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stemangiolastemangiola
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merge again commits wrongly added to change-name branch
Merge branch 'change-name' # Please enter a commit message to explain why this merge is necessary, # especially if it merges an updated upstream into a topic branch. # # Lines starting with '#' will be ignored, and an empty message aborts # the commit.
2 parents f7e220d + 865461b commit 5c8adff

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‎.gitignore‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -5,7 +5,7 @@
55
human_cell_atlas.Rproj
66
local_file.rds
77
slurm.status.*
8-
CuratedAtlasQuery.Rproj
8+
CuratedAtlasQueryR.Rproj
99
split_files.makeflow
1010
._*
1111
.DS_Store

‎DESCRIPTION‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
11
Type: Package
2-
Package: CuratedAtlasQuery
2+
Package: CuratedAtlasQueryR
33
Title: Queries the Human Cell Atlas
44
Version: 0.1.0
55
Authors@R: c(person("Stefano", "Mangiola", email = "[email protected]",

‎NEWS.md‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,3 @@
1-
# CuratedAtlasQuery 0.1.0
1+
# CuratedAtlasQueryR 0.1.0
22

33
* Added a `NEWS.md` file to track changes to the package.

‎dev/.gitignore‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -7,3 +7,4 @@ ERROR-1.output
77
core.26183
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curated_annotation.rds
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metadata.sqlite
10+
latex_equations.html

‎dev/DB2_files.R‎

Lines changed: 122 additions & 69 deletions
Original file line numberDiff line numberDiff line change
@@ -13,23 +13,24 @@ library(HDF5Array)
1313
library(openssl)
1414
library(stringr)
1515
library(HCAquery)
16+
library(purrr)
1617
## source("utility.R")
1718
options(scipen = 999)
1819
#
1920

