diff --git a/README.md b/README.md
index d1b0307..a17d8f7 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,8 @@ Worfklows are defined in [the `workflows` directory](workflows). There is [the `
 
 These workflows are set up to analyze spatial transcriptomics data: Nanostring GeoMx in WDL using command line, R, and Python scripts and 10x Visium in WDL using command line and Python scripts.
 
+_Note: Unlike our other workflows (e.g., [pmdbs-sc-rnaseq-wf](https://github.com/ASAP-CRN/pmdbs-sc-rnaseq-wf/tree/main)), the spatial workflows do not perform cross-team cohort analysis, as integrating spatial coordinates and mapping dimensions across datasets adds significant complexity._
+
 ## Nanostring GeoMx workflow overview
 
 For the Nanostring GeoMx workflow, we start with raw output files from the instrument and convert them into counts. Then, we clean the data by removing unreliable segments and genes, adjust for technical noise, and combine data from different slides. Finally, we cluster based on segment (which may contain many cells) and visualize their transcriptional profiles in a UMAP space.
@@ -45,7 +47,7 @@ Run once per slide. Intermediate files from previous runs are not reused and are
 
 ### Cohort analysis
 
-Run once per team (all slide from a single team) if `project.run_project_cohort_analysis` is set to `true`, and once for the whole cohort (all slides from all teams) if `run_cross_team_cohort_analysis` is set to `true`. Additional slides requires this entire analysis to be rerun. Intermediate files from previous runs are not reused and are stored in timestamped directories.
+Run once per team (all slide from a single team) if `project.run_project_cohort_analysis` is set to `true`. Additional slides requires this entire analysis to be rerun. Intermediate files from previous runs are not reused and are stored in timestamped directories.
 
 ## 10x Visium workflow overview
 
@@ -69,7 +71,7 @@ Run once per sample; only rerun when the preprocessing workflow version is updat
 
 ### Cohort analysis
 
-Run once per team (all samples from a single team) if `project.run_project_cohort_analysis` is set to `true`, and once for the whole cohort (all samples from all teams) if `run_cross_team_cohort_analysis` is set to `true`. This can be rerun using different sample subsets; including additional samples requires this entire analysis to be rerun. Intermediate files from previous runs are not reused and are stored in timestamped directories.
+Run once per team (all samples from a single team) if `project.run_project_cohort_analysis` is set to `true`. This can be rerun using different sample subsets; including additional samples requires this entire analysis to be rerun. Intermediate files from previous runs are not reused and are stored in timestamped directories.
 
 
 # Inputs
@@ -80,7 +82,6 @@ An input template file can be found at [workflows/pmdbs_spatial_geomx/inputs.jso
 
 | Type | Name | Description |
 | :- | :- | :- |
-| String | cohort_id | Name of the cohort; used to name output files during cross-team cohort analysis. |
 | Array[[Project](#nanostring-geomx-project)] | projects | The project ID, set of slides and their associated samples, reads and metadata, output bucket locations, and whether or not to run project-level cohort analysis. |
 | File | geomxngs_config_pkc | The GeoMx DSP configuration file to associate assay targets with GeoMx HybCode barcodes and Seq Code primers; see https://nanostring.com/products/geomx-digital-spatial-profiler/geomx-dsp-configuration-files/. |
 | Int? | min_segment_reads | Minimum number of segment reads. [1000] |
@@ -97,9 +98,6 @@ An input template file can be found at [workflows/pmdbs_spatial_geomx/inputs.jso
 | Int? | n_comps | Number of principal components to compute. [30] |
 | String? | batch_key | Key in AnnData object for batch information. ['batch_id'] |
 | Float? | leiden_resolution | Value controlling the coarseness of the Leiden clustering. [0.4] |
-| Boolean? | run_cross_team_cohort_analysis | Whether to run downstream harmonization steps on all samples across projects. If set to false, only preprocessing steps (GeoMxNGSPipeline and generating the initial adata object(s)) will run for samples. [false] |
-| String | cohort_raw_data_bucket | Bucket to upload cross-team cohort analysis intermediate files to. |
-| Array[String] | cohort_staging_data_buckets | Buckets to upload cross-team cohort analysis outputs to. |
 | String | container_registry | Container registry where workflow Docker images are hosted. |
 | String? | zones | Space-delimited set of GCP zones where compute will take place. ['us-central1-c us-central1-f'] |
 
@@ -109,7 +107,6 @@ An input template file can be found at [workflows/pmdbs_spatial_visium/inputs.js
 
 | Type | Name | Description |
 | :- | :- | :- |
-| String | cohort_id | Name of the cohort; used to name output files during cross-team cohort analysis. |
 | Array[[Project](#10x-visium-project)] | projects | The project ID, set of samples and their associated reads and metadata, output bucket locations, and whether or not to run project-level cohort analysis. |
 | File | spaceranger_reference_data | Space Ranger transcriptome reference data; see https://www.10xgenomics.com/support/software/space-ranger/downloads and [10x Visium notes](#10x-visium-notes). |
 | File | visium_probe_set_csv | Visium probe-based assays target genes in Space Ranger transcriptome; see https://www.10xgenomics.com/support/software/space-ranger/downloads and [10x Visium notes](#10x-visium-notes). |
@@ -122,9 +119,6 @@ An input template file can be found at [workflows/pmdbs_spatial_visium/inputs.js
 | Int? | n_comps | Number of principal components to compute. [30] |
 | String? | batch_key | Key in AnnData object for batch information. ['batch_id'] |
 | Float? | leiden_resolution | Value controlling the coarseness of the Leiden clustering. [0.4] |
-| Boolean? | run_cross_team_cohort_analysis | Whether to run downstream harmonization steps on all samples across projects. If set to false, only preprocessing steps ( and generating the initial adata object(s)) will run for samples. [false] |
-| String | cohort_raw_data_bucket | Bucket to upload cross-team cohort analysis intermediate files to. |
-| Array[String] | cohort_staging_data_buckets | Buckets to upload cross-team cohort analysis outputs to. |
 | String | container_registry | Container registry where workflow Docker images are hosted. |
 | String? | zones | Space-delimited set of GCP zones where compute will take place. ['us-central1-c us-central1-f'] |
 
@@ -195,7 +189,7 @@ An input template file can be found at [workflows/pmdbs_spatial_visium/inputs.js
 
 ## Generating the inputs JSON
 
-The inputs JSON may be generated manually, however when running a large number of samples, this can become unwieldly. The `generate_inputs` utility script may be used to automatically generate the inputs JSON (`inputs.{staging_env}.{source}-{cohort_dataset}.{date}.json` and a sample list TSV (`{team_id}.{source}-{cohort_dataset}.sample_list.{date}.tsv`; same as the one generated in [the write_cohort_sample_list task](https://github.com/ASAP-CRN/wf-common/wdl/tasks/write_cohort_sample_list.wdl)). The script requires the libraries outlined in [the requirements.txt file](https://github.com/ASAP-CRN/wf-common/util/requirements.txt) and the following inputs:
+The inputs JSON may be generated manually, however when running a large number of samples, this can become unwieldly. The [`generate_inputs` utility script](https://github.com/ASAP-CRN/wf-common/blob/main/util/generate_inputs) may be used to automatically generate the inputs JSON (`inputs.{staging_env}.{source}-{cohort_dataset}.{date}.json` and a sample list TSV (`{team_id}.{source}-{cohort_dataset}.sample_list.{date}.tsv`; same as the one generated in [the write_cohort_sample_list task](https://github.com/ASAP-CRN/wf-common/wdl/tasks/write_cohort_sample_list.wdl)). The script requires the libraries outlined in [the requirements.txt file](https://github.com/ASAP-CRN/wf-common/util/requirements.txt) and the following inputs:
 
 - `project-tsv`: One or more project TSVs with one row per sample and columns team_id, ASAP_dataset_id, ASAP_sample_id, batch, fastq_R1s, fastq_R2s, fastq_I1s, fastq_I2s, embargoed, source, dataset, dataset_DOI_url, and SPATIAL columns if applicable: geomx_config, geomx_dsp_config, geomx_annotation_file, visium_cytassist, visium_probe_set, visium_slide_ref, and visium_capture_area. All samples from all projects may be included in the same project TSV, or multiple project TSVs may be provided.
 	- `team_id`: A unique identifier for the team from which the sample(s) arose.
@@ -226,14 +220,14 @@ Example usage:
 ```bash
 ./wf-common/util/generate_inputs \
 	--project-tsv metadata.tsv \
-	--inputs-template workflows/inputs.json \
+	--inputs-template workflows/pmdbs_spatial_geomx/inputs.json \
 	--run-project-cohort-analysis \
 	--workflow-name pmdbs_spatial_geomx_analysis \
 	--cohort-dataset spatial-geomx
 
 ./wf-common/util/generate_inputs \
 	--project-tsv metadata.tsv \
-	--inputs-template workflows/inputs.json \
+	--inputs-template workflows/pmdbs_spatial_visium/inputs.json \
 	--run-project-cohort-analysis \
 	--workflow-name pmdbs_spatial_visium_analysis \
 	--cohort-dataset spatial-visium
@@ -246,7 +240,7 @@ Example usage:
 - `cohort_id`: either the `team_id` for project-level downstream analysis, or the `cohort_id` for the full cohort
 - `workflow_run_timestamp`: format: `%Y-%m-%dT%H-%M-%SZ`
 - The list of samples used to generate the cohort analysis will be output alongside other cohort analysis outputs in the staging data bucket (`${cohort_id}.sample_list.tsv`)
-- The MANIFEST.tsv file in the staging data bucket describes the file name, md5 hash, timestamp, workflow version, workflow name, and workflow release for the run used to generate each file in that directory
+- The `MANIFEST.tsv` file in the staging data bucket describes the file name, md5 hash, timestamp, workflow version, workflow name, and workflow release for the run used to generate each file in that directory
 
 ### Raw data (intermediate files and final outputs for all runs of the workflow)
 
@@ -298,20 +292,23 @@ asap-raw-{cohort,team-xxyy}-{source}-{dataset}
 
 ### Staging data (intermediate workflow objects and final workflow outputs for the latest run of the workflow)
 
-Following QC by researchers, the objects in the dev or uat bucket are synced into the curated data buckets, maintaining the same file structure. Curated data buckets are named `asap-curated-{cohort,team-xxyy}-{source}-{dataset}`.
+Following QC by researchers, the objects in the dev or uat bucket are synced into the curated data buckets, maintaining the same file structure. Curated data buckets are named `asap-curated-{team-xxyy}-{source}-{dataset}`.
 
 Data may be synced using [the `promote_staging_data` script](#promoting-staging-data).
 
