From 21da0794e4314db9f121f97e43a84d40fe52fa67 Mon Sep 17 00:00:00 2001 From: Andrew Butler Date: Fri, 29 Oct 2021 15:15:39 -0700 Subject: [PATCH 01/39] fix scaling options in FeaturePlot when using split.by --- NEWS.md | 1 + R/visualization.R | 8 ++++---- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/NEWS.md b/NEWS.md index 59a4570e7..48b884bad 100644 --- a/NEWS.md +++ b/NEWS.md @@ -5,6 +5,7 @@ - Add `min.cells.group` parameter to `FindConservedMarkers` ([#5079](https://github.com/satijalab/seurat/pull/5079)) - Set `do.center` to FALSE for `lsiproject` in `FindTransferAnchors` - Fix error message in `ReadMtx()` ([#5158](https://github.com/satijalab/seurat/issues/5158)) +- Fix scaling options when using `split.by` in `FeaturePlot()` ([#5243](https://github.com/satijalab/seurat/issues/5243)) # Seurat 4.0.5 (2020-10-04) ## Changes diff --git a/R/visualization.R b/R/visualization.R index e0d06af24..4f0881fd8 100644 --- a/R/visualization.R +++ b/R/visualization.R @@ -1332,8 +1332,8 @@ FeaturePlot <- function( } } if (!(is.null(x = keep.scale)) && keep.scale == "feature" && !blend) { - max.feature.value <- max(data.single[, feature]) - min.feature.value <- min(data.single[, feature]) + max.feature.value <- max(data[, feature]) + min.feature.value <- min(data[, feature]) plot <- suppressMessages(plot & scale_color_gradientn(colors = cols, limits = c(min.feature.value, max.feature.value))) } # Add coord_fixed @@ -1467,8 +1467,8 @@ FeaturePlot <- function( plots <- plots & NoLegend() } if (!(is.null(x = keep.scale)) && keep.scale == "all" && !blend) { - max.feature.value <- max(data.plot[, features]) - min.feature.value <- min(data.plot[, features]) + max.feature.value <- max(data[, features]) + min.feature.value <- min(data[, features]) plots <- suppressMessages(plots & scale_color_gradientn(colors = cols, limits = c(min.feature.value, max.feature.value))) } } From 88fc54d720a829aacb751b3c52d2b9ea0401bcf3 Mon Sep 17 00:00:00 2001 From: samuel-marsh Date: Wed, 15 Dec 2021 18:02:09 -0500 Subject: [PATCH 02/39] add param raster.dpi for geom_scattermore (updated branch) --- R/visualization.R | 54 ++++++++++++++++++++++++++++++++++++----------- 1 file changed, 42 insertions(+), 12 deletions(-) diff --git a/R/visualization.R b/R/visualization.R index 067af9fb2..c5463466e 100644 --- a/R/visualization.R +++ b/R/visualization.R @@ -759,6 +759,8 @@ ColorDimSplit <- function( #' ggplot object. If \code{FALSE}, return a list of ggplot objects #' @param raster Convert points to raster format, default is \code{NULL} which #' automatically rasterizes if plotting more than 100,000 cells +#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore(). +#' Default is c(512, 512) #' #' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if #' \code{combine = TRUE}; otherwise, a list of ggplot objects @@ -806,7 +808,8 @@ DimPlot <- function( na.value = 'grey50', ncol = NULL, combine = TRUE, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) { if (length(x = dims) != 2) { stop("'dims' must be a two-length vector") @@ -853,7 +856,8 @@ DimPlot <- function( cols.highlight = cols.highlight, sizes.highlight = sizes.highlight, na.value = na.value, - raster = raster + raster = raster, + raster.dpi = raster.dpi ) if (label) { plot <- LabelClusters( @@ -995,7 +999,8 @@ FeaturePlot <- function( sort.cell = NULL, interactive = FALSE, combine = TRUE, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) { # TODO: deprecate fully on 3.2.0 if (!is.null(x = sort.cell)) { @@ -1257,7 +1262,8 @@ FeaturePlot <- function( cols = cols.use, shape.by = shape.by, label = FALSE, - raster = raster + raster = raster, + raster.dpi = raster.dpi ) + scale_x_continuous(limits = xlims) + scale_y_continuous(limits = ylims) + @@ -1833,7 +1839,8 @@ CellScatter <- function( cols = NULL, pt.size = 1, smooth = FALSE, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) { features <- features %||% rownames(x = object) data <- FetchData( @@ -1848,7 +1855,8 @@ CellScatter <- function( pt.size = pt.size, rows.highlight = highlight, smooth = smooth, - raster = raster + raster = raster, + raster.dpi = raster.dpi ) return(plot) } @@ -1880,6 +1888,8 @@ CellScatter <- function( #' @param raster Convert points to raster format, default is \code{NULL} #' which will automatically use raster if the number of points plotted is greater than #' 100,000 +#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore(). +#' Default is c(512, 512) #' @param jitter Jitter for easier visualization of crowded points #' #' @return A ggplot object @@ -1913,6 +1923,7 @@ FeatureScatter <- function( slot = 'data', plot.cor = TRUE, raster = NULL, + raster.dpi = c(512, 512), jitter = TRUE ) { cells <- cells %||% colnames(x = object) @@ -1955,6 +1966,7 @@ FeatureScatter <- function( span = span, plot.cor = plot.cor, raster = raster, + raster.dpi = raster.dpi, jitter = jitter ) } @@ -2000,7 +2012,8 @@ VariableFeaturePlot <- function( log = NULL, selection.method = NULL, assay = NULL, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) { if (length(x = cols) != 2) { stop("'cols' must be of length 2") @@ -2034,7 +2047,8 @@ VariableFeaturePlot <- function( data = hvf.info, col.by = var.status, pt.size = pt.size, - raster = raster + raster = raster, + raster.dpi = raster.dpi ) if (length(x = unique(x = var.status)) == 1) { switch( @@ -6792,6 +6806,8 @@ globalVariables(names = '..density..', package = 'Seurat') #' @param raster Convert points to raster format, default is \code{NULL} #' which will automatically use raster if the number of points plotted is #' greater than 100,000 +#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore(). +#' Default is c(512, 512) #' @param plot.cor ... #' @param jitter Jitter for easier visualization of crowded points #' @@ -6819,6 +6835,7 @@ SingleCorPlot <- function( na.value = 'grey50', span = NULL, raster = NULL, + raster.dpi = NULL, plot.cor = TRUE, jitter = TRUE ) { @@ -6828,6 +6845,10 @@ SingleCorPlot <- function( "\nTo disable this behavior set `raster=FALSE`") } raster <- raster %||% (nrow(x = data) > 1e5) + if (!is.null(x = raster.dpi)) { + if (!is.numeric(x = raster.dpi) || length(x = raster.dpi) != 2) + stop("'raster.dpi' must be a two-length numeric vector") + } orig.names <- colnames(x = data) names.plot <- colnames(x = data) <- gsub( pattern = '-', @@ -6923,7 +6944,8 @@ SingleCorPlot <- function( plot <- plot + geom_scattermore( mapping = aes_string(color = 'colors'), position = position, - pointsize = pt.size + pointsize = pt.size, + pixels = raster.dpi ) } else { plot <- plot + geom_point( @@ -6934,7 +6956,7 @@ SingleCorPlot <- function( } } else { if (raster) { - plot <- plot + geom_scattermore(position = position, pointsize = pt.size) + plot <- plot + geom_scattermore(position = position, pointsize = pt.size, pixels = raster.dpi) } else { plot <- plot + geom_point(position = position, size = pt.size) } @@ -6994,6 +7016,8 @@ SingleCorPlot <- function( #' @param raster Convert points to raster format, default is \code{NULL} #' which will automatically use raster if the number of points plotted is #' greater than 100,000 +#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore(). +#' Default is c(512, 512) #' #' @return A ggplot2 object #' @@ -7022,7 +7046,8 @@ SingleDimPlot <- function( cols.highlight = '#DE2D26', sizes.highlight = 1, na.value = 'grey50', - raster = NULL + raster = NULL, + raster.dpi = NULL ) { pt.size <- pt.size %||% AutoPointSize(data = data, raster = raster) if ((nrow(x = data) > 1e5) & !isFALSE(raster)){ @@ -7030,6 +7055,10 @@ SingleDimPlot <- function( "\nTo disable this behavior set `raster=FALSE`") } raster <- raster %||% (nrow(x = data) > 1e5) + if (!is.null(x = raster.dpi)) { + if (!is.numeric(x = raster.dpi) || length(x = raster.dpi) != 2) + stop("'raster.dpi' must be a two-length numeric vector") + } if (length(x = dims) != 2) { stop("'dims' must be a two-length vector") } @@ -7113,7 +7142,8 @@ SingleDimPlot <- function( shape = shape.by, alpha = alpha.by ), - pointsize = pt.size + pointsize = pt.size, + pixels = raster.dpi ) } else { plot + geom_point( From d612d46d56b83e056b09342029de7d06bcb019b2 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Fri, 17 Dec 2021 13:38:14 -0500 Subject: [PATCH 03/39] Update documentation --- R/visualization.R | 8 ++++---- src/stats.o | Bin 435256 -> 447864 bytes 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/visualization.R b/R/visualization.R index c5463466e..513d176f5 100644 --- a/R/visualization.R +++ b/R/visualization.R @@ -759,8 +759,8 @@ ColorDimSplit <- function( #' ggplot object. If \code{FALSE}, return a list of ggplot objects #' @param raster Convert points to raster format, default is \code{NULL} which #' automatically rasterizes if plotting more than 100,000 cells -#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore(). -#' Default is c(512, 512) +#' @param raster.dpi Pixel resolution for rasterized plots, passed to geom_scattermore(). +#' Default is c(512, 512). #' #' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if #' \code{combine = TRUE}; otherwise, a list of ggplot objects @@ -1888,8 +1888,8 @@ CellScatter <- function( #' @param raster Convert points to raster format, default is \code{NULL} #' which will automatically use raster if the number of points plotted is greater than #' 100,000 -#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore(). -#' Default is c(512, 512) +#' @param raster.dpi Pixel resolution for rasterized plots, passed to geom_scattermore(). +#' 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zXv6=_?7yNXDw84d5A=W?Y;!*)5})?yr&zz~-$MrJ)9=5Au)>o4hZb{6Eh9)BfM$(SM#E45ZC{KaQK$zrmxQ z_*kev&Ivr}pJ3?U(390|8r#$6X%EiOFtw3-|zkjU-0KV{!g;L zzJDiv%l<{?i+{2|<`;TIhi8N*b_sWZAxycSs9^rIyT6Z{_%GORC4K>TKgIE<-1jTA zQT!it|Aa5`TiRs5;JMKMrjOF6L}HOMe97%KpNh94Z9O`ie>!*`q%vqDJ>1;#)#N8H N4BPJ{bph7={|9R?aJc{g From 226bef956df60387246d71bd1f47a11cd2f7266c Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Fri, 17 Dec 2021 13:46:40 -0500 Subject: [PATCH 04/39] Update documentation; Bump version --- DESCRIPTION | 2 +- NEWS.md | 5 +++++ man/CellScatter.Rd | 6 +++++- man/ColorDimSplit.Rd | 2 ++ man/DimPlot.Rd | 6 +++++- man/FeaturePlot.Rd | 6 +++++- man/FeatureScatter.Rd | 4 ++++ man/SingleCorPlot.Rd | 4 ++++ man/SingleDimPlot.Rd | 6 +++++- man/VariableFeaturePlot.Rd | 6 +++++- 10 files changed, 41 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ca4569e1f..2ad4301bf 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: Seurat -Version: 4.0.6 +Version: 4.0.6.9001 Date: 2021-12-16 Title: Tools for Single Cell Genomics Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. diff --git a/NEWS.md b/NEWS.md index 2877cc185..594f1f93a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,8 @@ +# Unreleased +## Added +- Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392)) + + # Seurat 4.0.6 (2021-12-16) ## Added diff --git a/man/CellScatter.Rd b/man/CellScatter.Rd index 97f8190c7..fc5a88356 100644 --- a/man/CellScatter.Rd +++ b/man/CellScatter.Rd @@ -14,7 +14,8 @@ CellScatter( cols = NULL, pt.size = 1, smooth = FALSE, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) } \arguments{ @@ -37,6 +38,9 @@ CellScatter( \item{raster}{Convert points to raster format, default is \code{NULL} which will automatically use raster if the number of points plotted is greater than 100,000} + +\item{raster.dpi}{Pixel resolution for rasterized plots, passed to geom_scattermore(). +Default is c(512, 512).} } \value{ A ggplot object diff --git a/man/ColorDimSplit.Rd b/man/ColorDimSplit.Rd index 6acbaa6ab..4dab0677c 100644 --- a/man/ColorDimSplit.Rd +++ b/man/ColorDimSplit.Rd @@ -69,6 +69,8 @@ groups in cells.highlight} ggplot object. If \code{FALSE}, return a list of ggplot objects} \item{\code{raster}}{Convert points to raster format, default is \code{NULL} which automatically rasterizes if plotting more than 100,000 cells} + \item{\code{raster.dpi}}{Pixel resolution for rasterized plots, passed to geom_scattermore(). +Default is c(512, 512).} }} } \value{ diff --git a/man/DimPlot.Rd b/man/DimPlot.Rd index 2927d53a1..c93a78b01 100644 --- a/man/DimPlot.Rd +++ b/man/DimPlot.Rd @@ -32,7 +32,8 @@ DimPlot( na.value = "grey50", ncol = NULL, combine = TRUE, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) PCAPlot(object, ...) @@ -109,6 +110,9 @@ ggplot object. If \code{FALSE}, return a list of ggplot objects} \item{raster}{Convert points to raster format, default is \code{NULL} which automatically rasterizes if plotting more than 100,000 cells} +\item{raster.dpi}{Pixel resolution for rasterized plots, passed to geom_scattermore(). +Default is c(512, 512).