | }
47 | # change first column name
48 | colnames(polya_data)[1] <- "read_id"
49 | # transcript names, if mapping to gencode transcriptome
50 | if (gencode == TRUE) {
51 | transcript_names <- gsub(".*?\\|.*?\\|.*?\\|.*?\\|.*?\\|(.*?)\\|.*", "\\1", polya_data$contig)
52 | polya_data$transcript <- transcript_names
53 | ensembl_transcript_ids <- gsub("^(.*?)\\|.*\\|.*", "\\1", polya_data$contig)
54 | ensembl_transcript_ids_short <- gsub("(.*)\\..*", "\\1", ensembl_transcript_ids) # without version number
55 | polya_data$ensembl_transcript_id_full <- ensembl_transcript_ids
56 | polya_data$ensembl_transcript_id_short <- ensembl_transcript_ids_short
57 | }
58 | else {
59 | polya_data <- polya_data %>% dplyr::rename(transcript = contig)
60 | }
61 |
62 | if(!is.na(sample_name)) {
63 | # set sample_name (if was set)
64 | if (! "sample_name" %in% colnames(polya_data)) {
65 | warning("sample_name was provided in the input file. Overwriting with the provided one")
66 | }
67 | polya_data$sample_name = sample_name
68 | polya_data$sample_name <- as.factor(polya_data$sample_name)
69 | }
70 |
71 | return(polya_data)
72 | }
73 |
74 |
75 | # TODO Benchmark na lapply/for loop
76 |
77 | #' Reads multiple nanopolish polyA predictions at once
78 | #'
79 | #' This function can be used to load any number of files with polyA predictions with single invocation,
80 | #' allowing for metadata specification.
81 | #'
82 | #'
83 | #' @param samples_table data.frame or tibble containing samples metadata and paths to files.
84 | #' Should have at least two columns: \itemize{
85 | #' \item polya_path - containing path to the polya predictions file
86 | #' \item sample_name - unique name of the sample
87 | #' }
88 | #' Additional columns can provide metadata which will be included in the final table
89 | #' @param ... - additional parameters to pass to read_polya_single(), like gencode=(TRUE/FALSE)
90 | #'
91 | #' @return a [tibble][tibble::tibble-package] containing polyA predictions for all specified samples, with metadata provided in samples_table
92 | #' stored as separate columns
93 | #'
94 | #' @seealso \link{read_polya_single}
95 | #'
96 | #' @export
97 | #'
98 | read_polya_multiple <- function(samples_table,...) {
99 |
100 | if (missing(samples_table)) {
101 | stop("Samples table argument is missing",
102 | call. = FALSE)
103 | }
104 |
105 | assertthat::assert_that(assertive::has_rows(samples_table),msg = "Empty data frame provided as an input (samples_table). Please provide samples_table describing data to load")
106 | assertthat::assert_that("polya_path" %in% colnames(samples_table),msg = "Samples table should contain at least polya_path and sample_name columns")
107 | assertthat::assert_that("sample_name" %in% colnames(samples_table),msg = "Samples table should contain at least polya_path and sample_name columns")
108 |
109 | samples_data <- samples_table %>% dplyr::as.tbl() %>% dplyr::mutate_if(is.character,as.factor) %>% dplyr::mutate(polya_path = as.character(polya_path)) %>% dplyr::group_by(sample_name) %>% dplyr::mutate(polya_contents=purrr::map(polya_path, function(x) read_polya_single(x))) %>% dplyr::ungroup() %>% dplyr::select(-polya_path)
110 | polya_data <- tidyr::unnest(samples_data)
111 |
112 | return(polya_data)
113 | }
114 |
115 |
116 | #' Removes reads which failed during Nanopolish polya processing
117 | #'
118 | #' Convenient function to quickly remove all reads failing during nanopolish polya processing
119 | #'
120 | #' @param polya_data output table from \link{read_polya_single} or \link{read_polya_multiple}
121 | #'
122 | #' @return a [tibble][tibble::tibble-package] with only reads having qc_tag=='PASS'
123 | #'
124 | #' @export
125 | #'
126 | #' @seealso \link{read_polya_single}, \link{read_polya_multiple}
127 | #'
128 | remove_failed_reads <- function(polya_data) {
129 |
130 | if (missing(polya_data)) {
131 | stop("Please provide data.frame with polyA predictions as an input.",
132 | call. = FALSE)
133 | }
134 |
135 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data frame provided as an input (polya_data). Please provide valid input")
136 |
137 | filtered_polya_data <- polya_data %>% dplyr::filter(qc_tag=='PASS')
138 | return(filtered_polya_data)
139 | }
140 |
141 |
142 |
--------------------------------------------------------------------------------
/tests/testthat.R:
--------------------------------------------------------------------------------
1 | library(testthat)
2 | library(nanotail)
3 |
4 |
5 | #define testing environment for all tests:
6 | empty_polya_data_table = data.frame()
7 | qc_values <- c(rep("PASS",10),"ADAPTER","READ_FAILED_LOAD","SUFFCLIP","NOREGION")
8 | one_line_polya_data_table = data.frame(sample_name="wt1",group="wt",read_id="1234567890",polya_length=100,position=1,contig="contig",leader_start=1,adapter_start=1,polya_start=1,qc_tag="PASS",transcript="transcript")
9 | two_line_polya_data_table = data.frame(sample_name=c("wt1","mut1"),group=c("wt","mut"),read_id=c("1234567890","1234567891"),polya_length=c(100,20),position=rep(1,2),contig=rep("contig1",2),leader_start=rep(1,2),adapter_start=rep(100,2),polya_start=rep(1,2),qc_tag=rep("PASS",2),transcript=rep("transcript",2))
10 |
11 | # generation of valid multi-sample polyA table (resembling output of read_polya_multiple())
12 | number_of_reads_per_sample=20000
13 | number_of_transcripts_per_sample=50
14 | example_polya_table_sample1 = data.frame(sample_name="sample1",group="group1",read_id=paste0("sample1_",seq(1,number_of_reads_per_sample)),transcript=rep(paste0("transcript",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),polya_length=c(rlnorm(number_of_reads_per_sample,meanlog=4,sdlog=1)),qc_tag=qc_values[sample(14,20000,replace = TRUE)],position=sample(1000,number_of_reads_per_sample,replace=TRUE),leader_start=sample(seq(1,100),number_of_reads_per_sample,replace=TRUE),adapter_start=sample(seq(100,200),number_of_reads_per_sample,replace=TRUE),polya_start=sample(seq(200,500),number_of_reads_per_sample,replace=TRUE),transcript_start=sample(seq(1000,10000),number_of_reads_per_sample,replace=TRUE),read_rate=rnorm(n = number_of_reads_per_sample,mean = 70,sd=20))
15 | example_polya_table_sample2 = data.frame(sample_name="sample2",group="group2",read_id=paste0("sample2_",seq(1,number_of_reads_per_sample)),transcript=rep(paste0("transcript",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),polya_length=c(rlnorm(number_of_reads_per_sample,meanlog=3,sdlog=1)),qc_tag=qc_values[sample(14,20000,replace = TRUE)],position=sample(1000,number_of_reads_per_sample,replace=TRUE),leader_start=sample(seq(1,100),number_of_reads_per_sample,replace=TRUE),adapter_start=sample(seq(100,200),number_of_reads_per_sample,replace=TRUE),polya_start=sample(seq(200,500),number_of_reads_per_sample,replace=TRUE),transcript_start=sample(seq(1000,10000),number_of_reads_per_sample,replace=TRUE),read_rate=rnorm(n = number_of_reads_per_sample,mean = 70,sd=20))
16 | example_polya_table_sample3 = data.frame(sample_name="sample3",group="group3",read_id=paste0("sample2_",seq(1,number_of_reads_per_sample)),transcript=rep(paste0("transcript",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),polya_length=c(rlnorm(number_of_reads_per_sample,meanlog=3,sdlog=1)),qc_tag=qc_values[sample(14,20000,replace = TRUE)],position=sample(1000,number_of_reads_per_sample,replace=TRUE),leader_start=sample(seq(1,100),number_of_reads_per_sample,replace=TRUE),adapter_start=sample(seq(100,200),number_of_reads_per_sample,replace=TRUE),polya_start=sample(seq(200,500),number_of_reads_per_sample,replace=TRUE),transcript_start=sample(seq(1000,10000),number_of_reads_per_sample,replace=TRUE),read_rate=rnorm(n = number_of_reads_per_sample,mean = 70,sd=20))
17 | example_valid_polya_table = rbind(example_polya_table_sample1,example_polya_table_sample2)
18 | example_valid_polya_table$group <- factor(example_valid_polya_table$group)
19 | example_valid_polya_table$sample_name <- factor(example_valid_polya_table$sample_name)
20 | example_valid_polya_table$qc_tag <- factor(example_valid_polya_table$qc_tag)
21 | example_valid_polya_table_3levels = rbind(example_polya_table_sample1,example_polya_table_sample2,example_polya_table_sample3)
22 | example_valid_polya_table_3levels$group <- factor(example_valid_polya_table_3levels$group)
23 | example_valid_polya_table_3levels$sample_name <- factor(example_valid_polya_table_3levels$sample_name)
24 | example_valid_polya_table_3levels$qc_tag <- factor(example_valid_polya_table_3levels$qc_tag)
25 |
26 |
27 | example_polya_table_mouse = data.frame(sample_name="sample1",group="group1",read_id=paste0("sample1_",seq(1,number_of_reads_per_sample)),transcript=rep(paste0("ENSMUST000001049",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),ensembl_transcript_id_short=rep(paste0("ENSMUST000001049",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),polya_length=c(rlnorm(number_of_reads_per_sample,meanlog=4,sdlog=1)),qc_tag=qc_values[sample(14,20000,replace = TRUE)],position=sample(1000,number_of_reads_per_sample,replace=TRUE),leader_start=sample(seq(1,100),number_of_reads_per_sample,replace=TRUE),adapter_start=sample(seq(100,200),number_of_reads_per_sample,replace=TRUE),polya_start=sample(seq(200,500),number_of_reads_per_sample,replace=TRUE),transcript_start=sample(seq(1000,10000),number_of_reads_per_sample,replace=TRUE),read_rate=rnorm(n = number_of_reads_per_sample,mean = 70,sd=20))
28 |
29 | # generation of valid polyA tables to be saved to temp files (for testing or data import functions)
30 | example_polya_table_sample1 = data.frame(read_id=paste0("sample1_",seq(1,number_of_reads_per_sample)),contig=rep(paste0("transcript",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),polya_length=c(rlnorm(number_of_reads_per_sample,meanlog=4,sdlog=1)),qc_tag=qc_values[sample(14,20000,replace = TRUE)],position=sample(1000,number_of_reads_per_sample,replace=TRUE),leader_start=sample(seq(1,100),number_of_reads_per_sample,replace=TRUE),adapter_start=sample(seq(100,200),number_of_reads_per_sample,replace=TRUE),polya_start=sample(seq(200,500),number_of_reads_per_sample,replace=TRUE),transcript_start=sample(seq(1000,10000),number_of_reads_per_sample,replace=TRUE),read_rate=rnorm(n = number_of_reads_per_sample,mean = 70,sd=20))
31 | example_polya_table_sample2 = data.frame(read_id=paste0("sample2_",seq(1,number_of_reads_per_sample)),contig=rep(paste0("transcript",seq(1,number_of_transcripts_per_sample)),number_of_reads_per_sample/number_of_transcripts_per_sample),polya_length=c(rlnorm(number_of_reads_per_sample,meanlog=3,sdlog=1)),qc_tag=qc_values[sample(14,20000,replace = TRUE)],position=sample(1000,number_of_reads_per_sample,replace=TRUE),leader_start=sample(seq(1,100),number_of_reads_per_sample,replace=TRUE),adapter_start=sample(seq(100,200),number_of_reads_per_sample,replace=TRUE),polya_start=sample(seq(200,500),number_of_reads_per_sample,replace=TRUE),transcript_start=sample(seq(1000,10000),number_of_reads_per_sample,replace=TRUE),read_rate=rnorm(n = number_of_reads_per_sample,mean = 70,sd=20))
32 |
33 | sample_tempfile1 <- tempfile()
34 | sample_tempfile2 <- tempfile()
35 |
36 | write.table(example_polya_table_sample1,sample_tempfile1,quote = FALSE,sep = "\t",col.names = TRUE,row.names = FALSE)
37 | write.table(example_polya_table_sample2,sample_tempfile2,quote = FALSE,sep = "\t",col.names = TRUE,row.names = FALSE)
38 |
39 | example_sample_table <- data.frame(polya_path=c(sample_tempfile1,sample_tempfile2),sample_name=c("sample1","sample2"),group=c("group1","group2"))
40 |
41 |
42 | test_check("nanotail")
43 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # nanotail
2 |
3 |
4 | [](https://travis-ci.org/smaegol/nanotail)
5 | [](https://codecov.io/gh/smaegol/nanotail)
6 | [](https://zenodo.org/badge/latestdoi/176952175)
7 | [](https://www.gnu.org/licenses/gpl-3.0.en.html)
8 | [
9 |
10 |
11 |
12 | The goal of **NanoTail** is to provide a set of functions to manipulate and analyze data coming from polyA lengths estimations done using Oxford Nanopore Direct RNA sequencing and Nanopolish software. Existing solutions, like [Pipeline for testing shifts in poly(A) tail lengths estimated by nanopolish](https://github.com/nanoporetech/pipeline-polya-diff/) are, in our opinion, not sufficient for in-depth analysis of such data.