20-
# CREATE MAKEFILE
21-
tab = "\t"
22-
root_directory = "/vast/projects/RCP/human_cell_atlas"
23-
raw_data_directory = glue("{root_directory}/raw_data")
24-
splitted_DB2_data_directory = glue("{root_directory}/splitted_DB2_data")
25-
file_cell_types_directory = glue("{root_directory}/file_cell_types")
26-
input_files_path = dir(file_cell_types_directory, full.names = TRUE)
27-
gene_names = glue("{root_directory}/gene_names.rds")
21+
# # CREATE MAKEFILE
22+
# tab = "\t"
23+
# root_directory = "/vast/projects/RCP/human_cell_atlas"
24+
# raw_data_directory = glue("{root_directory}/raw_data")
25+
# splitted_DB2_data_directory = glue("{root_directory}/splitted_DB2_data_0.2")
26+
# file_cell_types_directory = glue("{root_directory}/file_cell_types")
27+
# input_files_path = dir(file_cell_types_directory, full.names = TRUE)
28+
# gene_names = glue("{root_directory}/gene_names.rds")
2829
#
2930
#
3031
# ## metadata = readRDS(metadata_path)
3132
#
32-
# get_metadata() |>
33+
# get_metadata("/vast/projects/RCP/human_cell_atlas/metadata_annotated_0.2.sqlite") |>
3334
# distinct(file_id, cell_type) |>
3435
# as_tibble() |>
3536
#
@@ -45,23 +46,21 @@ gene_names = glue("{root_directory}/gene_names.rds")
4546
# mutate(memory = pmax(Mb * 2, 30000)) |>
4647
# mutate(
4748
# memory = case_when(
48-
# input_file_path %in% c(
49-
# "/vast/scratch/users/mangiola.s/human_cell_atlas/raw_data/b50b15f1-bf19-4775-ab89-02512ec941a6.H5AD",
50-
# "/vast/scratch/users/mangiola.s/human_cell_atlas/raw_data/56e0359f-ee8d-4ba5-a51d-159a183643e5.H5AD",
51-
# "/vast/scratch/users/mangiola.s/human_cell_atlas/raw_data/51f114ae-232a-4550-a910-934e175db814.H5AD",
52-
# "/vast/scratch/users/mangiola.s/human_cell_atlas/raw_data/21ca95b3-776b-4fa2-9956-09a07c0e5224.H5AD"
53-
# ) ~ 160000,
54-
# input_file_path == "/vast/scratch/users/mangiola.s/human_cell_atlas/raw_data/08247324-7ab7-45c5-8bd6-6c22676761ed.H5AD" ~ 200000,
55-
#
49+
# file_id_db2 %in% c(
50+
# '643d1a28d35ed6d9f087ed8732d0fbf0', 'f7cb9ed27af8bd19b8b05feeaf84ec3c', 'abb6ff154b70c74e395add7dca444bbc', '6690e8e6b89123b1e11eef9d4a153e49', '5734fb9bd18f48c88f1bab6e1ea6e5f7', 'd9f28c596bb5813d7da2e690b0473570', 'fea1eba9397c3763e77dc321ac1c35b8', '6b312db0bbe47249bf6dbb382a0f25aa', 'a252f45beddcd62c5ccc0894461861e2', '2d7cc7e650275abf80b672febbdbb47f', '64b9770f1679dc9a6d8b7b12613391ef', '133f0de6e86d712c079af21344ae4501', '2d6fa434060cd4e16987245c35294fdb', '5754ab342ca2f630f442c6fb11f7977f', 'b7411ae6d2d5025fe8735c9f5635bed3', '5a7c941fcf37216ce181e5af9857a897', 'b48aab951c5ac53a9b9b4250c309cb04', '9fe44e0afee2d48bbe9b9922490c4fdf', 'c4cda7f034432e9f88588905e96b8878', 'cb0fa793cff47ff26de5ebcf9d75d8d8', 'ec2cf3480224c44c2f87875b8a383416', '6c3915e70f391f4cc6d8460b8237e686', 'ecc5c83172b38a4c2d703cef9fee6600', '19bc78159fda9d0795ff82201a493627', '5e0dba8e536570c43c0e79bb4695671b', '680201e2dae91ed66c4cb2da45590607', '94c522ee71123a3b115bdebce647fb88', 'b448edda111f93495eb9eb0488c95613', 'ad33c246db845178ffa1d6d1de15946e', '672f90e815461cf1fde0fed7d83528b3', '95b234bb577816c848af6bdbf4e5f639', '5bf8f63f1b8435f51db88701578c9944', 'e604de2b6508a0056cf187b1790e2a2e', '7df8990ca100ac47df3dc857a739a531', 'a983aca109e8ef8cdfa2befbfec8a85d', 'e6cf0c9d608465fe69fc477e5d24a4da', 'a224c2660000d6fbc79296b31ddb05dd', '321ab8ef8102d8e9817588539216fcb2', 'd8fe285941ee94128c466ffe2fc72214', 'f464369e48799c7225ce613d4fac64e4', '60981c2a4aba42bfb81d4d52eb7d91c4', '2b2ca0d6dce081902657f28e6942a546', 