 ```bash
-asap-dev-{cohort,team-xxyy}-{source}-{dataset}
+asap-dev-{team-xxyy}-{source}-{dataset}
 └── pmdbs_spatial_geomx
     ├── cohort_analysis
-    │   ├── ${cohort_id}.sample_list.tsv
-    │   ├── ${cohort_id}.merged.h5ad
-    │   ├── ${cohort_id}.hvg_dispersion.png
-    │   ├── ${cohort_id}.merged_adata_metadata.csv
-    │   ├── ${cohort_id}.clustered.h5ad # Final
-    │   ├── ${cohort_id}.umap_cluster.png
+    │   ├── ${team_id}.sample_list.tsv
+    │   ├── ${team_id}.merged_metadata.csv
+    │   ├── ${team_id}.merged_processed.h5ad
+    │   ├── ${team_id}.all_genes.csv
+    │   ├── ${team_id}.hvg_genes.csv
+    │   ├── ${team_id}.hvg_dispersion.png
+    │   ├── ${team_id}.umap_cluster.png
+    │   ├── ${team_id}.final.h5ad
+    │   ├── ${team_id}.final_metadata.csv
     │   └── MANIFEST.tsv
     ├── process_to_adata
     │   ├── ${slideN_id}.segment_gene_detection_plot.png
@@ -338,25 +335,27 @@ asap-dev-{cohort,team-xxyy}-{source}-{dataset}
         ├── ${slideN_id}.gene_count.csv
         └── MANIFEST.tsv
 
-asap-dev-{cohort,team-xxyy}-{source}-{dataset}
+asap-dev-{team-xxyy}-{source}-{dataset}
 └── pmdbs_spatial_visium
     ├── cohort_analysis
-    │   ├── ${cohort_id}.sample_list.tsv
-    │   ├── ${cohort_id}.merged_adata_object.h5ad
-    │   ├── ${cohort_id}.qc_violin.png
-    │   ├── ${cohort_id}.qc_dist.png
-    │   ├── ${cohort_id}.hvg_dispersion.png
-    │   ├── ${cohort_id}.clustered.h5ad
-    │   ├── ${cohort_id}.umap_cluster.png
-    │   ├── ${cohort_id}.spatial_scatter.png
-    │   ├── ${cohort_id}.final_adata_object.h5ad
-    │   ├── ${cohort_id}.moran_top_10_variable_genes.csv
-    │   ├── ${cohort_id}.moran_top_4_variable_genes_spatial_scatter.png
+    │   ├── ${team_id}.sample_list.tsv
+    │   ├── ${team_id}.merged_cleaned_unfiltered.h5ad
+    │   ├── ${team_id}.merged_metadata.csv
+    │   ├── ${team_id}.all_genes.csv
+    │   ├── ${team_id}.hvg_genes.csv
+    │   ├── ${team_id}.qc_violin.png
+    │   ├── ${team_id}.qc_dist.png
+    │   ├── ${team_id}.hvg_dispersion.png
+    │   ├── ${team_id}.umap_cluster.png
+    │   ├── ${team_id}.spatial_scatter.png
+    │   ├── ${team_id}.final.h5ad
+    │   ├── ${team_id}.final_metadata.csv
+    │   ├── ${team_id}.moran_top_10_variable_genes.csv
+    │   ├── ${team_id}.moran_top_4_variable_genes_spatial_scatter.png
     │   └── MANIFEST.tsv
     └── preprocess
         ├── ${sampleA_id}.raw_feature_bc_matrix.h5
         ├── ${sampleA_id}.filtered_feature_bc_matrix.h5
-        ├── ${sampleA_id}.initial_adata_object.h5ad
         ├── ${sampleA_id}.molecule_info.h5
         ├── ${sampleA_id}.metrics_summary.csv
         ├── ${sampleA_id}.spaceranger_spatial_outputs.tar.gz
@@ -367,13 +366,12 @@ asap-dev-{cohort,team-xxyy}-{source}-{dataset}
         ├── ${sampleA_id}.scalefactors_json.json
         ├── ${sampleA_id}.tissue_positions.csv
         ├── ${sampleA_id}.spatial_enrichment.csv
-        ├── ${sampleA_id}.initial_adata_object.h5ad
+        ├── ${sampleA_id}.cleaned_unfiltered.h5ad
         ├── ${sampleA_id}.qc.h5ad
         ├── MANIFEST.tsv
         ├── ...
         ├── ${sampleN_id}.raw_feature_bc_matrix.h5
         ├── ${sampleN_id}.filtered_feature_bc_matrix.h5
-        ├── ${sampleN_id}.initial_adata_object.h5ad
         ├── ${sampleN_id}.molecule_info.h5
         ├── ${sampleN_id}.metrics_summary.csv
         ├── ${sampleN_id}.spaceranger_spatial_outputs.tar.gz
@@ -384,7 +382,7 @@ asap-dev-{cohort,team-xxyy}-{source}-{dataset}
         ├── ${sampleN_id}.scalefactors_json.json
         ├── ${sampleN_id}.tissue_positions.csv
         ├── ${sampleN_id}.spatial_enrichment.csv
-        ├── ${sampleN_id}.initial_adata_object.h5ad
+        ├── ${sampleN_id}.cleaned_unfiltered.h5ad
         ├── ${sampleN_id}.qc.h5ad
         └── MANIFEST.tsv
 ```
@@ -397,7 +395,7 @@ This script compiles bucket and file information for both the initial (staging)
 
 If data integrity tests pass, this script will upload a combined MANIFEST.tsv and the data promotion Markdown report under a metadata/{timestamp} directory in the staging bucket. Previous manifest files and reports will be kept. Next, it will rsync all files in the staging bucket to the curated bucket's upstream, downstream, cohort_analysis, and metadata directories. **Exercise caution when using this script**; files that are not present in the source (staging) bucket will be deleted at the destination (curated) bucket.
 
-If data integrity tests fail, staging data cannot be promoted. The combined MANFIEST.tsv, Markdown report, and promote_staging_data_script.log will be locally available.
+If data integrity tests fail, staging data cannot be promoted. The combined `MANIFEST.tsv`, Markdown report, and `promote_staging_data_script.log` will be locally available.
 
 The script defaults to a dry run, printing out the files that would be copied or deleted for each selected team.
 
@@ -411,7 +409,6 @@ The script defaults to a dry run, printing out the files that would be copied or
 -d  Space-delimited dataset name(s) in team bucket name, must follow the same order as {team}
 -w  Workflow name used as a directory in bucket
 -p  Promote data. If this option is not selected, data that would be copied or deleted is printed out, but files are not actually changed (dry run)
--e  Staging bucket type; options are 'uat' or 'dev' ['uat']
 ```
 
 ### Usage
@@ -421,11 +418,11 @@ The script defaults to a dry run, printing out the files that would be copied or
 ./wf-common/util/promote_staging_data -t cohort -l -s pmdbs -d spatial-geomx -w pmdbs_spatial_geomx
 ./wf-common/util/promote_staging_data -t cohort -l -s pmdbs -d spatial-visium -w pmdbs_spatial_visium
 
-# Print out the files that would be copied or deleted from the staging bucket to the curated bucket for teams team-hardy and team-biederer
-./wf-common/util/promote_staging_data -t team-hardy team-biederer -s pmdbs -d spatial-geomx -w pmdbs_spatial_geomx
+# Print out the files that would be copied or deleted from the staging bucket to the curated bucket for teams team-edwards and team-vila
+./wf-common/util/promote_staging_data -t team-edwards team-vila -s pmdbs -d spatial-geomx-th spatial-geomx-thlc -w pmdbs_spatial_geomx
 