13 | The software is still in the development phase so all suggestions are welcome. Please also expect the code to be changed frequently, so use it with caution.
14 |
15 | ## Installation
16 |
17 | ### Prerequisities
18 |
19 | As `asserrtive` package was [removed from CRAN](https://cran.r-project.org/web/packages/assertive/index.html) it is currently impossible to install nanotail without prior manual installation of assertive package. We are working on exchanging the `assertive` package with its equivalent. However, for now, it is required to install all assertive packages manually:
20 |
21 | ```r
22 | install.packages("devtools")
23 | devtools::install_bitbucket("richierocks/assertive.properties") # install this one first as other packages depend on it
24 | devtools::install_bitbucket(c("richierocks/assertive.files", "richierocks/assertive.strings", "richierocks/assertive.numbers", "richierocks/assertive.matrices", "richierocks/assertive.sets", "richierocks/assertive.strings", "richierocks/assertive.models", "richierocks/assertive.reflection", "richierocks/assertive.types", "richierocks/assertive.datetimes", "richierocks/assertive.data", "richierocks/assertive.data.uk", "richierocks/assertive.data.us", "richierocks/assertive.code"))
25 | devtools::install_bitbucket("richierocks/assertive.properties") # install this one first as other packages depend on it
26 | devtools::install_bitbucket("richierocks/assertive")
27 | ```
28 |
29 |
30 |
31 | Now you can install the developmental version of Nanotail with
32 |
33 | ``` r
34 | devtools::install_github('smaegol/nanotail')
35 | library(nanotail)
36 | ```
37 |
38 | ## Input data
39 |
40 | NanoTail needs output from [nanopolish](https://github.com/jts/nanopolish) polya to work. It can read a single output file with `read_polya_single`:
41 |
42 | ``` r
43 | path <- "/location/of/nanopolish/output"
44 | polya_data <- read_polya_single(path)
45 | ```
46 |
47 | It can also read multiple samples at once and associate any metadata with them.
48 | Let's assume we have performed an experiment, targeting one of the polyA polymerases. 2 replicates were sequenced for control samples, and 2 replicates sequenced for samples with mutant PAP, therefore after all analysis we have 4 files with [nanopolish](https://github.com/jts/nanopolish) polya output. To read all of them at once, we can use command `read_polya_multiple` and associate metadata using samples_table data.frame:
49 |
50 | ``` r
51 | samples_table <- data.frame(polya_path = c(path1,path2,path3,path4),
52 | sample_name =c("wt1","mu1","wt2","mut2"),
53 | group = c("wt","mut","wt","mut"))
54 | polya_data_multiple <- read_polya_multiple(samples_table)
55 | ```
56 |
57 | To obtain nanopolish predictions one can use [Pipeline for calling poly(A) tail lengths from nanopore direct RNA data using nanopolish](https://github.com/nanoporetech/pipeline-polya-ng)
58 |
59 | ## Shiny App
60 |
61 | Once data are imported they can be processed in the R environment using NanoTail functions described below or, more convenient, the interactive Shiny app can be launched, allowing for easy exploration of obtained data. To launch the app for the data imported above:
62 |
63 | ``` r
64 | nanoTailApp(polya_table = polya_data_multiple)
65 |
66 | ```
67 |
68 | ## Nanopolish output QC
69 |
70 | To get overall information about the output of NanoPolish polya analysis, please use `get_nanopolish_processing_info()` function. Obtained summary can be plotted using `plot_nanopolish_qc()`. Summary of the analysis is also shown in the *QC info* tab of the Shiny App.
71 |
72 | 
73 |
74 |
75 | ## Global distribution of polyA lengths
76 |
77 | Global distribution of polyA tails lengths can be plotted with `plot_polya_distribution()` function, which produces the density plot, allowing for comparison of the distribution of polyA lengths between samples. The same plot can be seen in the *Global polya distribution* tab of the Shiny App.
78 |
79 | 
80 |
81 |
82 |
83 | ## Statistical analysis of polyA predictions
84 |
85 | NanoTail is intended to analyze differential adenylation. For this purpose 3 statistical tests can be employed, allowing or comparison of polyA lengths of individual transcripts between selected conditions:
86 | * Wilcoxon rank-sum test ([Mann-Whitney U-test](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test))
87 | * [Kolmogorov-Smirnov test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) of the equality of distributions
88 | * [generalized linear model](https://en.wikipedia.org/wiki/Generalized_linear_model) with log(polya_length) as the response, and post-hoc Tukey test
89 |
90 | Differential adenylation analysis can be performed with the `calculate_polya_stats` function, or within the *Differential adenylation* tab in the Shiny App.
91 |
92 | 
93 |
94 |
95 | ## Differential expression analysis
96 |
97 | NanoTail provides also the possibility of very basic differential expression testing, using binomTest from the [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) package. This functionality is still in the development and may not work as expected. To calculate differential expression please use `calculate_diff_exp_binom()` function or use Shiny App.
98 |
99 | 
100 |
101 |
102 | ## Citation
103 |
104 | Please cite NanoTail as:
105 | Krawczyk PS et al., NanoTail - R package for exploratory analysis of Nanopore Direct RNA based polyA lengths estimations
106 |
107 | Preprint in the preparation.
108 |
109 |
110 | ## TBD & plans
111 |
112 | * Import of polya predictions from software other then NanoPolish (poreplex,tailfindr,?)
113 | * Analysis of predictions based on genome-mapping (now only transcriptome-mapping is supported)
114 | * Annotation of results and enrichment analysis
115 | * Squiggle visualization of polyA tails
116 |
117 | ## Support
118 |
119 | Any issues connected with the NanoTail should be addressed to Pawel Krawczyk (p.krawczyk (at) ibb.waw.pl).
120 |
--------------------------------------------------------------------------------
/R/polya_helper_functions.R:
--------------------------------------------------------------------------------
1 | #' Calculates geometric mean
2 | #'
3 | #' @param x input vector
4 | #' @param na.rm should NA values be removed?
5 | #'
6 | #' @return geometric mean of values provided as an input
7 | #' @export
8 | #'
9 | #' @examples
10 | #' a <- rnorm(100,33,5)
11 | #' gm_mean(a)
12 | gm_mean = function(x, na.rm=TRUE){
13 |
14 | assertthat::assert_that(is.vector(x),msg = "Please provide numeric vector as an input for gm_mean")
15 | assertthat::assert_that(length(x)>0,msg = "Empty vector provided as input")
16 | assertthat::assert_that(assertive::is_a_bool(na.rm),msg = "Please provide boolean value for na.rm option")
17 | assertthat::assert_that(assertive::is_numeric(x),msg = "Please provide numeric vector as input")
18 | if (length(x)==1) {
19 | gm_mean=x[1]
20 | }
21 | else {
22 | gm_mean = exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
23 | }
24 | return(gm_mean)
25 | }
26 |
27 |
28 |
29 | #' Subsample a date frame
30 | #'
31 | #' Uses base subsetting and \link{sample} or dplyr \link[dplyr]{sample_n} or \link[dplyr]{sample_frac} to get the subset of the bigger data.frame or tibble
32 | #'
33 | #' @param input_table input table for subsampling
34 | #' @param groupingFactor grouping factor(s)
35 | #' @param subsample specify absolute number of rows or fraction to subsample from the data frame (group-wise)
36 | #'
37 | #' @return \link{tibble}
38 | #' @export
39 | #'
40 | subsample_table <- function(input_table,groupingFactor=NA,subsample=NA)
41 | {
42 | if (missing(input_table)) {
43 | stop("PolyA predictions are missing. Please provide a valid polya_data argument",
44 | call. = FALSE)
45 | }
46 |
47 | #assertthat::assert_that(is.numeric(reads_to_subsample),"Non-numeric argument for reads_to_subsample")
48 |
49 | assertthat::assert_that(!is.na(subsample),msg = "Please provide subsample option as an integer or fraction")
50 |
51 | if (isTRUE(round(subsample)==subsample)) {
52 | subsample_number = TRUE
53 | }
54 | else {
55 | subsample_number = FALSE
56 | }
57 |
58 | #if set to 0 - do not subsample - return input table))
59 | if (subsample==0) {
60 | return(input_table)
61 | }
62 | else {
63 | if(!is.na(groupingFactor)) {
64 | # group, if required
65 | assertthat::assert_that(groupingFactor %in% colnames(input_table),msg=paste0(groupingFactor," is not a column of input dataset"))
66 | input_table <- input_table %>% group_by(.dots = groupingFactor)
67 | if (subsample_number) {
68 | input_table <- dplyr::sample_n(input_table,subsample)
69 | }
70 | else {
71 | input_table <- dplyr::sample_frac(input_table,subsample)
72 | }
73 | }
74 | else {
75 | if (any(class(polya_test_lymph2)=="grouped_df")) {
76 | grouping_var = dplyr::group_vars(input_table)
77 | #input_table %>% ungroup(input_table)
78 | }
79 | if (subsample_number) {
80 | input_table <- input_table[sample(nrow(input_table),subsample),]
81 | }
82 | else {
83 | input_table <- dplyr::sample_frac(input_table,subsample)
84 | }
85 | }
86 |
87 | return(input_table)
88 | }
89 | }
90 |
91 |
92 |
93 |
94 |
95 | axis_elements_size=15
96 | axis_titles_size=18
97 | #' Default theme for ggplot2-based plots in the NanoTail package
98 | #' @export
99 | nanotail_ggplot2_theme <- ggplot2::theme(axis.title = ggplot2::element_text(size=axis_titles_size),axis.text = ggplot2::element_text(size=axis_elements_size),legend.text = ggplot2::element_text(size=axis_elements_size),legend.title = ggplot2::element_text(size=axis_titles_size))
100 |
101 |
102 |
103 |
104 | # based on https://community.rstudio.com/t/spread-with-multiple-value-columns/5378/2
105 | #' Spread multiple columns
106 | #'
107 | #' @param df data frame to apply spread on
108 | #' @param key as in \link{spread}
109 | #' @param value vector of columns to be taken as value for \link{spread}
110 | #'
111 | #' @return \link{tibble}
112 | #' @export
113 | #'
114 | spread_multiple <- function(df, key, value) {
115 | # quote key
116 | keyq <- rlang::enquo(key)
117 | # break value vector into quotes
118 | valueq <- rlang::enquo(value)
119 | s <- rlang::quos(!!valueq)
120 | df %>% tidyr::gather(variable, value, !!!s) %>%
121 | tidyr::unite(temp, !!keyq, variable) %>%
122 | tidyr::spread(temp, value)
123 | }
124 |
125 |
126 | #' Calculates scaling vector for virtual gel plotting
127 | #'
128 | #' @param input_data input polyA table for calculation of scaling factor (count of reads)
129 | #' @param groupingFactor for which factor calculate counts
130 | #'
131 | #' @return named vector
132 | #' @export
133 | #'
134 | calculate_scaling_vector_for_virutal_gel <- function(input_data,groupingFactor) {
135 | scaling_vector <- summarize_polya(input_data,transcript_id_column = groupingFactor) %>% dplyr::select(!!rlang::sym(groupingFactor),counts) %>% tibble::deframe()
136 | return(scaling_vector)
137 | }
138 |
139 |
140 |
141 | StatMedianLine <- ggplot2::ggproto("StatMedianLine", ggplot2::Stat,
142 | compute_group = function(data, scales) {
143 | transform(data, yintercept=median(y))
144 | },
145 | required_aes = c("x", "y")
146 | )
147 |
148 | #' Helper function for calculating median stat for violin/boxpolot ggplot plots
149 | #'
150 | #' @param mapping
151 | #' @param data
152 | #' @param geom
153 | #' @param position
154 | #' @param na.rm
155 | #' @param show.legend
156 | #' @param inherit.aes
157 | #' @param ...
158 | #'
159 | #' @return
160 | #' @export
161 | #'
162 | stat_median_line <- function(mapping = NULL, data = NULL, geom = "hline",
163 | position = "identity", na.rm = FALSE, show.legend = NA,
164 | inherit.aes = TRUE, ...) {
165 | ggplot2::layer(
166 | stat = StatMedianLine, data = data, mapping = mapping, geom = geom,
167 | position = position, show.legend = show.legend, inherit.aes = inherit.aes,
168 | params = list(na.rm = na.rm, ...)
169 | )
170 | }
171 |
172 |
173 | #' Function calculating statistical mode of given vector.
174 | #'
175 | #' @param x [character] data for which the most frequent value is to be calculated
176 | #' (polya_data column with the lengths of the poly(A) tails)
177 | #'
178 | #' @param method [character] "density"/"value"; density mode is computed
179 | #' by default.
180 | #'
181 | #' @param na.rm [boolean] parameter defining whether to remove missing values or
182 | #' not. By a default set to false
183 | #'
184 | #' @return statistical mode of given vector.
185 | #' @export
186 | #'
187 | #' @examples
188 | #' \dontrun{
189 | #'
190 | #' getmode(x = polya_data$tail_length,
191 | #' method = "density",
192 | #' na.rm = FALSE)
193 | #'
194 | #' }
195 | #'
196 | getmode <- function(x, method ="density", na.rm = FALSE) {
197 | x <- unlist(x)
198 | if (na.rm) {
199 | x <- x[!is.na(x)]
200 | }
201 |
202 | if (method %in% c("value", "density", "") | is.na(method)) {
203 | # Return actual mode (from the real values in dataset)
204 | if (method %in% c("density", "")) {
205 |
206 | # Return density mode for normalized data - only for numeric!)
207 | d <- density(x)
208 | return(d$x[d$y==max(d$y)])
209 | #return(modeest::mlv(x,na.rm=TRUE,method="parzen", abc=T)) #in some cases this method produces weird output
210 |
211 | } else if (method %in% c("value")) {
212 |
213 | uniqx <- unique(x)
214 | n <- length(uniqx)
215 | freqx <- tabulate(match(x, uniqx))
216 | modex <- freqx == max(freqx)
217 | return(uniqx[which(modex)])
218 | }
219 | }
220 | }
--------------------------------------------------------------------------------
/tests/testthat/test-polya_plots.R:
--------------------------------------------------------------------------------
1 | context("Test plotting functions")
2 |
3 | library(assertthat)
4 | library(testthat)
5 | library(assertive)
6 |
7 |
8 | test_that("valid parameters are provided for plot_polya_distribution()",{
9 |
10 | expect_error(plot_polya_distribution())
11 | #expect_silent(plot_polya_distribution(example_valid_polya_table))
12 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "non-existent_group"))
13 | #expect_silent(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group"))
14 | #expect_silent(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group2"))
15 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="non-existent_group"))
16 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",condition2="group1",condition1="non-existent_group"))
17 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group1"))
18 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",scale_x_limit_low = 0))
19 | #expect_silent(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",scale_x_limit_low = 0,scale_x_limit_high = 200))
20 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",scale_x_limit_low = "0",scale_x_limit_high = 200))
21 | expect_error(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",scale_x_limit_low = 0,scale_x_limit_high = "200"))
22 | expect_warning(plot_polya_distribution(example_valid_polya_table,groupingFactor = "group",scale_x_limit_low = 0,scale_x_limit_high = 200,color_palette = "non_existent_palette"),"Please provide valid color palette",all = FALSE,fixed=TRUE)
23 | })
24 |
25 | test_that("valid parameters are provided for plot_polya_boxplot()",{
26 |
27 | expect_error(plot_polya_boxplot())
28 | expect_error(plot_polya_boxplot(example_valid_polya_table))
29 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "non-existent_group"))
30 | expect_silent(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group"))
31 | expect_silent(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group2"))
32 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="non-existent_group"))
33 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",condition2="group1",condition1="non-existent_group"))
34 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group1"))
35 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",scale_y_limit_low = 0))
36 | expect_silent(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",scale_y_limit_low = 0,scale_y_limit_high = 200))
37 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",scale_y_limit_low = "0",scale_y_limit_high = 200))
38 | expect_error(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",scale_y_limit_low = 0,scale_y_limit_high = "200"))
39 | expect_warning(plot_polya_boxplot(example_valid_polya_table,groupingFactor = "group",scale_y_limit_low = 0,scale_y_limit_high = 200,color_palette = "non_existent_palette"),"Please provide valid color palette",all = FALSE,fixed=TRUE)
40 | })
41 |
42 |
43 | test_that("valid parameters are provided for plot_counts_scatter()",{
44 |
45 | summarized_example_valid_polya_table <- summarize_polya(example_valid_polya_table,summary_factors = "group")
46 |
47 | expect_error(plot_counts_scatter())
48 | expect_error(plot_counts_scatter(example_valid_polya_table))
49 | expect_error(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group"))
50 | expect_warning(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group2"),"Ignoring unknown aesthetics: text",fixed=TRUE)
51 | expect_error(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="non-existent_group"))
52 | expect_error(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition2="group1",condition1="non-existent_group"))
53 | expect_error(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group1"))
54 | expect_error(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",max_counts = 0))
55 | expect_warning(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group2",max_counts=200),"Ignoring unknown aesthetics: text",fixed=TRUE)
56 | expect_error(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group2",max_counts="200"))
57 | expect_warning(plot_counts_scatter(summarized_example_valid_polya_table,groupingFactor = "group",condition1="group1",condition2="group2",max_counts=200,color_palette = "non_existent_palette"),"Please provide valid color palette",all = FALSE,fixed=TRUE)
58 |
59 | })
60 |
61 |
62 | test_that("valid parameters are provided for plot_nanopolish_qc()",{
63 |
64 | nanopolish_qc_of_example_valid_polya_table <- get_nanopolish_processing_info(example_valid_polya_table)
65 | grouped_nanopolish_qc_of_example_valid_polya_table <- get_nanopolish_processing_info(example_valid_polya_table,grouping_factor = "sample_name")
66 |
67 | expect_error(plot_nanopolish_qc())
68 | expect_error(plot_nanopolish_qc(example_valid_polya_table))
69 | expect_error(plot_nanopolish_qc(empty_polya_data_table))
70 | expect_silent(plot_nanopolish_qc(nanopolish_qc_of_example_valid_polya_table))
71 | expect_silent(plot_nanopolish_qc(nanopolish_qc_of_example_valid_polya_table,frequency = FALSE))
72 | expect_error(plot_nanopolish_qc(nanopolish_qc_of_example_valid_polya_table,frequency = "FALSE"))
73 | expect_warning(plot_nanopolish_qc(grouped_nanopolish_qc_of_example_valid_polya_table,color_palette = "non_existent_palette"),"Please provide valid color palette",all = FALSE,fixed=TRUE)
74 | })
75 |
76 |
77 |
78 | test_that("valid parameters are provided for plot_volcano()",{
79 |
80 | binom_test_output_of_example_valid_polya_table <- calculate_diff_exp_binom(example_valid_polya_table,grouping_factor = "group",condition1 = "group1",condition2 = "group2")
81 | expect_error
82 | expect_error(plot_volcano())
83 | expect_error(plot_volcano(empty_polya_data_table))
84 | expect_silent(plot_volcano(binom_test_output_of_example_valid_polya_table))
85 | expect_error(plot_volcano(binom_test_output_of_example_valid_polya_table %>% dplyr::select(-fold_change)))
86 | expect_error(plot_volcano(binom_test_output_of_example_valid_polya_table %>% dplyr::select(-padj)))
87 | expect_error(plot_volcano(binom_test_output_of_example_valid_polya_table %>% dplyr::select(-significance)))
88 | expect_warning(plot_volcano(binom_test_output_of_example_valid_polya_table,color_palette = "non_existent_palette"),"Please provide valid color palette",all = FALSE,fixed=TRUE)
89 | })
90 |
91 | test_that("valid parameters are provided for plot_MA()",{
92 |
93 | binom_test_output_of_example_valid_polya_table <- calculate_diff_exp_binom(example_valid_polya_table,grouping_factor = "group",condition1 = "group1",condition2 = "group2")
94 | expect_error(plot_MA())
95 | expect_error(plot_MA(example_valid_polya_table))
96 | expect_error(plot_MA(empty_polya_data_table))
97 | expect_silent(plot_MA(binom_test_output_of_example_valid_polya_table))
98 | expect_error(plot_MA(binom_test_output_of_example_valid_polya_table %>% dplyr::select(-fold_change)))
99 | expect_error(plot_MA(binom_test_output_of_example_valid_polya_table %>% dplyr::select(-mean_expr)))
100 | expect_error(plot_MA(binom_test_output_of_example_valid_polya_table %>% dplyr::select(-significance)))
101 | expect_warning(plot_MA(binom_test_output_of_example_valid_polya_table,color_palette = "non_existent_palette"),"Please provide valid color palette",all = FALSE,fixed=TRUE)
102 | })
103 |
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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212 | terms of section 4, provided that you also meet all of these conditions:
213 |
214 | a) The work must carry prominent notices stating that you modified
215 | it, and giving a relevant date.
216 |
217 | b) The work must carry prominent notices stating that it is
218 | released under this License and any conditions added under section
219 | 7. This requirement modifies the requirement in section 4 to
220 | "keep intact all notices".
221 |
222 | c) You must license the entire work, as a whole, under this
223 | License to anyone who comes into possession of a copy. This
224 | License will therefore apply, along with any applicable section 7
225 | additional terms, to the whole of the work, and all its parts,
226 | regardless of how they are packaged. This License gives no
227 | permission to license the work in any other way, but it does not
228 | invalidate such permission if you have separately received it.
229 |
230 | d) If the work has interactive user interfaces, each must display
231 | Appropriate Legal Notices; however, if the Program has interactive
232 | interfaces that do not display Appropriate Legal Notices, your
233 | work need not make them do so.
234 |
235 | A compilation of a covered work with other separate and independent
236 | works, which are not by their nature extensions of the covered work,
237 | and which are not combined with it such as to form a larger program,
238 | in or on a volume of a storage or distribution medium, is called an
239 | "aggregate" if the compilation and its resulting copyright are not
240 | used to limit the access or legal rights of the compilation's users
241 | beyond what the individual works permit. Inclusion of a covered work
242 | in an aggregate does not cause this License to apply to the other
243 | parts of the aggregate.
244 |
245 | 6. Conveying Non-Source Forms.
246 |
247 | You may convey a covered work in object code form under the terms
248 | of sections 4 and 5, provided that you also convey the
249 | machine-readable Corresponding Source under the terms of this License,
250 | in one of these ways:
251 |
252 | a) Convey the object code in, or embodied in, a physical product
253 | (including a physical distribution medium), accompanied by the
254 | Corresponding Source fixed on a durable physical medium
255 | customarily used for software interchange.
256 |
257 | b) Convey the object code in, or embodied in, a physical product
258 | (including a physical distribution medium), accompanied by a
259 | written offer, valid for at least three years and valid for as
260 | long as you offer spare parts or customer support for that product
261 | model, to give anyone who possesses the object code either (1) a
262 | copy of the Corresponding Source for all the software in the
263 | product that is covered by this License, on a durable physical
264 | medium customarily used for software interchange, for a price no
265 | more than your reasonable cost of physically performing this
266 | conveying of source, or (2) access to copy the
267 | Corresponding Source from a network server at no charge.