'd48585f19116758ac55715d36af29faf', '2763074a04eafc1590ec8e4195c2d30b', '894c26dfae23520d5b1ec13681b973d4', 'bb270e6b0aea2bba1f8f044402b50d0f', '3837b82ce7110024d6010eb668c4d65f', 'afd38219dfc9fa010b181a6f1dc167df', 'f29f36400dcaa5b550e055dee3a6bfab', '0031d2758f70b4602aa5980a96a96a12', '044fc974c7f546b2f61d15ae5c804ddf', '562594615b5d41f9ebe52c2489325045', '9f7827abf98794f8c37d87e5de01691b', '1994ae237838e3271488f98a7a5afc37', 'aa9b46796d3d0e7cf774c12e34cc872f', '2bc6ef5c0f153cdaba618788f75dff13', '124aa1104d8fed6fa9e98fa2ca5c00eb', 'c032b8738a8d06e9f261f76564de0ec8', 'c54f874f27a35cce97fcb3c67701c8c3', 'bc89346be5f9ffc76d8b920f117f244d', '136bcc485075f101e9d109d0222f7211', '9ed4da7076000ffac93a812b075812da', '90099dad83392f82d7cccaff14ab21a8', '2bcc0d793ed9db6ac939c86f4fa67044', 'e29a49f612b72c770276406500392790', '88eeb885ba02a8f5a30202a65f2314d2', '93cfef1122d69293a9f19c054e031b0a', 'cfe2deeeb735797b871828d9860eff41', '11b27ed03f34435b1ed9b513705a99cf', 'b72533ee50311aded927f2d4acc2d696', '63473d2b4aabefe0385fc0d8a0378115', '6909d30b9f20cbb1c4f22a596918bd69', '4c39fdd1e8eb9a146f6e0766a8fbe4c7', 'ff572a8430b657bfc79fb6c6c3760ac1', 'f01c20412cab27a5e8feacd170cfc900', '0ae84745b3261a021cc0cfe055ca8c46', 'b61dc9d6067dc0aae0db5007e4d7f876', '1b855ddca92afe1bed522bb07dc5f788', '8aff79b4657c0aff54c9016ac1eef46e', '1d85feaf473d55854639514693504c02', 'b2377c149a40b99ec331dbf5ab1cf4ce', '77a8cd32e8188e707ed83575fcbed904', '7e8e47f7eb2537368d06cec14ac3a458', '03ff63330952859dbf86f41a48df6a83', '96c252abaf78877e2f400d838bf69166', '42b08eb49a5390b9e66ddc98663eb269', '148e3d4c60692354e3d1948519e4a0ea', 'f92e17f4efd838108af21129becd7b19', '5b56fc4fb694c62d4de406c24c7ca5e7', '63fbfcf952e803f5c39473fafb599251',
51+
# 'b531a86d7188be31428a03603d7405c6', '768700aa330a2a5891ad22b67ea49dc1', '2f36daf2a3a8aedd48db06acd6727b0a', '01a6385ee03ac917f245d7e52faa9f66', '02ffa11eb4dbc3106eda5dbdec29e817', '542e6a445f3a4d9dad4e32acc525f2c4', '3513dd06f3363335e0561979a5e5f2d1', '3c88cfffed093f566b06783c7033948f', '45c95a5fb1f303c138b0f60b54a94c2c', '848c8bf283e6c39555ed5085b3eba735', '2b9f5b251111939dd8689ddf86309c7b', '93ce665d647aa0319fc444596eb2bb5a', 'e61d313adfe3604cafc773210f461fc1', 'c6ebf4689bb0b3fe66b0139ccaf83ac2', '0b934d2125cf7ca9564ccdab65797c11', '396a6963d3b402548b38676200b9a544', 'dcc9e9def75bc9bae1b893da13daf378', 'c4aafa1124d87da0606bc67bb125d702', '018d85cd9702db9339920f0cb91d7afc', 'b628ccb3d8721fc019ae2ef279375a08', 'ca1de9c53113407dc7373bd33cad2a57', '92852e108c7612c0d7ff4ceccd59bdca', '133f20d89ba49eb68a7e231e590a0ec7', '90679d805829232802b794baf6a09338', '255400de1eda3bec328dcb932a0ce70e', 'ea03efb1ec2466ff0c3925a2c25255bc', '69bd89af9e84e240de4ae1a9c4730b78', '4b92fdac8484757e1ed805f2ef485aff', 'cea187ba19982b64b651046d0c55d683', '09a1bc602a7ad508f10b1c83785e3f5f', 'e27843805c7e5e9743202ad132f0cb74', 'd8216db7dd67453b948025092631e8f0', '76cb634ba33e4438517fa388f472413f', '0e3b0626806414deb561d4c95edf6cc9', 'd6dde5273fdf62bf1776a24007d59ae2', 'f95b23918aa5bcab2c0cc9dcc85e3f2f', 'ba1017fcabbc3f4552554cac3e1e8d51', 'd7096504ae4361e995f97dbcdcdea94e', 'ee701a2474084b3477ce862f2cef54d0', 'b708a95d6bc07e1d79dab68972fde016', '495f8be32619aa791c71947b66a6e44c', 'e48af5a58cf363b65dc2c027616155c3', 'a837f0376b0af3ee2243a26f96003fb6', 'bf522b5a8a05ef06876b1166cf76cd76', 'f2e97e6eb83c6ae6315735c3a58475dd', 'db3dc0285cc2e4866c211e151559df40', '4d957fd55b43b527b17f82a2e8534337', 