dev script for connecting cellxgene to census

dev/split-large_samples.R

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+# R Script for Processing Sample Metadata in Cellxgene Data
+# This script processes sample metadata related to Cellxgene datasets, focusing on Homo sapiens data.
+# It filters datasets based on certain criteria like primary data, accepted assays, and large sample size thresholds.
+# Additionally, it modifies cell identifiers and merges this information with related datasets to generate final outputs for further analysis.
+# The script employs several R packages like arrow, targets, glue, dplyr, and more for data manipulation and storage operations.
+
+
+library(arrow)
+library(targets)
+library(glue)
+library(dplyr)
+library(cellxgene.census)
+library(stringr)
+library(purrr)
+result_directory = "/vast/projects/cellxgene_curated/metadata_cellxgenedp_Apr_2024"
+# # Sample metadata
+# sample_meta <- tar_read(metadata_dataset_id_common_sample_columns, store = glue("{result_directory}/_targets"))
+# saveRDS(sample_meta, "~/scratch/Census/cellxgene_to_census/sample_meta.rds")
+sample_meta <- readRDS("~/scratch/Census/cellxgene_to_census/sample_meta.rds")
+
+# Sample to cell link
+sample_to_cell <- tar_read(metadata_dataset_id_cell_to_sample_mapping, store = glue("{result_directory}/_targets"))
+sample_to_cell_primary <- sample_to_cell |> filter(is_primary_data == TRUE)
+#saveRDS(sample_to_cell_primary, "~/scratch/Census/cellxgene_to_census/sample_to_cell_primary.rds")
+sample_to_cell_primary <-  readRDS("~/scratch/Census/cellxgene_to_census/sample_to_cell_primary.rds")
+
+sample_to_cell_primary_human <- sample_to_cell_primary |> 
+  left_join(sample_meta, by = c("sample_","dataset_id")) |> 
+  filter(organism == "Homo sapiens") |> 
+  select(observation_joinid, cell_, sample_, donor_id.x, dataset_id, is_primary_data.x, 
+         sample_heuristic.x, organism, tissue, development_stage, assay, collection_id,
+         sex, self_reported_ethnicity, disease, cell_type)
+
+# accepted_assays from census 
+accepted_assays <- read.csv("~/projects/CuratedAtlasQueryR/cellxgene-to-census/census_accepted_assays.csv", header=TRUE)
+sample_to_cell_primary_human_accepted_assay <- sample_to_cell_primary_human |> filter(assay %in% accepted_assays$assay)
+
+large_samples <- sample_to_cell_primary_human_accepted_assay |> 
+  dplyr::count(sample_, assay, collection_id, dataset_id) |> 
+  mutate(above_threshold = n > 15000)
+
+large_samples_collection_id <- large_samples |> ungroup() |> 
+  dplyr::count(collection_id) |> arrange(desc(n))
+
+# function to discard nucleotide in cell_ ---------------------------------
+# cell pattern repeated across samples. 
+# Decision: use modified_cell and sample_ to split data
+
+# drop cell ID if cell ID is a series of numbers
+# ACGT more than 5, drops
+# drop cellID if does not have special cahracter : - _
+remove_nucleotides_and_separators <- function(x) {
+  # convert integer cell ID or contain numerics surrounded by special characters to NA
+  x[str_detect(x, "^[0-9:_\\-*]+$")] <- NA
+
+  # drop sequence having a consistent stretch of 5 characters from ACGT 
+  modified <- str_replace_all(x, "[ACGT]{5,}", "")
+
+  #remove nucleotides surrounded by optional separators
+  modified <- str_replace_all(modified, "[:_-]{2,}", "_")
+}
+
+# List of collection IDs for sample cells great than 10K
+collection_ids <- large_samples_collection_id$collection_id
+
+process_collection <- function(id) {
+  filtered_data <- sample_to_cell_primary_human_accepted_assay |>
+    filter(collection_id == id) |>
+    select(cell_, sample_)
+
+  filtered_data$cell_modified <- remove_nucleotides_and_separators(filtered_data$cell_)
+  filtered_data
+}
+
+final_result <- map_df(collection_ids, process_collection)
+
+# conditional generating sample_2 based on whether number of cells > 10K.
