Error in rownames(st_data[[st_assay]]@data) : no slot of name "data" for this object of class "Assay5", Possible Seurat v5.0 error #39
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Got some progress on this issue, I've tried converting the object to a similar format to v3 using satijalab/seurat#7409 (comment) code, but currently encounter this problem |
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I am facing the same question as you. Have you known how to solve it? |
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Same question :( |
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The issue is associated with object sctructure, I can´t change from Assay5 to Assay3 but I changed the functions code of CellTrek using the follow code: #### CellTrek Functions ####
# traint ####
traint <- function (
st_data, sc_data, st_assay='Spatial', sc_assay='scint',
norm='LogNormalize', nfeatures=2000,
cell_names='cell_names', coord_xy=c('imagerow', 'imagecol'),
gene_kept=NULL, ...) {
st_data$id <- names(st_data$orig.ident)
sc_data$id <- names(sc_data$orig.ident)
sc_data$cell_names <- make.names(sc_data@meta.data[, cell_names])
st_data$type <- 'st'
sc_data$type <- 'sc'
st_data$coord_x <- st_data@images[[1]]@coordinates[, coord_xy[1]]
st_data$coord_y <- st_data@images[[1]]@coordinates[, coord_xy[2]]
DefaultAssay(st_data) <- st_assay
DefaultAssay(sc_data) <- sc_assay
cat('Finding transfer anchors... \n')
st_idx <- st_data$id
sc_idx <- sc_data$id
## Integration features ##
sc_st_list <- list(st_data=st_data, sc_data=sc_data)
sc_st_features <- Seurat::SelectIntegrationFeatures(
sc_st_list, nfeatures=nfeatures)
if (!is.null(gene_kept)) {
sc_st_features <- union(sc_st_features, gene_kept)
}
sc_st_features <-
sc_st_features[(sc_st_features %in% rownames(st_data[[st_assay]]@data)) &
(sc_st_features %in% rownames(sc_data[[sc_assay]]@data))]
cat('Using', length(sc_st_features), 'features for integration... \n')
###
sc_st_anchors <- Seurat::FindTransferAnchors(
reference = sc_data, query = st_data,
reference.assay = sc_assay, query.assay = st_assay,
normalization.method = norm, features = sc_st_features,
reduction = 'cca')
cat('Data transfering... \n')
st_data_trans <- Seurat::TransferData(
anchorset = sc_st_anchors,
refdata = GetAssayData(sc_data)[sc_st_features, ],
weight.reduction = 'cca')
st_data@assays$transfer <- st_data_trans
cat('Creating new Seurat object... \n')
sc_st_meta <- dplyr::bind_rows(st_data@meta.data, sc_data@meta.data)
counts_temp <- cbind(
data.frame(st_data[['transfer']]@data),
data.frame(sc_data[[sc_assay]]@data[sc_st_features, ] %>% data.frame))
rownames(sc_st_meta) <- make.names(sc_st_meta$id)
colnames(counts_temp) <- make.names(sc_st_meta$id)
sc_st_int <- CreateSeuratObject(
counts = counts_temp,
assay = 'traint',
meta.data = sc_st_meta, project = "SeuratProject"
)
cat('Scaling -> PCA -> UMAP... \n')
sc_st_int <- ScaleData(
sc_st_int,
features = sc_st_features,
assay = 'traint', layer = 'counts') %>%
RunPCA(features = sc_st_features)
sc_st_int <- RunUMAP(sc_st_int, dims = 1:30)
sc_st_int@images <- st_data@images
library(magrittr)
sc_st_int@images[[1]]@coordinates <- data.frame(
imagerow=sc_st_int@meta.data$coord_x,
imagecol=sc_st_int@meta.data$coord_y) %>%
set_rownames(rownames(sc_st_int@meta.data))
return (sc_st_int)
}
# celltrek ####
celltrek <- function (
st_sc_int, int_assay='traint', sc_data=NULL, sc_assay='RNA',
reduction='pca', intp=T, intp_pnt=10000, intp_lin=F, nPCs=30, ntree=1000,
dist_thresh=.4, top_spot=10, spot_n=10, repel_r=5, repel_iter=10,
keep_model=F, ...) {
dist_res <- celltrek_dist(
st_sc_int=st_sc_int, int_assay=int_assay, reduction=reduction, intp=intp,
intp_pnt=intp_pnt, intp_lin=intp_lin, nPCs=nPCs, ntree=ntree, keep_model=T)
spot_dis_intp <- mediaspot_dis_intp <- mediaspot_dis_intp <- median(
unlist(dbscan::kNN(dist_res$coord_df[, c('coord_x', 'coord_y')], k=4)$dist))
if (is.null(repel_r)) {repel_r=spot_dis_intp/4}
sc_coord_list <- celltrek_chart(
dist_mat=dist_res$celltrek_dist, coord_df=dist_res$coord_df,
dist_cut=ntree*dist_thresh, top_spot=top_spot, spot_n=spot_n,
repel_r=repel_r, repel_iter=repel_iter)
sc_coord_raw <- sc_coord_list[[1]]
sc_coord <- sc_coord_list[[2]]
cat('Creating Seurat Object... \n')
if (!is.null(sc_data)) {
cat('sc data...')