'c646e9fb94b3331944050967ea820d11', '051579deb85a5b7eabe7c6df3264d302', 'ebdee2282dfd6ea3cb60844087f4c923', '3795117167beb9768034b7443fe5b754', 'cf1cf8debfc581c217707fb09d1c0cc9', '0c34089dbd4db0d48af97873baa8cb10', '867797d630b0a67438a7b1b6ddb9f46f', '44600d9ff423b2f8a674e4fdf928c36f', 'd8036a66b91006fb87312ab73b6f2647', 'd7ab07844e32d4e609e3fae4f6e624b5', '37decab3aa35040d72af0eeff9e557c2', '98435ec4176e33164ac2dd092911518b', 'b957873f07b73bc9fe2858de7560d354', '52643bbd6767cf5904b0bec3eaafe283', '453b598c0fa37d3df507932d8b742eca', '25be440bc59f97942579d25419517781', '48730f8b9ea4a4eb67c4826cec75e6d7', '6d4ceccbec07d4b3f41b8d73a5cc54a4', '099e4f9788ea5646b01899a4565b449b', '3cd5853986f381dc40e02414f76ae0a4', '63f6d25357ac92d889d2a2e08a6d95f1', 'ccf04cce8134efe5e667e2c0caa57474', 'd0f11393c659cf2f70dc989d21a682fa', '5b845723652627151f4ff121333fede1', 'a3802f7db8829109ac98eb4552231d54', '558dd61fb7a6a47ef76b97384898de27', 'cc556cae149faa24f2d011806d65168e', 'c3d01f08f2eb4f769b0fa3497e8222ff', '64bcbbf198f7e191e06173b054cff02a', 'a50e764e1e49e7877bf91864665de354', '4269e17cd671c56a9865b1ed5e316a61', '5a8d3746fadcec538c3d70490cc888ed', '2e5d52ce7ee10ea5bb37769409449e01', 'd0ad238960be64ad4cd8f67fa58a946b', '40d764a54eae749b6852910655e7c35c', '749821212c25ca95abb1831c48764d2a', '1625d480e0c3160c5e34c53006cd85ee', '354f1780bb909fe141b16a5ce155caf8', '51ae4a30442db8f7fcc3f77899370100', '4bb27edd1f01bf191c77271b580a186c', 'e22f00a4abd2d8d9ebe2ca5a6055a0d5', '4d3eeb032b517f91b8ac75babbd8a13b', 'c5db9e3b4ecc205b7d5d09ca660a0c4b', '314dce2d49ecf8b3d83d97f854b471ef', 'd58447f6ceb1c959538846df532bf82a', '346c5e08ea5bafee68a471f0f04fe397', '2c162ef8a4bdfef1ab5dd2d45389b232', 'f55a91bc0057a859f94245b047087dbf', '24dad6be9f859b50fb4f00e1a7981f82', '59a53c1c0078ad9cb88bdbf161c11800', 'c5a05f23f9784a3be3bfa651198a48eb', '18d13cca5e607b917f3c60bd75f863cd', '521b9b73c5b032f8e2809a5032d18266', '18ecc71ecc0df104a1628bdd4e529f85', '7aec18c648d573f34641b8329a2afe3e'
52+
# ) ~ 60000,
53+
# TRUE ~ memory
54+
# )
55+
# ) |>
56+
# mutate(
57+
# memory = case_when(
58+
# file_id_db2 %in% c(
59+
# 'f7cb9ed27af8bd19b8b05feeaf84ec3c', 'abb6ff154b70c74e395add7dca444bbc', '2d6fa434060cd4e16987245c35294fdb', '5a7c941fcf37216ce181e5af9857a897', '2c162ef8a4bdfef1ab5dd2d45389b232', 'f55a91bc0057a859f94245b047087dbf', '24dad6be9f859b50fb4f00e1a7981f82', 'c5a05f23f9784a3be3bfa651198a48eb'
60+
# ) ~ 120000,
5661
# TRUE ~ memory
5762
# )
5863
# ) |>
59-
# mutate(memory = if_else(file_id_db2 %in% c(
60-
# readRDS("dev/DB2_files_list_that_need_more_memory.rds"),
61-
# readRDS("dev/DB2_files_list_that_need_more_memory_2.rds")
62-
# ), 100000, memory)) |>
63-
# mutate(memory = if_else(file_id_db2 %in% c('a0a56250b7b38ab3c30df2ded38d1c1f', '317b03f4caa6af8d147fd74d44c04adf', 'beda5a9dd9ab716431b0e2db194ffbe0', '7db83cd468bfcb5cf125996437412b96', '74e3dc029ccd9e5e1c82a64ece3c616e', '5e75192c96af82a0a715998bcb417cb1', '11585c1edaa11913c306477bb3d9d552', '8c0ee4326521ac2daeb411e161cbf6dd', 'aea9854f73ec194517b3eb525b203cf4', 'e37562a76ad784eca662bf5c7a3ca409'), 200000, memory)) |>
64-
#
6564
# rowid_to_column() |>
6665
# mutate(commands = pmap(
6766
# list(output_file_path, input_file_path, memory, rowid),
@@ -75,14 +74,14 @@ gene_names = glue("{root_directory}/gene_names.rds")
7574
# )) |>
7675
# pull(commands) |>
7776
# unlist() |>
78-
# write_lines(glue("dev/DB2_files.makeflow"))
77+
# write_lines(glue("DB2_files.makeflow"))
7978