-# Promote data for team-hardy and cohort
-./wf-common/util/promote_staging_data -t team-hardy cohort -s pmdbs -d spatial-geomx -w pmdbs_spatial_geomx -p -e dev
+# Promote data for team-edwards and team-vila
+./wf-common/util/promote_staging_data -t team-edwards team-vila -s pmdbs -d spatial-geomx-th spatial-geomx-thlc -w pmdbs_spatial_geomx -p
 ```
 
 # Docker images
diff --git a/docker/spatial_py/requirements.txt b/docker/spatial_py/requirements.txt
index b0daffd..4c74280 100644
--- a/docker/spatial_py/requirements.txt
+++ b/docker/spatial_py/requirements.txt
@@ -1,13 +1,13 @@
 argparse==1.4.0
-anndata==0.10.9
-numpy==2.0.1
-pandas==2.2.2
-scipy==1.15.2
+anndata==0.11.4
+numpy==2.2.0
+pandas==2.3.1
+scipy==1.15.3
 squidpy==1.6.2
 matplotlib==3.10.0
 seaborn==0.13.2
 harmonypy==0.0.10
-scanpy==1.10.4
+scanpy==1.11.3
 dask==2024.11.2
 distributed==2024.11.2
 numcodecs==0.11.0
diff --git a/docker/spatial_py/scripts/geomx_export_final_artifacts b/docker/spatial_py/scripts/geomx_export_final_artifacts
new file mode 100755
index 0000000..b1b5f6d
--- /dev/null
+++ b/docker/spatial_py/scripts/geomx_export_final_artifacts
@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+
+import argparse
+import scanpy as sc
+
+
+def main(args):
+    ############################
+    ## EXPORT FINAL ARTIFACTS ##
+    ############################
+    adata = sc.read_h5ad(args.adata_input)
+
+    # Save outputs
+    metadata = adata.obs
+    metadata.to_csv(f"{args.cohort_id}.final_metadata.csv")
+    adata.write_h5ad(filename=args.adata_output, compression="gzip")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Export final AnnData object and its metadata"
+    )
+    parser.add_argument(
+        "-c",
+        "--cohort-id",
+        type=str,
+        required=True,
+        help="Cohort ID"
+    )
+    parser.add_argument(
+        "-i",
+        "--adata-input",
+        type=str,
+        required=True,
+        help="Adata object to finalize"
+    )
+    parser.add_argument(
+        "-o",
+        "--adata-output",
+        type=str,
+        required=True,
+        help="Output file name for the final AnnData object"
+    )
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/docker/spatial_py/scripts/geomx_merge_and_prep b/docker/spatial_py/scripts/geomx_merge_and_prep
index 952db7c..385d61e 100755
--- a/docker/spatial_py/scripts/geomx_merge_and_prep
+++ b/docker/spatial_py/scripts/geomx_merge_and_prep
@@ -24,16 +24,31 @@ def main(args):
     ##################
     ## ANNOTATE HVG ##
     ##################
+    merged_adata.layers["counts"] = merged_adata.X.copy()
     sc.pp.log1p(merged_adata)
     
     sc.pp.highly_variable_genes(
         merged_adata,
-        flavor="seurat",
         n_top_genes=args.n_top_genes,
+        flavor="seurat",
         inplace=True,
+        batch_key=args.batch_key,
+    )
+    full_features = merged_adata.var.copy()
+    hvg_full = (
+        merged_adata.var[merged_adata.var["highly_variable"]]
+        .sort_values(
+            by=["highly_variable_nbatches", "dispersions_norm"],
+            ascending=[False, False]
+        )
+        .head(args.n_top_genes)
+        .index.tolist()
     )
+    hvg_adata = merged_adata[:, merged_adata.var.index.isin(hvg_full)]
+    hvg_features = hvg_adata.var.copy()
+
     sc.pl.highly_variable_genes(
-        merged_adata,
+        hvg_adata,
     )
     fig = plt.gcf()
     fig.suptitle(f"Highly variable genes dispersion plot - {args.output_prefix}", va="center", ha="center", fontsize=16)
@@ -44,20 +59,22 @@ def main(args):
     ## DIMENSIONALITY REDUCTION ##
     ##############################
     sc.pp.pca(
-        merged_adata,
+        hvg_adata,
         n_comps=args.n_comps,
         svd_solver="arpack",
     )
 
     # Save outputs
     metadata = merged_adata.obs
-    metadata.to_csv(f"{args.output_prefix}.merged_adata_metadata.csv")
-    merged_adata.write_h5ad(filename=args.adata_output, compression="gzip")
+    metadata.to_csv(f"{args.output_prefix}.merged_metadata.csv")
+    full_features.to_csv(f"{args.output_prefix}.all_genes.csv", index=True)
+    hvg_features.to_csv(f"{args.output_prefix}.hvg_genes.csv", index=True)
+    hvg_adata.write_h5ad(filename=args.adata_output, compression="gzip")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Merge adata objects and process to prepare for downstream analysis"
+        description="Merge adata objects and process to prepare for downstream analysis by identifying highly variable genes (HVGs) and PCA"
     )
     parser.add_argument(
         "-i",
@@ -80,6 +97,13 @@ if __name__ == "__main__":
         required=True,
         help="Number of principal components to compute using `scanpy.pp.pca` [30]"
     )
+    parser.add_argument(
+        "-b",
+        "--batch-key",
+        type=str,
+        required=True,
+        help="Key in AnnData object for batch information ['batch_id']"
+    )
     parser.add_argument(
         "-p",
         "--output-prefix",
diff --git a/docker/spatial_py/scripts/visium_merge_and_plot_qc b/docker/spatial_py/scripts/visium_merge_and_plot_qc
index 8dadad5..512c4c4 100755
--- a/docker/spatial_py/scripts/visium_merge_and_plot_qc
+++ b/docker/spatial_py/scripts/visium_merge_and_plot_qc
@@ -41,39 +41,41 @@ def main(args):
         multi_panel=True,
     )
     fig = plt.gcf()
-    fig.suptitle(f"QC violin plot - {args.qc_plots_prefix}", va="center", ha="center", fontsize=16)
-    plt.savefig(f"{args.qc_plots_prefix}.qc_violin.png", dpi=300, bbox_inches="tight")
+    fig.suptitle(f"QC violin plot - {args.output_prefix}", va="center", ha="center", fontsize=16)
+    plt.savefig(f"{args.output_prefix}.qc_violin.png", dpi=300, bbox_inches="tight")
 
     # Total counts and n genes by counts distribution plots
     fig, axs = plt.subplots(1, 4, figsize=(15,4))
-    fig.suptitle(f"Covariates for filtering - {args.qc_plots_prefix}")
-    sns.distplot(
+    fig.suptitle(f"Covariates for filtering - {args.output_prefix}")
+    sns.histplot(
         merged_adata.obs["total_counts"],
         kde=False,
         ax=axs[0],
     )
-    sns.distplot(
+    sns.histplot(
         merged_adata.obs["total_counts"][merged_adata.obs["total_counts"]<10000],
         kde=False,
         bins=40,
         ax=axs[1],
     )
-    sns.distplot(
+    sns.histplot(
         merged_adata.obs["n_genes_by_counts"],
         kde=False,
         bins=60,
         ax=axs[2],
     )
-    sns.distplot(
+    sns.histplot(
         merged_adata.obs["n_genes_by_counts"][merged_adata.obs["n_genes_by_counts"]<4000],
         kde=False,
         bins=60,
         ax=axs[3],
     )
 
-    plt.savefig(f"{args.qc_plots_prefix}.qc_dist.png", dpi=300, bbox_inches="tight")
+    plt.savefig(f"{args.output_prefix}.qc_dist.png", dpi=300, bbox_inches="tight")
 
     # Save outputs
+    metadata = merged_adata.obs
+    metadata.to_csv(f"{args.output_prefix}.merged_metadata.csv")
     merged_adata.write_h5ad(filename=args.merged_adata_output, compression="gzip")
 
 
@@ -90,10 +92,10 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "-p",
-        "--qc-plots-prefix",
+        "--output-prefix",
         type=str,
         required=True,
-        help="Output file name prefix for the QC violin and distribution plots"
+        help="Output file name prefix for the QC violin, distribution plots, and metadata"
     )
     parser.add_argument(
         "-o",
diff --git a/docker/spatial_py/scripts/visium_process b/docker/spatial_py/scripts/visium_process
index dedf5f0..e06f2bb 100755
--- a/docker/spatial_py/scripts/visium_process
+++ b/docker/spatial_py/scripts/visium_process
@@ -43,29 +43,45 @@ def main(args):
     ##################
     sc.pp.highly_variable_genes(
         adata,
-        flavor="seurat",
         n_top_genes=args.n_top_genes,
+        flavor="seurat",
         inplace=True,
+        batch_key=args.batch_key,
+    )
+    full_features = adata.var.copy()
+    hvg_full = (
+        adata.var[adata.var["highly_variable"]]
+        .sort_values(
+            by=["highly_variable_nbatches", "dispersions_norm"],
+            ascending=[False, False]
+        )
+        .head(args.n_top_genes)
+        .index.tolist()
     )
+    hvg_adata = adata[:, adata.var.index.isin(hvg_full)]
+    hvg_features = hvg_adata.var.copy()
+
     sc.pl.highly_variable_genes(
-        adata,
+        hvg_adata,
     )
     fig = plt.gcf()
-    fig.suptitle(f"Highly variable genes dispersion plot - {args.plots_prefix}", va="center", ha="center", fontsize=16)
-    plt.savefig(f"{args.plots_prefix}.hvg_dispersion.png", dpi=300, bbox_inches="tight")
+    fig.suptitle(f"Highly variable genes dispersion plot - {args.output_prefix}", va="center", ha="center", fontsize=16)
+    plt.savefig(f"{args.output_prefix}.hvg_dispersion.png", dpi=300, bbox_inches="tight")
 
 
     ##############################
     ## DIMENSIONALITY REDUCTION ##
     ##############################
     sc.pp.pca(
-        adata,
+        hvg_adata,
         n_comps=args.n_comps,
         svd_solver="arpack",
     )
 
     # Save outputs
-    adata.write_h5ad(filename=args.adata_output, compression="gzip")
+    full_features.to_csv(f"{args.output_prefix}.all_genes.csv", index=True)
+    hvg_features.to_csv(f"{args.output_prefix}.hvg_genes.csv", index=True)
+    hvg_adata.write_h5ad(filename=args.adata_output, compression="gzip")
 
 
 if __name__ == "__main__":
@@ -128,12 +144,19 @@ if __name__ == "__main__":
         required=True,
         help="Number of principal components to compute using `scanpy.pp.pca` [30]"