sc_data$id <- Seurat::Cells(sc_data)
sc_out <- CreateSeuratObject(
counts=sc_data[[sc_assay]]@data[, sc_coord$id_raw] %>%
set_colnames(sc_coord$id_new),
project='celltrek',
assay=sc_assay,
meta.data=sc_data@meta.data[sc_coord$id_raw, ] %>%
dplyr::rename(id_raw=id) %>%
mutate(id_new=sc_coord$id_new) %>%
set_rownames(sc_coord$id_new))
sc_out@meta.data <- dplyr::left_join(
sc_out@meta.data, sc_coord) %>%
data.frame %>%
set_rownames(sc_out$id_new)
sc_coord_raw_df <- CreateDimReducObject(
embeddings=sc_coord_raw %>%
dplyr::mutate(
coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x
) %>%
dplyr::select(c(coord1, coord2)) %>%
set_rownames(sc_coord_raw$id_new) %>%
as.matrix, assay=sc_assay, key='celltrek_raw')
sc_coord_dr <- CreateDimReducObject(
embeddings=sc_coord %>%
dplyr::mutate(
coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
dplyr::select(c(coord1, coord2)) %>%
set_rownames(sc_coord$id_new) %>%
as.matrix, assay=sc_assay, key='celltrek')
sc_out@reductions$celltrek <- sc_coord_dr
sc_out@reductions$celltrek_raw <- sc_coord_raw_df
if ('pca' %in% names(sc_data@reductions)) {
sc_pca_dr <- CreateDimReducObject(
embeddings=sc_data@reductions$pca@cell.embeddings[sc_coord$id_raw, ] %>%
set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay, key='pca'
)
sc_out@reductions$pca <- sc_pca_dr
}
if ('umap' %in% names(sc_data@reductions)) {
sc_umap_dr <- CreateDimReducObject(
embeddings=sc_data@reductions$umap@cell.embeddings[sc_coord$id_raw,] %>%
set_rownames(sc_coord$id_new) %>% as.matrix, assay=sc_assay,
key='umap'
)
sc_out@reductions$umap <- sc_umap_dr
}
if ('tsne' %in% names(sc_data@reductions)) {
sc_tsne_dr <- CreateDimReducObject(
embeddings=sc_data@reductions$tsne@cell.embeddings[sc_coord$id_raw, ] %>%
set_rownames(sc_coord$id_new) %>%
as.matrix, assay=sc_assay, key='tsne')
sc_out@reductions$tsne <- sc_tsne_dr
}
} else {
cat('no sc data...')
sc_out <- CreateSeuratObject(
counts=st_sc_int[[int_assay]]@data[, sc_coord$id_raw] %>%
set_colnames(sc_coord$id_new),
project='celltrek', assay=int_assay,
meta.data=st_sc_int@meta.data[sc_coord$id_raw, ] %>%
dplyr::rename(id_raw=id) %>%
mutate(id_new=sc_coord$id_new) %>%
set_rownames(sc_coord$id_new)
)
sc_out$coord_x <- sc_coord$coord_x[match(sc_coord$id_new, sc_out$id_new)]
sc_out$coord_y <- sc_coord$coord_y[match(sc_coord$id_new, sc_out$id_new)]
sc_out[[int_assay]]@scale.data <-
st_sc_int[[int_assay]]@scale.data[, sc_coord$id_raw] %>%
set_colnames(sc_coord$id_new)
sc_coord_raw_df <- CreateDimReducObject(
embeddings=sc_coord_raw %>%
dplyr::mutate(
coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
dplyr::select(c(coord1, coord2)) %>%
set_rownames(sc_coord_raw$id_new) %>%
as.matrix, assay=sc_assay, key='celltrek_raw'
)
sc_coord_dr <- CreateDimReducObject(
embeddings = sc_coord %>%
dplyr::mutate(
coord1=coord_y, coord2=max(coord_x)+min(coord_x)-coord_x) %>%
dplyr::select(c(coord1, coord2)) %>%
set_rownames(sc_coord$id_new) %>%
as.matrix, assay=int_assay, key='celltrek'
)
