Version 0.2.6

LazyData: true

Depends: R (>= 3.4.0)

Imports: Matrix, tximport, sparseDRIMSeq, stageR, rtracklayer, formattable, DT, Gviz, assertthat, scales, ggplot2, reshape2, BiocParallel, Matrix.utils, pheatmap, methods, GenomicRanges, htmltools, htmlwidgets, knitr, stringi 

Suggests: GenomeInfoDb, Seurat (>= 3.0.0), webshot, webshot2, magick

Suggests: GenomeInfoDb, Seurat (>= 3.0.0), webshot, webshot2, magick, knitr

Remotes: github::TobiTekath/sparseDRIMSeq

RoxygenNote: 7.1.0

Roxygen: list(markdown = TRUE)

BuildType: Package

PackageUseDevtools: Yes

PackageInstallArgs: --no-multiarch --with-keep.source

PackageCheckArgs: --no-vignettes --no-build-vignettes

knitr::opts_chunk$set(echo = TRUE)

```

# DTUrtle 0.2.6

## Changes

- add `add_to_table` parameter to plotting functions, to add plot savepaths directly to `dtu_table`.

- `run_drimseq()`: added description of predefined filter strategies.

- `run_drimseq()`: added check if two sample groups are left after excluding unused samples.

- minor update of function documentation.

- `plot_dtu_table()`: add `min_page_length` parameter.

- `plot_dtu_table()`: slightly change icon and heading alignment.

- added conditional vignette evaluation.

- added `knitr` in Suggests for vignette building.

# DTUrtle 0.2.5

## Changes

- initial first version.

DTUrtle News

================

# DTUrtle 0.2.6

## Changes

  - add `add_to_table` parameter to plotting functions, to add plot

    savepaths directly to `dtu_table`.

  - `run_drimseq()`: added description of predefined filter strategies.

  - `run_drimseq()`: added check if two sample groups are left after

    excluding unused samples.

  - minor update of function documentation.

  - `plot_dtu_table()`: add `min_page_length` parameter.

  - `plot_dtu_table()`: slightly change icon and heading alignment.

  - added conditional vignette evaluation.

  - added `knitr` in Suggests for vignette building.

# DTUrtle 0.2.5

## Changes

#' @inheritDotParams tximport::tximport -files -type

#'

#' @return - For bulk data: A combined count matrix for all specified samples.

#' - For single-cell data (`type='alevin'`): A list of count matrices per sample. Should be combined and optionally added to a Seurat object with [combine_to_matrix()].

#' - For single-cell data: A list of count matrices per sample. Should be combined and optionally added to a Seurat object with [combine_to_matrix()].

#' @family DTUrtle

#' @export

#' @seealso Please see [import_gtf()], [move_columns_to_front()] and [one_to_one_mapping()] to help with tx2gene creation. See also [combine_to_matrix()], when output is a list of single-cell runs.

#' Combine sparse matrices.

#'

#' Combine list of sparse transcription count matrices. Presumably coming from [import_counts()] with single-cell data.

#' Combine list of sparse transcription count matrices. Presumably coming from [import_counts()] in conjunction with single-cell data.

#'

#' This function adds a cellname extension if necessary.

#' Can optionally add the combined matrix to a existing Seurat object (keeping the cellname extension of the object and matching the cells).

#' Also removes overall non expressed features.

#'

#' @param tx_list List of sparse transcription count matrices, as returned by [import_counts()] for single-cell data.

#' @param cell_extensions Optional list of cellname extensions that are added to the cellnames of the samples. The original cellnames and the extension are separated by an underscore '_'.

#' @param cell_extensions Optional vector of cellname extensions that are added to the cellnames of the samples. The original cellnames and the extension are separated by an underscore '_'. Defaults to an increasing integer per sample.

#' @param seurat_obj Optional seurat object, where the combined matrix is added as an assay. This has the advantage, that the cells are matched and subsetted if necessary. Currently only Seurat 3 objects are supported.

#' @param tx2gene Optional tx2gene or metadata data frame, can only be used in conjunction with a seurat object. Metadata is added as feature-level meta data to the created assay. The first column of the data frame must contain transcript names/ids. The same transcript names/ids as in the `tx_list` objects must be used.

#' @param assay_name If the combined matrix should be added to an existing Seurat object, the name of the assay can be specified here.

}

#TODO: Specify predefined strategies!

#' Main DRIMSeq results

#'

#' Compute the main DRIMSeq results.

#' @param cond_col Name of the column in `pd`, where the comparison groups/conditions are defined. If more than 2 levels/groups are present, the groups that should be used must be specified in `cond_levels`.

#' @param cond_levels Define two levels/groups of `cond_col`, that should be compared. The order of the levels states the comparison formula (i.e. `cond_col[1]-cond_col[2]`).

#' @param filtering_strategy Define the filtering strategy to reduce and noise and increase statistical power.

#' - `'bulk'`: Predefined strategy for bulk RNAseq data. (default)

#' - `'sc'`: Predefined strategy for single-cell RNAseq data.

#' - `'own'`: Can be used to specify a user-defined strategy via the `...` argument (using the parameters of `dmFilter()`).

#' - `'bulk'`: Predefined strategy for bulk RNAseq data (default): Features must contribute at least 5% of the total expression in at least 50% of the samples of the smallest group. Additionally the total gene expression must be 5 or more for at least 50% of the samples of the smallest group.

#' - `'sc'`: Predefined strategy for single-cell RNAseq data: Features must contribute at least 5% of the total expression in at least 5% of the cells of the smallest group.