} + \item{...}{Extra parameters passed to \code{DimPlot}} } \value{ diff --git a/man/FeaturePlot.Rd b/man/FeaturePlot.Rd index 9d334631c..239c0a8c2 100644 --- a/man/FeaturePlot.Rd +++ b/man/FeaturePlot.Rd @@ -33,7 +33,8 @@ FeaturePlot( sort.cell = NULL, interactive = FALSE, combine = TRUE, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) } \arguments{ @@ -115,6 +116,9 @@ ggplot object. If \code{FALSE}, return a list of ggplot objects} \item{raster}{Convert points to raster format, default is \code{NULL} which automatically rasterizes if plotting more than 100,000 cells} + +\item{raster.dpi}{Pixel resolution for rasterized plots, passed to geom_scattermore(). +Default is c(512, 512).} } \value{ A \code{\link[patchwork]{patchwork}ed} ggplot object if diff --git a/man/FeatureScatter.Rd b/man/FeatureScatter.Rd index c3181f38e..ab20ab9ae 100644 --- a/man/FeatureScatter.Rd +++ b/man/FeatureScatter.Rd @@ -22,6 +22,7 @@ FeatureScatter( slot = "data", plot.cor = TRUE, raster = NULL, + raster.dpi = c(512, 512), jitter = TRUE ) } @@ -63,6 +64,9 @@ useful for crowded plots if points of interest are being buried. (default is FAL which will automatically use raster if the number of points plotted is greater than 100,000} +\item{raster.dpi}{Pixel resolution for rasterized plots, passed to geom_scattermore(). +Default is c(512, 512).} + \item{jitter}{Jitter for easier visualization of crowded points} } \value{ diff --git a/man/SingleCorPlot.Rd b/man/SingleCorPlot.Rd index 3fc762922..b250c32c6 100644 --- a/man/SingleCorPlot.Rd +++ b/man/SingleCorPlot.Rd @@ -15,6 +15,7 @@ SingleCorPlot( na.value = "grey50", span = NULL, raster = NULL, + raster.dpi = NULL, plot.cor = TRUE, jitter = TRUE ) @@ -36,6 +37,9 @@ SingleCorPlot( which will automatically use raster if the number of points plotted is greater than 100,000} +\item{raster.dpi}{the pixel resolution for rastered plots, passed to geom_scattermore(). +Default is c(512, 512)} + \item{plot.cor}{...} \item{jitter}{Jitter for easier visualization of crowded points} diff --git a/man/SingleDimPlot.Rd b/man/SingleDimPlot.Rd index 5ec9911af..6fb2f6956 100644 --- a/man/SingleDimPlot.Rd +++ b/man/SingleDimPlot.Rd @@ -20,7 +20,8 @@ SingleDimPlot( cols.highlight = "#DE2D26", sizes.highlight = 1, na.value = "grey50", - raster = NULL + raster = NULL, + raster.dpi = NULL ) } \arguments{ @@ -70,6 +71,9 @@ groups in cells.highlight} \item{raster}{Convert points to raster format, default is \code{NULL} which will automatically use raster if the number of points plotted is greater than 100,000} + +\item{raster.dpi}{the pixel resolution for rastered plots, passed to geom_scattermore(). +Default is c(512, 512)} } \value{ A ggplot2 object diff --git a/man/VariableFeaturePlot.Rd b/man/VariableFeaturePlot.Rd index f049ca38a..9320b3f3d 100644 --- a/man/VariableFeaturePlot.Rd +++ b/man/VariableFeaturePlot.Rd @@ -13,7 +13,8 @@ VariableFeaturePlot( log = NULL, selection.method = NULL, assay = NULL, - raster = NULL + raster = NULL, + raster.dpi = c(512, 512) ) } \arguments{ @@ -45,6 +46,9 @@ For \code{SVFInfo} and \code{SpatiallyVariableFeatures}, choose from: \item{raster}{Convert points to raster format, default is \code{NULL} which will automatically use raster if the number of points plotted is greater than 100,000} + +\item{raster.dpi}{Pixel resolution for rasterized plots, passed to geom_scattermore(). +Default is c(512, 512).} } \value{ A ggplot object From c6932a65e60d0ded5ea19b5dceb425939a4e641d Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Fri, 17 Dec 2021 15:17:20 -0500 Subject: [PATCH 05/39] Remove object file --- src/stats.o | Bin 447864 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 src/stats.o diff --git a/src/stats.o b/src/stats.o deleted file mode 100644 index b881888afbf03ad8cdafed104c890f02a63309b1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 447864 zcmeFa2bdMb)-Jqj53|`!Gl3`!Sx}N>h76KMh71CNMgO}iF&54A$J-MhSP001K~^?pM`L?jn6@Nl#S0tc(jcl zgK(aW&qsKyjW0mB(8iBLc)X3DV8cZ=T#RrDjuUPABpaS=!&4A0#c`@lpJv0;ZFmO4 zGjS}l>E$+DVZ*Z#o@3)H5w5cF)d 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174858ca574254417cdab3ba3d1fd91f6ba1c52b Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Sat, 25 Dec 2021 16:43:23 -0500 Subject: [PATCH 06/39] Add PrepSCTFindMarkers; sct2 vignette --- NAMESPACE | 1 + R/differential_expression.R | 109 +++++++++++++++ R/objects.R | 22 ++- man/PrepSCTFindMarkers.Rd | 26 ++++ man/SCTAssay-class.Rd | 2 + vignettes/sctransform_v2_vignette.Rmd | 191 ++++++++++++++++++++++++++ 6 files changed, 345 insertions(+), 6 deletions(-) create mode 100644 man/PrepSCTFindMarkers.Rd create mode 100644 vignettes/sctransform_v2_vignette.Rmd diff --git a/NAMESPACE b/NAMESPACE index 8700c46f3..73c8aead5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -259,6 +259,7 @@ export(PolyDimPlot) export(PolyFeaturePlot) export(PredictAssay) export(PrepLDA) +export(PrepSCTFindMarkers) export(PrepSCTIntegration) export(Project) export(ProjectDim) diff --git a/R/differential_expression.R b/R/differential_expression.R index b4134fa14..3d54bbb85 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -845,6 +845,20 @@ FindMarkers.Seurat <- function( # select which data to use if (is.null(x = reduction)) { assay <- assay %||% DefaultAssay(object = object) + + if (inherits(x = object[[assay]], what = "SCTAssay") && length(x = levels(x = object[[assay]])) > 1) { + cell_attributes <- SCTResults(object = object[[assay]], slot = "cell.attributes") + observed_median_umis <- lapply( + X = cell_attributes, + FUN = function(x) median(x[, "umi"]) + ) + model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") + min_median_umi <- min(unlist(x = observed_median_umis)) + if (any(unlist(model_median_umis) != min_median_umi)){ + stop("Object contains multiple models with unequal library sizes. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") + } + } + data.use <- object[[assay]] cellnames.use <- colnames(x = data.use) } else { @@ -1877,6 +1891,101 @@ PerformDE <- function( return(de.results) } +#' Prepare object to run differential expression on SCT assay with multiple models +#' +#' Given a merged object with multiple SCT models, this function uses minimum +#' of the median UMI (calculated using the raw UMI counts) of individual objects +#' to reverse the individual SCT regression model using minimum of median UMI +#' as the sequencing depth covariate. +#' The counts slot of the SCT assay is replaced with recorrected counts and +#' the data slot is replaced with log1p of recorrected counts. +#' @param object Seurat object with SCT assays +#' @param assay Assay name where for SCT objects are stored; Default is 'SCT' +#' @param verbose Print messages and progress +#' @importFrom Matrix Matrix +#' @importFrom sctransform correct_counts +#' +#' @return object Seurat object with recorrected counts and data in the SCT assay. +#' @export +PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { + if (length(x = levels(x = object[[assay]])) == 1) { + if (verbose) { + message("Only one SCT model is stored - skipping recalculating corrected counts") + } + return(object) + } + observed_median_umis <- lapply( + X = SCTResults(object = object[[assay]], slot = "cell.attributes"), + FUN = function(x) median(x[, "umi"]) + ) + model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") + min_median_umi <- min(unlist(x = observed_median_umis)) + if (all(unlist(model_median_umis) == min_median_umi)){ + if (verbose){ + message("Minimum UMI unchnaged. Skipping re-correction.") + } + return (object) + } + if (verbose) { + message(paste0("Found ", length(x = levels(x = object[[assay]])), " SCT models.", + " Recorrecting SCT counts using minimum median counts: ", min_median_umi)) + } + umi.assay <- unique( + x = unlist( + x = SCTResults(object = object[[assay]], slot = "umi.assay") + ) + ) + if (length(x = umi.assay) > 1) { + stop("Multiple UMI assays are used for SCTransform: ", + paste(umi.assay, collapse = ", ") + ) + } + raw_umi <- GetAssayData(object = object, assay = umi.assay, slot = "counts") + corrected_counts <- Matrix( + nrow = nrow(x = raw_umi), + ncol = ncol(x = raw_umi), + data = 0, + dimnames = dimnames(x = raw_umi), + sparse = TRUE + ) + cell_attr <- SCTResults(object = object[[assay]], slot = "cell.attributes") + model_pars_fit <- lapply( + X = SCTResults(object = object[[assay]], slot = "feature.attributes"), + FUN = function(x) x[, c("theta", "(Intercept)", "log_umi")] + ) + arguments <- SCTResults(object = object[[assay]], slot = "arguments") + model_str <- SCTResults(object = object[[assay]], slot = "model") + set_median_umi <- rep(min_median_umi, length(levels(x = object[[assay]]))) + names(set_median_umi) <- levels(x = object[[assay]]) + set_median_umi <- as.list(set_median_umi) + # correct counts + for (model_name in levels(x = object[[assay]]) ) { + model_genes <- rownames(x = model_pars_fit[[model_name]]) + x <- list( + model_str = model_str[[model_name]], + arguments = arguments[[model_name]], + model_pars_fit = as.matrix(x = model_pars_fit[[model_name]]), + cell_attr = cell_attr[[model_name]] + ) + cells <- rownames(x = cell_attr[[model_name]]) + umi <- raw_umi[model_genes, cells] + + umi_corrected <- correct_counts( + x = x, + umi = umi, + verbosity = 0, + scale_factor = min_median_umi + ) + corrected_counts[rownames(umi_corrected), colnames(umi_corrected)] <- umi_corrected + } + corrected_data <- log1p(corrected_counts) + suppressWarnings({object <- SetAssayData(object = object, assay = assay, slot = "counts", new.data = corrected_counts)}) + suppressWarnings({object <- SetAssayData(object = object, assay = assay, slot = "data", new.data = corrected_data)}) + SCTResults(object = object[[assay]], slot = "median_umi") <- set_median_umi + + return(object) +} + # given a UMI count matrix, estimate NB theta parameter for each gene # and use fit of relationship with mean to assign regularized theta to each gene # diff --git a/R/objects.R b/R/objects.R index f772f8e33..316ab6051 100644 --- a/R/objects.R +++ b/R/objects.R @@ -159,6 +159,7 @@ IntegrationData <- setClass( #' and the default is RNA #' @slot model A formula used in SCTransform #' @slot arguments other information used in SCTransform +#' @slot median_umi Median UMI (or scale factor) used to calculate corrected counts #' #' @seealso \code{\link{Assay}} #' @@ -180,7 +181,8 @@ SCTModel <- setClass( clips = 'list', umi.assay = 'character', model = 'character', - arguments = "list" + arguments = 'list', + median_umi = 'numeric' ) ) @@ -1660,7 +1662,7 @@ RenameCells.VisiumV1 <- function(object, new.names = NULL, ...) { #' SCTResults.SCTModel <- function(object, slot, ...) { CheckDots(...) - slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments') + slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments', 'median_umi') if (!slot %in% slots.use) { stop( "'slot' must be one of ", @@ -1677,7 +1679,7 @@ SCTResults.SCTModel <- function(object, slot, ...) { #' @method SCTResults<- SCTModel #' "SCTResults<-.SCTModel" <- function(object, slot, ..., value) { - slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments') + slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments', 'median_umi') if (!slot %in% slots.use) { stop( "'slot' must be one of ", @@ -1705,7 +1707,7 @@ SCTResults.SCTModel <- function(object, slot, ...) { #' SCTResults.SCTAssay <- function(object, slot, model = NULL, ...) { CheckDots(...) - slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments') + slots.use <- c('feature.attributes', 'cell.attributes', 'clips', 'umi.assay', 'model', 'arguments', 'median_umi') if (!slot %in% slots.use) { stop( "'slot' must be one of ", @@ -1728,7 +1730,7 @@ SCTResults.SCTAssay <- function(object, slot, model = NULL, ...) { #' @method SCTResults<- SCTAssay #' "SCTResults<-.SCTAssay" <- function(object, slot, model = NULL, ..., value) { - slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments') + slots.use <- c('feature.attributes', 'cell.attributes', 'clips','umi.assay', 'model', 'arguments', 'median_umi') if (!slot %in% slots.use) { stop( "'slot' must be one of ", @@ -2566,13 +2568,21 @@ PrepVSTResults <- function(vst.res, cell.names) { 'vst' = vst.res$arguments$res_clip_range, 'sct' = vst.res$arguments$sct.clip.range ) + median_umi <- NA + # check if a custom scale_factor was provided to vst() + if ("scale_factor" %in% names(vst.res$arguments)){ + median_umi <- vst.res$arguments$scale_factor + } + if (is.na(median_umi)) median_umi <- median(cell.attrs$umi) + vst.res.SCTModel <- SCTModel( feature.attributes = feature.attrs, cell.attributes = cell.attrs, clips = clips, umi.assay = vst.res$umi.assay %||% "RNA", model = vst.res$model_str, - arguments = vst.res$arguments + arguments = vst.res$arguments, + median_umi = median_umi ) return(vst.res.SCTModel) } diff --git a/man/PrepSCTFindMarkers.Rd b/man/PrepSCTFindMarkers.Rd new file mode 100644 index 000000000..9c8a3ca31 --- /dev/null +++ b/man/PrepSCTFindMarkers.