8079

8180

8281
# Read arguments
8382
args = commandArgs(trailingOnly = TRUE)
8483
input_file = args[[1]]
85-
all_gene_names = args[[2]]
84+
gene_names = args[[2]]
8685
output_file = args[[3]]
8786

8887
output_file |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE)
@@ -92,29 +91,108 @@ file_id_db2 = basename(output_file) |> tools::file_path_sans_ext()
9291
cells_to_keep =
9392
get_metadata() |>
9493
filter(file_id == !!file_id) |>
95-
select(.cell, .sample, file_id, cell_type) |>
94+
dplyr::select(.cell, .sample, file_id, cell_type) |>
9695
as_tibble() |>
9796
unite("file_id_db2", c(file_id, cell_type), remove = FALSE) |>
9897
mutate(file_id_db2 = file_id_db2 |> md5() |> as.character()) |>
9998
filter(file_id_db2 == !!file_id_db2) |>
100-
mutate(.cell_original = .cell |> str_remove(.sample) |> str_remove("_$"))
99+
mutate(.cell_original = .cell |> str_remove(.sample) |> str_remove("_$")) |>
100+
pull(.cell_original)
101+
102+
# Read
103+
data = readH5AD(input_file, use_hdf5 = TRUE, reader = "R")
104+
data_for_rows_and_columns = readH5AD(input_file, use_hdf5 = TRUE, skip_assays = TRUE)
105+
colnames(data) = data_for_rows_and_columns |> colnames()
106+
rownames(data) = data_for_rows_and_columns|> rownames()
107+
rm(data_for_rows_and_columns)
108+
gc()
109+
110+
# Subset cells
111+
data = data[,cells_to_keep] |> select(.cell)
112+
gc()
113+
114+
col_sums = colSums(data@assays@data$X)
115+
116+
# If I have negative values
117+
if((data@assays@data$X[,1:min(10000, ncol(data@assays@data$X))] |> min()) < 0)
118+
data@assays@data$X[data@assays@data$X<0] <- 0
119+
120+
# If there are huge value cap
121+
if(max(col_sums) > 1e100) {
122+
temp = data@assays@data$X[,sample(1:ncol(data), size = 10000, replace = T), drop=T]
123+
q = quantile(temp[temp>0], 0.9)
124+
data@assays@data$X[data@assays@data$X>1e100] = q
125+
}
126+
127+
# Drop all 0 cells
128+
data = data[,col_sums>0]
129+
130+
# Set gene names
131+
rownames(data) = AnnotationDbi::mapIds(org.Hs.eg.db::org.Hs.eg.db,
132+
keys = rownames(data),
133+
keytype = "ENSEMBL",
134+
column = "SYMBOL",
135+
multiVals = "first"
136+
)
137+
data = data[!is.na(rownames(data)),]
138+
rownames(data@assays@data$X) = rownames(data)
139+
140+
# Merge duplicate genes
141+
split_matrix_by_column <- function(matrix, chunk_size) {
142+
ncols <- ncol(matrix)
143+
chunks <- lapply(seq(1, ncols, by = chunk_size), function(i) {
144+
if(i + chunk_size -1 > ncols){
145+
matrix[, i:ncols, drop = FALSE]
146+
}else{
147+
matrix[, i:(i + chunk_size - 1), drop = FALSE]
148+
}
149+
})
150+
return(chunks)
151+
}
152+
153+
duplicated_genes = data[duplicated(rownames(data)),] |> rownames()
154+
deduplicated_matrix =
155+
156+
data[rownames(data) %in% duplicated_genes,]@assays@data$X %>%
157+
split_matrix_by_column(10000) |>
158+
map(~ rowsum(.x, rownames(.x))) |>
159+
reduce(cbind) %>%
160+
{
161+
.x = writeHDF5Array(., as.sparse = T)
162+
rownames(.x) = rownames(.)
163+
.x
164+
}
165+
166+
sce = SingleCellExperiment(list(X = rbind(
167+
data[!rownames(data) %in% duplicated_genes,]@assays@data$X ,
168+
deduplicated_matrix
169+
)[rownames(data),,drop=FALSE]
170+
))
171+
colnames(sce) = colnames(data)
172+
173+
rm(data)
174+
rm(deduplicated_matrix)
175+
gc()
101176

