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Adding anndata concatenation with bioalpha to main TileDB branch #116

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51fe44f
Using bioalpha for concatenating more than 100 anndatas
Oct 6, 2025
7af8f31
Fix typo
Oct 6, 2025
bd064b5
Using gpu for bioalpha
Oct 6, 2025
9eb70ff
Forgot to load newly added module CONCAT_ANNDATA_BA
Oct 8, 2025
3b7d238
Forgot about coverage in anndata obs
ariannalandini Oct 16, 2025
b168322
Making sure to remove actual values for SNPs outside of the extended cs
ariannalandini Oct 16, 2025
6a48a84
Add also function to fix var after concatenation
Oct 16, 2025
cbc1df8
Instead of setting to 0, actually filter out SNPs outside of cs - avo…
ariannalandini Oct 16, 2025
38b1ad9
Tidy up branches - adding OT to annData part into most updated TileDB…
ariannalandini Dec 4, 2025
1540b68
Adding latest improvment to the TileDB version using bioalpha for ann…
bruno-ariano Jan 20, 2026
c28a5b5
Merge branch 'tiledb_locusbreaker' into bioalpha_ad_concat
ariannalandini Jan 20, 2026
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11 changes: 9 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ nextflow run Biostatistics-Unit-HT/Flanders -r 1.0 -profile [docker|singulari

### Example: Run Only Colocalization (with existing `.h5ad`)
```bash
nextflow run Biostatistics-Unit-HT/Flanders -r 1.0 -profile [docker|singularity|conda] --coloc_input /path/to/finemapping_output.h5ad --run_colocalization true --coloc_id my_coloc_run -w ./work -resume
nextflow run Biostatistics-Unit-HT/Flanders -r 1.0 -profile [docker|singularity|conda] --coloc_h5ad_input /path/to/finemapping_output.h5ad --run_colocalization true --coloc_id my_coloc_run -w ./work -resume
```

### Quick run with example dataset
Expand Down Expand Up @@ -84,6 +84,14 @@ Flanders separates the fine-mapping and colocalization process into two distinct
</br>
</br>

#### 4.1 Getting external credible sets from Open Targets
- It is possible to create an Anndata using credible-sets already genereted from Open Targets. To do that follow these steps
- Download the Open Targets [credible-set](https://platform.opentargets.org/downloads/credible_set/access) and [study](https://platform.opentargets.org/downloads/study/access).
- Create a conda environment with the following packages: anndata, polars, pyarrow, scipy, math, argparse
- Run the python script in the bin folder called parse_credible_sets_refined.py following the instructions from the arguments
</br>
</br>

### Step 2: Colocalization analysis

#### Inputs
Expand Down Expand Up @@ -125,7 +133,6 @@ iCOLOC approach allows to:
</br>
</br>


## 👩‍🔬 Credits
Developed by the Biostatistics and Genome Analysis Units at [Human Technopole](https://humantechnopole.it/en/)<br>
- [Arianna Landini](mailto:arianna.landini@fht.org)<br>
Expand Down
6 changes: 6 additions & 0 deletions assets/OT_mapping_credset_table.txt
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@@ -0,0 +1,6 @@
project,finemapping,data,description
GCST,SuSiE-inf,gwas,GWAS catalog data
GTEx,SuSie-inf,eqtl,eQTLs from GTEx
UKB_PPP_EUR,SuSiE-inf,pqtl,Data from UKB-PPP considering only Europeans
FINNGEN_R12,SuSie,gwas,Data from Finngen consortium

11 changes: 8 additions & 3 deletions bin/funs_locus_breaker_cojo_finemap_all_at_once.R
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Expand Up @@ -583,7 +583,7 @@ expand_cs <- function(fitted) {
#' cs_idx <- which(lbf_vec > 2)
#' find_threshold(lbf_vec, original_idx = cs_idx)
find_threshold <- function(vector, original_idx = NULL) {
if (var(vector) != 0) {
if ( var(vector) != 0 && !is.na(var(vector)) ) {
thres <- optim(
fn = function(th) thresholder(th, vector),
p = 0,
Expand Down Expand Up @@ -716,7 +716,7 @@ from_susie_to_anndata <- function(finemap_list=NULL, cs_indices=NULL, analysis_i
lABFs_list <- c(lABFs_list, lABFs)
min_res_labf_vec <- c(min_res_labf_vec, min_res_labf)
top_pvalue_vec <- c(top_pvalue_vec, top_pvalue)
purity_df <- rbind(purity_df, finemap$sets$purity |> dplyr::mutate(logsum.logABF=logsum.logABF))
purity_df <- rbind(purity_df, finemap$sets$purity |> dplyr::mutate(logsum.logABF=logsum.logABF, coverage = finemap$sets$requested_coverage))
comment_section <- c(comment_section, rep(finemap$comment_section, length(finemap$sets$cs_index)))
comment_section[is.na(comment_section)] <- "NaN"
metadata_df <- rbind(metadata_df, finemap$metadata)
Expand All @@ -731,7 +731,7 @@ from_susie_to_anndata <- function(finemap_list=NULL, cs_indices=NULL, analysis_i
metadata_df$L_index,
sep = "::"
)

# Prepare `obs_df` metadata
obs_df <- metadata_df |> dplyr::select(-L_index) |> dplyr::rename(type=TYPE)
obs_df$chr <- paste0("chr", obs_df$chr)
Expand Down Expand Up @@ -767,6 +767,11 @@ from_susie_to_anndata <- function(finemap_list=NULL, cs_indices=NULL, analysis_i
)
}

# Filter out values for SNPs out of the credible set right before matrices creation
for (cs in seq_along(lABFs_list)) {
lABFs_list[[cs]] <- lABFs_list[[cs]] |> dplyr::filter(is_cs)
}

lABF_matrix_sparse <- create_sparse_matrix("lABF")
beta_matrix_sparse <- create_sparse_matrix("bC")
se_matrix_sparse <- create_sparse_matrix("bC_se")
Expand Down
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