268 |
269 | c) Convey individual copies of the object code with a copy of the
270 | written offer to provide the Corresponding Source. This
271 | alternative is allowed only occasionally and noncommercially, and
272 | only if you received the object code with such an offer, in accord
273 | with subsection 6b.
274 |
275 | d) Convey the object code by offering access from a designated
276 | place (gratis or for a charge), and offer equivalent access to the
277 | Corresponding Source in the same way through the same place at no
278 | further charge. You need not require recipients to copy the
279 | Corresponding Source along with the object code. If the place to
280 | copy the object code is a network server, the Corresponding Source
281 | may be on a different server (operated by you or a third party)
282 | that supports equivalent copying facilities, provided you maintain
283 | clear directions next to the object code saying where to find the
284 | Corresponding Source. Regardless of what server hosts the
285 | Corresponding Source, you remain obligated to ensure that it is
286 | available for as long as needed to satisfy these requirements.
287 |
288 | e) Convey the object code using peer-to-peer transmission, provided
289 | you inform other peers where the object code and Corresponding
290 | Source of the work are being offered to the general public at no
291 | charge under subsection 6d.
292 |
293 | A separable portion of the object code, whose source code is excluded
294 | from the Corresponding Source as a System Library, need not be
295 | included in conveying the object code work.
296 |
297 | A "User Product" is either (1) a "consumer product", which means any
298 | tangible personal property which is normally used for personal, family,
299 | or household purposes, or (2) anything designed or sold for incorporation
300 | into a dwelling. In determining whether a product is a consumer product,
301 | doubtful cases shall be resolved in favor of coverage. For a particular
302 | product received by a particular user, "normally used" refers to a
303 | typical or common use of that class of product, regardless of the status
304 | of the particular user or of the way in which the particular user
305 | actually uses, or expects or is expected to use, the product. A product
306 | is a consumer product regardless of whether the product has substantial
307 | commercial, industrial or non-consumer uses, unless such uses represent
308 | the only significant mode of use of the product.
309 |
310 | "Installation Information" for a User Product means any methods,
311 | procedures, authorization keys, or other information required to install
312 | and execute modified versions of a covered work in that User Product from
313 | a modified version of its Corresponding Source. The information must
314 | suffice to ensure that the continued functioning of the modified object
315 | code is in no case prevented or interfered with solely because
316 | modification has been made.
317 |
318 | If you convey an object code work under this section in, or with, or
319 | specifically for use in, a User Product, and the conveying occurs as
320 | part of a transaction in which the right of possession and use of the
321 | User Product is transferred to the recipient in perpetuity or for a
322 | fixed term (regardless of how the transaction is characterized), the
323 | Corresponding Source conveyed under this section must be accompanied
324 | by the Installation Information. But this requirement does not apply
325 | if neither you nor any third party retains the ability to install
326 | modified object code on the User Product (for example, the work has
327 | been installed in ROM).
328 |
329 | The requirement to provide Installation Information does not include a
330 | requirement to continue to provide support service, warranty, or updates
331 | for a work that has been modified or installed by the recipient, or for
332 | the User Product in which it has been modified or installed. Access to a
333 | network may be denied when the modification itself materially and
334 | adversely affects the operation of the network or violates the rules and
335 | protocols for communication across the network.
336 |
337 | Corresponding Source conveyed, and Installation Information provided,
338 | in accord with this section must be in a format that is publicly
339 | documented (and with an implementation available to the public in
340 | source code form), and must require no special password or key for
341 | unpacking, reading or copying.
342 |
343 | 7. Additional Terms.
344 |
345 | "Additional permissions" are terms that supplement the terms of this
346 | License by making exceptions from one or more of its conditions.
347 | Additional permissions that are applicable to the entire Program shall
348 | be treated as though they were included in this License, to the extent
349 | that they are valid under applicable law. If additional permissions
350 | apply only to part of the Program, that part may be used separately
351 | under those permissions, but the entire Program remains governed by
352 | this License without regard to the additional permissions.
353 |
354 | When you convey a copy of a covered work, you may at your option
355 | remove any additional permissions from that copy, or from any part of
356 | it. (Additional permissions may be written to require their own
357 | removal in certain cases when you modify the work.) You may place
358 | additional permissions on material, added by you to a covered work,
359 | for which you have or can give appropriate copyright permission.
360 |
361 | Notwithstanding any other provision of this License, for material you
362 | add to a covered work, you may (if authorized by the copyright holders of
363 | that material) supplement the terms of this License with terms:
364 |
365 | a) Disclaiming warranty or limiting liability differently from the
366 | terms of sections 15 and 16 of this License; or
367 |
368 | b) Requiring preservation of specified reasonable legal notices or
369 | author attributions in that material or in the Appropriate Legal
370 | Notices displayed by works containing it; or
371 |
372 | c) Prohibiting misrepresentation of the origin of that material, or
373 | requiring that modified versions of such material be marked in
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375 |
376 | d) Limiting the use for publicity purposes of names of licensors or
377 | authors of the material; or
378 |
379 | e) Declining to grant rights under trademark law for use of some
380 | trade names, trademarks, or service marks; or
381 |
382 | f) Requiring indemnification of licensors and authors of that
383 | material by anyone who conveys the material (or modified versions of
384 | it) with contractual assumptions of liability to the recipient, for
385 | any liability that these contractual assumptions directly impose on
386 | those licensors and authors.
387 |
388 | All other non-permissive additional terms are considered "further
389 | restrictions" within the meaning of section 10. If the Program as you
390 | received it, or any part of it, contains a notice stating that it is
391 | governed by this License along with a term that is a further
392 | restriction, you may remove that term. If a license document contains
393 | a further restriction but permits relicensing or conveying under this
394 | License, you may add to a covered work material governed by the terms
395 | of that license document, provided that the further restriction does
396 | not survive such relicensing or conveying.
397 |
398 | If you add terms to a covered work in accord with this section, you
399 | must place, in the relevant source files, a statement of the
400 | additional terms that apply to those files, or a notice indicating
401 | where to find the applicable terms.
402 |
403 | Additional terms, permissive or non-permissive, may be stated in the
404 | form of a separately written license, or stated as exceptions;
405 | the above requirements apply either way.
406 |
407 | 8. Termination.
408 |
409 | You may not propagate or modify a covered work except as expressly
410 | provided under this License. Any attempt otherwise to propagate or
411 | modify it is void, and will automatically terminate your rights under
412 | this License (including any patent licenses granted under the third
413 | paragraph of section 11).
414 |
415 | However, if you cease all violation of this License, then your
416 | license from a particular copyright holder is reinstated (a)
417 | provisionally, unless and until the copyright holder explicitly and
418 | finally terminates your license, and (b) permanently, if the copyright
419 | holder fails to notify you of the violation by some reasonable means
420 | prior to 60 days after the cessation.
421 |
422 | Moreover, your license from a particular copyright holder is
423 | reinstated permanently if the copyright holder notifies you of the
424 | violation by some reasonable means, this is the first time you have
425 | received notice of violation of this License (for any work) from that
426 | copyright holder, and you cure the violation prior to 30 days after
427 | your receipt of the notice.
428 |
429 | Termination of your rights under this section does not terminate the
430 | licenses of parties who have received copies or rights from you under
431 | this License. If your rights have been terminated and not permanently
432 | reinstated, you do not qualify to receive new licenses for the same
433 | material under section 10.
434 |
435 | 9. Acceptance Not Required for Having Copies.
436 |
437 | You are not required to accept this License in order to receive or
438 | run a copy of the Program. Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance. However,
441 | nothing other than this License grants you permission to propagate or
442 | modify any covered work. These actions infringe copyright if you do
443 | not accept this License. Therefore, by modifying or propagating a
444 | covered work, you indicate your acceptance of this License to do so.
445 |
446 | 10. Automatic Licensing of Downstream Recipients.
447 |
448 | Each time you convey a covered work, the recipient automatically
449 | receives a license from the original licensors, to run, modify and
450 | propagate that work, subject to this License. You are not responsible
451 | for enforcing compliance by third parties with this License.
452 |
453 | An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
455 | organization, or merging organizations. If propagation of a covered
456 | work results from an entity transaction, each party to that
457 | transaction who receives a copy of the work also receives whatever
458 | licenses to the work the party's predecessor in interest had or could
459 | give under the previous paragraph, plus a right to possession of the
460 | Corresponding Source of the work from the predecessor in interest, if
461 | the predecessor has it or can get it with reasonable efforts.
462 |
463 | You may not impose any further restrictions on the exercise of the
464 | rights granted or affirmed under this License. For example, you may
465 | not impose a license fee, royalty, or other charge for exercise of
466 | rights granted under this License, and you may not initiate litigation
467 | (including a cross-claim or counterclaim in a lawsuit) alleging that
468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 |
471 | 11. Patents.
472 |
473 | A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based. The
475 | work thus licensed is called the contributor's "contributor version".
476 |
477 | A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version. For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
485 | this License.
486 |
487 | Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
489 | make, use, sell, offer for sale, import and otherwise run, modify and
490 | propagate the contents of its contributor version.
491 |
492 | In the following three paragraphs, a "patent license" is any express
493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
495 | sue for patent infringement). To "grant" such a patent license to a
496 | party means to make such an agreement or commitment not to enforce a
497 | patent against the party.
498 |
499 | If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
501 | to copy, free of charge and under the terms of this License, through a
502 | publicly available network server or other readily accessible means,
503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients. "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 |
513 | If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 |
521 | A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License. You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 |
536 | Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License. If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all. For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
553 |
554 | Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work. The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 |
563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time. Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. Interpretation of Sections 15 and 16.
613 |
614 | If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 |
621 | END OF TERMS AND CONDITIONS
622 |
--------------------------------------------------------------------------------
/LICENSE.md:
--------------------------------------------------------------------------------
1 | GNU General Public License
2 | ==========================
3 |
4 | _Version 3, 29 June 2007_
5 | _Copyright © 2007 Free Software Foundation, Inc. <>_
6 |
7 | Everyone is permitted to copy and distribute verbatim copies of this license
8 | document, but changing it is not allowed.
9 |
10 | ## Preamble
11 |
12 | The GNU General Public License is a free, copyleft license for software and other
13 | kinds of works.
14 |
15 | The licenses for most software and other practical works are designed to take away
16 | your freedom to share and change the works. By contrast, the GNU General Public
17 | License is intended to guarantee your freedom to share and change all versions of a
18 | program--to make sure it remains free software for all its users. We, the Free
19 | Software Foundation, use the GNU General Public License for most of our software; it
20 | applies also to any other work released this way by its authors. You can apply it to
21 | your programs, too.
22 |
23 | When we speak of free software, we are referring to freedom, not price. Our General
24 | Public Licenses are designed to make sure that you have the freedom to distribute
25 | copies of free software (and charge for them if you wish), that you receive source
26 | code or can get it if you want it, that you can change the software or use pieces of
27 | it in new free programs, and that you know you can do these things.
28 |
29 | To protect your rights, we need to prevent others from denying you these rights or
30 | asking you to surrender the rights. Therefore, you have certain responsibilities if
31 | you distribute copies of the software, or if you modify it: responsibilities to
32 | respect the freedom of others.
33 |
34 | For example, if you distribute copies of such a program, whether gratis or for a fee,
35 | you must pass on to the recipients the same freedoms that you received. You must make
36 | sure that they, too, receive or can get the source code. And you must show them these
37 | terms so they know their rights.
38 |
39 | Developers that use the GNU GPL protect your rights with two steps: **(1)** assert
40 | copyright on the software, and **(2)** offer you this License giving you legal permission
41 | to copy, distribute and/or modify it.
42 |
43 | For the developers' and authors' protection, the GPL clearly explains that there is
44 | no warranty for this free software. For both users' and authors' sake, the GPL
45 | requires that modified versions be marked as changed, so that their problems will not
46 | be attributed erroneously to authors of previous versions.