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/differential_expression.R +\name{PrepSCTFindMarkers} +\alias{PrepSCTFindMarkers} +\title{Prepare object to run differential expression on SCT assay with multiple models} +\usage{ +PrepSCTFindMarkers(object, assay = "SCT", verbose = TRUE) +} +\arguments{ +\item{object}{Seurat object with SCT assays} + +\item{assay}{Assay name where for SCT objects are stored; Default is 'SCT'} + +\item{verbose}{Print messages and progress} +} +\value{ +object Seurat object with recorrected counts and data in the SCT assay. +} +\description{ +Given a merged object with multiple SCT models, this function uses minimum +of the median UMI (calculated using the raw UMI counts) of individual objects +to reverse the individual SCT regression model using minimum of median UMI +as the sequencing depth covariate. +The counts slot of the SCT assay is replaced with recorrected counts and +the data slot is replaced with log1p of recorrected counts. +} diff --git a/man/SCTAssay-class.Rd b/man/SCTAssay-class.Rd index 347894fbb..b06638089 100644 --- a/man/SCTAssay-class.Rd +++ b/man/SCTAssay-class.Rd @@ -48,6 +48,8 @@ and the default is RNA} \item{\code{arguments}}{other information used in SCTransform} +\item{\code{median_umi}}{Median UMI (or scale factor) used to calculate corrected counts} + \item{\code{SCTModel.list}}{A list containing SCT models} }} diff --git a/vignettes/sctransform_v2_vignette.Rmd b/vignettes/sctransform_v2_vignette.Rmd new file mode 100644 index 000000000..922dd8f6d --- /dev/null +++ b/vignettes/sctransform_v2_vignette.Rmd @@ -0,0 +1,191 @@ +--- +title: 'Introduction to sctransform v2' +output: + html_document: + theme: united + pdf_document: default +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +--- + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + fig.width = 10, + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +``` + +## Introduction + +Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. In [Choudhary and Satija, 2021](https://www.biorxiv.org/content/10.1101/2021.07.07.451498v1) we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis. + +In this vignette, we use [sctransform v2](https://github.com/satijalab/sctransform/) based workflow to perform a comparative analysis of human immune cells (PBMC) in either a [resting or interferon-stimulated state](https://www.nature.com/articles/nbt.4042). In this vignette we apply sctransform-v2 based normalization to perform the following tasks: + +* Create an 'integrated' data assay for downstream analysis +* Compare the datasets to find cell-type specific responses to stimulation +* Obtain cell type markers that are conserved in both control and stimulated cells + + +## Setup the Seurat objects + + +```{r data} +library(Seurat) +library(SeuratData) +library(patchwork) +library(dplyr) +library(ggplot2) +``` +The dataset is available throuh our [SeuratData](https://github.com/satijalab/seurat-data) package. + +```{r installdata, eval=FALSE} +# install dataset +InstallData("ifnb") +``` + +```{r init, results='hide', message=FALSE, fig.keep='none'} +# load dataset +LoadData("ifnb") + +# split the dataset into a list of two seurat objects (stim and CTRL) +ifnb.list <- SplitObject(ifnb, split.by = "stim") + +ctrl <- ifnb.list[["CTRL"]] +stim <- ifnb.list[["STIM"]] +``` + +## Perform normalization and dimensionality reduction + +To perform normalization, we invoke `SCTransform` with an additional flag `vst.flavor="v2"` to invoke +the v2 regularization. This flavor provides some improvements over our original approach first introduced in [Hafemeister and Satija, 2019](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1). + +* We fix the slope parameter of the GLM to $\ln(10)$ with $\log_{10}(\text{total UMI})$ used as the predictor as proposed by [Lause et al.](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02451-7) +* We remove a subset of genes prior to performing regularization where there is no evidence for overdispersion because of very low abundance ($\mu < 0.001$), or where the variance of count values did not exceed the average abundance (i.e. $\sigma^2 \leq \mu$) +* We place a lower bound on gene-level standard deviation when calculating Pearson residuals. This prevents genes with extremely low expression (only 1-2 detected UMIs) from having a high pearson residual. + + +```{r ctrldimreduc, fig.width=10, fig.height=5} +# normalize and run dimensionality reduction on control dataset +ctrl <- SCTransform(ctrl, vst.flavor = "v2", verbose = FALSE) %>% + RunPCA(npcs = 30, verbose = FALSE) %>% + RunUMAP(reduction = "pca", dims = 1:30, verbose = FALSE) %>% + FindNeighbors(reduction = "pca", dims = 1:30, verbose = FALSE) %>% + FindClusters(resolution = 0.7, verbose = FALSE) + +p1 <- DimPlot(ctrl, label = T, repel = T) + ggtitle("Unsupervised clustering") +p2 <- DimPlot(ctrl, label = T, repel = T, group.by = "seurat_annotations") + ggtitle("Annotated celltypes") + +p1 | p2 +``` + +## Perform integration using pearson residuals + +To perform integration using the pearson residuals calculaated above, we use the `PrepSCTIntegration()` function after selecting a list of informative features using `SelectIntegrationFeatures()`: + +```{r prepinteg} +stim <- SCTransform(stim, vst.flavor = "v2", verbose = FALSE) %>% RunPCA(npcs = 30, verbose = FALSE) +ifnb.list <- list(ctrl = ctrl, stim = stim) +features <- SelectIntegrationFeatures(object.list = ifnb.list, nfeatures = 3000) +ifnb.list <- PrepSCTIntegration(object.list = ifnb.list, anchor.features = features) +``` + +To integrate the two datasets, we use the `FindIntegrationAnchors()` function, which takes a list of Seurat objects as input, and use these anchors to integrate the two datasets together with `IntegrateData()`. + +```{r ifnb.cca.sct.anchors} +immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, normalization.method = "SCT", anchor.features = features) +immune.combined.sct <- IntegrateData(anchorset = immune.anchors, normalization.method = "SCT") +``` + +## Perform an integrated analysis + +Now we can run a single integrated analysis on all cells: + +```{r ifnb.cca.sct.clustering, results='hide', message=FALSE} +immune.combined.sct <- RunPCA(immune.combined.sct, verbose = FALSE) +immune.combined.sct <- RunUMAP(immune.combined.sct, reduction = "pca", dims = 1:30, verbose = FALSE) +immune.combined.sct <- FindNeighbors(immune.combined.sct, reduction = "pca", dims = 1:30) +immune.combined.sct <- FindClusters(immune.combined.sct, resolution = 0.3) +``` + +To visualize the two conditions side-by-side, we can use the `split.by` argument to show each condition colored by cluster. + +```{r split.dim} +DimPlot(immune.combined.sct, reduction = "umap", split.by = "stim") +``` + +We can also visualize the distribution of annotated celltypes across control and stimulated datasets: + +```{r immunesca.cca.sct.split.dims, fig.width=13, fig.height=4.5} +p1 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "stim") +p2 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) +p3 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_annotations", label = TRUE, repel = TRUE) +p1 | p2 | p3 +``` + + +## Identify differential expressed genes across conditions + +Using the normalized datasets with known celltype annotation, we can ask what genes change in different conditions for cells of the same type. First, we create a column in the meta.data slot to hold both the cell type and stimulation information and switch the current ident to that column. + +```{r de.genes} +immune.combined.sct$celltype.stim <- paste(immune.combined.sct$seurat_annotations, immune.combined.sct$stim, sep = "_") +Idents(immune.combined.sct) <- "celltype.stim" +``` + +To run differential expression, we make use of 'corrected counts' that are stored in the `data` slot of the the `SCT` assay. While the primary output of the sctransform procedure is a set of Pearson residuals, we can also estimate 'corrected' counts for each gene in a cell. These corrected counts should no longer exhibit technical variation driven by differences in sequencing depth, and can be used for downstream visualization and differential expression analyses. Corrected counts are obtained by setting the sequencing depth for all the cells to a fixed value and reversing the learned regularized negative-binomial regression model. +When calculating corrected counts for sctransform v2, we calculated the median sequencing depth across cells for both datasets, and use the minimum to calculate corrected counts using `PrepSCTFindMarkers()`. Then we use `FindMarkers(assay="SCT")` to find the genes that are different between stimulated and control B cells. + +```{r runde} +immune.combined.sct <- PrepSCTFindMarkers(immune.combined.sct) +b.interferon.response <- FindMarkers(immune.combined.sct, assay = "SCT", ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE) +head(b.interferon.response, n = 15) +``` + +We can also use the corrected counts for visualization: + +```{r feature.heatmaps, fig.height = 14} +Idents(immune.combined.sct) <- "seurat_annotations" +DefaultAssay(immune.combined.sct) <- "SCT" +FeaturePlot(immune.combined.sct, features = c("CD3D", "GNLY", "IFI6"), split.by = "stim", max.cutoff = 3, cols = c("grey", "red")) +``` + +```{r splitvln, fig.height = 12} +plots <- VlnPlot(immune.combined.sct, features = c("LYZ", "ISG15", "CXCL10"), split.by = "stim", group.by = "seurat_annotations", pt.size = 0, combine = FALSE) +wrap_plots(plots = plots, ncol = 1) +``` + +### Identify conserved cell type markers + +To identify canonical cell type marker genes that are conserved across conditions, we provide the `FindConservedMarkers()` function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. For example, we can calculated the genes that are conserved markers irrespective of stimulation condition in NK cells: + +```{r conserved.markers, warning=FALSE} +nk.markers <- FindConservedMarkers(immune.combined.sct, assay = "SCT", ident.1 = "NK", grouping.var = "stim", verbose = FALSE) +head(nk.markers) +``` + +```{r save.times, include = FALSE} +# write.csv(x = t(as.data.frame(all_times)), file = "../output/timings/sctransform2.csv") +``` + +

      + **Session Info** +```{r} +sessionInfo() +``` +