102-
gene_names =
103-
(
104-
readH5AD(
105-
input_file, use_hdf5 = TRUE,
106-
layers=FALSE,uns=FALSE,obs=FALSE,varm=FALSE,obsm=FALSE,varp=FALSE,obsp=FALSE,raw=FALSE
107-
) |> rowData()
108-
)$feature_name
177+
# Complete gene set
178+
missing_genes = readRDS(gene_names) |> setdiff(rownames(sce))
179+
180+
missing_matrix =
181+
HDF5RealizationSink(c(length(missing_genes),ncol(sce)), as.sparse = TRUE) |>
182+
as("DelayedArray")
183+
rownames(missing_matrix) = missing_genes
109184

110-
X =
111-
readH5AD(
112-
input_file, use_hdf5 = TRUE,
113-
layers=FALSE,uns=FALSE,var=FALSE, obs=FALSE,varm=FALSE,obsm=FALSE,varp=FALSE,obsp=FALSE,raw=FALSE
114-
)[,cells_to_keep |> pull(.cell_original)]@assays@data$X
185+
missing_sce = SingleCellExperiment(list(X=missing_matrix))
186+
colnames(missing_sce) = colnames(sce)
187+
missing_sce = missing_sce[!is.na(rownames(missing_sce)),]
115188

116-
rownames(X) = gene_names
189+
# Make cell name unique
190+
sce = sce |> rbind(missing_sce )
191+
sce = sce[sce |> rownames() |> sort(),]
192+
rm(missing_sce)
193+
gc()
117194

195+
# Tranform
118196
transformation =
119197
get_metadata() |>
120198
filter(file_id == !!file_id) |>
@@ -128,7 +206,6 @@ transformation =
128206
pull(transformation) |>
129207
unique()
130208

131-
# Ad hoc transformations, not declared as log
132209
if(file_id %in% c(
133210
"1e81a742-e457-4fc6-9c39-c55189ec9dc2",
134211
"b51bfa2d-22b2-4c65-9803-d36d4de973fa",
@@ -139,8 +216,10 @@ if(file_id %in% c(
139216
"575513b2-6e53-41e2-85a9-bc08a6233ce4"
140217
)) transformation = "log"
141218

142-
X =
143-
X |>
219+
220+
221+
sce@assays@data$X =
222+
sce@assays@data$X |>
144223
when(
145224
transformation=="log" ~ {
146225
.x = expm1(.)
@@ -159,32 +238,6 @@ X =
159238
}
160239
)
161240

162-
# If I have negative values
163-
if(min(X) < 0)
164-
X[X<0] <- 0
165-
166-
# Select just the X assay
167-
sce = SingleCellExperiment(list(X = X))
168-
rownames(sce) = rownames(X)
169-
colnames(sce) = colnames(X)
170-
171-
rm(X)
172-
gc()
173-
174-
# Add missing genes
175-
missing_genes = readRDS(all_gene_names) |> setdiff(rownames(sce))
176-
missing_matrix =
177-
HDF5RealizationSink(c(length(missing_genes),ncol(sce)), as.sparse = TRUE) |>
178-
as("DelayedArray")
179-
180-
rownames(missing_matrix) = missing_genes
181-
colnames(missing_matrix) = colnames(sce)
182-
183-
missing_sce = SingleCellExperiment(list(X=missing_matrix), colData=colData(sce))
184-
missing_sce@int_colData = sce@int_colData
185-
186-
# Make cell name unique
187-
sce = sce |> rbind(missing_sce)
188241

189242
sce |> saveHDF5SummarizedExperiment(output_file, replace=TRUE)
190243

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