     )
+    parser.add_argument(
+        "-b",
+        "--batch-key",
+        type=str,
+        required=True,
+        help="Key in AnnData object for batch information ['batch_id']"
+    )
     parser.add_argument(
         "-p",
-        "--plots-prefix",
+        "--output-prefix",
         type=str,
         required=True,
-        help="Output file name prefix for the HVGs plot"
+        help="Output file name prefix for the HVGs plot and CSVs"
     )
     parser.add_argument(
         "-o",
diff --git a/docker/spatial_py/scripts/visium_spatially_variable_genes b/docker/spatial_py/scripts/visium_spatially_variable_genes
index ffe0d78..98dafc0 100755
--- a/docker/spatial_py/scripts/visium_spatially_variable_genes
+++ b/docker/spatial_py/scripts/visium_spatially_variable_genes
@@ -43,6 +43,8 @@ def main(args):
 
     # Save table and adata object
     top_10_variable_genes.to_csv(f"{args.cohort_id}.moran_top_10_variable_genes.csv")
+    metadata = adata.obs
+    metadata.to_csv(f"{args.cohort_id}.final_metadata.csv")
     adata.write_h5ad(filename=args.adata_output, compression="gzip")
 
 
diff --git a/wdl-ci.config.json b/wdl-ci.config.json
index 15d8e78..93f805e 100644
--- a/wdl-ci.config.json
+++ b/wdl-ci.config.json
@@ -7,7 +7,7 @@
       "tasks": {
         "merge_and_prep": {
           "key": "merge_and_prep",
-          "digest": "z6qqmruro2r42bxwxxsbig34t5qfjhir",
+          "digest": "cgyktioycaor6b3vkycc5qmefyw65lmo",
           "tests": [
             {
               "inputs": {
@@ -18,6 +18,7 @@
                 ],
                 "n_top_genes": 3000,
                 "n_comps": 30,
+                "batch_key": "batch_id",
                 "raw_data_path": "${geomx_cohort_analysis_raw_data_path}",
                 "workflow_info": "${geomx_workflow_info}",
                 "billing_project": "${billing_project}",
@@ -25,13 +26,40 @@
                 "zones": "${zones}"
               },
               "output_tests": {
-                "merged_adata_object": {
-                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.merged.h5ad",
+                "merged_and_processed_adata_object": {
+                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.merged_processed.h5ad",
                   "test_tasks": [
                     "compare_file_basename",
                     "check_hdf5"
                   ]
                 },
+                "merged_metadata_csv": {
+                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.merged_metadata.csv",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_empty_lines",
+                    "check_comma_separated",
+                    "count_columns"
+                  ]
+                },
+                "all_genes_csv": {
+                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.all_genes.csv",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_empty_lines",
+                    "check_comma_separated",
+                    "count_columns"
+                  ]
+                },
+                "hvg_genes_csv": {
+                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.hvg_genes.csv",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_empty_lines",
+                    "check_comma_separated",
+                    "count_columns"
+                  ]
+                },
                 "hvg_plot_png": {
                   "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.hvg_dispersion.png",
                   "test_tasks": [
@@ -39,9 +67,35 @@
                     "png_validator",
                     "image_validator"
                   ]
+                }
+              }
+            }
+          ]
+        },
+        "export_final_artifacts": {
+          "key": "export_final_artifacts",
+          "digest": "i6i56p6qrv5iew6tthkzl7p6mlmkyvsl",
+          "tests": [
+            {
+              "inputs": {
+                "cohort_id": "${geomx_team_id}",
+                "clustered_adata_object": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.clustered.h5ad",
+                "raw_data_path": "${geomx_cohort_analysis_raw_data_path}",
+                "workflow_info": "${geomx_workflow_info}",
+                "billing_project": "${billing_project}",
+                "container_registry": "${container_registry}",
+                "zones": "${zones}"
+              },
+              "output_tests": {
+                "final_adata_object": {
+                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.final.h5ad",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_hdf5"
+                  ]
                 },
-                "merged_adata_metadata_csv": {
-                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.merged_adata_metadata.csv",
+                "final_metadata_csv": {
+                  "value": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.final_metadata.csv",
                   "test_tasks": [
                     "compare_file_basename",
                     "check_empty_lines",
@@ -213,7 +267,7 @@
             {
               "inputs": {
                 "cohort_id": "${geomx_team_id}",
-                "merged_and_processed_adata_object": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.merged.h5ad",
+                "merged_and_processed_adata_object": "${geomx_input_file_path}/${cohort_analysis_workflow_name}/${geomx_team_id}.merged_processed.h5ad",
                 "batch_key": "batch_id",
                 "container_registry": "${container_registry}",
                 "zones": "${zones}"
@@ -232,7 +286,7 @@
         },
         "cluster": {
           "key": "cluster",
-          "digest": "chquhvgfycakclmctuyqjyeanrbtlilo",
+          "digest": "qouf6flpm3rprnfz4fztlh3ybrumqcfb",
           "tests": [
             {
               "inputs": {
@@ -275,7 +329,7 @@
       "tasks": {
         "merge_and_plot_qc_metrics": {
           "key": "merge_and_plot_qc_metrics",
-          "digest": "wmyxopfjayx7b473xjcvghxm4zyu32fw",
+          "digest": "kppsqzekmaf2vvut6ijgchkdof56io7u",
           "tests": [
             {
               "inputs": {
@@ -292,7 +346,7 @@
               },
               "output_tests": {
                 "merged_adata_object": {
-                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.merged_adata_object.h5ad",
+                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.merged_cleaned_unfiltered.h5ad",
                   "test_tasks": [
                     "compare_file_basename",
                     "check_hdf5"
@@ -315,12 +369,12 @@
         },
         "filter_and_normalize": {
           "key": "filter_and_normalize",
-          "digest": "hrix2jd7skzunlvqtbddvosxosslqlac",
+          "digest": "lujek6d7l3dinbjtu23rvf2bplcbdkl5",
           "tests": [
             {
               "inputs": {
                 "cohort_id": "${visium_team_id}",
-                "merged_adata_object": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.merged_adata_object.h5ad",
+                "merged_adata_object": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.merged_cleaned_unfiltered.h5ad",
                 "filter_cells_min_counts": 5000,
                 "filter_cells_min_genes": 3000,
                 "filter_genes_min_cells": 10,
@@ -328,6 +382,7 @@
                 "normalize_target_sum": 10000,
                 "n_top_genes": 3000,
                 "n_comps": 30,
+                "batch_key": "batch_id",
                 "raw_data_path": "${visium_cohort_analysis_raw_data_path}",
                 "workflow_info": "${visium_workflow_info}",
                 "billing_project": "${billing_project}",
@@ -342,6 +397,24 @@
                     "check_hdf5"
                   ]
                 },
+                "all_genes_csv": {
+                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.all_genes.csv",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_empty_lines",
+                    "check_comma_separated",
+                    "count_columns"
+                  ]
+                },
+                "hvg_genes_csv": {
+                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.hvg_genes.csv",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_empty_lines",
+                    "check_comma_separated",
+                    "count_columns"
+                  ]
+                },
                 "hvg_plot_png": {
                   "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.hvg_dispersion.png",
                   "test_tasks": [
@@ -390,7 +463,7 @@
       "tasks": {
         "spatially_variable_gene_analysis": {
           "key": "spatially_variable_gene_analysis",
-          "digest": "evtq6h2q4bix4eglhh5ptj6hvgarmoqu",
+          "digest": "swz5i2qx4fxynsb77wgp53zvqraibph3",
           "tests": [
             {
               "inputs": {
@@ -404,12 +477,21 @@
               },
               "output_tests": {
                 "final_adata_object": {
-                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.final_adata_object.h5ad",
+                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.final.h5ad",
                   "test_tasks": [
                     "compare_file_basename",
                     "check_hdf5"
                   ]
                 },
+                "final_metadata_csv": {
+                  "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.final_metadata.csv",
+                  "test_tasks": [
+                    "compare_file_basename",