#' - `'own'`: Can be used to specify a user-defined strategy via the `...` argument (using the parameters of \code{\link[sparseDRIMSeq:dmFilter]{dmFilter}}).

#' @param BPPARAM If multicore processing should be used, specify a `BiocParallelParam` object here. Among others, can be `SerialParam()` (default) for non-multicore processing or `MulticoreParam('number_cores')` for multicore processing. See \code{\link[BiocParallel:BiocParallel-package]{BiocParallel}} for more information.

#' @param force_dense If you do not want to use a sparse Matrix for DRIMSeq calculations, you can force a dense conversion by specifying `TRUE`. Increases memory usage, but also reduces runtime drastically (currently).

#' @param carry_over_metadata Specify if compatible additional columns of `tx2gene` shall be carried over to the gene and transcript level `meta_table` in the results. Columns with `NA` values are not carried over.

#' @param ... If `filtering_strategy='own'` specify the wished parameters of \code{\link[sparseDRIMSeq:dmFilter]{dmFilter}} here.

#' @inheritDotParams sparseDRIMSeq::dmFilter

#'

#' @return `dturtle` object with the key results, that can be used in the DTUrtle steps hereafter. The object is just a easily accesible list with the following items:

#' - `meta_table_gene`: Data frame of the expressed-in ratio of all genes. Expressed-in is defined as expression > 0. Can be used to add gene level meta-information for plotting.

#' - `meta_table_tx`: Data frame of the expressed-in ratio of all transcripts. Expressed-in is defined as expression > 0. Can be used to add transcript level meta-information for plotting.

#' - `meta_table_sample`: Data frame of the provided sample level information (`pd`). Can be used to add sample level meta-information for plotting.

#' - `drim`: The results of the DRIMSeq statistical computations (`dmTest()`).

#' - `design`: The design matrix generated from the specified `pd` columns.

#' - `drim`: Results of the DRIMSeq statistical computations (`dmTest()`).

#' - `design`: Design matrix generated from the specified `pd` columns.

#' - `group`: Vector which sample/cell belongs to which comparison group.

#' - `used_filtering_options`: Vector of the used filtering options.

#' - `used_filtering_options`: List of the used filtering options.

#'

#' @family DTUrtle

#' @export

        counts <- counts[ , !(colnames(counts) %in% exclude)]

    }

    message("\nProceed with cells/samples: ",paste0(utils::capture.output(table(samp$condition)), collapse = "\n"))

    assertthat::assert_that(length(unique(samp$condition))==2, msg="No two sample groups left for comparison. Aborting!")

    #TODO: Reevaluate!

    message("\nFiltering...\n")

    total_sample <- round(nrow(samp)*0.8)

    smallest_group <- min(min(table(samp$condition)), 10)

    filter_opt_list <- list("min_samps_gene_expr" = 0,

                             "min_samps_feature_expr" = 0, "min_samps_feature_prop" = 0,

                             "min_gene_expr" = 0, "min_feature_expr" = 0, "min_feature_prop" = 0,

                             "run_gene_twice" = FALSE)

    switch(filtering_strategy,

    sc={

        smallest_group <- min(table(samp$condition))*0.05

        filter_opt_list <- utils::modifyList(filter_opt_list, list("min_samps_feature_prop" = smallest_group,

                                      "min_feature_prop" = 0.1, "run_gene_twice" = T))

                                      "min_feature_prop" = 0.05, "run_gene_twice" = T))

    },

    bulk={

        smallest_group <- min(table(samp$condition))*0.5

        filter_opt_list <- utils::modifyList(filter_opt_list,

                                      list("min_samps_feature_expr" = smallest_group,

                                           "min_feature_expr" = 1,

                                           "min_samps_gene_expr" = total_sample,

                                      list("min_samps_gene_expr" = smallest_group,

                                           "min_gene_expr" = 5,

                                           "min_samps_feature_prop" = smallest_group,

                                           "min_feature_prop" = 0.05, "run_gene_twice" = T))

#TODO: implement Noise - IQR filtering

#' Posthoc filtering and two-staged statistical tests

#'

#' Perform optional posthoc filtering and run two-staged statistical tests.

#' Perform optional post-hoc filtering and run two-staged statistical tests.

#'

#' The posthoc filter excludes transcripts, which standard deviation of the proportion per cell/sample is below the threshold.

#' The post-hoc filter excludes transcripts, which standard deviation of the proportion per cell/sample is below the threshold.

#' The two-staged statistical test performed by stageR first determines if any of the transcripts of a gene is showing signs of DTU.

#' The second step tries to identify on singular transcript level the significantly different transcripts.

#'

#' @param dturtle Result object of [run_drimseq()]. Must be of class `dturtle`.

#' @param ofdr Overall false discovery rate (OFDR) threshold.

#' @param posthoc Specify the minimal standard deviation of a transcripts porportion level that should be kept when performing posthoc filtering. To disbale poshoc filtering 0 or `FALSE` can be provided.

#' @param posthoc Specify the minimal standard deviation of a transcripts porportion level that should be kept when performing post-hoc filtering. To disbale poshoc filtering 0 or `FALSE` can be provided.

#' @return An extended `dturtle` object. Additional slots include:

#' - `sig_gene`: A character vector of all genes where the first stageR step was significant. Basically the significant genes, that showed signs of DTU.

#' - `sig_tx` : A named character vector of transcripts where the second stageR step was significant. Basically the significant transcripts of the significant genes.