Merge pull request #3 from AustinHartman/gh-pages

.RData

src/*.so

src/*.o

**/*.html

.ipynb_checkpoints

**/.ipynb_checkpoints

    return(hash)

}

#' Collapse counts

#' Collapse counts based on multiple categorical metadata columns

#'

#' @param counts_mat counts matrix where columns represent cells and rows

#' represent features

#' @param meta_data data.frame containing cell metadata

#' @param varnames subset of `meta_data` column names

#' @param min_cells_per_group minimum cells to keep collapsed group

#' @param keep_n keep or drop the `N`` column containing the number of

#' @param keep_n keep or drop the `N` column containing the number of

#' cells in each group. Default is `FALSE`

#' @param how method of collapsing counts from groups. `sum` or `mean`

#'

#' @importFrom data.table data.table

#'

#' @examples

#' library(Seurat)

#' m <- matrix(sample.int(8, 100*500, replace=TRUE), nrow=100, ncol=500)

#' rownames(m) <- paste0("G", 1:100)

#' colnames(m) <- paste0("C", 1:500)

#' o <- CreateSeuratObject(m)

#' o$md1 <- sample(c("a", "b"), 500, replace=TRUE)

#' o$md2 <- sample(c("c", "d"), 500, replace=TRUE)

#' data_collapsed <- collapse_counts(

#'     o@assays$RNA@counts, o@meta.data, c('md1', 'md2')

#' )

#' md1 <- sample(c("a", "b"), 500, replace=TRUE)

#' md2 <- sample(c("c", "d"), 500, replace=TRUE)

#' df <- data.frame(md1, md2)

#' data_collapsed <- collapse_counts(m, df, c("md1", "md2"))

#' head(data_collapsed$counts_mat)

#'

#' @export

#'

#'

#' @examples

#' \dontrun{

#'     library(Seurat)

#'     m <- matrix(sample.int(8, 100*500, replace=TRUE),nrow=100, ncol=500)

#'     rownames(m) <- paste0("G", 1:100)

#'     colnames(m) <- paste0("C", 1:500)

#'     o <- CreateSeuratObject(m)

#'     o$md1 <- sample(c("a", "b"), 500, replace = TRUE)

#'     o$md2 <- sample(c("c", "d"), 500, replace = TRUE)

#'     data_collapsed <- collapse_counts(

#'         o@assays$RNA@counts, o@meta.data, c('md1', 'md2')

#'     )

#'     md1 <- sample(c("a", "b"), 500, replace=TRUE)

#'     md2 <- sample(c("c", "d"), 500, replace=TRUE)

#'     df <- data.frame(md1, md2)

#'     data_collapsed <- collapse_counts(m, df, c("md1", "md2"))

#'     res_mat <- pseudobulk_deseq2(

#'         ~md1 + md1,

#'         data_collapsed$meta_data,

#' @export

sumGroups <- function(X, y, MARGIN = 2) {

    if (MARGIN == 2 & nrow(X) != length(y)) {

        stop("wrong dims")

        stop(

            "nrow(X) != length(y) - the number of rows in the matrix is not

            the same length as group labels"

        )

    } else if (MARGIN == 1 & ncol(X) != length(y)) {

        stop("wrong dims")

        stop(

            "ncol(X) != length(y) - the number of columns in the matrix is not

             the same length as group labels"

        )

    }

    UseMethod("sumGroups")

}

# Installation

We are working on getting presto into CRAN. For now, install Presto from github directly:

Install presto from CRAN:

```{r}

library(devtools)

install_github('immunogenomics/presto')

install.packages('presto')

```

# Usage

wilcoxauc(sce_object, 'group_name')

```

For examples, see `?wilcoxauc` and the [vignette](http://htmlpreview.github.io/?https://github.com/immunogenomics/presto/blob/master/docs/getting-started.html)

For examples, see `?wilcoxauc` and the [vignette](https://austinhartman.github.io/presto/articles/getting-started.html)