+                    "check_empty_lines",
+                    "check_comma_separated",
+                    "count_columns"
+                  ]
+                },
                 "moran_top_10_variable_genes_csv": {
                   "value": "${visium_input_file_path}/${cohort_analysis_workflow_name}/${visium_team_id}.moran_top_10_variable_genes.csv",
                   "test_tasks": [
@@ -545,7 +627,7 @@
         },
         "counts_to_adata": {
           "key": "counts_to_adata",
-          "digest": "j53mpjhjsfrcldiwhxeqm5jyhmkganmh",
+          "digest": "uty3rh5gqsqcymnegbiacpejn2mhklcm",
           "tests": [
             {
               "inputs": {
@@ -564,7 +646,7 @@
               },
               "output_tests": {
                 "initial_adata_object": {
-                  "value": "${visium_input_file_path}/${preprocess_workflow_name}/${visium_sample_id}.initial_adata_object.h5ad",
+                  "value": "${visium_input_file_path}/${preprocess_workflow_name}/${visium_sample_id}.cleaned_unfiltered.h5ad",
                   "test_tasks": [
                     "compare_file_basename",
                     "check_hdf5"
@@ -581,7 +663,7 @@
             {
               "inputs": {
                 "sample_id": "${visium_sample_id}",
-                "initial_adata_object": "${visium_input_file_path}/${preprocess_workflow_name}/${visium_sample_id}.initial_adata_object.h5ad",
+                "initial_adata_object": "${visium_input_file_path}/${preprocess_workflow_name}/${visium_sample_id}.cleaned_unfiltered.h5ad",
                 "raw_data_path": "${visium_qc_raw_data_path}",
                 "workflow_info": "${visium_workflow_info}",
                 "billing_project": "${billing_project}",
diff --git a/wf-common b/wf-common
index 68cccc9..4bf08b0 160000
--- a/wf-common
+++ b/wf-common
@@ -1 +1 @@
-Subproject commit 68cccc9b07eedb063ab92c1475c98fd5e4fa7318
+Subproject commit 4bf08b09cbf8f7cc0c6ee61ad31cf6e2e230696b
diff --git a/workflows/integrate_data/integrate_data.wdl b/workflows/integrate_data/integrate_data.wdl
index c5dc59a..1241820 100644
--- a/workflows/integrate_data/integrate_data.wdl
+++ b/workflows/integrate_data/integrate_data.wdl
@@ -42,7 +42,7 @@ workflow integrate_data {
 
 	output {
 		File integrated_adata_object = integrate_sample_data.integrated_adata_object
-		File clustered_adata_object = cluster.clustered_adata_object #!FileCoercion
+		File clustered_adata_object = cluster.clustered_adata_object
 		File umap_cluster_plots_png = cluster.umap_cluster_plots_png #!FileCoercion
 	}
 
@@ -147,12 +147,11 @@ task cluster {
 			-b ~{billing_project} \
 			-d ~{raw_data_path} \
 			-i ~{write_tsv(workflow_info)} \
-			-o "~{cohort_id}.clustered.h5ad" \
 			-o "~{cohort_id}.umap_cluster.png"
 	>>>
 
 	output {
-		String clustered_adata_object = "~{raw_data_path}/~{cohort_id}.clustered.h5ad"
+		File clustered_adata_object = "~{cohort_id}.clustered.h5ad"
 		String umap_cluster_plots_png = "~{raw_data_path}/~{cohort_id}.umap_cluster.png"
 	}
 
diff --git a/workflows/pmdbs_spatial_geomx/cohort_analysis/cohort_analysis.wdl b/workflows/pmdbs_spatial_geomx/cohort_analysis/cohort_analysis.wdl
index 230b259..34a3bde 100644
--- a/workflows/pmdbs_spatial_geomx/cohort_analysis/cohort_analysis.wdl
+++ b/workflows/pmdbs_spatial_geomx/cohort_analysis/cohort_analysis.wdl
@@ -57,6 +57,7 @@ workflow cohort_analysis {
 			processed_adata_objects = processed_adata_objects,
 			n_top_genes = n_top_genes,
 			n_comps = n_comps,
+			batch_key = batch_key,
 			raw_data_path = raw_data_path,
 			workflow_info = workflow_info,
 			billing_project = billing_project,
@@ -67,7 +68,7 @@ workflow cohort_analysis {
 	call IntegrateData.integrate_data {
 		input:
 			cohort_id = cohort_id,
-			merged_and_processed_adata_object = merge_and_prep.merged_adata_object, #!FileCoercion
+			merged_and_processed_adata_object = merge_and_prep.merged_and_processed_adata_object, #!FileCoercion
 			n_comps = n_comps,
 			batch_key = batch_key,
 			leiden_resolution = leiden_resolution,
@@ -78,6 +79,17 @@ workflow cohort_analysis {
 			zones = zones
 	}
 
+	call export_final_artifacts {
+		input:
+			cohort_id = cohort_id,
+			clustered_adata_object = integrate_data.clustered_adata_object,
+			raw_data_path = raw_data_path,
+			workflow_info = workflow_info,
+			billing_project = billing_project,
+			container_registry = container_registry,
+			zones = zones
+	}
+
 	call UploadFinalOutputs.upload_final_outputs as upload_preprocess_files {
 		input:
 			output_file_paths = preprocessing_output_file_paths,
@@ -101,13 +113,18 @@ workflow cohort_analysis {
 			write_cohort_sample_list.cohort_sample_list
 		],
 		[
-			merge_and_prep.merged_adata_object,
-			merge_and_prep.hvg_plot_png,
-			merge_and_prep.merged_adata_metadata_csv
+			merge_and_prep.merged_metadata_csv,
+			merge_and_prep.merged_and_processed_adata_object,
+			merge_and_prep.all_genes_csv,
+			merge_and_prep.hvg_genes_csv,
+			merge_and_prep.hvg_plot_png
 		],
 		[
-			integrate_data.clustered_adata_object,
 			integrate_data.umap_cluster_plots_png
+		],
+		[
+			export_final_artifacts.final_adata_object,
+			export_final_artifacts.final_metadata_csv
 		]
 	]) #!StringCoercion
 
@@ -124,15 +141,21 @@ workflow cohort_analysis {
 		File cohort_sample_list = write_cohort_sample_list.cohort_sample_list #!FileCoercion
 
 		# Merged and prepped AnnData object
-		File merged_adata_object = merge_and_prep.merged_adata_object #!FileCoercion
+		File merged_metadata_csv = merge_and_prep.merged_metadata_csv #!FileCoercion
+		File merged_and_processed_adata_object = merge_and_prep.merged_and_processed_adata_object #!FileCoercion
+		File all_genes_csv = merge_and_prep.all_genes_csv #!FileCoercion
+		File hvg_genes_csv = merge_and_prep.hvg_genes_csv #!FileCoercion
 		File hvg_plot_png = merge_and_prep.hvg_plot_png #!FileCoercion
-		File merged_adata_metadata_csv = merge_and_prep.merged_adata_metadata_csv #!FileCoercion
 
 		# Integrate data outputs
 		File integrated_adata_object = integrate_data.integrated_adata_object
 		File clustered_adata_object = integrate_data.clustered_adata_object
 		File umap_cluster_plots_png = integrate_data.umap_cluster_plots_png
 
+		# Export final outputs
+		File final_adata_object = export_final_artifacts.final_adata_object #!FileCoercion
+		File final_metadata_csv = export_final_artifacts.final_metadata_csv #!FileCoercion
+
 		Array[File] preprocess_manifest_tsvs = upload_preprocess_files.manifests #!FileCoercion
 		Array[File] process_to_adata_manifest_tsvs = upload_process_to_adata_files.manifests #!FileCoercion
 		Array[File] cohort_analysis_manifest_tsvs = upload_cohort_analysis_files.manifests #!FileCoercion
@@ -171,6 +194,7 @@ task merge_and_prep {
 
 		Int n_top_genes
 		Int n_comps
+		String batch_key
 
 		String raw_data_path
 		Array[Array[String]] workflow_info
@@ -189,23 +213,28 @@ task merge_and_prep {
 			--adata-paths-input ~{sep=' ' processed_adata_objects} \
 			--n-top-genes ~{n_top_genes} \
 			--n-comps ~{n_comps} \
+			--batch-key ~{batch_key} \
 			--output-prefix ~{cohort_id} \
-			--adata-output ~{cohort_id}.merged.h5ad
+			--adata-output ~{cohort_id}.merged_processed.h5ad
 
 		upload_outputs \
 			-b ~{billing_project} \
 			-d ~{raw_data_path} \
 			-i ~{write_tsv(workflow_info)} \
-			-o "~{cohort_id}.merged.h5ad" \
-			-o "~{cohort_id}.hvg_dispersion.png" \
-			-o "~{cohort_id}.merged_adata_metadata.csv"
+			-o "~{cohort_id}.merged_metadata.csv" \
+			-o "~{cohort_id}.merged_processed.h5ad" \
+			-o "~{cohort_id}.all_genes.csv" \
+			-o "~{cohort_id}.hvg_genes.csv" \
+			-o "~{cohort_id}.hvg_dispersion.png"
 
 	>>>
 
 	output {
-		String merged_adata_object = "~{raw_data_path}/~{cohort_id}.merged.h5ad"
+		String merged_metadata_csv = "~{raw_data_path}/~{cohort_id}.merged_metadata.csv"
+		String merged_and_processed_adata_object = "~{raw_data_path}/~{cohort_id}.merged_processed.h5ad"
+		String all_genes_csv = "~{raw_data_path}/~{cohort_id}.all_genes.csv"
+		String hvg_genes_csv = "~{raw_data_path}/~{cohort_id}.hvg_genes.csv"
 		String hvg_plot_png = "~{raw_data_path}/~{cohort_id}.hvg_dispersion.png"
-		String merged_adata_metadata_csv = "~{raw_data_path}/~{cohort_id}.merged_adata_metadata.csv"
 	}
 
 	runtime {
@@ -228,6 +257,70 @@ task merge_and_prep {
 		processed_adata_objects: {help: "An array of processed AnnData object to merge."}
 		n_top_genes: {help: "Number of highly-variable genes to keep. [3000]"}
 		n_comps: {help: "Number of principal components to compute. [30]"}
+		batch_key: {help: "Key in AnnData object for batch information. ['batch_id']"}
+		raw_data_path: {help: "Raw data bucket path for merged adata and HVG plot outputs; location of raw bucket to upload task outputs to (`<raw_data_bucket>/workflow_execution/cohort_analysis/<cohort_analysis_version>/<run_timestamp>`)."}
+		workflow_info: {help: "UTC timestamp, workflow name, workflow version, and GitHub release; stored in the file-level manifest and final manifest with all saved files."}
+		billing_project: {help: "Billing project to charge GCP costs."}
+		container_registry: {help: "Container registry where workflow Docker images are hosted."}
+		zones: {help: "Space-delimited set of GCP zones where compute will take place. ['us-central1-c us-central1-f']"}
+	}
+}
+
+task export_final_artifacts {
+	input {
+		String cohort_id
+		File clustered_adata_object
+
+		String raw_data_path
+		Array[Array[String]] workflow_info
+		String billing_project
+		String container_registry
+		String zones
+	}