      From d3c31137d5aeeb6b2c1f6c31d9a20200cbf280b6 Mon Sep 17 00:00:00 2001 From: Efi Papalexi Date: Wed, 5 Jan 2022 15:07:39 -0800 Subject: [PATCH 07/39] fixed function to be compatible with latest split-pipe 0.9.6p output files --- R/convenience.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/convenience.R b/R/convenience.R index edd606c26..aee4b254a 100644 --- a/R/convenience.R +++ b/R/convenience.R @@ -186,7 +186,7 @@ SpecificDimPlot <- function(object, ...) { ReadParseBio <- function(data.dir, ...) { mtx <- file.path(data.dir, "DGE.mtx") cells <- file.path(data.dir, "cell_metadata.csv") - features <- file.path(data.dir, "genes.csv") + features <- file.path(data.dir, "all_genes.csv") return(ReadMtx( mtx = mtx, cells = cells, From 0c025e601d5763f4cd8be2cf21981f7bd3f85f8d Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Wed, 5 Jan 2022 19:21:26 -0500 Subject: [PATCH 08/39] Handle old seurat objects; update vignette --- DESCRIPTION | 2 +- R/differential_expression.R | 22 +++++++++++++ vignettes/sctransform_v2_vignette.Rmd | 45 +++++++++++++++++++++------ 3 files changed, 58 insertions(+), 11 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2ad4301bf..b092887e0 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -95,7 +95,7 @@ Collate: 'tree.R' 'utilities.R' 'zzz.R' -RoxygenNote: 7.1.1 +RoxygenNote: 7.1.2 Encoding: UTF-8 Suggests: ape, diff --git a/R/differential_expression.R b/R/differential_expression.R index 3d54bbb85..7d5734baf 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -852,6 +852,15 @@ FindMarkers.Seurat <- function( X = cell_attributes, FUN = function(x) median(x[, "umi"]) ) + model.list <- slot(object = object[[assay]], "SCTModel.list") + median_umi.status <- lapply(X = model.list, + FUN = function(x) { return(tryCatch( + expr = slot(object = x, name = 'median_umi'), + error = function(...) {return(NULL)}) + )}) + if (any(is.null(unlist(median_umi.status)))){ + stop("SCT assay does not contain median UMI information. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") + } model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") min_median_umi <- min(unlist(x = observed_median_umis)) if (any(unlist(model_median_umis) != min_median_umi)){ @@ -1918,6 +1927,19 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { X = SCTResults(object = object[[assay]], slot = "cell.attributes"), FUN = function(x) median(x[, "umi"]) ) + model.list <- slot(object = object[[assay]], "SCTModel.list") + median_umi.status <- lapply(X = model.list, + FUN = function(x) { return(tryCatch( + expr = slot(object = x, name = 'median_umi'), + error = function(...) {return(NULL)}) + )}) + if (any(is.null(unlist(median_umi.status)))){ + # For old SCT objects median_umi is set to median umi as calculated from + slot(object = object[[assay]], "SCTModel.list") <- lapply(model.list, + FUN = UpdateSlots) + SCTResults(object = object[[assay]], slot = "median_umi") <- observed_median_umis + + } model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") min_median_umi <- min(unlist(x = observed_median_umis)) if (all(unlist(model_median_umis) == min_median_umi)){ diff --git a/vignettes/sctransform_v2_vignette.Rmd b/vignettes/sctransform_v2_vignette.Rmd index 922dd8f6d..4a2b41768 100644 --- a/vignettes/sctransform_v2_vignette.Rmd +++ b/vignettes/sctransform_v2_vignette.Rmd @@ -31,6 +31,19 @@ knitr::opts_chunk$set( ``` +## TL;DR + +We recently introduced [sctransform](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1) to perform normalization and variance stabilization of scRNA-seq datasets. We now release an updated version ('v2'), based on [our broad analysis](https://www.biorxiv.org/content/10.1101/2021.07.07.451498v1) of 59 scRNA-seq datasets spanning a range of technologies, systems, and sequencing depths. This update improves speed and memory consumption, the stability of parameter estimates, the identification of variable features, and the the ability to perform downstream differential expression analyses. + +Users can install sctransform v2 from Github (CRAN update coming soon), and invoke the use of the updated method via the `vst.flavor` argument. + +```{r tldr, eval=FALSE} +# install sctransform +devtools::install_github("satijalab/sctransform", ref="develop") +# invoke sctransform +object <- SCTransform(object, vst.flavor = 'v2') +``` + ## Introduction Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. In [Choudhary and Satija, 2021](https://www.biorxiv.org/content/10.1101/2021.07.07.451498v1) we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis. @@ -41,6 +54,17 @@ In this vignette, we use [sctransform v2](https://github.com/satijalab/sctransfo * Compare the datasets to find cell-type specific responses to stimulation * Obtain cell type markers that are conserved in both control and stimulated cells +## Install sctransform + +We will install sctransform v2 from Github. We also recommend installing [glmGamPoi](https://bioconductor.org/packages/release/bioc/html/glmGamPoi.html) package which substantially improves the speed of the learning procedure. + +```{r} +# install glmGamPoi +##if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") +##BiocManager::install("glmGamPoi") +# install sctransform +##devtools::install("satijalab/sctransform", ref="develop") +``` ## Setup the Seurat objects @@ -52,7 +76,7 @@ library(patchwork) library(dplyr) library(ggplot2) ``` -The dataset is available throuh our [SeuratData](https://github.com/satijalab/seurat-data) package. +The dataset is available through our [SeuratData](https://github.com/satijalab/seurat-data) package. ```{r installdata, eval=FALSE} # install dataset @@ -73,14 +97,14 @@ stim <- ifnb.list[["STIM"]] ## Perform normalization and dimensionality reduction To perform normalization, we invoke `SCTransform` with an additional flag `vst.flavor="v2"` to invoke -the v2 regularization. This flavor provides some improvements over our original approach first introduced in [Hafemeister and Satija, 2019](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1). +the v2 regularization. This provides some improvements over our original approach first introduced in [Hafemeister and Satija, 2019](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1). * We fix the slope parameter of the GLM to $\ln(10)$ with $\log_{10}(\text{total UMI})$ used as the predictor as proposed by [Lause et al.](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02451-7) -* We remove a subset of genes prior to performing regularization where there is no evidence for overdispersion because of very low abundance ($\mu < 0.001$), or where the variance of count values did not exceed the average abundance (i.e. $\sigma^2 \leq \mu$) +* We utilize an improved parameter estimation procedure that alleviates uncertainty and bias that result from fitting GLM models for very lowly expressed genes. * We place a lower bound on gene-level standard deviation when calculating Pearson residuals. This prevents genes with extremely low expression (only 1-2 detected UMIs) from having a high pearson residual. -```{r ctrldimreduc, fig.width=10, fig.height=5} +```{r ctrldimreduc, fig.width=10, fig.height=4} # normalize and run dimensionality reduction on control dataset ctrl <- SCTransform(ctrl, vst.flavor = "v2", verbose = FALSE) %>% RunPCA(npcs = 30, verbose = FALSE) %>% @@ -131,7 +155,7 @@ DimPlot(immune.combined.sct, reduction = "umap", split.by = "stim") We can also visualize the distribution of annotated celltypes across control and stimulated datasets: -```{r immunesca.cca.sct.split.dims, fig.width=13, fig.height=4.5} +```{r immunesca.cca.sct.split.dims, fig.width=12, fig.height=4} p1 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "stim") p2 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) p3 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_annotations", label = TRUE, repel = TRUE) @@ -144,16 +168,17 @@ p1 | p2 | p3 Using the normalized datasets with known celltype annotation, we can ask what genes change in different conditions for cells of the same type. First, we create a column in the meta.data slot to hold both the cell type and stimulation information and switch the current ident to that column. ```{r de.genes} -immune.combined.sct$celltype.stim <- paste(immune.combined.sct$seurat_annotations, immune.combined.sct$stim, sep = "_") +immune.combined.sct$celltype.stim <- paste(immune.combined.sct$seurat_annotations, + immune.combined.sct$stim, sep = "_") Idents(immune.combined.sct) <- "celltype.stim" ``` -To run differential expression, we make use of 'corrected counts' that are stored in the `data` slot of the the `SCT` assay. While the primary output of the sctransform procedure is a set of Pearson residuals, we can also estimate 'corrected' counts for each gene in a cell. These corrected counts should no longer exhibit technical variation driven by differences in sequencing depth, and can be used for downstream visualization and differential expression analyses. Corrected counts are obtained by setting the sequencing depth for all the cells to a fixed value and reversing the learned regularized negative-binomial regression model. -When calculating corrected counts for sctransform v2, we calculated the median sequencing depth across cells for both datasets, and use the minimum to calculate corrected counts using `PrepSCTFindMarkers()`. Then we use `FindMarkers(assay="SCT")` to find the genes that are different between stimulated and control B cells. +To run differential expression, we make use of 'corrected counts' that are stored in the `data` slot of the the `SCT` assay. Corrected counts are obtained by setting the sequencing depth for all the cells to a fixed value and reversing the learned regularized negative-binomial regression model. Prior to performing differential expression, we first run `PrepSCTFindMarkers`, which ensures that the fixed value is set properly. Then we use `FindMarkers(assay="SCT")` to find differentially expressed genes. Here, we aim to identify genes that are differently expressed between stimulated and control B cells. ```{r runde} immune.combined.sct <- PrepSCTFindMarkers(immune.combined.sct) -b.interferon.response <- FindMarkers(immune.combined.sct, assay = "SCT", ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE) +b.interferon.response <- FindMarkers(immune.combined.sct, assay = "SCT", + ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE) head(b.interferon.response, n = 15) ``` @@ -172,7 +197,7 @@ wrap_plots(plots = plots, ncol = 1) ### Identify conserved cell type markers -To identify canonical cell type marker genes that are conserved across conditions, we provide the `FindConservedMarkers()` function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. For example, we can calculated the genes that are conserved markers irrespective of stimulation condition in NK cells: +To identify canonical cell type marker genes that are conserved across conditions, we provide the `FindConservedMarkers()` function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. For example, we can identify genes that are conserved markers irrespective of stimulation condition in NK cells. Note that the `PrepSCTFindMarkers` command does not to be rerun here. ```{r conserved.markers, warning=FALSE} nk.markers <- FindConservedMarkers(immune.combined.sct, assay = "SCT", ident.1 = "NK", grouping.var = "stim", verbose = FALSE) From 183d1aab63ba23e90fb5850b4e587c7a2d1c6cc3 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Wed, 5 Jan 2022 19:23:47 -0500 Subject: [PATCH 09/39] document --- R/differential_expression.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index 7d5734baf..5f0c8970d 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -1934,7 +1934,7 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { error = function(...) {return(NULL)}) )}) if (any(is.null(unlist(median_umi.status)))){ - # For old SCT objects median_umi is set to median umi as calculated from + # For old SCT objects median_umi is set to median umi as calculated from obserbed UMIs slot(object = object[[assay]], "SCTModel.list") <- lapply(model.list, FUN = UpdateSlots) SCTResults(object = object[[assay]], slot = "median_umi") <- observed_median_umis From e64e7f4ad88b3449a7d8385e6ec7422393cbc42f Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Wed, 5 Jan 2022 19:42:18 -0500 Subject: [PATCH 10/39] Bump develop version --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2ad4301bf..ed749344d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: Seurat -Version: 4.0.6.9001 -Date: 2021-12-16 +Version: 4.0.6.9002 +Date: 2022-01-05 Title: Tools for Single Cell Genomics Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Authors@R: c( From 9100c4a08fa5b9d745cabb5c2932a2d485a13f0b Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Wed, 5 Jan 2022 19:42:25 -0500 Subject: [PATCH 11/39] Update changelog --- NEWS.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/NEWS.md b/NEWS.md index 594f1f93a..6cd97b112 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,6 +2,8 @@ ## Added - Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392)) +## Changes +- Update `ReadParseBio` to support split-pipe 0.9.6p ([#5446](https://github.com/satijalab/seurat/pull/5446)) # Seurat 4.0.6 (2021-12-16) ## Added From bbf2685b8e9e67cb48739a82ebfacb2aae11bac0 Mon Sep 17 00:00:00 2001 From: yuhanH Date: Thu, 6 Jan 2022 16:53:45 -0500 Subject: [PATCH 12/39] update style --- R/differential_expression.R | 79 +++++++++++++++++++++++++------------ 1 file changed, 53 insertions(+), 26 deletions(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index 5f0c8970d..42714b15c 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -846,8 +846,12 @@ FindMarkers.Seurat <- function( if (is.null(x = reduction)) { assay <- assay %||% DefaultAssay(object = object) - if (inherits(x = object[[assay]], what = "SCTAssay") && length(x = levels(x = object[[assay]])) > 1) { - cell_attributes <- SCTResults(object = object[[assay]], slot = "cell.attributes") + if (inherits(x = object[[assay]], what = "SCTAssay") && + length(x = levels(x = object[[assay]])) > 1) { + cell_attributes <- SCTResults( + object = object[[assay]], + slot = "cell.attributes" + ) observed_median_umis <- lapply( X = cell_attributes, FUN = function(x) median(x[, "umi"]) @@ -858,21 +862,24 @@ FindMarkers.Seurat <- function( expr = slot(object = x, name = 'median_umi'), error = function(...) {return(NULL)}) )}) - if (any(is.null(unlist(median_umi.status)))){ + if (any(is.null(unlist(x = median_umi.status)))) { stop("SCT assay does not contain median UMI information. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") } - model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") + model_median_umis <- SCTResults( + object = object[[assay]], + slot = "median_umi" + ) min_median_umi <- min(unlist(x = observed_median_umis)) - if (any(unlist(model_median_umis) != min_median_umi)){ - stop("Object contains multiple models with unequal library sizes. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") + if (any(unlist(model_median_umis) != min_median_umi)) { + stop("Object contains multiple models with unequal library sizes. ", + "Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") } } - data.use <- object[[assay]] cellnames.use <- colnames(x = data.use) } else { data.use <- object[[reduction]] - cellnames.use <- rownames(data.use) + cellnames.use <- rownames(x = data.use) } cells <- IdentsToCells( object = object, @@ -1927,30 +1934,40 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { X = SCTResults(object = object[[assay]], slot = "cell.attributes"), FUN = function(x) median(x[, "umi"]) ) - model.list <- slot(object = object[[assay]], "SCTModel.list") + model.list <- slot(object = object[[assay]], name = "SCTModel.list") median_umi.status <- lapply(X = model.list, FUN = function(x) { return(tryCatch( expr = slot(object = x, name = 'median_umi'), error = function(...) {return(NULL)}) - )}) - if (any(is.null(unlist(median_umi.status)))){ + )} + ) + if (any(is.null(unlist(x = median_umi.status)))) { # For old SCT objects median_umi is set to median umi as calculated from obserbed UMIs - slot(object = object[[assay]], "SCTModel.list") <- lapply(model.list, - FUN = UpdateSlots) - SCTResults(object = object[[assay]], slot = "median_umi") <- observed_median_umis - + slot(object = object[[assay]], name = "SCTModel.list") <- lapply( + X = model.list, + FUN = UpdateSlots + ) + SCTResults(object = object[[assay]], + slot = "median_umi") <- observed_median_umis } model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") min_median_umi <- min(unlist(x = observed_median_umis)) - if (all(unlist(model_median_umis) == min_median_umi)){ - if (verbose){ + if (all(unlist(x = model_median_umis) == min_median_umi)) { + if (verbose) { message("Minimum UMI unchnaged. Skipping re-correction.") } - return (object) + return(object) } if (verbose) { - message(paste0("Found ", length(x = levels(x = object[[assay]])), " SCT models.", - " Recorrecting SCT counts using minimum median counts: ", min_median_umi)) + message( + paste0( + "Found ", + length(x = levels(x = object[[assay]])), + " SCT models.", + " Recorrecting SCT counts using minimum median counts: ", + min_median_umi + ) + ) } umi.assay <- unique( x = unlist( @@ -1981,7 +1998,7 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { names(set_median_umi) <- levels(x = object[[assay]]) set_median_umi <- as.list(set_median_umi) # correct counts - for (model_name in levels(x = object[[assay]]) ) { + for (model_name in levels(x = object[[assay]])) { model_genes <- rownames(x = model_pars_fit[[model_name]]) x <- list( model_str = model_str[[model_name]], @@ -1991,7 +2008,6 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { ) cells <- rownames(x = cell_attr[[model_name]]) umi <- raw_umi[model_genes, cells] - umi_corrected <- correct_counts( x = x, umi = umi, @@ -2000,11 +2016,22 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { ) corrected_counts[rownames(umi_corrected), colnames(umi_corrected)] <- umi_corrected } - corrected_data <- log1p(corrected_counts) - suppressWarnings({object <- SetAssayData(object = object, assay = assay, slot = "counts", new.data = corrected_counts)}) - suppressWarnings({object <- SetAssayData(object = object, assay = assay, slot = "data", new.data = corrected_data)}) + corrected_data <- log1p(x = corrected_counts) + suppressWarnings({object <- SetAssayData( + object = object, + assay = assay, + slot = "counts", + new.data = corrected_counts + ) + }) + suppressWarnings({object <- SetAssayData( + object = object, + assay = assay, + slot = "data", + new.data = corrected_data + ) + }) SCTResults(object = object[[assay]], slot = "median_umi") <- set_median_umi - return(object) } From 937180e1592f97df48b866bc76f2c89a9d434dec Mon Sep 17 00:00:00 2001 From: yuhanH Date: Thu, 6 Jan 2022 20:42:16 -0500 Subject: [PATCH 13/39] update docu --- R/differential_expression.R | 2 +- man/PrepSCTFindMarkers.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index 42714b15c..ce74ed94a 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -1921,7 +1921,7 @@ PerformDE <- function( #' @importFrom Matrix Matrix #' @importFrom sctransform correct_counts #' -#' @return object Seurat object with recorrected counts and data in the SCT assay. +#' @return Returns a Seurat object with recorrected counts and data in the SCT assay. #' @export PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { if (length(x = levels(x = object[[assay]])) == 1) { diff --git a/man/PrepSCTFindMarkers.Rd b/man/PrepSCTFindMarkers.Rd index 9c8a3ca31..a190ccdef 100644 --- a/man/PrepSCTFindMarkers.Rd +++ b/man/PrepSCTFindMarkers.Rd @@ -14,7 +14,7 @@ PrepSCTFindMarkers(object, assay = "SCT", verbose = TRUE) \item{verbose}{Print messages and progress} } \value{ -object Seurat object with recorrected counts and data in the SCT assay. +Returns a Seurat object with recorrected counts and data in the SCT assay. } \description{ Given a merged object with multiple SCT models, this function uses minimum From 65f743e1ce3060baa186d3a8509d9e93857b6b96 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Fri, 7 Jan 2022 13:08:41 -0500 Subject: [PATCH 14/39] Add FindMarkers.SCTAssay --- NAMESPACE | 1 + R/differential_expression.R | 139 +++++++++++++++++++++----- man/FindMarkers.Rd | 27 +++++ man/PrepSCTFindMarkers.Rd | 11 ++ vignettes/sctransform_v2_vignette.Rmd | 16 ++- 5 files changed, 167 insertions(+), 27 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 73c8aead5..20b61af05 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,6 +16,7 @@ S3method(FindClusters,Seurat) S3method(FindClusters,default) S3method(FindMarkers,Assay) S3method(FindMarkers,DimReduc) +S3method(FindMarkers,SCTAssay) S3method(FindMarkers,Seurat) S3method(FindMarkers,default) S3method(FindNeighbors,Assay) diff --git a/R/differential_expression.R b/R/differential_expression.R index 5f0c8970d..aacf073b0 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -680,6 +680,108 @@ FindMarkers.Assay <- function( return(de.results) } + +#' @rdname FindMarkers +#' @concept differential_expression +#' @export +#' @method FindMarkers SCTAssay +#' +FindMarkers.SCTAssay <- function( + object, + slot = "data", + cells.1 = NULL, + cells.2 = NULL, + features = NULL, + logfc.threshold = 0.25, + test.use = 'wilcox', + min.pct = 0.1, + min.diff.pct = -Inf, + verbose = TRUE, + only.pos = FALSE, + max.cells.per.ident = Inf, + random.seed = 1, + latent.vars = NULL, + min.cells.feature = 3, + min.cells.group = 3, + pseudocount.use = 1, + mean.fxn = NULL, + fc.name = NULL, + base = 2, + densify = FALSE, + recorrect_umi = TRUE, + ... +) { + data.slot <- ifelse( + test = test.use %in% DEmethods_counts(), + yes = 'counts', + no = slot + ) + if (recorrect_umi && length(x = levels(x = object)) > 1) { + cell_attributes <- SCTResults(object = object, slot = "cell.attributes") + observed_median_umis <- lapply( + X = cell_attributes, + FUN = function(x) median(x[, "umi"]) + ) + model.list <- slot(object = object, "SCTModel.list") + median_umi.status <- lapply(X = model.list, + FUN = function(x) { return(tryCatch( + expr = slot(object = x, name = 'median_umi'), + error = function(...) {return(NULL)}) + )}) + if (any(is.null(unlist(median_umi.status)))){ + stop("SCT assay does not contain median UMI information. Run `PrepSCTFindMarkers()` before running `FindMarkers()` or invoke `FindMarkers(recorrect_umi=FALSE)`") + } + model_median_umis <- SCTResults(object = object, slot = "median_umi") + min_median_umi <- min(unlist(x = observed_median_umis)) + if (any(unlist(model_median_umis) != min_median_umi)){ + stop("Object contains multiple models with unequal library sizes. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") + } + } + + data.use <- GetAssayData(object = object, slot = data.slot) + counts <- switch( + EXPR = data.slot, + 'scale.data' = GetAssayData(object = object, slot = "counts"), + numeric() + ) + fc.results <- FoldChange( + object = object, + slot = data.slot, + cells.1 = cells.1, + cells.2 = cells.2, + features = features, + pseudocount.use = pseudocount.use, + mean.fxn = mean.fxn, + fc.name = fc.name, + base = base + ) + de.results <- FindMarkers( + object = data.use, + slot = data.slot, + counts = counts, + cells.1 = cells.1, + cells.2 = cells.2, + features = features, + logfc.threshold = logfc.threshold, + test.use = test.use, + min.pct = min.pct, + min.diff.pct = min.diff.pct, + verbose = verbose, + only.pos = only.pos, + max.cells.per.ident = max.cells.per.ident, + random.seed = random.seed, + latent.vars = latent.vars, + min.cells.feature = min.cells.feature, + min.cells.group = min.cells.group, + pseudocount.use = pseudocount.use, + fc.results = fc.results, + densify = densify, + ... + ) + return(de.results) +} + + #' @importFrom Matrix rowMeans #' @rdname FindMarkers #' @concept differential_expression @@ -845,29 +947,6 @@ FindMarkers.Seurat <- function( # select which data to use if (is.null(x = reduction)) { assay <- assay %||% DefaultAssay(object = object) - - if (inherits(x = object[[assay]], what = "SCTAssay") && length(x = levels(x = object[[assay]])) > 1) { - cell_attributes <- SCTResults(object = object[[assay]], slot = "cell.attributes") - observed_median_umis <- lapply( - X = cell_attributes, - FUN = function(x) median(x[, "umi"]) - ) - model.list <- slot(object = object[[assay]], "SCTModel.list") - median_umi.status <- lapply(X = model.list, - FUN = function(x) { return(tryCatch( - expr = slot(object = x, name = 'median_umi'), - error = function(...) {return(NULL)}) - )}) - if (any(is.null(unlist(median_umi.status)))){ - stop("SCT assay does not contain median UMI information. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") - } - model_median_umis <- SCTResults(object = object[[assay]], slot = "median_umi") - min_median_umi <- min(unlist(x = observed_median_umis)) - if (any(unlist(model_median_umis) != min_median_umi)){ - stop("Object contains multiple models with unequal library sizes. Run `PrepSCTFindMarkers()` before running `FindMarkers()`.") - } - } - data.use <- object[[assay]] cellnames.use <- colnames(x = data.use) } else { @@ -1916,6 +1995,18 @@ PerformDE <- function( #' #' @return object Seurat object with recorrected counts and data in the SCT assay. #' @export +#' +#' @concept differential_expression +#' @examples +#' data("pbmc_small") +#' pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20) +#' pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) +#' pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) +#' pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) +#' markers <- FindMarkers(object = pbmc_merged, ident.1="0", ident.2="1", assay="SCT") +#' pbmc_subset <- subset(pbmc_merged, idents =Run c("0", "1")) +#' markers_subset <- FindMarkers(object = pbmcx, ident.1="0", ident.2="1", assay="SCT", recorrect_umi = FALSE) + PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { if (length(x = levels(x = object[[assay]])) == 1) { if (verbose) { @@ -1944,7 +2035,7 @@ PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { min_median_umi <- min(unlist(x = observed_median_umis)) if (all(unlist(model_median_umis) == min_median_umi)){ if (verbose){ - message("Minimum UMI unchnaged. Skipping re-correction.") + message("Minimum UMI unchanged. Skipping re-correction.") } return (object) } diff --git a/man/FindMarkers.Rd b/man/FindMarkers.Rd index 0bd77a8fd..5cb3f0ba2 100644 --- a/man/FindMarkers.Rd +++ b/man/FindMarkers.Rd @@ -5,6 +5,7 @@ \alias{FindMarkersNode} \alias{FindMarkers.default} \alias{FindMarkers.Assay} +\alias{FindMarkers.SCTAssay} \alias{FindMarkers.DimReduc} \alias{FindMarkers.Seurat} \title{Gene expression markers of identity classes} @@ -60,6 +61,32 @@ FindMarkers(object, ...) ... ) +\method{FindMarkers}{SCTAssay}( + object, + slot = "data", + cells.1 = NULL, + cells.2 = NULL, + features = NULL, + logfc.threshold = 0.25, + test.use = "wilcox", + min.pct = 0.1, + min.diff.pct = -Inf, + verbose = TRUE, + only.pos = FALSE, + max.cells.per.ident = Inf, + random.seed = 1, + latent.vars = NULL, + min.cells.feature = 3, + min.cells.group = 3, + pseudocount.use = 1, + mean.fxn = NULL, + fc.name = NULL, + base = 2, + densify = FALSE, + recorrect_umi = TRUE, + ... +) + \method{FindMarkers}{DimReduc}( object, cells.1 = NULL, diff --git a/man/PrepSCTFindMarkers.Rd b/man/PrepSCTFindMarkers.Rd index 9c8a3ca31..287a3f3f4 100644 --- a/man/PrepSCTFindMarkers.Rd +++ b/man/PrepSCTFindMarkers.Rd @@ -24,3 +24,14 @@ as the sequencing depth covariate. The counts slot of the SCT assay is replaced with recorrected counts and the data slot is replaced with log1p of recorrected counts. } +\examples{ +data("pbmc_small") +pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20) +pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) +pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) +pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) +markers <- FindMarkers(object = pbmc_merged, ident.1="0", ident.2="1", assay="SCT") +pbmc_subset <- subset(pbmc_merged, idents =Run c("0", "1")) +markers_subset <- FindMarkers(object = pbmcx, ident.1="0", ident.2="1", assay="SCT", recorrect_umi = FALSE) +} +\concept{differential_expression} diff --git a/vignettes/sctransform_v2_vignette.Rmd b/vignettes/sctransform_v2_vignette.Rmd index 4a2b41768..a10150473 100644 --- a/vignettes/sctransform_v2_vignette.Rmd +++ b/vignettes/sctransform_v2_vignette.Rmd @@ -56,12 +56,12 @@ In this vignette, we use [sctransform v2](https://github.com/satijalab/sctransfo ## Install sctransform -We will install sctransform v2 from Github. We also recommend installing [glmGamPoi](https://bioconductor.org/packages/release/bioc/html/glmGamPoi.html) package which substantially improves the speed of the learning procedure. +We will install sctransform v2 from Github. We will also install the [glmGamPoi](https://bioconductor.org/packages/release/bioc/html/glmGamPoi.html) package which substantially improves the speed of the learning procedure. ```{r} # install glmGamPoi -##if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") -##BiocManager::install("glmGamPoi") +if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") +BiocManager::install("glmGamPoi") # install sctransform ##devtools::install("satijalab/sctransform", ref="develop") ``` @@ -177,11 +177,21 @@ To run differential expression, we make use of 'corrected counts' that are store ```{r runde} immune.combined.sct <- PrepSCTFindMarkers(immune.combined.sct) + b.interferon.response <- FindMarkers(immune.combined.sct, assay = "SCT", ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE) head(b.interferon.response, n = 15) ``` +If running on a subset of the original object after running `PrepSCTFindMarkers()`, `FindMarkers()` should be invoked with `recorrect_umi = FALSE` to use the existing corrected counts: + +```{r runde2} +immune.combined.sct.subset <- subset(immune.combined.sct, idents = c("B_STIM", "B_CTRL")) +b.interferon.response.subset <- FindMarkers(immune.combined.sct.subset, assay = "SCT", + ident.1 = "B_STIM", ident.2 = "B_CTRL", + verbose = FALSE, recorrect_umi = FALSE) +``` + We can also use the corrected counts for visualization: ```{r feature.heatmaps, fig.height = 14} From d92eb88c7f9861eeac75eae58180844c1175aa50 Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Fri, 7 Jan 2022 17:56:10 -0500 Subject: [PATCH 15/39] Bump develop version Automatically install satijalab/sctransform@develop --- DESCRIPTION | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ef2548fd5..d815fd618 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: Seurat -Version: 4.0.6.9002 -Date: 2022-01-05 +Version: 4.0.6.9003 +Date: 2022-01-07 Title: Tools for Single Cell Genomics Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Authors@R: c( @@ -24,6 +24,7 @@ Authors@R: c( ) URL: https://satijalab.org/seurat, https://github.com/satijalab/seurat BugReports: https://github.com/satijalab/seurat/issues +Remotes: satijalab/sctransform@develop Depends: R (>= 4.0.0), methods From c782f960409c247707ae117f8bff1c743e79435b Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Fri, 7 Jan 2022 17:57:40 -0500 Subject: [PATCH 16/39] Update changelog --- NEWS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/NEWS.md b/NEWS.md index 6cd97b112..94115abdb 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,7 @@ # Unreleased ## Added - Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392)) +- Add support for sctransform v2, differential expression on with SCT ## Changes - Update `ReadParseBio` to support split-pipe 0.9.6p ([#5446](https://github.com/satijalab/seurat/pull/5446)) From 5a734f4d610a640a317689c68f3116fc9a35c7fa Mon Sep 17 00:00:00 2001 From: yuhanH Date: Fri, 7 Jan 2022 23:46:35 -0500 Subject: [PATCH 17/39] fix sct update --- R/objects.R | 3 --- R/preprocessing.R | 3 ++- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/R/objects.R b/R/objects.R index 316ab6051..eca9752eb 100644 --- a/R/objects.R +++ b/R/objects.R @@ -2258,9 +2258,6 @@ setAs( ) names(x = vst.res) <- paste0("model", 1:length(x = vst.res)) } - if (length(x = vst.res) > 1) { - vst.res <- merge(x = vst.res[[1]], y = vst.res[2:length(x = vst.res)]) - } object.list$misc[[vst.use]] <- NULL object.list$SCTModel.list <- vst.res } diff --git a/R/preprocessing.R b/R/preprocessing.R index f04537494..fde9f30b0 100644 --- a/R/preprocessing.R +++ b/R/preprocessing.R @@ -408,7 +408,8 @@ GetResidual <- function( } features <- intersect(x = features.orig, y = features) if (length(x = sct.models) > 1 & verbose) { - message("This SCTAssay contains multiple SCT models. Computing residuals for cells using") + message("This SCTAssay contains multiple SCT models. ", + "Computing residuals for cells using different models") } new.residuals <- lapply( X = sct.models, From 6e3c21437ebb87bc2ccdd65db879ffe9f5215bf3 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Sat, 8 Jan 2022 17:16:10 -0500 Subject: [PATCH 18/39] SCT v2 vignette updates --- vignettes/sctransform_v2_vignette.Rmd | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/vignettes/sctransform_v2_vignette.Rmd b/vignettes/sctransform_v2_vignette.Rmd index a99e3d436..1064de774 100644 --- a/vignettes/sctransform_v2_vignette.Rmd +++ b/vignettes/sctransform_v2_vignette.Rmd @@ -38,10 +38,10 @@ We recently introduced [sctransform](https://genomebiology.biomedcentral.com/art Users can install sctransform v2 from Github (CRAN update coming soon), and invoke the use of the updated method via the `vst.flavor` argument. ```{r tldr, eval=FALSE} -# install sctransform -devtools::install_github("satijalab/sctransform", ref="develop") +# install Seurat from Github (automatically updates sctransform) +devtools::install_github("satijalab/seurat", ref="develop") # invoke sctransform -object <- SCTransform(object, vst.flavor = 'v2') +object <- SCTransform(object, vst.flavor = "v2") ``` ## Introduction @@ -58,12 +58,12 @@ In this vignette, we use [sctransform v2](https://github.com/satijalab/sctransfo We will install sctransform v2 from Github. We will also install the [glmGamPoi](https://bioconductor.org/packages/release/bioc/html/glmGamPoi.html) package which substantially improves the speed of the learning procedure. -```{r} +```{r results='hide', message=FALSE, warning=FALSE} # install glmGamPoi if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("glmGamPoi") -# install sctransform -##devtools::install("satijalab/sctransform", ref="develop") +# install sctransform from Github +devtools::install_github("satijalab/sctransform", ref="develop") ``` ## Setup the Seurat objects @@ -132,7 +132,8 @@ ifnb.list <- PrepSCTIntegration(object.list = ifnb.list, anchor.features = featu To integrate the two datasets, we use the `FindIntegrationAnchors()` function, which takes a list of Seurat objects as input, and use these anchors to integrate the two datasets together with `IntegrateData()`. ```{r ifnb.cca.sct.anchors} -immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, normalization.method = "SCT", anchor.features = features) +immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, + normalization.method = "SCT", anchor.features = features) immune.combined.sct <- IntegrateData(anchorset = immune.anchors, normalization.method = "SCT") ``` @@ -155,7 +156,7 @@ DimPlot(immune.combined.sct, reduction = "umap", split.by = "stim") We can also visualize the distribution of annotated celltypes across control and stimulated datasets: -```{r immunesca.cca.sct.split.dims, fig.width=12, fig.height=4} +```{r immunesca.cca.sct.split.dims, fig.width=13, fig.height=4} p1 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "stim") p2 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) p3 <- DimPlot(immune.combined.sct, reduction = "umap", group.by = "seurat_annotations", label = TRUE, repel = TRUE) @@ -197,11 +198,13 @@ We can also use the corrected counts for visualization: ```{r feature.heatmaps, fig.height = 14} Idents(immune.combined.sct) <- "seurat_annotations" DefaultAssay(immune.combined.sct) <- "SCT" -FeaturePlot(immune.combined.sct, features = c("CD3D", "GNLY", "IFI6"), split.by = "stim", max.cutoff = 3, cols = c("grey", "red")) +FeaturePlot(immune.combined.sct, features = c("CD3D", "GNLY", "IFI6"), + split.by = "stim", max.cutoff = 3, cols = c("grey", "red")) ``` ```{r splitvln, fig.height = 12} -plots <- VlnPlot(immune.combined.sct, features = c("LYZ", "ISG15", "CXCL10"), split.by = "stim", group.by = "seurat_annotations", pt.size = 0, combine = FALSE) +plots <- VlnPlot(immune.combined.sct, features = c("LYZ", "ISG15", "CXCL10"), + split.by = "stim", group.by = "seurat_annotations", pt.size = 0, combine = FALSE) wrap_plots(plots = plots, ncol = 1) ``` From 99f36a6e11ba3e452d5941be90aeeb99d46bfa2d Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Mon, 10 Jan 2022 13:50:50 -0500 Subject: [PATCH 19/39] Update preprocessing.R --- R/preprocessing.R | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/R/preprocessing.R b/R/preprocessing.R index fde9f30b0..1c54fc605 100644 --- a/R/preprocessing.R +++ b/R/preprocessing.R @@ -407,9 +407,10 @@ GetResidual <- function( } } features <- intersect(x = features.orig, y = features) - if (length(x = sct.models) > 1 & verbose) { - message("This SCTAssay contains multiple SCT models. ", - "Computing residuals for cells using different models") + if (length(x = sct.models) > 1 && verbose) { + message( + "This SCTAssay contains multiple SCT models. Computing residuals for cells using different models" + ) } new.residuals <- lapply( X = sct.models, From d40141c8f2bcc0a57ce04676e1754432f89aa7f7 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Mon, 10 Jan 2022 15:04:41 -0500 Subject: [PATCH 20/39] Fix sct2 example and tests --- R/differential_expression.R | 6 +++--- R/integration.R | 2 +- R/objects.R | 12 +++++++++--- man/PrepSCTFindMarkers.Rd | 6 +++--- tests/testthat/test_preprocessing.R | 18 +++++++++--------- 5 files changed, 25 insertions(+), 19 deletions(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index 6b526c186..a7751df8e 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -2004,9 +2004,9 @@ PerformDE <- function( #' pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) #' pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) #' pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) -#' markers <- FindMarkers(object = pbmc_merged, ident.1="0", ident.2="1", assay="SCT") -#' pbmc_subset <- subset(pbmc_merged, idents =Run c("0", "1")) -#' markers_subset <- FindMarkers(object = pbmcx, ident.1="0", ident.2="1", assay="SCT", recorrect_umi = FALSE) +#' markers <- FindMarkers(object = pbmc_merged, ident.1 = "0", ident.2 = "1", assay = "SCT") +#' pbmc_subset <- subset(pbmc_merged, idents = c("0", "1")) +#' markers_subset <- FindMarkers(object = pbmc_subset, ident.1 = "0", ident.2 = "1", assay = "SCT", recorrect_umi = FALSE) PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { if (length(x = levels(x = object[[assay]])) == 1) { diff --git a/R/integration.R b/R/integration.R index 00b7e5e58..99a3ebe8f 100644 --- a/R/integration.R +++ b/R/integration.R @@ -1912,7 +1912,7 @@ LocalStruct <- function( #' } #' #' @importFrom rlang invoke -#' +#' #' @export #' @concept integration #' diff --git a/R/objects.R b/R/objects.R index 316ab6051..ee8847ac5 100644 --- a/R/objects.R +++ b/R/objects.R @@ -2253,7 +2253,8 @@ setAs( X = 1:length(x = vst.res), FUN = function(i) { vst.res[[i]]$umi.assay <- umi.assay[[i]] - return(PrepVSTResults(vst.res = vst.res[[i]], cell.names = colnames(x = from))) + return(PrepVSTResults(vst.res = vst.res[[i]], + cell.names = colnames(x = from))) } ) names(x = vst.res) <- paste0("model", 1:length(x = vst.res)) @@ -2573,8 +2574,13 @@ PrepVSTResults <- function(vst.res, cell.names) { if ("scale_factor" %in% names(vst.res$arguments)){ median_umi <- vst.res$arguments$scale_factor } - if (is.na(median_umi)) median_umi <- median(cell.attrs$umi) - + if (is.na(median_umi)) { + if ("umi" %in% colnames(x = cell.attrs)) { + median_umi <- median(cell.attrs$umi) + } else if ("log_umi" %in% colnames(x = cell.attrs)) { + median_umi <- median(10 ^ cell.attrs$umi) + } + } vst.res.SCTModel <- SCTModel( feature.attributes = feature.attrs, cell.attributes = cell.attrs, diff --git a/man/PrepSCTFindMarkers.Rd b/man/PrepSCTFindMarkers.Rd index 9ff4379af..e618a7bfe 100644 --- a/man/PrepSCTFindMarkers.Rd +++ b/man/PrepSCTFindMarkers.Rd @@ -30,8 +30,8 @@ pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) -markers <- FindMarkers(object = pbmc_merged, ident.1="0", ident.2="1", assay="SCT") -pbmc_subset <- subset(pbmc_merged, idents =Run c("0", "1")) -markers_subset <- FindMarkers(object = pbmcx, ident.1="0", ident.2="1", assay="SCT", recorrect_umi = FALSE) +markers <- FindMarkers(object = pbmc_merged, ident.1 = "0", ident.2 = "1", assay = "SCT") +pbmc_subset <- subset(pbmc_merged, idents = c("0", "1")) +markers_subset <- FindMarkers(object = pbmc_subset, ident.1 = "0", ident.2 = "1", assay = "SCT", recorrect_umi = FALSE) } \concept{differential_expression} diff --git a/tests/testthat/test_preprocessing.R b/tests/testthat/test_preprocessing.R index 18ccdbb88..f5e39d892 100644 --- a/tests/testthat/test_preprocessing.R +++ b/tests/testthat/test_preprocessing.R @@ -304,26 +304,26 @@ test_that("SCTransform wrapper works as expected", { expect_equal(fa["MS4A1", "detection_rate"], 0.15) expect_equal(fa["MS4A1", "gmean"], 0.2027364, tolerance = 1e-6) expect_equal(fa["MS4A1", "variance"], 1.025158, tolerance = 1e-6) - expect_equal(fa["MS4A1", "residual_mean"], 0.2362887026, tolerance = 1e-6) - expect_equal(fa["MS4A1", "residual_variance"], 2.875760656, tolerance = 1e-6) + expect_equal(fa["MS4A1", "residual_mean"], 0.2362887, tolerance = 1e-6) + expect_equal(fa["MS4A1", "residual_variance"], 2.875761, tolerance = 1e-6) }) object <- suppressWarnings(SCTransform(object = object, ncells = 40, verbose = FALSE)) test_that("SCTransform ncells param works", { expect_true("SCT" %in% names(object)) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 11.47923, tolerance = 1e6) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 11.8332, tolerance = 1e6) expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[5]), 0) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 55.42253476, tolerance = 1e-6) - expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.36674295, tolerance = 1e-6) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 119) - expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 26) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 54.59918, tolerance = 1e-6) + expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.74404, tolerance = 1e-6) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 117) + expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 28) expect_equal(length(VariableFeatures(object[["SCT"]])), 220) fa <- SCTResults(object = object, assay = "SCT", slot = "feature.attributes") expect_equal(fa["MS4A1", "detection_rate"], 0.15) expect_equal(fa["MS4A1", "gmean"], 0.2027364, tolerance = 1e-6) expect_equal(fa["MS4A1", "variance"], 1.025158, tolerance = 1e-6) - expect_equal(fa["MS4A1", "residual_mean"], 0.3084931639, tolerance = 1e-6) - expect_equal(fa["MS4A1", "residual_variance"], 3.721067314, tolerance = 1e-6) + expect_equal(fa["MS4A1", "residual_mean"], 0.3110813, tolerance = 1e-3) + expect_equal(fa["MS4A1", "residual_variance"], 3.871326, tolerance = 1e-3) }) suppressWarnings(object[["SCT_SAVE"]] <- object[["SCT"]]) From e23fe2dc15e21d4946e8c47d496bb0a33d40f26b Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Mon, 10 Jan 2022 17:18:00 -0500 Subject: [PATCH 21/39] Update objects.R --- R/objects.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/R/objects.R b/R/objects.R index ee8847ac5..2d1ca71ef 100644 --- a/R/objects.R +++ b/R/objects.R @@ -2253,8 +2253,10 @@ setAs( X = 1:length(x = vst.res), FUN = function(i) { vst.res[[i]]$umi.assay <- umi.assay[[i]] - return(PrepVSTResults(vst.res = vst.res[[i]], - cell.names = colnames(x = from))) + return(PrepVSTResults( + vst.res = vst.res[[i]], + cell.names = colnames(x = from) + )) } ) names(x = vst.res) <- paste0("model", 1:length(x = vst.res)) From accc4012b95a940c361e7abc7d0d400f41f2d703 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Mon, 10 Jan 2022 17:27:00 -0500 Subject: [PATCH 22/39] Add sct2 vignette to get started --- vignettes/assets/sctransform_v2.png | Bin 0 -> 482390 bytes vignettes/get_started.Rmd | 2 +- vignettes/vignettes.yaml | 19 ++++++++++++------- 3 files changed, 13 insertions(+), 8 deletions(-) create mode 100644 vignettes/assets/sctransform_v2.png diff --git a/vignettes/assets/sctransform_v2.png 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integrating very large scRNA-seq datasets (including >200,000 cells). image: bm280k_integrated.jpg - title: Integrating scRNA-seq and scATAC-seq data name: atacseq_integration_vignette summary: | - Annotate, visualize, and interpret an scATAC-seq experiment using scRNA-seq data from the same biological system + Annotate, visualize, and interpret an scATAC-seq experiment using scRNA-seq data from the same biological system. image: atacseq_integration_vignette.jpg - title: Multimodal Reference Mapping @@ -61,7 +61,7 @@ - title: Weighted Nearest Neighbor Analysis name: weighted_nearest_neighbor_analysis summary: | - Analyze multimodal single-cell data with weighted nearest neighbor analysis in Seurat v4 + Analyze multimodal single-cell data with weighted nearest neighbor analysis in Seurat v4. image: weighted_nearest_neighbor_analysis.jpg - title: Mixscape @@ -76,6 +76,11 @@ Examples of how to use the SCTransform wrapper in Seurat. image: assets/sctransform.png + - title: sctransform, v2 regularization + name: sctransform_v2_vignette + summary: | + Examples of how to perform normalization, feature selection, integration, and differential expression with an updated version of sctransform. + image: assets/sctransform_v2.png - category: Other vignettes: - title: Visualization @@ -87,7 +92,7 @@ - title: Cell Cycle Regression name: cell_cycle_vignette summary: | - Mitigate the effects of cell cycle heterogeneity by computing cell cycle phase scores based on marker genes + Mitigate the effects of cell cycle heterogeneity by computing cell cycle phase scores based on marker genes. image: cell_cycle_vignette.jpg - title: Differential Expression Testing From abf6840498c6b54cd644f7a9c95860ea8ac8df14 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Mon, 10 Jan 2022 17:35:15 -0500 Subject: [PATCH 23/39] Fix log_umi in attributes --- R/objects.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/objects.R b/R/objects.R index 49145c5d3..db9f67983 100644 --- a/R/objects.R +++ b/R/objects.R @@ -2577,7 +2577,7 @@ PrepVSTResults <- function(vst.res, cell.names) { if ("umi" %in% colnames(x = cell.attrs)) { median_umi <- median(cell.attrs$umi) } else if ("log_umi" %in% colnames(x = cell.attrs)) { - median_umi <- median(10 ^ cell.attrs$umi) + median_umi <- median(10 ^ cell.attrs$log_umi) } } vst.res.SCTModel <- SCTModel( From 1b115522de59da244692d96ab2f4cdf75e526651 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Tue, 11 Jan 2022 08:06:50 -0500 Subject: [PATCH 24/39] Fix MAST DE test --- R/differential_expression.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index a7751df8e..d5e5eb20b 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -1812,7 +1812,7 @@ MASTDETest <- function( object = paste0(" ~ ", paste(latent.vars.names, collapse = "+")) ) zlmCond <- MAST::zlm(formula = fmla, sca = sca, ...) - summaryCond <- summary(object = 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[PATCH 26/39] Update vignette organization --- _pkgdown.yaml | 2 ++ vignettes/get_started.Rmd | 1 + vignettes/sctransform_v2_vignette.Rmd | 2 +- vignettes/vignettes.yaml | 2 +- 4 files changed, 5 insertions(+), 2 deletions(-) diff --git a/_pkgdown.yaml b/_pkgdown.yaml index 37dd42fd5..17c2a9464 100644 --- a/_pkgdown.yaml +++ b/_pkgdown.yaml @@ -48,6 +48,8 @@ navbar: href: articles/mixscape_vignette.html - text: "Using sctransform in Seurat" href: articles/sctransform_vignette.html + - text: "SCTransform, v2 regularization" + href: articles/sctransform_v2_vignette.html - text: ------- - text: Other - text: "Data visualization vignette" diff --git a/vignettes/get_started.Rmd b/vignettes/get_started.Rmd index 8f0f9984f..fd28ff622 100644 --- a/vignettes/get_started.Rmd +++ b/vignettes/get_started.Rmd @@ -126,6 +126,7 @@ Seurat also offers additional novel statistical methods for analyzing single-cel * Weighted-nearest neighbor (WNN) analysis: to define cell state based on multiple modalities [[paper](https://doi.org/10.1016/j.cell.2021.04.048)] * Mixscape: to analyze data from pooled single-cell CRISPR screens [[paper](https://doi.org/10.1038/s41588-021-00778-2)] * SCTransform: Improved normalization for single-cell RNA-seq data [[paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1)]] +* SCTransform, v2 regularization [[paper](https://www.biorxiv.org/content/10.1101/2021.07.07.451498v1.full)]] ```{r results='asis', echo=FALSE, warning=FALSE, message = FALSE} make_vignette_card_section(vdat = vdat, cat = 3) diff --git a/vignettes/sctransform_v2_vignette.Rmd b/vignettes/sctransform_v2_vignette.Rmd index 1064de774..1b33b0700 100644 --- a/vignettes/sctransform_v2_vignette.Rmd +++ b/vignettes/sctransform_v2_vignette.Rmd @@ -1,5 +1,5 @@ --- -title: 'Introduction to sctransform v2' +title: 'Introduction to SCTransform, v2 regularization' output: html_document: theme: united diff --git a/vignettes/vignettes.yaml b/vignettes/vignettes.yaml index 1cda39339..7ed3c016c 100644 --- a/vignettes/vignettes.yaml +++ b/vignettes/vignettes.yaml @@ -76,7 +76,7 @@ Examples of how to use the SCTransform wrapper in Seurat. image: assets/sctransform.png - - title: sctransform, v2 regularization + - title: SCTransform, v2 regularization name: sctransform_v2_vignette summary: | Examples of how to perform normalization, feature selection, integration, and differential expression with an updated version of sctransform. From 371881c222bb99f48657b2d62794219fa0a2285e Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Wed, 12 Jan 2022 17:41:04 -0500 Subject: [PATCH 27/39] Update changelog --- NEWS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/NEWS.md b/NEWS.md index 94115abdb..b9a282299 100644 --- a/NEWS.md +++ b/NEWS.md @@ -5,6 +5,7 @@ ## Changes - Update `ReadParseBio` to support split-pipe 0.9.6p ([#5446](https://github.com/satijalab/seurat/pull/5446)) +- Fixes for MAST differential expression ([#5441](https://github.com/satijalab/seurat/issues/5441)) # Seurat 4.0.6 (2021-12-16) ## Added From 833e1b2d361c3e510a0b00139353dc91757f5099 Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Wed, 12 Jan 2022 17:41:56 -0500 Subject: [PATCH 28/39] Bump develop version, prep for v4.1 --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index d815fd618..d987e88be 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: Seurat -Version: 4.0.6.9003 -Date: 2022-01-07 +Version: 4.0.6.9900 +Date: 2022-01-12 Title: Tools for Single Cell Genomics Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Authors@R: c( From d58ccfbeb32dddc1fcd70d83c6c4c9c9c670e1fe Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Thu, 13 Jan 2022 13:01:45 -0500 Subject: [PATCH 29/39] Fix sct tests --- tests/testthat/test_preprocessing.R | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tests/testthat/test_preprocessing.R b/tests/testthat/test_preprocessing.R index f5e39d892..b624273fa 100644 --- a/tests/testthat/test_preprocessing.R +++ b/tests/testthat/test_preprocessing.R @@ -308,22 +308,22 @@ test_that("SCTransform wrapper works as expected", { expect_equal(fa["MS4A1", "residual_variance"], 2.875761, tolerance = 1e-6) }) -object <- suppressWarnings(SCTransform(object = object, ncells = 40, verbose = FALSE)) +object <- suppressWarnings(SCTransform(object = object, ncells = 40, verbose = FALSE, seed.use = 42)) test_that("SCTransform ncells param works", { expect_true("SCT" %in% names(object)) expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 11.8332, tolerance = 1e6) expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[5]), 0) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 54.59918, tolerance = 1e-6) - expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.74404, tolerance = 1e-6) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 117) - expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 28) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 52.86886, tolerance = 1e-6) + expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.36674, tolerance = 1e-6) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 118) + expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 26) expect_equal(length(VariableFeatures(object[["SCT"]])), 220) fa <- SCTResults(object = object, assay = "SCT", slot = "feature.attributes") expect_equal(fa["MS4A1", "detection_rate"], 0.15) expect_equal(fa["MS4A1", "gmean"], 0.2027364, tolerance = 1e-6) expect_equal(fa["MS4A1", "variance"], 1.025158, tolerance = 1e-6) - expect_equal(fa["MS4A1", "residual_mean"], 0.3110813, tolerance = 1e-3) - expect_equal(fa["MS4A1", "residual_variance"], 3.871326, tolerance = 1e-3) + expect_equal(fa["MS4A1", "residual_mean"], 0.1255668, tolerance = 1e-3) + expect_equal(fa["MS4A1", "residual_variance"], 2.259242, tolerance = 1e-3) }) suppressWarnings(object[["SCT_SAVE"]] <- object[["SCT"]]) From 47cc514fbe860a537c72625270fe1629268a5cbf Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Thu, 13 Jan 2022 13:35:51 -0500 Subject: [PATCH 30/39] Use old RNGversion for SCT tests --- tests/testthat/test_preprocessing.R | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/tests/testthat/test_preprocessing.R b/tests/testthat/test_preprocessing.R index b624273fa..89474010e 100644 --- a/tests/testthat/test_preprocessing.R +++ b/tests/testthat/test_preprocessing.R @@ -308,22 +308,23 @@ test_that("SCTransform wrapper works as expected", { expect_equal(fa["MS4A1", "residual_variance"], 2.875761, tolerance = 1e-6) }) +suppressWarnings(RNGversion(vstr = "3.5.0")) object <- suppressWarnings(SCTransform(object = object, ncells = 40, verbose = FALSE, seed.use = 42)) test_that("SCTransform ncells param works", { expect_true("SCT" %in% names(object)) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 11.8332, tolerance = 1e6) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[1]), 12.02126, tolerance = 1e6) expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "scale.data"))[5]), 0) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 52.86886, tolerance = 1e-6) - expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.36674, tolerance = 1e-6) - expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 118) - expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 26) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "data"))[1]), 60.65299, tolerance = 1e-6) + expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "data"))[5]), 11.74404, tolerance = 1e-6) + expect_equal(as.numeric(colSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[1]), 136) + expect_equal(as.numeric(rowSums(GetAssayData(object = object[["SCT"]], slot = "counts"))[5]), 28) expect_equal(length(VariableFeatures(object[["SCT"]])), 220) fa <- SCTResults(object = object, assay = "SCT", slot = "feature.attributes") expect_equal(fa["MS4A1", "detection_rate"], 0.15) expect_equal(fa["MS4A1", "gmean"], 0.2027364, tolerance = 1e-6) expect_equal(fa["MS4A1", "variance"], 1.025158, tolerance = 1e-6) - expect_equal(fa["MS4A1", "residual_mean"], 0.1255668, tolerance = 1e-3) - expect_equal(fa["MS4A1", "residual_variance"], 2.259242, tolerance = 1e-3) + expect_equal(fa["MS4A1", "residual_mean"], 0.2829672, tolerance = 1e-3) + expect_equal(fa["MS4A1", "residual_variance"], 3.674079, tolerance = 1e-3) }) suppressWarnings(object[["SCT_SAVE"]] <- object[["SCT"]]) From ec58e7bb07d62bdc3a13d1c78c3e7fa552171546 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Thu, 13 Jan 2022 13:51:40 -0500 Subject: [PATCH 31/39] Fix documentation --- R/differential_expression.R | 3 ++- man/FindMarkers.Rd | 2 ++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index d5e5eb20b..fb1aea19f 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -680,7 +680,8 @@ FindMarkers.Assay <- function( return(de.results) } - +#' @param recorrect_umi Recalculate corrected UMI counts using minimum of the median UMIs when performing DE using multiple SCT objects; default is TRUE +#' #' @rdname FindMarkers #' @concept differential_expression #' @export diff --git a/man/FindMarkers.Rd b/man/FindMarkers.Rd index 5cb3f0ba2..70805a8cc 100644 --- a/man/FindMarkers.Rd +++ b/man/FindMarkers.Rd @@ -243,6 +243,8 @@ slot "avg_diff".} \item{base}{The base with respect to which logarithms are computed.} +\item{recorrect_umi}{Recalculate corrected UMI counts using minimum of the median UMIs when performing DE using multiple SCT objects; default is TRUE} + \item{ident.1}{Identity class to define markers for; pass an object of class \code{phylo} or 'clustertree' to find markers for a node in a cluster tree; passing 'clustertree' requires \code{\link{BuildClusterTree}} to have been run} From 1c17f7b5bb40e23bc928a4867034c1c58654b7c5 Mon Sep 17 00:00:00 2001 From: Saket Choudhary Date: Thu, 13 Jan 2022 14:18:00 -0500 Subject: [PATCH 32/39] Depend on sctransform 0.3.3 --- DESCRIPTION | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index d987e88be..10351d209 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -24,7 +24,6 @@ Authors@R: c( ) URL: https://satijalab.org/seurat, https://github.com/satijalab/seurat BugReports: https://github.com/satijalab/seurat/issues -Remotes: satijalab/sctransform@develop Depends: R (>= 4.0.0), methods @@ -66,7 +65,7 @@ Imports: Rtsne, scales, scattermore (>= 0.7), - sctransform (>= 0.3.2), + sctransform (>= 0.3.3), SeuratObject (>= 4.0.4), shiny, spatstat.core, From b9642f43a03f20de6d96f5135faa0b77900a8440 Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:08:21 -0500 Subject: [PATCH 33/39] Bump version --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 10351d209..4d26cbe00 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: Seurat -Version: 4.0.6.9900 -Date: 2022-01-12 +Version: 4.1.0 +Date: 2022-01-14 Title: Tools for Single Cell Genomics Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Authors@R: c( From f9da710dc422b2062a57ead31e493cf6554cedde Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:08:27 -0500 Subject: [PATCH 34/39] Update changelog --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index b9a282299..fa72a7e43 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# Unreleased +# Seurat 4.1.0 (2021-01-13) ## Added - Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392)) - Add support for sctransform v2, differential expression on with SCT From 2d86c3f01b80a15e65f3135fb34a5d369c8b19bb Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:08:36 -0500 Subject: [PATCH 35/39] Update CRAN comments --- cran-comments.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cran-comments.md b/cran-comments.md index 5799d0411..ee8f02d38 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,4 @@ -# Seurat v4.0.6 +# Seurat v4.1.0 ## Test environments * local Ubuntu 20.04 install, R 4.1.2 From 9e1fbcbb8fb16a8ec7db75196518158ee2fa6084 Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:10:04 -0500 Subject: [PATCH 36/39] Update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e46dc6215..fffc2a393 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![CRAN Version](https://www.r-pkg.org/badges/version/Seurat)](https://cran.r-project.org/package=Seurat) [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/Seurat)](https://cran.r-project.org/package=Seurat) -# Seurat v4.0 +# Seurat v4.1 Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC. From aeeed4a02a24ade139381c99c54f772678941e98 Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:10:28 -0500 Subject: [PATCH 37/39] Update NEWS.md --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index fa72a7e43..490d58ffe 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# Seurat 4.1.0 (2021-01-13) +# Seurat 4.1.0 (2021-01-14) ## Added - Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392)) - Add support for sctransform v2, differential expression on with SCT From 71feba8bcdb1853670e9cb7af26c164a3e4067ea Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:14:15 -0500 Subject: [PATCH 38/39] Update dates --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index fa72a7e43..d3b5f079a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# Seurat 4.1.0 (2021-01-13) +# Seurat 4.1.0 (2022-01-13) ## Added - Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392)) - Add support for sctransform v2, differential expression on with SCT From 0d211e50b15fa6b40f530b01cf9689ce997ec8ab Mon Sep 17 00:00:00 2001 From: Paul Hoffman Date: Thu, 13 Jan 2022 15:39:07 -0500 Subject: [PATCH 39/39] Fix CRAN notes --- R/differential_expression.R | 17 ++++++++++++++--- man/PrepSCTFindMarkers.Rd | 16 ++++++++++++++-- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/R/differential_expression.R b/R/differential_expression.R index fb1aea19f..aac07e55c 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -2005,10 +2005,21 @@ PerformDE <- function( #' pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) #' pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) #' pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) -#' markers <- FindMarkers(object = pbmc_merged, ident.1 = "0", ident.2 = "1", assay = "SCT") +#' markers <- FindMarkers( +#' object = pbmc_merged, +#' ident.1 = "0", +#' ident.2 = "1", +#' assay = "SCT" +#' ) #' pbmc_subset <- subset(pbmc_merged, idents = c("0", "1")) -#' markers_subset <- FindMarkers(object = pbmc_subset, ident.1 = "0", ident.2 = "1", assay = "SCT", recorrect_umi = FALSE) - +#' markers_subset <- FindMarkers( +#' object = pbmc_subset, +#' ident.1 = "0", +#' ident.2 = "1", +#' assay = "SCT", +#' recorrect_umi = FALSE +#' ) +#' PrepSCTFindMarkers <- function(object, assay = "SCT", verbose = TRUE) { if (length(x = levels(x = object[[assay]])) == 1) { if (verbose) { diff --git a/man/PrepSCTFindMarkers.Rd b/man/PrepSCTFindMarkers.Rd index e618a7bfe..efec41676 100644 --- a/man/PrepSCTFindMarkers.Rd +++ b/man/PrepSCTFindMarkers.Rd @@ -30,8 +30,20 @@ pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) -markers <- FindMarkers(object = pbmc_merged, ident.1 = "0", ident.2 = "1", assay = "SCT") +markers <- FindMarkers( + object = pbmc_merged, + ident.1 = "0", + ident.2 = "1", + assay = "SCT" +) pbmc_subset <- subset(pbmc_merged, idents = c("0", "1")) -markers_subset <- FindMarkers(object = pbmc_subset, ident.1 = "0", ident.2 = "1", assay = "SCT", recorrect_umi = FALSE) +markers_subset <- FindMarkers( + object = pbmc_subset, + ident.1 = "0", + ident.2 = "1", + assay = "SCT", + recorrect_umi = FALSE +) + } \concept{differential_expression}

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