47 |
48 | Some devices are designed to deny users access to install or run modified versions of
49 | the software inside them, although the manufacturer can do so. This is fundamentally
50 | incompatible with the aim of protecting users' freedom to change the software. The
51 | systematic pattern of such abuse occurs in the area of products for individuals to
52 | use, which is precisely where it is most unacceptable. Therefore, we have designed
53 | this version of the GPL to prohibit the practice for those products. If such problems
54 | arise substantially in other domains, we stand ready to extend this provision to
55 | those domains in future versions of the GPL, as needed to protect the freedom of
56 | users.
57 |
58 | Finally, every program is threatened constantly by software patents. States should
59 | not allow patents to restrict development and use of software on general-purpose
60 | computers, but in those that do, we wish to avoid the special danger that patents
61 | applied to a free program could make it effectively proprietary. To prevent this, the
62 | GPL assures that patents cannot be used to render the program non-free.
63 |
64 | The precise terms and conditions for copying, distribution and modification follow.
65 |
66 | ## TERMS AND CONDITIONS
67 |
68 | ### 0. Definitions
69 |
70 | “This License” refers to version 3 of the GNU General Public License.
71 |
72 | “Copyright” also means copyright-like laws that apply to other kinds of
73 | works, such as semiconductor masks.
74 |
75 | “The Program” refers to any copyrightable work licensed under this
76 | License. Each licensee is addressed as “you”. “Licensees” and
77 | “recipients” may be individuals or organizations.
78 |
79 | To “modify” a work means to copy from or adapt all or part of the work in
80 | a fashion requiring copyright permission, other than the making of an exact copy. The
81 | resulting work is called a “modified version” of the earlier work or a
82 | work “based on” the earlier work.
83 |
84 | A “covered work” means either the unmodified Program or a work based on
85 | the Program.
86 |
87 | To “propagate” a work means to do anything with it that, without
88 | permission, would make you directly or secondarily liable for infringement under
89 | applicable copyright law, except executing it on a computer or modifying a private
90 | copy. Propagation includes copying, distribution (with or without modification),
91 | making available to the public, and in some countries other activities as well.
92 |
93 | To “convey” a work means any kind of propagation that enables other
94 | parties to make or receive copies. Mere interaction with a user through a computer
95 | network, with no transfer of a copy, is not conveying.
96 |
97 | An interactive user interface displays “Appropriate Legal Notices” to the
98 | extent that it includes a convenient and prominently visible feature that **(1)**
99 | displays an appropriate copyright notice, and **(2)** tells the user that there is no
100 | warranty for the work (except to the extent that warranties are provided), that
101 | licensees may convey the work under this License, and how to view a copy of this
102 | License. If the interface presents a list of user commands or options, such as a
103 | menu, a prominent item in the list meets this criterion.
104 |
105 | ### 1. Source Code
106 |
107 | The “source code” for a work means the preferred form of the work for
108 | making modifications to it. “Object code” means any non-source form of a
109 | work.
110 |
111 | A “Standard Interface” means an interface that either is an official
112 | standard defined by a recognized standards body, or, in the case of interfaces
113 | specified for a particular programming language, one that is widely used among
114 | developers working in that language.
115 |
116 | The “System Libraries” of an executable work include anything, other than
117 | the work as a whole, that **(a)** is included in the normal form of packaging a Major
118 | Component, but which is not part of that Major Component, and **(b)** serves only to
119 | enable use of the work with that Major Component, or to implement a Standard
120 | Interface for which an implementation is available to the public in source code form.
121 | A “Major Component”, in this context, means a major essential component
122 | (kernel, window system, and so on) of the specific operating system (if any) on which
123 | the executable work runs, or a compiler used to produce the work, or an object code
124 | interpreter used to run it.
125 |
126 | The “Corresponding Source” for a work in object code form means all the
127 | source code needed to generate, install, and (for an executable work) run the object
128 | code and to modify the work, including scripts to control those activities. However,
129 | it does not include the work's System Libraries, or general-purpose tools or
130 | generally available free programs which are used unmodified in performing those
131 | activities but which are not part of the work. For example, Corresponding Source
132 | includes interface definition files associated with source files for the work, and
133 | the source code for shared libraries and dynamically linked subprograms that the work
134 | is specifically designed to require, such as by intimate data communication or
135 | control flow between those subprograms and other parts of the work.
136 |
137 | The Corresponding Source need not include anything that users can regenerate
138 | automatically from other parts of the Corresponding Source.
139 |
140 | The Corresponding Source for a work in source code form is that same work.
141 |
142 | ### 2. Basic Permissions
143 |
144 | All rights granted under this License are granted for the term of copyright on the
145 | Program, and are irrevocable provided the stated conditions are met. This License
146 | explicitly affirms your unlimited permission to run the unmodified Program. The
147 | output from running a covered work is covered by this License only if the output,
148 | given its content, constitutes a covered work. This License acknowledges your rights
149 | of fair use or other equivalent, as provided by copyright law.
150 |
151 | You may make, run and propagate covered works that you do not convey, without
152 | conditions so long as your license otherwise remains in force. You may convey covered
153 | works to others for the sole purpose of having them make modifications exclusively
154 | for you, or provide you with facilities for running those works, provided that you
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299 | ### 7. Additional Terms
300 |
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409 |
410 | ### 11. Patents
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470 |
471 | ### 12. No Surrender of Others' Freedom
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481 |
482 | ### 13. Use with the GNU Affero General Public License
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490 |
491 | ### 14. Revised Versions of this License
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504 |
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508 |
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511 | your choosing to follow a later version.
512 |
513 | ### 15. Disclaimer of Warranty
514 |
515 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
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522 |
523 | ### 16. Limitation of Liability
524 |
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531 | WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
532 | POSSIBILITY OF SUCH DAMAGES.
533 |
534 | ### 17. Interpretation of Sections 15 and 16
535 |
536 | If the disclaimer of warranty and limitation of liability provided above cannot be
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538 | law that most closely approximates an absolute waiver of all civil liability in
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540 | a copy of the Program in return for a fee.
541 |
542 | _END OF TERMS AND CONDITIONS_
543 |
544 | ## How to Apply These Terms to Your New Programs
545 |
546 | If you develop a new program, and you want it to be of the greatest possible use to
547 | the public, the best way to achieve this is to make it free software which everyone
548 | can redistribute and change under these terms.
549 |
550 | To do so, attach the following notices to the program. It is safest to attach them
551 | to the start of each source file to most effectively state the exclusion of warranty;
552 | and each file should have at least the “copyright” line and a pointer to
553 | where the full notice is found.
554 |
555 |
556 | Copyright (C) 2019 Pawel Krawczyk
557 |
558 | This program is free software: you can redistribute it and/or modify
559 | it under the terms of the GNU General Public License as published by
560 | the Free Software Foundation, either version 3 of the License, or
561 | (at your option) any later version.
562 |
563 | This program is distributed in the hope that it will be useful,
564 | but WITHOUT ANY WARRANTY; without even the implied warranty of
565 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
566 | GNU General Public License for more details.
567 |
568 | You should have received a copy of the GNU General Public License
569 | along with this program. If not, see .
570 |
571 | Also add information on how to contact you by electronic and paper mail.
572 |
573 | If the program does terminal interaction, make it output a short notice like this
574 | when it starts in an interactive mode:
575 |
576 | nanotail Copyright (C) 2019 Pawel Krawczyk
577 | This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
578 | This is free software, and you are welcome to redistribute it
579 | under certain conditions; type 'show c' for details.
580 |
581 | The hypothetical commands `show w` and `show c` should show the appropriate parts of
582 | the General Public License. Of course, your program's commands might be different;
583 | for a GUI interface, you would use an “about box”.
584 |
585 | You should also get your employer (if you work as a programmer) or school, if any, to
586 | sign a “copyright disclaimer” for the program, if necessary. For more
587 | information on this, and how to apply and follow the GNU GPL, see
588 | <>.
589 |
590 | The GNU General Public License does not permit incorporating your program into
591 | proprietary programs. If your program is a subroutine library, you may consider it
592 | more useful to permit linking proprietary applications with the library. If this is
593 | what you want to do, use the GNU Lesser General Public License instead of this
594 | License. But first, please read
595 | <>.
596 |
--------------------------------------------------------------------------------
/R/polya_stats.R:
--------------------------------------------------------------------------------
1 | #' Calculates basic statistics for polya lengths
2 | #'
3 | #' Takes polyA predictions table as input and checks if there is significant difference in polyA lengths between chosen conditions for each transcript.
4 | #' By default, Wilcoxon Rank Sum (\link{wilcox.test}) test is used.
5 | #'
6 | #' @param polya_data input table with polyA predictions
7 | #' @param min_reads minimum number of reads to include transcript in the analysis
8 | #' @param grouping_factor which column defines groups (default: sample_name)
9 | #' @param condition1 if `grouping_factor` has more than 2 levels, which level use for comparison
10 | #' @param condition2 if `grouping_factor` has more than 2 levels, which level use for comparison
11 | #' @param transcript_id_column - name of the column with transcript ids (default = "transcript")
12 | #' @param alpha - alpha value to consider a hit significant (default - 0.05)
13 | #' @param add_summary - add summary (mean polya lengths, counts) to statistics results?
14 | #' @param length_summary_to_show - which length summary to show ("median"/"mean"/"gm_mean")
15 | #' @param ... - additional parameters to pass to .polya_stats (custom_glm_formula,use_dwell_time)
16 | #' @param stat_test what statistical test to use for testing, currently supports "Wilcoxon" (for \link{wilcox.test}), "KS" (for \link[FSA]{ksTest} from FSA package) or "glm" (for \link{glm})
17 | #'
18 | #' @return summary table with pvalues and median/mean values associated to each transcript
19 | #'
20 | #' @export
21 | #'
22 | calculate_polya_stats <- function(polya_data, transcript_id_column = "transcript", min_reads = 0, grouping_factor = "sample_name",condition1=NA,condition2=NA,stat_test="Wilcoxon",alpha=0.05,add_summary=TRUE,length_summary_to_show = "gm_mean",...)