+
+	Int mem_gb = ceil(size(clustered_adata_object, "GB") * 2 + 20)
+	Int disk_size = ceil(size(clustered_adata_object, "GB") * 2 + 50)
+
+	command <<<
+		set -euo pipefail
+
+		geomx_export_final_artifacts \
+			--cohort-id ~{cohort_id} \
+			--adata-input ~{clustered_adata_object} \
+			--adata-output ~{cohort_id}.final.h5ad
+
+		upload_outputs \
+			-b ~{billing_project} \
+			-d ~{raw_data_path} \
+			-i ~{write_tsv(workflow_info)} \
+			-o "~{cohort_id}.final.h5ad" \
+			-o "~{cohort_id}.final_metadata.csv"
+
+	>>>
+
+	output {
+		String final_adata_object = "~{raw_data_path}/~{cohort_id}.final.h5ad"
+		String final_metadata_csv = "~{raw_data_path}/~{cohort_id}.final_metadata.csv"
+	}
+
+	runtime {
+		docker: "~{container_registry}/spatial_py:1.0.0"
+		cpu: 2
+		memory: "~{mem_gb} GB"
+		disks: "local-disk ~{disk_size} HDD"
+		preemptible: 3
+		maxRetries: 2
+		bootDiskSizeGb: 15
+		zones: zones
+	}
+
+	meta {
+		description: "Export clustered AnnData object as final and grab the metadata."
+	}
+
+	parameter_meta {
+		cohort_id: {help: "Name of the cohort; used to name output files."}
+		clustered_adata_object: {help: "Clustered AnnData object."}
 		raw_data_path: {help: "Raw data bucket path for merged adata and HVG plot outputs; location of raw bucket to upload task outputs to (`<raw_data_bucket>/workflow_execution/cohort_analysis/<cohort_analysis_version>/<run_timestamp>`)."}
 		workflow_info: {help: "UTC timestamp, workflow name, workflow version, and GitHub release; stored in the file-level manifest and final manifest with all saved files."}
 		billing_project: {help: "Billing project to charge GCP costs."}
diff --git a/workflows/pmdbs_spatial_geomx/inputs.json b/workflows/pmdbs_spatial_geomx/inputs.json
index 68418e8..89a7f40 100644
--- a/workflows/pmdbs_spatial_geomx/inputs.json
+++ b/workflows/pmdbs_spatial_geomx/inputs.json
@@ -1,5 +1,4 @@
 {
-  "pmdbs_spatial_geomx_analysis.cohort_id": "String",
   "pmdbs_spatial_geomx_analysis.projects": "Array[WomCompositeType {\n slides -> Array[WomCompositeType {\n slide_id -> String\ngeomx_lab_annotation_xlsx -> File\nsamples -> Array[WomCompositeType {\n fastq_I1s -> Array[File]\nsample_id -> String\nfastq_R1s -> Array[File]+\nbatch -> String?\nfastq_I2s -> Array[File]\nfastq_R2s -> Array[File]+ \n}] \n}]\nproject_sample_metadata_csv -> File\nteam_id -> String\ndataset_id -> String\ngeomx_config_ini -> File\nstaging_data_buckets -> Array[String]\nrun_project_cohort_analysis -> Boolean\ndataset_doi_url -> String\nraw_data_bucket -> String \n}]",
   "pmdbs_spatial_geomx_analysis.geomxngs_config_pkc": "File",
   "pmdbs_spatial_geomx_analysis.min_segment_reads": "Int (optional, default = 1000)",
@@ -17,9 +16,6 @@
   "pmdbs_spatial_geomx_analysis.n_comps": "Int (optional, default = 30)",
   "pmdbs_spatial_geomx_analysis.batch_key": "String (optional, default = \"batch_id\")",
   "pmdbs_spatial_geomx_analysis.leiden_resolution": "Float (optional, default = 0.4)",
-  "pmdbs_spatial_geomx_analysis.run_cross_team_cohort_analysis": "Boolean (optional, default = false)",
-  "pmdbs_spatial_geomx_analysis.cohort_raw_data_bucket": "String",
-  "pmdbs_spatial_geomx_analysis.cohort_staging_data_buckets": "Array[String]",
   "pmdbs_spatial_geomx_analysis.container_registry": "String",
   "pmdbs_spatial_geomx_analysis.zones": "String (optional, default = \"us-central1-c us-central1-f\")"
 }
diff --git a/workflows/pmdbs_spatial_geomx/main.wdl b/workflows/pmdbs_spatial_geomx/main.wdl
index 12e7931..8fa6423 100644
--- a/workflows/pmdbs_spatial_geomx/main.wdl
+++ b/workflows/pmdbs_spatial_geomx/main.wdl
@@ -10,7 +10,6 @@ import "cohort_analysis/cohort_analysis.wdl" as CohortAnalysis
 
 workflow pmdbs_spatial_geomx_analysis {
 	input {
-		String cohort_id
 		Array[Project] projects
 
 		File geomxngs_config_pkc
@@ -36,11 +35,6 @@ workflow pmdbs_spatial_geomx_analysis {
 		String batch_key = "batch_id"
 		Float leiden_resolution = 0.4
 
-		# Cohort analysis
-		Boolean run_cross_team_cohort_analysis = false
-		String cohort_raw_data_bucket
-		Array[String] cohort_staging_data_buckets
-
 		String container_registry
 		String zones = "us-central1-c us-central1-f"
 	}
@@ -143,32 +137,6 @@ workflow pmdbs_spatial_geomx_analysis {
 		}
 	}
 
-	if (run_cross_team_cohort_analysis) {
-		String cohort_raw_data_path_prefix = "~{cohort_raw_data_bucket}/~{workflow_execution_path}/~{workflow_name}"
-
-		call CohortAnalysis.cohort_analysis as cross_team_cohort_analysis {
-			input:
-				cohort_id = cohort_id,
-				project_sample_ids = flatten(preprocess.project_sample_ids),
-				processed_adata_objects = flatten(process_to_adata.processed_adata_objects),
-				preprocessing_output_file_paths = flatten(preprocessing_output_file_paths),
-				processing_output_file_paths = flatten(processing_output_file_paths),
-				n_top_genes = n_top_genes,
-				n_comps = n_comps,
-				batch_key = batch_key,
-				leiden_resolution = leiden_resolution,
-				workflow_name = workflow_name,
-				workflow_version = workflow_version,
-				workflow_release = workflow_release,
-				run_timestamp = get_workflow_metadata.timestamp,
-				raw_data_path_prefix = cohort_raw_data_path_prefix,
-				staging_data_buckets = cohort_staging_data_buckets,
-				billing_project = get_workflow_metadata.billing_project,
-				container_registry = container_registry,
-				zones = zones
-		}
-	}
-
 	output {
 		# Sample-level outputs
 		## Sample list
@@ -197,30 +165,20 @@ workflow pmdbs_spatial_geomx_analysis {
 		Array[File?] project_cohort_sample_list = project_cohort_analysis.cohort_sample_list
 
 		## Merged, integrated and clustered adata objects, and plots
-		Array[File?] project_merged_adata_object = project_cohort_analysis.merged_adata_object
+		Array[File?] project_merged_metadata_csv = project_cohort_analysis.merged_metadata_csv
+		Array[File?] project_merged_and_processed_adata_object = project_cohort_analysis.merged_and_processed_adata_object
+		Array[File?] project_all_genes_csv = project_cohort_analysis.all_genes_csv
+		Array[File?] project_hvg_genes_csv = project_cohort_analysis.hvg_genes_csv
 		Array[File?] project_hvg_plot_png = project_cohort_analysis.hvg_plot_png
-		Array[File?] project_merged_adata_metadata_csv = project_cohort_analysis.merged_adata_metadata_csv
 		Array[File?] project_integrated_adata_object = project_cohort_analysis.integrated_adata_object
 		Array[File?] project_clustered_adata_object = project_cohort_analysis.clustered_adata_object
 		Array[File?] project_umap_cluster_plots_png = project_cohort_analysis.umap_cluster_plots_png
+		Array[File?] project_final_adata_object = project_cohort_analysis.final_adata_object
+		Array[File?] project_final_metadata_csv = project_cohort_analysis.final_metadata_csv
 
 		Array[Array[File]?] preprocess_manifests = project_cohort_analysis.preprocess_manifest_tsvs
 		Array[Array[File]?] process_to_adata_manifests = project_cohort_analysis.process_to_adata_manifest_tsvs
 		Array[Array[File]?] project_manifests = project_cohort_analysis.cohort_analysis_manifest_tsvs
-
-		# Cross-team cohort analysis outputs
-		## List of samples included in the cohort
-		File? cohort_cohort_sample_list = cross_team_cohort_analysis.cohort_sample_list
-
-		## Merged, integrated and clustered adata objects, and plots
-		File? cohort_merged_adata_object = cross_team_cohort_analysis.merged_adata_object
-		File? cohort_hvg_plot_png = cross_team_cohort_analysis.hvg_plot_png
-		File? cohort_merged_adata_metadata_csv = cross_team_cohort_analysis.merged_adata_metadata_csv
-		File? cohort_integrated_adata_object = cross_team_cohort_analysis.integrated_adata_object
-		File? cohort_clustered_adata_object = cross_team_cohort_analysis.clustered_adata_object
-		File? cohort_umap_cluster_plots_png = cross_team_cohort_analysis.umap_cluster_plots_png
-
-		Array[File]? cohort_manifests = cross_team_cohort_analysis.cohort_analysis_manifest_tsvs
 	}
 
 	meta {
@@ -228,7 +186,6 @@ workflow pmdbs_spatial_geomx_analysis {
 	}
 
 	parameter_meta {
-		cohort_id: {help: "Name of the cohort; used to name output files during cross-team downstream analysis."}
 		projects: {help: "The project ID, set of slides and their associated samples, reads and metadata, output bucket locations, and whether or not to run project-level downstream analysis."}
 		geomxngs_config_pkc: {help: "The GeoMx DSP configuration file to associate assay targets with GeoMx HybCode barcodes and Seq Code primers."}
 		min_segment_reads: {help: "Minimum number of segment reads. [1000]"}
@@ -246,9 +203,6 @@ workflow pmdbs_spatial_geomx_analysis {
 		n_comps: {help: "Number of principal components to compute. [30]"}
 		batch_key: {help: "Key in AnnData object for batch information. ['batch_id']"}
 		leiden_resolution: {help: "Value controlling the coarseness of the Leiden clustering. [0.4]"}
-		run_cross_team_cohort_analysis: {help: "Whether to run downstream harmonization steps on all samples across projects. If set to false, only preprocessing steps (GeoMxNGSPipeline and generating the initial adata object(s)) will run for samples. [false]"}
-		cohort_raw_data_bucket: {help: "Bucket to upload cross-team downstream intermediate files to."}
-		cohort_staging_data_buckets: {help: "Set of buckets to stage cross-team downstream analysis outputs in."}
 		container_registry: {help: "Container registry where workflow Docker images are hosted."}
 		zones: {help: "Space-delimited set of GCP zones where compute will take place."}
 	}
diff --git a/workflows/pmdbs_spatial_visium/cohort_analysis/cohort_analysis.wdl b/workflows/pmdbs_spatial_visium/cohort_analysis/cohort_analysis.wdl