+sample_to_cell_primary_human_accepted_assay <- sample_to_cell_primary_human_accepted_assay |> 
+  left_join(large_samples, by = c("sample_", "assay","collection_id","dataset_id"))
+
+sample_to_cell_primary_human_accepted_assay_sample_2 <- 
+  sample_to_cell_primary_human_accepted_assay |> 
+  left_join(final_result, by = c("cell_","sample_")) |>
+  # manual adjust 
+  mutate(
+    cell_modified = ifelse(dataset_id == "b2dda353-0c96-42df-8dcd-1ea7429a6feb" & sample_ == "5951a81f1d40153bab5d2b808e384f39",
+                           "s14",
+                           cell_modified),
+    cell_modified = ifelse(dataset_id == "b2dda353-0c96-42df-8dcd-1ea7429a6feb" & sample_ == "7313173de022921da50c34ea2f87c7af",
+                           "s3",
+                           cell_modified)
+  ) |>
+  mutate(sample_2 = if_else(above_threshold,
+                            paste(sample_, cell_modified, sep = "___"),
+                            sample_)
+  )
+# save result
+#sample_to_cell_primary_human_accepted_assay_sample_2 |> arrow::write_parquet("~/scratch/Census_rerun/sample_to_cell_primary_human_accepted_assay_sample_2_modify.parquet")
+
+# Load Census census_version = "2024-07-01"
+census <- open_soma(census_version = "stable")
+metadata <- census$get("census_data")$get("homo_sapiens")$get("obs")
+selected_columns <- c('assay', 'disease', 'donor_id', 'sex', 'self_reported_ethnicity', 'tissue', 'development_stage','is_primary_data','dataset_id','observation_joinid',
+                      "cell_type", "cell_type_ontology_term_id")
+samples <- metadata$read(column_names = selected_columns,
+                         value_filter = "is_primary_data == 'TRUE'")$concat()
+samples <- samples |> as.data.frame() |> distinct() 
+#samples |> saveRDS("~/scratch/Census/cellxgene_to_census/census_samples_701.rds")
+
+######## READ
+sample_to_cell_primary_human_accepted_assay_sample_2 <- arrow::read_parquet("~/scratch/Census_rerun/sample_to_cell_primary_human_accepted_assay_sample_2.parquet")
+samples <- readRDS("~/scratch/Census/cellxgene_to_census/census_samples_701.rds")
+
+census_samples_to_download <- samples |> 
+  left_join(sample_to_cell_primary_human_accepted_assay_sample_2,
+            by = c("observation_joinid", "dataset_id"),
+            relationship = "many-to-many") |>
+  select(-donor_id.x,
+         -is_primary_data.x,
+         -tissue.y,
+         -development_stage.y,
+         -assay.y,
+         -sex.y,
+         -self_reported_ethnicity.y,
+         -disease.y) |>
+  rename(assay = assay.x,
+         disease = disease.x,
+         sex = sex.x,
+         self_reported_ethnicity = self_reported_ethnicity.x,
+         tissue = tissue.x,
+         development_stage = development_stage.x
+         ) |> 
+  as_tibble() |>
+  # remove space in the sample_2, as sample_2 will be regarded as filename 
+  mutate(sample_2 = if_else(str_detect(sample_2, " "), str_replace_all(sample_2, " ",""), sample_2))
+
+#census_samples_to_download |> arrow::write_parquet("~/scratch/Census_rerun/census_samples_to_download.parquet")
+con <- duckdb::dbConnect(duckdb::duckdb(), dbdir = ":memory:")
+parquet_file = "~/scratch/Census_rerun/census_samples_to_download.parquet"
+
+census_samples_to_download <- tbl(con, sql(paste0("SELECT * FROM read_parquet('", parquet_file, "')")))
+
+# This is important: please make sure observation_joinid and cell_ is unique per sample (sample_2) in census_samples_to_download
+census_samples_to_download |> dplyr::count(observation_joinid, sample_2) |> dplyr::count(n)
+census_samples_to_download |> dplyr::count(cell_, sample_2) |> dplyr::count(n)
+
+
+census_samples_to_download |> group_by(dataset_id, sample_2)  |> 
+  summarise(observation_joinid = list(observation_joinid), .groups = "drop") |> as_tibble() |> mutate(list_length = map_dbl(observation_joinid, length)) |>
+  filter(list_length >=100) |>
+  arrow::write_parquet("~/scratch/Census_rerun/census_samples_to_download_groups.parquet")
+
+census_samples_to_download_groups <- arrow::read_parquet("~/scratch/Census_rerun/census_samples_to_download_groups.parquet")
+
+
+
+
+# # census metadata
+# files <- readRDS("~/git_control/HPCell/data/files_3.rds")
+# metadata <- files |> left_join(census_samples_to_download, by = c("dataset_id", "sample_2")) |> filter(cell_number != 0)
+# 
+# # write to parquet
+# metadata |> filter(is_primary_data == TRUE) |> select(-transformation_function) |> 
+#   arrow::write_parquet("~/cellxgene_curated/census_samples/primary_metatadata.parquet")
+# 
+# # Calculate stats
+# metadata |> filter(!is.na(is_primary_data), cell_number != 0) |> distinct(dataset_id)
+