23 | {
24 |
25 |
26 | if (missing(polya_data)) {
27 | stop("PolyA predictions are missing. Please provide a valid polya_data argument",
28 | call. = FALSE)
29 | }
30 |
31 |
32 |
33 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data.frame provided as an input")
34 |
35 |
36 |
37 |
38 |
39 | # if grouping factor has more than two levels
40 | if (length(levels(polya_data[[grouping_factor]]))>2) {
41 | if(is.na(condition1) && is.na(condition2)) {
42 | #throw error when no conditions for comparison are specified
43 | stop(paste0("grouping_factor ",grouping_factor," has more than 2 levels. Please specify condtion1 and condition2 to select comparison pairs"))
44 | }
45 | else {
46 | # filter input data leaving only specified conditions, dropping other factor levels
47 | assertthat::assert_that(condition1 %in% levels(polya_data[[grouping_factor]]),msg=paste0(condition1," is not a level of ",grouping_factor," (grouping_factor)"))
48 | assertthat::assert_that(condition2 %in% levels(polya_data[[grouping_factor]]),msg=paste0(condition2," is not a level of ",grouping_factor," (grouping_factor)"))
49 | assertthat::assert_that(condition2 != condition1,msg="condition2 should be different than condition1")
50 | polya_data <- polya_data %>% dplyr::filter(!!rlang::sym(grouping_factor) %in% c(condition1,condition2)) %>% dplyr::mutate() %>% droplevels()
51 | }
52 | }
53 | else if (length(levels(polya_data[[grouping_factor]]))==1) {
54 | stop("Only 1 level present for grouping factor. Choose another groping factor for comparison")
55 | }
56 | else {
57 | condition1 = levels(polya_data[[grouping_factor]])[1]
58 | condition2 = levels(polya_data[[grouping_factor]])[2]
59 | }
60 |
61 | polya_data_stat <-
62 | polya_data %>% dplyr::mutate(transcript2=transcript) %>% dplyr::group_by(.dots = c(transcript_id_column)) %>% tidyr::nest()
63 |
64 | #future::plan(future::multiprocess())
65 | polya_data_stat <- polya_data_stat %>% dplyr::mutate(stats=purrr::map(data,.polya_stats,grouping_factor=grouping_factor,stat_test=stat_test,min_reads=min_reads,...)) %>% dplyr::select(-data) %>% tidyr::unnest()
66 | message("calculating statistics")
67 | #polya_data_stat <- polya_data_stat %>% dplyr::mutate(stats=furrr::future_map(data,.polya_stats,grouping_factor=grouping_factor,stat_test=stat_test,min_reads=min_reads,...)) %>% dplyr::select(-data) %>% tidyr::unnest()
68 | message("Finished")
69 | if (add_summary) {
70 | polyA_data_stat_summary <- summarize_polya(polya_data,summary_factors = grouping_factor,transcript_id_column = transcript_id_column) %>% dplyr::select(!!rlang::sym(transcript_id_column),counts,!!rlang::sym(grouping_factor),!!rlang::sym(paste0("polya_",length_summary_to_show))) %>% spread_multiple(!!rlang::sym(grouping_factor),c(counts,!!rlang::sym(paste0("polya_",length_summary_to_show))))
71 | polya_data_stat <- polya_data_stat %>% dplyr::full_join(polyA_data_stat_summary,by=transcript_id_column)
72 | polya_data_stat$length_diff <- polya_data_stat[[paste0(condition2,"_polya_",length_summary_to_show)]] - polya_data_stat[[paste0(condition1,"_polya_",length_summary_to_show)]]
73 | polya_data_stat$fold_change <- polya_data_stat[[paste0(condition2,"_polya_",length_summary_to_show)]] / polya_data_stat[[paste0(condition1,"_polya_",length_summary_to_show)]]
74 | }
75 | message("Adjusting p.value")
76 |
77 | polya_data_stat$padj <- p.adjust(polya_data_stat$p.value, method = "BH",n = nrow(polya_data_stat[!is.na(polya_data_stat$p.value),]))
78 |
79 | polya_data_stat<- polya_data_stat %>% dplyr::mutate(effect_size=dplyr::case_when((abs(cohen_d))<0.2 ~ "negligible",(abs(cohen_d)<0.5) ~ "small", (abs(cohen_d)<0.8) ~ "medium",(abs(cohen_d)>=0.8) ~ "large",TRUE ~ "NA"))
80 |
81 | # create significance factor
82 | polya_data_stat <-
83 | polya_data_stat %>% dplyr::mutate(significance = dplyr::case_when(is.na(padj) ~ "NotSig",
84 | (padj < alpha) ~ paste0("FDR<", alpha),
85 | TRUE ~ "NotSig"))
86 |
87 | polya_data_stat$stats_code <- sapply(polya_data_stat$stats_code,FUN = function(x) {stat_codes_list[[x]]},simplify = "vector",USE.NAMES = FALSE) %>% unlist()
88 |
89 |
90 |
91 | polya_data_stat <- polya_data_stat %>% dplyr::arrange(padj)
92 | #polya_data_stat_short <- polya_data_stat %>% dplyr::select(!!rlang::sym(transcript_id_column),dplyr::ends_with("counts"),dplyr::ends_with("gm_mean"),p.value,padj,stats_code)
93 |
94 |
95 | #return(
96 | # list(summary = polya_data_stat,summary_short = polya_data_stat_short))
97 | return(polya_data_stat)
98 | }
99 |
100 |
101 |
102 | stat_codes_list = list(OK = "OK",
103 | G1_NA = "GROUP1_NA",
104 | G2_NA = "GROUP2_NA",
105 | G1_LC = "G1_LOW_COUNT",
106 | G2_LC = "G2_LOW_COUNT",
107 | B_NA = "DATA FOR BOTH GROUPS NOT AVAILABLE",
108 | B_LC = "LOW COUNTS FOR BOTH GROUPS",
109 | G_LC = "LOW COUNT FOR ONE GROUP",
110 | G_NA = "DATA FOR ONE GROUP NOT AVAILABLE",
111 | ERR = "OTHER ERROR",
112 | GLM_GROUP = "MISSING FACTOR LEVEL IN GLM CALL")
113 |
114 | #' Calculates polyA statistics for single group of reads (for single transcript)
115 | #'
116 | #' @param polya_data - input data frame with polyA predictions
117 | #' @param stat_test - statistical test to use. One of : Wilcoxon, KS (Kolmogorov-Smirnov) or glm (Generalized Linear Model). All tests use log2(polya_length) as a response variable
118 | #' @param grouping_factor - factor defining groups (Need to have 2 levels)
119 | #' @param min_reads - minimum reads per group to include in the statistics calculation
120 | #' @param use_dwell_time - use dwell time instead of calculated polya length for statistics
121 | #' @param custom_glm_formula - custom glm formula (when using glm for statistics)
122 | #'
123 | #' @return data frame
124 | #'
125 | .polya_stats <- function(polya_data,stat_test,grouping_factor,min_reads=0,use_dwell_time = FALSE,custom_glm_formula = NA) {
126 |
127 |
128 | if (missing(polya_data)) {
129 | stop("PolyA predictions are missing. Please provide a valid polya_data argument",
130 | call. = FALSE)
131 | }
132 |
133 |
134 | available_statistical_tests = c("Wilcoxon","KS","glm")
135 |
136 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data.frame provided as an input")
137 |
138 | assertthat::assert_that(stat_test %in% available_statistical_tests,msg = "Please provide one of available statistical tests (Wilcoxon, KS or glm)")
139 | assertthat::assert_that(assertthat::is.number(min_reads),msg = "Non-numeric parameter provided (min_reads)")
140 | assertthat::assert_that(assertive::is_a_bool(use_dwell_time),msg="Non-boolean value provided for option use_dwell_time.")
141 | assertthat::assert_that(grouping_factor %in% colnames(polya_data),msg=paste0(grouping_factor," is not a column of input dataset"))
142 |
143 |
144 |
145 | # if grouping factor has more than two levels
146 | if (length(levels(polya_data[[grouping_factor]]))>2) {
147 | if(is.na(condition1) && is.na(condition2)) {
148 | #throw error when no conditions for comparison are specified
149 | stop(paste0("grouping_factor ",grouping_factor," has more than 2 levels. Please specify condtion1 and condition2 to select comparison pairs"))
150 | }
151 | else {
152 | # filter input data leaving only specified conditions, dropping other factor levels
153 | assertthat::assert_that(condition1 %in% levels(polya_data[[grouping_factor]]),msg=paste0(condition1," is not a level of ",grouping_factor," (grouping_factor)"))
154 | assertthat::assert_that(condition2 %in% levels(polya_data[[grouping_factor]]),msg=paste0(condition2," is not a level of ",grouping_factor," (grouping_factor)"))
155 | assertthat::assert_that(condition2 != condition1,msg="condition2 should be different than condition1")
156 | polya_data <- polya_data %>% dplyr::filter(!!rlang::sym(grouping_factor) %in% c(condition1,condition2)) %>% droplevels()
157 | }
158 | }
159 | else if (length(levels(polya_data[[grouping_factor]]))==1) {
160 | stop("Only 1 level present for grouping factor. Choose another groping factor for comparison")
161 | }
162 | else {
163 | condition1 = levels(polya_data[[grouping_factor]])[1]
164 | condition2 = levels(polya_data[[grouping_factor]])[2]
165 | }
166 |
167 |
168 | # initial status code
169 | stats_code = codes_stats = "OK"
170 | # calculate group counts
171 | group_counts = polya_data %>% dplyr::group_by(.dots = c(grouping_factor)) %>% dplyr::count()
172 |
173 | stats <- NA
174 | cohend <- NA
175 | if (use_dwell_time) {
176 | statistics_formula <- paste0("dwell_time ~",grouping_factor)
177 | }
178 | else {
179 | statistics_formula <- paste0("log2(polya_length) ~",grouping_factor)
180 | }
181 |
182 | if ((!missing(custom_glm_formula)) & (stat_test!='glm')) {
183 | warning("custom_glm_formula specified but glm is not used for statistics calculation. Formula will be ignored")
184 | }
185 |
186 |
187 | if (nrow(group_counts)==2) {
188 | if (group_counts[1,]$n < min_reads) {
189 | if (group_counts[2,]$n < min_reads) {
190 | #message("Not enough counts for both groups")
191 | stats_code = "B_LC"
192 | }
193 | else {
194 | #message(paste0("Not enough counts for group ",group_counts[1,]$group))
195 | stats_code = "G_LC"
196 | }
197 | }
198 | else if (group_counts[2,]$n < min_reads) {
199 | #message(paste0("Not enough counts for group ",group_counts[2,]$group))
200 | stats_code = "G_LC"
201 | }
202 | else {
203 | #calculate cohen's d parameter
204 | cohend<-effsize::cohen.d(data=polya_data,as.formula(paste0("polya_length ~",grouping_factor)))$estimate
205 | if (stat_test=="Wilcoxon") {
206 | #print(polya_data$transcript2)
207 | stats <- suppressWarnings(wilcox.test(as.formula(statistics_formula),polya_data))$p.value
208 | }
209 | else if (stat_test=="KS") {
210 | stats <- FSA::ksTest(as.formula(statistics_formula),data=polya_data)$p.value
211 | }
212 | else if (stat_test=="glm") {
213 | valid_glm_groups = TRUE
214 | if(!missing(custom_glm_formula)) {
215 | custom_glm_formula <- substitute(custom_glm_formula)
216 | glm_groups<-all.vars(as.formula(custom_glm_formula))[-1]
217 | assertthat::assert_that(length(glm_groups)>0,msg="Please provide valid custom_glm_formula (with the correct categorical explanatory variable)")
218 | assertthat::assert_that(all(glm_groups %in% colnames(polya_data)),msg = "wrong custom glm formula. Please provide valid column names as explanatory variable")
219 | # check that at least 2 levels present for each explanatory variable
220 | for (glm_group in glm_groups) {
221 | if (length(unique(polya_data[[glm_group]]))<2) {
222 | valid_glm_groups = FALSE
223 | }
224 | }
225 | # check that each explanatory variable has the same number of levels for each other(for example batches for batch effect formula)
226 | # will fail if there is any intersect of explanatory variables with 0 count
227 | if(!all(table(polya_data %>% dplyr::select(glm_groups))>0)) {
228 | valid_glm_groups = FALSE
229 | }
230 | statistics_formula = custom_glm_formula
231 | }
232 |
233 | if (valid_glm_groups) {
234 | # required, as log2(0) produces inf, throwing error in glm
235 | polya_data <- polya_data %>% dplyr::mutate(polya_length = ifelse(polya_length==0,1,polya_length))
236 | mcp_call <- paste0("multcomp::mcp(",grouping_factor,' = "Tukey")')
237 | stats <- summary(multcomp::glht(glm(formula = as.formula(statistics_formula),data=polya_data),eval(parse(text = mcp_call))))$test$pvalues[1]
238 |
239 | #polya_data_stat <- polya_data_stat %>% dplyr::mutate(stats = suppressWarnings(coef(summary(glm(formula = as.formula(statistics_formula))))[2,4]))
240 | }
241 | else{
242 | stats <- NA
243 | stats_code <- "GLM_GROUP"
244 | }
245 | }
246 | else {
247 | stop("wrong stat_test parameter provided")
248 | }
249 | }
250 | }
251 | else if (nrow(group_counts)==1) {
252 | stats_code = "G_NA"
253 | }
254 | else if (nrow(group_counts)==0) {
255 | stats_code = "B_NA"
256 | }
257 | else {
258 | stats_code = "ERR"
259 | }
260 |
261 |
262 | stats<-tibble::tibble(p.value=stats,stats_code=as.character(stats_code),cohen_d=cohend)
263 |
264 | return(stats)
265 |
266 | }
267 |
268 |
269 | #' Summarizes input polya table
270 | #'
271 | #' Summarizes input table with polyA predictions, calculating medians, mean, geometric means and standard deviation values for each transcript (default).
272 | #' To get overall summary for each sample or group, specify `transcript_id_column=NULL`
273 | #'
274 | #' @param polya_data input table with polyA predictions
275 | #' @param summary_factors specifies column used for grouping (default: group)
276 | #' @param transcript_id_column specifies which column use as transcript identifier (default: transcript). Set to `NULL` to omit per-transcript stats
277 | #'
278 | #' @return long-format \link[tibble]{tibble} with per-transcript statistics for each sample
279 | #' @export
280 | #'
281 | summarize_polya <- function(polya_data,summary_factors = c("group"),transcript_id_column = c("transcript")) {
282 |
283 | if (missing(polya_data)) {
284 | stop("PolyA predictions are missing. Please provide a valid polya_data argument",
285 | call. = FALSE)
286 | }
287 |
288 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data.frame provided as an input")
289 | assertthat::assert_that(assertive::is_character(summary_factors),msg = "Non-character argument is not alowed for `summary factors`. Please provide either string or vector of strings")
290 | assertthat::assert_that(all(summary_factors %in% colnames(polya_data)),msg="Non-existent column name provided as the argument (summary_factors)")
291 |
292 | polya_data_summarized <-
293 | polya_data %>% dplyr::ungroup() %>% dplyr::group_by(.dots = c(transcript_id_column,summary_factors)) %>% dplyr::summarise(
294 | counts = dplyr::n(),
295 | polya_mean = mean(polya_length),
296 | polya_sd = sd(polya_length),
297 | polya_median = median(polya_length),
298 | polya_gm_mean = gm_mean(polya_length),
299 | polya_sem = polya_sd/sqrt(counts)
300 | )
301 | return(polya_data_summarized)
302 | }
303 |
304 | calculate_quantiles <- function(x,probs=c(0.1,0.9)) {
305 |
306 | data.frame(quantile_val=quantile(x,probs),quantile=probs)
307 | }
308 |
309 |
310 | #' Summarize poly(A) data per transcript
311 | #'
312 | #' @param polya_data input table with poly(A) data.
313 | #' @param summary_factors vector of grouping columns. Set to NULL to omit grouping
314 | #' @param transcript_id_column column with transcript identifier. Default to "transcript"
315 | #' @param summary_functions list of summary functions. Set to NA to get only counts per transcript
316 | #' @param quantiles vector with quantile values (optional)
317 | #'
318 | #' @return a tibble with summarized poly(A) length data
319 | #' @export
320 | #'
321 | #' @examples
322 | summarize_polya_per_transcript <- function(polya_data,groupBy=NULL,transcript_id_column=transcript,summary_functions=list("median","mean"),quantiles=NA) {
323 |
324 | if (missing(polya_data)) {
325 | stop("PolyA predictions are missing. Please provide a valid polya_data argument",
326 | call. = FALSE)
327 | }
328 |
329 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data.frame provided as an input")
330 | # assertthat::assert_that(assertive::is_character(summary_factors),msg = "Non-character argument is not allowed for `summary factors`. Please provide either string or vector of strings")
331 | #assertthat::assert_that(all(as.character(summary_factors) %in% colnames(polya_data)),msg="Non-existent column name provided as the argument (summary_factors)")
332 |
333 | #summary_label = paste0("polya_",summary_function)
334 |
335 |
336 | if(!is.na(summary_functions)) {
337 |
338 | names(summary_functions)= summary_functions
339 | summary_functions <- sapply(summary_functions,get)
340 |
341 | polya_data_summarized <-
342 | polya_data %>% dplyr::ungroup() %>% dplyr::group_by(across(c({{ transcript_id_column }},{{ groupBy }}))) %>% dplyr::summarise(counts = dplyr::n(),across(polya_length,.fns=summary_functions))
343 |
344 | }
345 | else {
346 | polya_data_summarized <-
347 | polya_data %>% dplyr::ungroup() %>% dplyr::group_by(across(c({{ transcript_id_column }},{{ groupBy }}))) %>% dplyr::summarise(counts = dplyr::n())
348 |
349 | }
350 | if (!is.na(quantiles)) {
351 | polya_data_summarized_quantiles <-
352 | polya_data %>% dplyr::ungroup() %>% dplyr::group_by(across(c({{ transcript_id_column }},{{ groupBy }}))) %>% dplyr::summarise(calculate_quantiles(polya_length,probs=quantiles)) %>% tidyr::pivot_wider(names_from="quantile",values_from="quantile_val",names_prefix = "q_")
353 | polya_data_summarized <- polya_data_summarized %>% dplyr::left_join(polya_data_summarized_quantiles)
354 |
355 | }
356 |
357 |
358 | return(polya_data_summarized)
359 | }
360 |
361 |
362 | #' Calculates PCA using polya predictions or counts
363 | #'
364 | #' Needs polyA predictions table summarized by \link{summarize_polya} function, using "sample_name" as summary_factors
365 | #'
366 | #' @param polya_data_summarized summarized polyA predictions. Generate use \link{summarize_polya}
367 | #' @param parameter - parameter used for PCA calculation. One of: polya_median,polya_mean,polya_gm_mean,counts
368 | #' @param transcript_id_column column which respresnrt transcript id
369 | #'
370 | #' @return pca object
371 | #' @export
372 | #'
373 | calculate_pca <- function(polya_data_summarized,parameter="polya_median",transcript_id_column = "transcript") {
374 |
375 |
376 | if (missing(polya_data_summarized)) {
377 | stop("Summarized PolyA predictions are missing. Please provide a valid polya_data_summarized argument",
378 | call. = FALSE)
379 | }
380 |
381 | assertthat::assert_that(assertive::has_rows(polya_data_summarized),msg = "Empty data.frame provided as an input")
382 | assertthat::assert_that(assertive::is_character(parameter),msg = "Non-character argument is not alowed for `parameter`.")
383 | assertthat::assert_that(transcript_id_column %in% colnames(polya_data_summarized),msg=paste0("Required `transcript`` column is missing from input dataset."))
384 | assertthat::assert_that("sample_name" %in% colnames(polya_data_summarized),msg=paste0("Required `sample_name` column is missing from input dataset."))
385 | assertthat::assert_that(parameter %in% colnames(polya_data_summarized),msg=paste0(parameter," is not a column of input dataset."))
386 |
387 | polya_data_summarized <- polya_data_summarized %>% dplyr::select(!!rlang::sym(transcript_id_column),sample_name,!!rlang::sym(parameter)) %>% tidyr::spread(sample_name,!!rlang::sym(parameter)) %>% as.data.frame()
388 | polya_data_summarized[is.na(polya_data_summarized)] <- 0
389 | sample_names <- colnames(polya_data_summarized[,-1])
390 | transcript_names <- polya_data_summarized[,1]
391 | polya_data_summarized_t<-t(polya_data_summarized[,-1])
392 | # step required to remove zero-variance columns (based on https://stackoverflow.com/questions/40315227/how-to-solve-prcomp-default-cannot-rescale-a-constant-zero-column-to-unit-var)
393 | zero_variance_transcripts<-which(apply(polya_data_summarized_t, 2, var)==0)
394 | if (length(zero_variance_transcripts>0)) {
395 | polya_data_summarized_t <- polya_data_summarized_t[ , -zero_variance_transcripts]
396 | colnames(polya_data_summarized_t) <- transcript_names[-zero_variance_transcripts]
397 | }
398 | else {
399 | colnames(polya_data_summarized_t) <- transcript_names
400 | }
401 | pca.test <- prcomp(polya_data_summarized_t,center=T,scale=T)
402 |
403 | return_list <- list(pca = pca.test,sample_names = sample_names)
404 | return(return_list)
405 | }
406 |
407 |
408 |
409 |
410 |
411 | #' Get information about nanopolish processing
412 | #'
413 | #' Process the information returned by \code{nanopolish polya} in the \code{qc_tag} column
414 | #'
415 | #' @param polya_data A data.frame or tibble containig unfiltered polya output from Nanopolish,
416 | #' @param grouping_factor How to group results (e.g. by sample_name)
417 | #' best read with \link[nanotail]{read_polya_single} or \link[nanotail]{read_polya_multiple}
418 | #' @return A \link[tibble]{tibble} with counts for each processing state
419 |
420 | #' @export
421 | #'
422 | get_nanopolish_processing_info <- function(polya_data,grouping_factor=NA) {
423 |
424 |
425 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data.frame provided as an input")
426 |
427 |
428 | if(!is.na(grouping_factor)) {
429 | assertthat::assert_that(grouping_factor %in% colnames(polya_data),msg=paste0(grouping_factor," is not a column of input dataset"))
430 | processing_info <- polya_data %>% dplyr::mutate(qc_tag=forcats::fct_relevel(qc_tag,"PASS", after = Inf)) %>% dplyr::group_by(!!rlang::sym(grouping_factor),qc_tag) %>% dplyr::count()
431 | }
432 | else {
433 | processing_info <- polya_data %>% dplyr::mutate(qc_tag=forcats::fct_relevel(qc_tag,"PASS", after = Inf)) %>% dplyr::group_by(qc_tag) %>% dplyr::count()
434 | }
435 |
436 | return (processing_info)
437 | }
438 |
439 |
440 |
441 | #' Performs differential expression analysis
442 | #'
443 | #' Uses counts for each identified transcript to calculate differential expression between specified groups.
444 | #' This function is a wrapper for \code{\link[edgeR]{binomTest}} from \code{edgeR} package
445 | #'
446 | #'
447 | #' @param polya_data polya_data tibble
448 | #' @param grouping_factor name of column containing factor with groups for comparison
449 | #' @param condition1 first condition to compare
450 | #' @param condition2 second condition to compare
451 | #' @param alpha threshold for a pvalue, to treat the result as significant (default = 0.05)
452 | #' @param summarized_input is input table already summarized?
453 | #'
454 | #' @return a tibble with differential expression results
455 | #' @export
456 | #'
457 | #' @seealso \link[edgeR]{binomTest}
458 | #'
459 | calculate_diff_exp_binom <- function(polya_data,grouping_factor=NA,condition1=NA,condition2=NA,alpha=0.05,summarized_input=FALSE) {
460 |
461 |
462 |
463 | if (missing(polya_data)) {
464 | stop("PolyA data are missing. Please provide a valid polya_data argument",
465 | call. = FALSE)
466 | }
467 |
468 | if (missing(grouping_factor)) {
469 | stop("Grouping factor is missing. Please specify one",
470 | call. = FALSE)
471 | }
472 |
473 | assertthat::assert_that(assertive::is_a_bool(summarized_input),msg="Non-boolean value provided for option summarized_input")
474 | assertthat::assert_that(assertive::has_rows(polya_data),msg = "Empty data.frame provided as an input")
475 | if (!is.na(grouping_factor)) {
476 | assertthat::assert_that(grouping_factor %in% colnames(polya_data),msg=paste0(grouping_factor," is not a column of input dataset"))
477 | if (!is.na(condition1)) {
478 | if(!is.na(condition2)) {
479 | assertthat::assert_that(condition1 %in% levels(polya_data[[grouping_factor]]),msg=paste0(condition1," is not a level of ",grouping_factor," (grouping_factor)"))
480 | assertthat::assert_that(condition2 %in% levels(polya_data[[grouping_factor]]),msg=paste0(condition2," is not a level of ",grouping_factor," (grouping_factor)"))
481 | assertthat::assert_that(condition2 != condition1,msg="condition2 should be different than condition1")
482 | }
483 | else {
484 | stop("Please provide both condition1 and condition2 for comparison")
485 | }
486 | }
487 | else {
488 | stop("Please provide both condition1 and condition2 for comparison")
489 | }
490 | }
491 | else {
492 | stop("Please provide valid grouping_factor")
493 | }
494 |
495 | assertthat::assert_that(is.numeric(alpha),msg = "Non-numeric parameter provided (alpha)")
496 |
497 | if (summarized_input){
498 | polya_data_summarized <- polya_data
499 | }
500 | else{
501 | polya_data_summarized <- summarize_polya(polya_data,summary_factors = grouping_factor)
502 | }
503 |
504 | libsizes <- polya_data_summarized %>% dplyr::ungroup() %>% dplyr::group_by(!!rlang::sym(grouping_factor)) %>% dplyr::summarize(lib_size=sum(counts)) %>% dplyr::mutate(sizeFactor=lib_size/mean(lib_size))
505 |
506 | #sum counts for all samples in given group
507 | polyA_data_counts_summarized<-polya_data_summarized %>% dplyr::group_by(transcript,!!rlang::sym(grouping_factor)) %>% dplyr::summarize(counts_sum=sum(counts)) %>% tidyr::spread(!!rlang::sym(grouping_factor),counts_sum) %>% as.data.frame()
508 |
509 | polyA_data_counts_summarized[is.na(polyA_data_counts_summarized)] <- 0
510 |
511 | polyA_data_counts_summarized <- cbind(polyA_data_counts_summarized$transcript,polyA_data_counts_summarized[,-1]/libsizes$sizeFactor)
512 | colnames(polyA_data_counts_summarized)[1] <- "transcript"
513 |
514 |
515 | binom_test_pvalues<-edgeR::binomTest(y1=polyA_data_counts_summarized[[condition1]],y2=polyA_data_counts_summarized[[condition2]],n1=sum(polyA_data_counts_summarized[[condition1]]),n2=sum(polyA_data_counts_summarized[[condition2]]))
516 | binom_test_adjusted_pvalues <- p.adjust(binom_test_pvalues,method="BH")
517 |
518 | binom_test_results <-
519 | data.frame(transcript = polyA_data_counts_summarized$transcript,
520 | pvalue = binom_test_pvalues,
521 | padj = binom_test_adjusted_pvalues) %>%
522 | dplyr::left_join(polyA_data_counts_summarized) %>%
523 | dplyr::mutate(fold_change = (!! rlang::sym(condition2))/(!! rlang::sym(condition1))) %>%
524 | #dplyr::mutate_(fold_change = ifelse((condition2 > 0 & condition1 > 0), fold_change, 0)) %>%
525 | dplyr::mutate(significance = ifelse(padj < alpha, paste0("FDR<", alpha), "NotSig")) %>%
526 | dplyr::mutate(mean_expr = ((!!rlang::sym(condition1)) + (!!rlang::sym(condition2)))/2) %>%
527 | dplyr::arrange(padj)
528 |
529 | return(binom_test_results)
530 |
531 | }
532 |
533 |
534 | #' Compute Kruskal-Wallis test on poly(A) data
535 | #'
536 | #' @param input_data - input tibble/data.frame with nanopolish output.
537 | #' @param grouping_factor - which column contains group information
538 | #' @param transcript_id column which transcript ids
539 | #'
540 | #' @return data.frame with statistis
541 | #' @export
542 | #'
543 | #' @examples
544 | #' \dontrun{
545 | #' polya_table <- nanotail::read_polya_single("nanopolish.tsv")
546 | #' kruskal_polya(polya_table,grouping_factor="group",transcript_id="transcript",verbose=T)
547 | #' }
548 | kruskal_polya <- function(input_data,grouping_factor="sample_name",transcript_id="transcript",verbose=F) {
549 |
550 | data_frame_check<-checkmate::assert_data_frame(input_data,min.rows=10) #minimum 10 rows in the input data.frame required
551 | groups_check<-checkmate::assertFactor(input_data[[grouping_factor]],min.levels=2,empty.levels.ok = F) #at least two levels of grouping factor required
552 |
553 | if (verbose) {
554 | message("Correct input data provided")
555 | }
556 |
557 | input_data_split <- split(input_data,input_data[[transcript_id]]) # split data.frame by transcript
558 | if (verbose) {
559 | message("succesfully splitted data by transcripts")
560 | }
561 |
562 | return_stats<-do.call(rbind,lapply(input_data_split,function(x) {.kruskal_polya(x,grouping_factor = grouping_factor,verbose=verbose) })) # compute statistics using helper function
563 |
564 | if (verbose) {
565 | message("Finished statistics computation")
566 | }
567 |
568 | return(return_stats)
569 | }
570 |
571 | #' Compute Kruskal-Wallis test on single poly(A) data
572 | #'
573 | #' @param input_data input data.frame (for single transcript)
574 | #' @param grouping_factor which column contains group information
575 | #'
576 | #' @return data.frame with test statistics
577 | .kruskal_polya <- function(input_data,grouping_factor="sample_name",verbose=F) {
578 |
579 | data_frame_check<-checkmate::check_data_frame(input_data,min.rows=10) #minimum 10 rows (observations) required in the data.frame
580 | groups_check<-checkmate::checkFactor(input_data[[grouping_factor]],min.levels=2,empty.levels.ok = F) #at least 2 factor levels
581 |
582 | # if all asserts are met, do statistics computation
583 | if (is.logical(data_frame_check) & isTRUE(data_frame_check) & is.logical(groups_check) & isTRUE(groups_check)) {
584 | test_formula <- as.formula(paste("polya_length ~ ",grouping_factor,sep=""))
585 | kruskal_stat <- kruskal.test(test_formula,input_data) # kruskal test
586 | test_effectsize <- effectsize::rank_epsilon_squared(test_formula,input_data) #effect size using rank_epsilon_squared
587 | return_data_frame <- data.frame(df=kruskal_stat$parameter,p.value=as.numeric(kruskal_stat$p.value),statistic=kruskal_stat$statistic,effectsize=test_effectsize$rank_epsilon_squared,data_name=kruskal_stat$data.name,data.frame(t(summary(input_data[[grouping_factor]]))))
588 | }
589 | else {
590 | #when asserts where not true, return associated messages (when verbose) and data.frame with NAs instead of statistics values
591 | if (verbose) {
592 | message(paste(data_frame_check,groups_check))
593 | }
594 | return_data_frame<-data.frame(df=NA,p.value=NA,statistic=NA,data_name=NA,data.frame(t(summary(input_data[[grouping_factor]]))))
595 | }
596 |
597 | }
598 |
599 |
600 | #' Compute Kruskal-Wallis test on poly(A) data
601 | #'
602 | #' @param input_data - input tibble/data.frame with nanopolish output.
603 | #' @param grouping_factor - which column contains group information
604 | #' @param transcript_id column which transcript ids
605 | #' @param verbose verbose output
606 | #' @param verbosity_level how verbose the output should be (levels 1 - little verbosity, or 2 - very verbose)
607 | #'
608 | #' @return data.frame with statistis
609 | #' @export
610 | #'
611 | #' @examples
612 | #' \dontrun{
613 | #' polya_table <- nanotail::read_polya_single("nanopolish.tsv")
614 | #' kruskal_polya(polya_table,grouping_factor="group",transcript_id="transcript",verbose=T)
615 | #' }
616 | kruskal_polya <- function(input_data,grouping_factor="sample_name",transcript_id="transcript",verbose=F,verbosity_level=1) {
617 |
618 | data_frame_check<-checkmate::assert_data_frame(input_data,min.rows=10) #minimum 10 rows in the input data.frame required
619 | groups_check<-checkmate::assertFactor(input_data[[grouping_factor]],min.levels=2,empty.levels.ok = F) #at least two levels of grouping factor required
620 |
621 | if (verbose) {
622 | message("Correct input data provided")
623 | }
624 |
625 | input_data_split <- split(input_data,input_data[[transcript_id]]) # split data.frame by transcript
626 | if (verbose) {
627 | message("succesfully splitted data by transcripts")
628 | }
629 |
630 | return_stats<-do.call(rbind,future.apply::future_lapply(input_data_split,function(x) {.kruskal_polya(x,grouping_factor = grouping_factor,verbose=verbose,verbosity_level = verbosity_level) })) # compute statistics using helper function
631 |
632 |
633 | return_stats$padj <- p.adjust(return_stats$p.value,method="BH") # adjust p.values
634 |
635 | if (verbose) {
636 | message("Adjusted p.values using Benjamini-Hochberg correction")
637 | }
638 |
639 | return_stats$transcript <- rownames(return_stats)
640 | rownames(return_stats) <- NULL
641 | return_stats <- return_stats[,c("transcript","p.value","padj","statistic","effectsize")]
642 |
643 | if (verbose) {
644 | message("Finished statistics computation")
645 | }
646 |
647 | return(return_stats)
648 | }
649 |
650 | #' Compute Kruskal-Wallis test on single poly(A) data
651 | #'
652 | #' @param input_data input data.frame (for single transcript)
653 | #' @param grouping_factor which column contains group information
654 | #' @param verbose verbose output
655 | #' @param verbosity_level how verbose the output should be (levels 1 - little verbosity, or 2 - very verbose)
656 | #'
657 | #' @return data.frame with test statistics
658 | .kruskal_polya <- function(input_data,grouping_factor="sample_name",verbose=F,verbosity_level=1) {
659 |
660 | data_frame_check<-checkmate::check_data_frame(input_data,min.rows=10) #minimum 10 rows (observations) required in the data.frame
661 | groups_check<-checkmate::checkFactor(input_data[[grouping_factor]],min.levels=2,empty.levels.ok = F) #at least 2 factor levels
662 |
663 | # if all asserts are met, do statistics computation
664 | if (is.logical(data_frame_check) & isTRUE(data_frame_check) & is.logical(groups_check) & isTRUE(groups_check)) {
665 | test_formula <- as.formula(paste("polya_length ~ ",grouping_factor,sep=""))
666 | kruskal_stat <- kruskal.test(test_formula,data=input_data) # kruskal test
667 | test_effectsize <- effectsize::rank_epsilon_squared(test_formula,data=input_data) #effect size using rank_epsilon_squared
668 | return_data_frame <- data.frame(df=kruskal_stat$parameter,p.value=as.numeric(kruskal_stat$p.value),statistic=kruskal_stat$statistic,effectsize=test_effectsize$rank_epsilon_squared,data_name=kruskal_stat$data.name,data.frame(t(summary(input_data[[grouping_factor]]))))
669 | }
670 | else {
671 | #when asserts where not true, return associated messages (when verbose) and data.frame with NAs instead of statistics values
672 | if (verbose & verbosity_level>1) {
673 | message(paste(data_frame_check,groups_check))
674 | }
675 | return_data_frame<-data.frame(df=NA,p.value=NA,statistic=NA,effectsize=NA,data_name=NA,data.frame(t(summary(input_data[[grouping_factor]]))))
676 | }
677 |
678 | }
679 |
680 |
681 |
682 | #' Normalize counts to sequencingdepth
683 | #'
684 | #' @param summarized_data - output of summarize_polya_per_transcript()
685 | #' @param raw_data - raw polyA data (loaded with read_polya_single() or read_polya_multiple())
686 | #' @param spike_in_data - spike-in data for normalization (optional) (raw polya data loaded with read_polya_single() or read_polya_multiple(), with the same metadata as raw data)
687 | #' @param groupBy - grouping variable
688 | #' @param force - force recalculation
689 | #'
690 | #' @return data.frame (tibble) with normalized data
691 | #' @export
692 | #'
693 |
694 | normalize_counts_to_depth <- function(summarized_data,raw_data,spike_in_data=NULL,groupBy,force=F) {
695 |
696 |
697 |
698 | if ("norm_counts" %in% colnames(summarized_data) && force==F) {
699 | stop("Input table contains already normalized counts, if you want to rerun normalization on existing data please use force=T")
700 | }
701 | else if ("norm_counts" %in% colnames(summarized_data) && force==T) {
702 |
703 | summarized_data$temp_counts <- summarized_data$counts
704 | }
705 | else if ("counts" %in% colnames(summarized_data)) {
706 | summarized_data$temp_counts <- summarized_data$counts
707 | }
708 | else {
709 | stop("Missing counts column in the input data")
710 | }
711 |
712 | if(!is.null(spike_in_data)) {
713 | spike_in_norm_factors <- spike_in_data %>% dplyr::group_by(across({{groupBy}})) %>% dplyr::count() %>% dplyr::ungroup() %>% dplyr::mutate(norm_factor=n/min(n))
714 | }
715 |
716 | norm_factors <- raw_data %>% dplyr::group_by(across({{groupBy}})) %>% dplyr::count() %>% dplyr::ungroup() %>% dplyr::mutate(norm_factor=n/min(n))
717 |
718 | print(norm_factors)
719 |
720 | normalized_data <- summarized_data %>% dplyr::left_join(norm_factors,by={{groupBy}}) %>% dplyr::mutate(norm_counts=temp_counts/norm_factor) %>% dplyr::select(-c(temp_counts,norm_factor,n))
721 |
722 |
723 | if(!is.null(spike_in_data)) {
724 | normalized_data$temp_counts <- normalized_data$norm_counts
725 | normalized_data <- normalized_data %>% dplyr::left_join(spike_in_norm_factors,by={{groupBy}}) %>% dplyr::mutate(norm_counts=temp_counts/norm_factor) %>% dplyr::select(-c(temp_counts,norm_factor,n))
726 | }
727 |
728 | return(normalized_data)
729 | }
730 |
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