index 02c7314..0cadcfd 100644
--- a/workflows/pmdbs_spatial_visium/cohort_analysis/cohort_analysis.wdl
+++ b/workflows/pmdbs_spatial_visium/cohort_analysis/cohort_analysis.wdl
@@ -78,6 +78,7 @@ workflow cohort_analysis {
 			normalize_target_sum = normalize_target_sum,
 			n_top_genes = n_top_genes,
 			n_comps = n_comps,
+			batch_key = batch_key,
 			raw_data_path = raw_data_path,
 			workflow_info = workflow_info,
 			billing_project = billing_project,
@@ -135,14 +136,16 @@ workflow cohort_analysis {
 			write_cohort_sample_list.cohort_sample_list
 		],
 		[
-			merge_and_plot_qc_metrics.merged_adata_object
+			merge_and_plot_qc_metrics.merged_adata_object,
+			merge_and_plot_qc_metrics.merged_metadata_csv
 		],
 		merge_and_plot_qc_metrics.qc_plots_png,
 		[
+			filter_and_normalize.all_genes_csv,
+			filter_and_normalize.hvg_genes_csv,
 			filter_and_normalize.hvg_plot_png
 		],
 		[
-			integrate_data.clustered_adata_object,
 			integrate_data.umap_cluster_plots_png
 		],
 		[
@@ -150,6 +153,7 @@ workflow cohort_analysis {
 		],
 		[	
 			spatial_statistics.final_adata_object,
+			spatial_statistics.final_metadata_csv,
 			spatial_statistics.moran_top_10_variable_genes_csv,
 			spatial_statistics.moran_top_4_variable_genes_spatial_scatter_plot_png
 		]
@@ -169,10 +173,13 @@ workflow cohort_analysis {
 
 		# Merged adata objects and QC plots
 		File merged_adata_object = merge_and_plot_qc_metrics.merged_adata_object #!FileCoercion
+		File merged_metadata_csv = merge_and_plot_qc_metrics.merged_metadata_csv #!FileCoercion
 		Array[File] qc_plots_png = merge_and_plot_qc_metrics.qc_plots_png #!FileCoercion
 
 		# Processed outputs
 		File processed_adata_object = filter_and_normalize.processed_adata_object
+		File all_genes_csv = filter_and_normalize.all_genes_csv #!FileCoercion
+		File hvg_genes_csv = filter_and_normalize.hvg_genes_csv #!FileCoercion
 		File hvg_plot_png = filter_and_normalize.hvg_plot_png #!FileCoercion
 
 		# Integrate data outputs
@@ -185,6 +192,7 @@ workflow cohort_analysis {
 
 		# Spatial statistics outputs
 		File final_adata_object = spatial_statistics.final_adata_object
+		File final_metadata_csv = spatial_statistics.final_metadata_csv
 		File moran_top_10_variable_genes_csv = spatial_statistics.moran_top_10_variable_genes_csv
 		File moran_top_4_variable_genes_spatial_scatter_plot_png = spatial_statistics.moran_top_4_variable_genes_spatial_scatter_plot_png
 
@@ -242,20 +250,22 @@ task merge_and_plot_qc_metrics {
 
 		visium_merge_and_plot_qc \
 			--adata-paths-input ~{sep=' ' preprocessed_adata_objects} \
-			--qc-plots-prefix ~{cohort_id} \
-			--merged-adata-output ~{cohort_id}.merged_adata_object.h5ad
+			--output-prefix ~{cohort_id} \
+			--merged-adata-output ~{cohort_id}.merged_cleaned_unfiltered.h5ad
 
 		upload_outputs \
 			-b ~{billing_project} \
 			-d ~{raw_data_path} \
 			-i ~{write_tsv(workflow_info)} \
-			-o "~{cohort_id}.merged_adata_object.h5ad" \
+			-o "~{cohort_id}.merged_cleaned_unfiltered.h5ad" \
+			-o "~{cohort_id}.merged_metadata.csv" \
 			-o "~{cohort_id}.qc_violin.png" \
 			-o "~{cohort_id}.qc_dist.png"
 	>>>
 
 	output {
-		String merged_adata_object = "~{raw_data_path}/~{cohort_id}.merged_adata_object.h5ad"
+		String merged_adata_object = "~{raw_data_path}/~{cohort_id}.merged_cleaned_unfiltered.h5ad"
+		String merged_metadata_csv = "~{raw_data_path}/~{cohort_id}.merged_metadata.csv"
 		Array[String] qc_plots_png = [
 			"~{raw_data_path}/~{cohort_id}.qc_violin.png",
 			"~{raw_data_path}/~{cohort_id}.qc_dist.png"
@@ -300,6 +310,7 @@ task filter_and_normalize {
 		Float normalize_target_sum
 		Int n_top_genes
 		Int n_comps
+		String batch_key
 
 		String raw_data_path
 		Array[Array[String]] workflow_info
@@ -323,18 +334,23 @@ task filter_and_normalize {
 			--target-sum ~{normalize_target_sum} \
 			--n-top-genes ~{n_top_genes} \
 			--n-comps ~{n_comps} \
-			--plots-prefix ~{cohort_id} \
+			--batch-key ~{batch_key} \
+			--output-prefix ~{cohort_id} \
 			--adata-output ~{cohort_id}.processed.h5ad
 
 		upload_outputs \
 			-b ~{billing_project} \
 			-d ~{raw_data_path} \
 			-i ~{write_tsv(workflow_info)} \
+			-o "~{cohort_id}.all_genes.csv" \
+			-o "~{cohort_id}.hvg_genes.csv" \
 			-o "~{cohort_id}.hvg_dispersion.png"
 	>>>
 
 	output {
 		File processed_adata_object = "~{cohort_id}.processed.h5ad"
+		String all_genes_csv = "~{raw_data_path}/~{cohort_id}.all_genes.csv"
+		String hvg_genes_csv = "~{raw_data_path}/~{cohort_id}.hvg_genes.csv"
 		String hvg_plot_png = "~{raw_data_path}/~{cohort_id}.hvg_dispersion.png"
 	}
 
@@ -363,6 +379,7 @@ task filter_and_normalize {
 		normalize_target_sum: {help: "The total count to which each cell's gene expression values will be normalized. [10000]"}
 		n_top_genes: {help: "Number of highly-variable genes to keep. [3000]"}
 		n_comps: {help: "Number of principal components to compute. [30]"}
+		batch_key: {help: "Key in AnnData object for batch information. ['batch_id']"}
 		raw_data_path: {help: "Raw data bucket path for processed adata and HVG plot outputs; location of raw bucket to upload task outputs to (`<raw_data_bucket>/workflow_execution/cohort_analysis/<cohort_analysis_version>/<run_timestamp>`)."}
 		workflow_info: {help: "UTC timestamp, workflow name, workflow version, and GitHub release; stored in the file-level manifest and final manifest with all saved files."}
 		billing_project: {help: "Billing project to charge GCP costs."}
diff --git a/workflows/pmdbs_spatial_visium/cohort_analysis/spatial_statistics/spatial_statistics.wdl b/workflows/pmdbs_spatial_visium/cohort_analysis/spatial_statistics/spatial_statistics.wdl
index f192e33..d5c8060 100644
--- a/workflows/pmdbs_spatial_visium/cohort_analysis/spatial_statistics/spatial_statistics.wdl
+++ b/workflows/pmdbs_spatial_visium/cohort_analysis/spatial_statistics/spatial_statistics.wdl
@@ -28,6 +28,7 @@ workflow spatial_statistics {
 	output {
 		# Moran’s I global spatial auto-correlation statistics
 		File final_adata_object = spatially_variable_gene_analysis.final_adata_object #!FileCoercion
+		File final_metadata_csv = spatially_variable_gene_analysis.final_metadata_csv #!FileCoercion
 		File moran_top_10_variable_genes_csv = spatially_variable_gene_analysis.moran_top_10_variable_genes_csv #!FileCoercion
 		File moran_top_4_variable_genes_spatial_scatter_plot_png = spatially_variable_gene_analysis.moran_top_4_variable_genes_spatial_scatter_plot_png #!FileCoercion
 	}
@@ -68,19 +69,21 @@ task spatially_variable_gene_analysis {
 		visium_spatially_variable_genes \
 			--cohort-id ~{cohort_id} \
 			--adata-input ~{clustered_adata_object} \
-			--adata-output ~{cohort_id}.final_adata_object.h5ad
+			--adata-output ~{cohort_id}.final.h5ad
 
 		upload_outputs \
 			-b ~{billing_project} \
 			-d ~{raw_data_path} \
 			-i ~{write_tsv(workflow_info)} \
-			-o "~{cohort_id}.final_adata_object.h5ad" \
+			-o "~{cohort_id}.final.h5ad" \
+			-o "~{cohort_id}.final_metadata.csv" \
 			-o "~{cohort_id}.moran_top_10_variable_genes.csv" \
 			-o "~{cohort_id}.moran_top_4_variable_genes_spatial_scatter.png"
 	>>>
 
 	output {
-		String final_adata_object = "~{raw_data_path}/~{cohort_id}.final_adata_object.h5ad"
+		String final_adata_object = "~{raw_data_path}/~{cohort_id}.final.h5ad"
+		String final_metadata_csv = "~{raw_data_path}/~{cohort_id}.final_metadata.csv"
 		String moran_top_10_variable_genes_csv = "~{raw_data_path}/~{cohort_id}.moran_top_10_variable_genes.csv"
 		String moran_top_4_variable_genes_spatial_scatter_plot_png = "~{raw_data_path}/~{cohort_id}.moran_top_4_variable_genes_spatial_scatter.png"
 	}
diff --git a/workflows/pmdbs_spatial_visium/inputs.json b/workflows/pmdbs_spatial_visium/inputs.json
index b1a17aa..d165e2c 100644
--- a/workflows/pmdbs_spatial_visium/inputs.json
+++ b/workflows/pmdbs_spatial_visium/inputs.json
@@ -1,5 +1,4 @@
 {
-  "pmdbs_spatial_visium_analysis.cohort_id": "String",
   "pmdbs_spatial_visium_analysis.projects": "Array[WomCompositeType {\n team_id -> String\ndataset_id -> String\nsamples -> Array[WomCompositeType {\n fastq_I1s -> Array[File]\nsample_id -> String\nfastq_R1s -> Array[File]+\nbatch -> String?\nvisium_capture_area -> String\nvisium_brightfield_image -> File\nfastq_I2s -> Array[File]\nvisium_slide_serial_number -> String\nfastq_R2s -> Array[File]+ \n}]\nstaging_data_buckets -> Array[String]\nrun_project_cohort_analysis -> Boolean\ndataset_doi_url -> String\nraw_data_bucket -> String \n}]",
   "pmdbs_spatial_visium_analysis.spaceranger_reference_data": "File",
   "pmdbs_spatial_visium_analysis.visium_probe_set_csv": "File? (optional)",
@@ -12,9 +11,6 @@
   "pmdbs_spatial_visium_analysis.n_comps": "Int (optional, default = 30)",
   "pmdbs_spatial_visium_analysis.batch_key": "String (optional, default = \"batch_id\")",
   "pmdbs_spatial_visium_analysis.leiden_resolution": "Float (optional, default = 0.4)",
-  "pmdbs_spatial_visium_analysis.run_cross_team_cohort_analysis": "Boolean (optional, default = false)",
-  "pmdbs_spatial_visium_analysis.cohort_raw_data_bucket": "String",
-  "pmdbs_spatial_visium_analysis.cohort_staging_data_buckets": "Array[String]",
   "pmdbs_spatial_visium_analysis.container_registry": "String",
   "pmdbs_spatial_visium_analysis.zones": "String (optional, default = \"us-central1-c us-central1-f\")"
 }
diff --git a/workflows/pmdbs_spatial_visium/main.wdl b/workflows/pmdbs_spatial_visium/main.wdl
index c9ffa94..8f8a6b0 100644
--- a/workflows/pmdbs_spatial_visium/main.wdl
+++ b/workflows/pmdbs_spatial_visium/main.wdl