sc_out@reductions$celltrek <- sc_coord_dr
sc_out@reductions$celltrek_raw <- sc_coord_raw_df
if ('pca' %in% names(st_sc_int@reductions)) {
sc_pca_dr <- CreateDimReducObject(
embeddings=st_sc_int@reductions$pca@cell.embeddings[sc_coord$id_raw, ] %>%
set_rownames(sc_coord$id_new) %>%
as.matrix, assay=int_assay, key='pca'
)
sc_out@reductions$pca <- sc_pca_dr
}
if ('umap' %in% names(st_sc_int@reductions)) {
sc_umap_dr <- CreateDimReducObject(
embeddings=st_sc_int@reductions$umap@cell.embeddings[sc_coord$id_raw,] %>%
set_rownames(sc_coord$id_new) %>%
as.matrix, assay=int_assay, key='umap'
)
sc_out@reductions$umap <- sc_umap_dr
}
if ('tsne' %in% names(st_sc_int@reductions)) {
sc_tsne_dr <- CreateDimReducObject(
embeddings=st_sc_int@reductions$tsne@cell.embeddings[sc_coord$id_raw,] %>%
set_rownames(sc_coord$id_new) %>%
as.matrix, assay=int_assay, key='tsne'
)
sc_out@reductions$tsne <- sc_tsne_dr
}
}
sc_out@images <- st_sc_int@images
sc_out@images[[1]]@assay <- DefaultAssay(sc_out)
sc_out@images[[1]]@coordinates <- data.frame(
imagerow=sc_coord$coord_x, imagecol=sc_coord$coord_y) %>%
set_rownames(sc_coord$id_new)
sc_out@images[[1]]@scale.factors$spot_dis <- dist_res$spot_d
sc_out@images[[1]]@scale.factors$spot_dis_intp <- spot_dis_intp
output <- list(celltrek=sc_out)
if (keep_model) {
output[[length(output)+1]] <- dist_res$model
names(output)[length(output)] <- 'model'
}
return(output)
}
# celltrek_dist ####
celltrek_dist <- function (
st_sc_int, int_assay='traint', reduction='pca', intp = T, intp_pnt=10000,
intp_lin=F, nPCs=30, ntree=1000, keep_model=T) {
DefaultAssay(st_sc_int) <- int_assay
kNN_dist <- dbscan::kNN(
na.omit(st_sc_int@meta.data[, c('coord_x', 'coord_y')]), k=6)$dist
spot_dis <- median(kNN_dist) %>% round
cat('Distance between spots is:', spot_dis, '\n')
st_sc_int$id <- names(st_sc_int$orig.ident)
st_idx <- st_sc_int$id[st_sc_int$type=='st']
sc_idx <- st_sc_int$id[st_sc_int$type=='sc']
meta_df <- data.frame(st_sc_int@meta.data)
st_sc_int_pca <-
st_sc_int@reductions[[reduction]]@cell.embeddings[, 1:nPCs] %>%
data.frame %>%
mutate(id=st_sc_int$id, type=st_sc_int$type, class=st_sc_int$cell_names,
coord_x=st_sc_int$coord_x, coord_y=st_sc_int$coord_y)
st_pca <- st_sc_int_pca %>%
dplyr::filter(type=='st') %>%
dplyr::select(-c(id:class))
## Interpolation ##
## Uniform sampling ##
if (intp) {
cat ('Interpolating...\n')
spot_ratio <- intp_pnt/nrow(st_pca)
st_intp_df <- apply(st_pca[, c('coord_x', 'coord_y')], 1, function(row_x) {
runif_test <- runif(1)
if (runif_test < spot_ratio%%1) {
theta <- runif(ceiling(spot_ratio), 0, 2*pi)
alpha <- sqrt(runif(ceiling(spot_ratio), 0, 1))
coord_x <- row_x[1] + (spot_dis/2)*sin(theta)*alpha
coord_y <- row_x[2] + (spot_dis/2)*cos(theta)*alpha
} else {
theta <- runif(floor(spot_ratio), 0, 2*pi)
alpha <- sqrt(runif(floor(spot_ratio), 0, 1))
coord_x <- row_x[1] + (spot_dis/2)*sin(theta)*alpha
coord_y <- row_x[2] + (spot_dis/2)*cos(theta)*alpha
}
data.frame(coord_x, coord_y)
}) %>% Reduce(rbind, .)