@@ -9,7 +9,6 @@ import "cohort_analysis/cohort_analysis.wdl" as CohortAnalysis
 
 workflow pmdbs_spatial_visium_analysis {
 	input {
-		String cohort_id
 		Array[Project] projects
 
 		File spaceranger_reference_data
@@ -26,11 +25,6 @@ workflow pmdbs_spatial_visium_analysis {
 		String batch_key = "batch_id"
 		Float leiden_resolution = 0.4
 
-		# Cohort analysis
-		Boolean run_cross_team_cohort_analysis = false
-		String cohort_raw_data_bucket
-		Array[String] cohort_staging_data_buckets
-
 		String container_registry
 		String zones = "us-central1-c us-central1-f"
 	}
@@ -109,36 +103,6 @@ workflow pmdbs_spatial_visium_analysis {
 		}
 	}
 
-	if (run_cross_team_cohort_analysis) {
-		String cohort_raw_data_path_prefix = "~{cohort_raw_data_bucket}/~{workflow_execution_path}/~{workflow_name}"
-
-		call CohortAnalysis.cohort_analysis as cross_team_cohort_analysis {
-			input:
-				cohort_id = cohort_id,
-				project_sample_ids = flatten(preprocess.project_sample_ids),
-				preprocessed_adata_objects = flatten(preprocess.qc_adata_object),
-				preprocessing_output_file_paths = flatten(preprocessing_output_file_paths),
-				filter_cells_min_counts = filter_cells_min_counts,
-				filter_cells_min_genes = filter_cells_min_genes,
-				filter_genes_min_cells = filter_genes_min_cells,
-				filter_mt_max_percent = filter_mt_max_percent,
-				normalize_target_sum = normalize_target_sum,
-				n_top_genes = n_top_genes,
-				n_comps = n_comps,
-				batch_key = batch_key,
-				leiden_resolution = leiden_resolution,
-				workflow_name = workflow_name,
-				workflow_version = workflow_version,
-				workflow_release = workflow_release,
-				run_timestamp = get_workflow_metadata.timestamp,
-				raw_data_path_prefix = cohort_raw_data_path_prefix,
-				staging_data_buckets = cohort_staging_data_buckets,
-				billing_project = get_workflow_metadata.billing_project,
-				container_registry = container_registry,
-				zones = zones
-		}
-	}
-
 	output {
 		# Sample-level outputs
 		## Sample list
@@ -163,6 +127,9 @@ workflow pmdbs_spatial_visium_analysis {
 
 		# Merged, processed (filtered, normalized, dimensionality reduced), integrated, and clustered adata objects, and plots
 		Array[File?] project_merged_adata_object = project_cohort_analysis.merged_adata_object
+		Array[File?] project_merged_metadata_csv = project_cohort_analysis.merged_metadata_csv
+		Array[File?] project_all_genes_csv = project_cohort_analysis.all_genes_csv
+		Array[File?] project_hvg_genes_csv = project_cohort_analysis.hvg_genes_csv
 		Array[Array[File]?] project_qc_plots_png = project_cohort_analysis.qc_plots_png
 		Array[File?] project_processed_adata_object = project_cohort_analysis.processed_adata_object
 		Array[File?] project_hvg_plot_png = project_cohort_analysis.hvg_plot_png
@@ -175,34 +142,12 @@ workflow pmdbs_spatial_visium_analysis {
 
 		# Spatial statistics outputs
 		Array[File?] project_final_adata_object = project_cohort_analysis.final_adata_object
+		Array[File?] project_final_metadata_csv = project_cohort_analysis.final_metadata_csv
 		Array[File?] project_moran_top_10_variable_genes_csv = project_cohort_analysis.moran_top_10_variable_genes_csv
 		Array[File?] project_moran_top_4_variable_genes_spatial_scatter_plot_png = project_cohort_analysis.moran_top_4_variable_genes_spatial_scatter_plot_png
 
 		Array[Array[File]?] preprocess_manifests = project_cohort_analysis.preprocess_manifest_tsvs
 		Array[Array[File]?] project_manifests = project_cohort_analysis.cohort_analysis_manifest_tsvs
-
-		# Cross-team cohort analysis outputs
-		## List of samples included in the cohort
-		File? cohort_cohort_sample_list = cross_team_cohort_analysis.cohort_sample_list
-
-		# Merged, processed (filtered, normalized, dimensionality reduced), integrated, and clustered adata objects, and plots
-		File? cohort_merged_adata_object = cross_team_cohort_analysis.merged_adata_object
-		Array[File]? cohort_qc_plots_png = cross_team_cohort_analysis.qc_plots_png
-		File? cohort_processed_adata_object = cross_team_cohort_analysis.processed_adata_object
-		File? cohort_hvg_plot_png = cross_team_cohort_analysis.hvg_plot_png
-		File? cohort_integrated_adata_object = cross_team_cohort_analysis.integrated_adata_object
-		File? cohort_clustered_adata_object = cross_team_cohort_analysis.clustered_adata_object
-		File? cohort_umap_cluster_plots_png = cross_team_cohort_analysis.umap_cluster_plots_png
-
-		# Spatial plots
-		File? cohort_spatial_scatter_plot_png = cross_team_cohort_analysis.spatial_scatter_plot_png
-
-		# Spatial statistics outputs
-		File? cohort_final_adata_object = cross_team_cohort_analysis.final_adata_object
-		File? cohort_moran_top_10_variable_genes_csv = cross_team_cohort_analysis.moran_top_10_variable_genes_csv
-		File? cohort_moran_top_4_variable_genes_spatial_scatter_plot_png = cross_team_cohort_analysis.moran_top_4_variable_genes_spatial_scatter_plot_png
-
-		Array[File]? cohort_manifests = cross_team_cohort_analysis.cohort_analysis_manifest_tsvs
 	}
 
 	meta {
@@ -210,7 +155,6 @@ workflow pmdbs_spatial_visium_analysis {
 	}
 
 	parameter_meta {
-		cohort_id: {help: "Name of the cohort; used to name output files during cross-team downstream analysis."}
 		projects: {help: "The project ID, set of samples and their associated reads and metadata, output bucket locations, and whether or not to run project-level downstream analysis."}
 		spaceranger_reference_data: {help: "Space Ranger transcriptome reference data; see https://www.10xgenomics.com/support/software/space-ranger/downloads."}
 		visium_probe_set_csv: {help: "Visium probe-based assays target genes in Space Ranger transcriptome; see https://www.10xgenomics.com/support/software/space-ranger/downloads."}
@@ -223,9 +167,6 @@ workflow pmdbs_spatial_visium_analysis {
 		n_comps: {help: "Number of principal components to compute. [30]"}
 		batch_key: {help: "Key in AnnData object for batch information. ['batch_id']"}
 		leiden_resolution: {help: "Value controlling the coarseness of the Leiden clustering. [0.4]"}
-		run_cross_team_cohort_analysis: {help: "Whether to run downstream harmonization steps on all samples across projects. If set to false, only preprocessing steps (GeoMxNGSPipeline and generating the initial adata object(s)) will run for samples. [false]"}
-		cohort_raw_data_bucket: {help: "Bucket to upload cross-team downstream intermediate files to."}
-		cohort_staging_data_buckets: {help: "Set of buckets to stage cross-team downstream analysis outputs in."}
 		container_registry: {help: "Container registry where workflow Docker images are hosted."}
 		zones: {help: "Space-delimited set of GCP zones where compute will take place. ['us-central1-c us-central1-f']"}
 	}
diff --git a/workflows/pmdbs_spatial_visium/preprocess/preprocess.wdl b/workflows/pmdbs_spatial_visium/preprocess/preprocess.wdl
index a8d7341..f282c8b 100644
--- a/workflows/pmdbs_spatial_visium/preprocess/preprocess.wdl
+++ b/workflows/pmdbs_spatial_visium/preprocess/preprocess.wdl
@@ -39,7 +39,7 @@ workflow preprocess {
 
 	scatter (sample_object in samples) {
 		String spaceranger_count_output = "~{spaceranger_raw_data_path}/~{sample_object.sample_id}.raw_feature_bc_matrix.h5"
-		String counts_to_adata_output = "~{adata_raw_data_path}/~{sample_object.sample_id}.initial_adata_object.h5ad"
+		String counts_to_adata_output = "~{adata_raw_data_path}/~{sample_object.sample_id}.cleaned_unfiltered.h5ad"
 		String qc_output = "~{qc_raw_data_path}/~{sample_object.sample_id}.qc.h5ad"
 	}
 
@@ -108,7 +108,7 @@ workflow preprocess {
 		File tissue_positions_csv_output = select_first([spaceranger_count.tissue_positions_csv, spaceranger_tissue_positions_csv]) #!FileCoercion
 		File spatial_enrichment_csv_output = select_first([spaceranger_count.spatial_enrichment_csv, spaceranger_spatial_enrichment_csv]) #!FileCoercion
 
-		String counts_to_adata_object = "~{adata_raw_data_path}/~{sample.sample_id}.initial_adata_object.h5ad"
+		String counts_to_adata_object = "~{adata_raw_data_path}/~{sample.sample_id}.cleaned_unfiltered.h5ad"
 
 		if (counts_to_adata_complete == "false") {
 			call counts_to_adata {
@@ -250,7 +250,7 @@ task check_output_files_exist {
 
 	parameter_meta {
 		spaceranger_count_output_files: {help: "Spaceranger count output file to detect (`<sample>.raw_feature_bc_matrix.h5`)."}
-		counts_to_adata_output_files: {help: "Converted AnnData object output file to detect (`<sample>.initial_adata_object.h5ad`)."}
+		counts_to_adata_output_files: {help: "Converted AnnData object output file to detect (`<sample>.cleaned_unfiltered.h5ad`)."}
 		qc_output_files: {help: "QC'ed output file to detect (`<sample>.qc.h5ad`)."}
 		billing_project: {help: "Billing project to charge GCP costs."}
 		zones: {help: "Space-delimited set of GCP zones where compute will take place. ['us-central1-c us-central1-f']"}
@@ -449,17 +449,17 @@ task counts_to_adata {
 			--slide ~{visium_slide_serial_number} \
 			--area ~{visium_capture_area} \
 			--spaceranger-spatial-dir spatial_outputs \
-			--adata-output ~{sample_id}.initial_adata_object.h5ad
+			--adata-output ~{sample_id}.cleaned_unfiltered.h5ad
 
 		upload_outputs \
 			-b ~{billing_project} \
 			-d ~{raw_data_path} \
 			-i ~{write_tsv(workflow_info)} \
-			-o "~{sample_id}.initial_adata_object.h5ad"
+			-o "~{sample_id}.cleaned_unfiltered.h5ad"
 	>>>
 
 	output {
-		String initial_adata_object = "~{raw_data_path}/~{sample_id}.initial_adata_object.h5ad"
+		String initial_adata_object = "~{raw_data_path}/~{sample_id}.cleaned_unfiltered.h5ad"
 	}
 
 	runtime {