st_intp_df <- apply(st_pca[, 1:nPCs], 2, function(col_x) {
akima::interpp(x=st_pca$coord_x, y=st_pca$coord_y, z=col_x,
linear=intp_lin, xo=st_intp_df$coord_x,
yo=st_intp_df$coord_y) %>%
magrittr::extract2('z')
}) %>% data.frame(., id='X', type='st_intp', st_intp_df) %>% na.omit
st_intp_df$id <- make.names(st_intp_df$id, unique = T)
st_sc_int_pca <- bind_rows(st_sc_int_pca, st_intp_df)
}
cat('Random Forest training... \n')
## Training on ST ##
data_train <- st_sc_int_pca %>%
dplyr::filter(type=='st') %>%
dplyr::select(-c(id:class))
rf_train <- randomForestSRC::rfsrc(
Multivar(coord_x, coord_y) ~ ., data_train, block.size=5, ntree=ntree)
cat('Random Forest prediction... \n')
## Testing on all ##
data_test <- st_sc_int_pca
rf_pred <- randomForestSRC::predict.rfsrc(
rf_train, newdata=data_test[, c(1:nPCs)], distance='all')
cat('Making distance matrix... \n')
rf_pred_dist <-
rf_pred$distance[data_test$type=='sc', data_test$type!='sc'] %>%
set_rownames(data_test$id[data_test$type=='sc']) %>%
set_colnames(data_test$id[data_test$type!='sc'])
output <- list()
output$spot_d <- spot_dis
output$celltrek_dist <- rf_pred_dist
output$coord_df <- st_sc_int_pca[, c('id', 'type', 'coord_x', 'coord_y')] %>%
dplyr::filter(type!='sc') %>%
magrittr::set_rownames(.$id) %>%
dplyr::select(-id)
if (keep_model) {
output$model <- rf_train
}
return (output)
}
# celltrek_chart ####
celltrek_chart <- function (dist_mat, coord_df, dist_cut=500, top_spot=10,
spot_n=10, repel_r=5, repel_iter=10) {
cat('Making graph... \n')
dist_mat[dist_mat>dist_cut] <- NA
dist_mat_dt <- data.table::data.table(Var1=rownames(dist_mat), dist_mat)
dist_edge_list <- data.table::melt(dist_mat_dt, id=1, na.rm=T)
colnames(dist_edge_list) <- c('Var1', 'Var2', 'value')
dist_edge_list$val_rsc <- scales::rescale(
dist_edge_list$value, to=c(0, repel_r))
dist_edge_list$Var1 %<>% as.character
dist_edge_list$Var2 %<>% as.character
dist_edge_list$Var1_type <- 'sc'
dist_edge_list$Var2_type <- 'non-sc'
cat('Pruning graph...\n')
dist_edge_list_sub <- dplyr::inner_join(
dist_edge_list %>%
group_by(Var1) %>%
top_n(n=top_spot, wt=-value),
dist_edge_list %>% group_by(Var2) %>%
top_n(n=spot_n, wt=-value)) %>%
data.frame
cat('Spatial Charting SC data...\n')
sc_coord <- sc_coord_raw <- data.frame(
id_raw=dist_edge_list_sub$Var1, id_new=make.names(dist_edge_list_sub$Var1,
unique = T))
sc_coord$coord_x <-
sc_coord_raw$coord_x <-
coord_df$coord_x[match(dist_edge_list_sub$Var2, rownames(coord_df))]
sc_coord$coord_y <-
sc_coord_raw$coord_y <-
coord_df$coord_y[match(dist_edge_list_sub$Var2, rownames(coord_df))]
## Add noise ##
theta <- runif(nrow(dist_edge_list_sub), 0, 2*pi)
alpha <- sqrt(runif(nrow(dist_edge_list_sub), 0, 1))
sc_coord$coord_x <-
sc_coord$coord_x + dist_edge_list_sub$val_rsc*sin(theta)*alpha
sc_coord$coord_y <-
sc_coord$coord_y + dist_edge_list_sub$val_rsc*cos(theta)*alpha
## Point repelling ##
cat('Repelling points...\n')
sc_repel_input <- data.frame(
sc_coord[, c('coord_x', 'coord_y')], repel_r=repel_r)
sc_repel <- packcircles::circleRepelLayout(
sc_repel_input, sizetype='radius', maxiter=repel_iter)
sc_coord$coord_x <- sc_repel$layout$x
sc_coord$coord_y <- sc_repel$layout$y
return(list(sc_coord_raw, sc_coord))
}Running the code constructs the functions of CellTrek in your environment. Now, the example of CellTrek run. Probably, other changes are necessary for specific cases. Other functions like celltrek_vis() run successfully using celltrek original library, it's mean CellTrek::celltrek_vis(). |
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Matrix_1.6-3 |
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@panxueling what version are you used? |
thanks, my dear friends. |
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Hiya, got a problem here
I think the error is caused by Seurat v5 object's format change. This is the format of my object, which uses Seurat v5
The count and data are wrapped in layer object(?)/variable(?). I wonder if someone has a tool to downgrade Seurat object, since my preprocessing code didn't work on the latest v4...
p.s: Tried to just copy and paste the count and data outside the layer object(?)/variable(?), didn't work...
sessionInfo()The text was updated successfully, but these errors were encountered: