diff --git a/.github/workflows/run_cell-type-consensus.yml b/.github/workflows/run_cell-type-consensus.yml
index e3d110e04..ddd13b0b9 100644
--- a/.github/workflows/run_cell-type-consensus.yml
+++ b/.github/workflows/run_cell-type-consensus.yml
@@ -53,3 +53,4 @@ jobs:
         run: |
           cd ${MODULE_PATH}
           # run module script(s) here
+          ./assign-consensus-celltypes.sh
diff --git a/analyses/cell-type-consensus/README.md b/analyses/cell-type-consensus/README.md
index 3346815b9..9789083d4 100644
--- a/analyses/cell-type-consensus/README.md
+++ b/analyses/cell-type-consensus/README.md
@@ -3,7 +3,7 @@
 This module explores creating rules that can be used to identify a consensus cell type label.
 Specifically, the cell type annotations obtained from both `SingleR` and `CellAssign` will be used to create a single cell type label in an ontology aware manner.
 
-## Description
+## Creating a reference for consensus cell types 
 
 The goal of this module is to create a reference that can be used to define an ontology aware consensus cell type label for all cells across all ScPCA samples. 
 This module performs a series of steps to accomplish that goal: 
@@ -25,23 +25,70 @@ If that is the case all other LCA terms are removed and `hematopoietic precursor
   - When the LCA is `epithelial cell` and the annotation from `BlueprintEncodeData` is `Epithelial cells`, then `epithelial cell` is used as the consensus label. 
   - If the LCA is `bone cell`, `lining cell`, `blood cell`, `progenitor cell`, or `supporting cell`, no consensus label is defined. 
 
+See the [`scripts/README.md`](./scripts/README.md) for instructions on running the individual scripts used to generate the reference. 
 
-## Usage
+## Assigning consensus cell types for ScPCA samples
 
-See the [`scripts/README.md`](./scripts/README.md) for instructions on running the scripts in this module. 
+The `assign-consensus-celltypes.sh` script can be used to assign a consensus cell type for all samples in ScPCA. 
+This script outputs a single TSV file with cell type annotations for all cells in ScPCA (excluding cell line samples). 
+Cell type annotations assigned using `SingleR` with the `BlueprintEncodeData` reference and `CellAssign` using the `PanglaoDB` reference are included along side the assigned consensus cell type annotation and ontology identifier. 
 
-## Input files
+To run this script use the following command: 
 
-TBD
+```sh
+./assign-consensus-celltypes.sh
+```
 
-## Output files
+### Input files
 
-TBD
+
+The `assign-consensus-celltypes.sh` script requires the processed `SingleCellExperiment` objects (`_processed.rds`) for all ScPCA samples.
+These files were obtained using the `download-data.py` script:
+
+```sh
+# download SCE objects
+./download-data.py
+```
+
+This script also requires two reference files, `panglao-cell-type-ontologies.tsv` and `consensus-cell-type-reference.tsv`. 
+See [Creating a reference for consensus cell types](#creating-a-reference-for-consensus-cell-types) and the [README.md in the references directory](./references/README.md) to learn more about the content of these files. 
+
+### Output files
+
+Running the `assign-consensus-celltypes.sh` script will generate the following output files in `results`. 
+
+```
+results
+├── scpca-consensus-celltype-assignments.tsv
+├── original-celltype-assignments
+    ├── <library_id>_celltype-assignments.tsv
+    └── <library_id>_celltype-assignments.tsv
+```
+
+The `original-celltyp-assignments` folder contains a single TSV file for each library in ScPCA, except for libraries obtained from cell lines.
+These TSV files have the cell type annotations from running `SingleR` and `CellAssign` that can be found in the `colData` of the processed SCE objects. 
+
+The `scpca-consensus-celltype-assignments.tsv` file contains cell type annotations for all cells in all ScPCA samples with the following columns: 
+
+| | |
+| --- | --- | 
+| `project_id` | ScPCA project id |
+| `sample_id` | ScPCA sample id | 
+| `library_id` |  ScPCA library id |
+| `barcodes` | cell barcode |
+| `singler_celltype_ontology` | Cell type ontology term assigned by `SingleR` | 
+| `singler_celltype_annotation` | Name associated with cell type ontology term assigned by `SingleR`; this term is equivalent to the `label.main` term in the `BlueprintEncodeData` reference | 
+| `cellassign_celltype_annotation` | Cell type assigned by `CellAssign`; this term is the original term found in the `PanglaoDB` reference file | 
+| `panglao_ontology` | Cell type ontology term associated with the term found in `cellassign_celltype_annotation` column | 
+| `panglao_annotation` | Name associated with the cell type ontology term in `panglao_ontology` | 
+| `blueprint_annotation_fine` | Fine grained cell type annotation (`label.fine`) from `BlueprintEncodeData` associated with the `singler_celltype_ontology` term |
+| `consensus_ontology` | Cell type ontology term assigned as the consensus cell type | 
+| `consensus_annotation` | Name associated with the assigned consensus cell type in `consensus_ontology` | 
 
 ## Software requirements
 
-TBD
+This module uses `renv` to manage software dependencies. 
 
 ## Computational resources
 
-TBD
+This module does not require compute beyond what is generally available on a laptop. 
diff --git a/analyses/cell-type-consensus/assign-consensus-celltypes.sh b/analyses/cell-type-consensus/assign-consensus-celltypes.sh
new file mode 100755
index 000000000..83897d93b
--- /dev/null
+++ b/analyses/cell-type-consensus/assign-consensus-celltypes.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+
+# This script is used to create a single table with cell type assignments all cells from all ScPCA samples  
+# The existing cell type annotations from SingleR and CellAssign are saved to a TSV file for each sample 
+# Then all TSV files are combined into a single file and consensus cell types are assigned 
+
+# Usage: ./assign-consensus-celltypes.sh 
+
+
+set -euo pipefail
+
+# navigate to where script lives
+cd $(dirname "$0")
+#module_dir=$(pwd)
+
+data_dir="../../data/current"
+# path to save consensus results 
+scpca_consensus_assignments_file="results/scpca-consensus-celltype-assignments.tsv.gz"
+# directory to store all individual tsv files 
+celltype_tsv_dir="results/original-celltype-assignments" 
+mkdir -p ${celltype_tsv_dir}
+
+# define reference input files 
+panglao_ref_file="references/panglao-cell-type-ontologies.tsv"
+consensus_ref_file="references/consensus-cell-type-reference.tsv"
+
+# run script to export tsv file on all processed objects
+for sce_file in $data_dir/SCPCP*/SCPCS*/*_processed.rds; do
+
+    # define library ID
+    library_id=$(basename $sce_file | sed 's/_processed.rds$//')
+    
+    echo "Grabbing cell types for ${library_id}"
+    # get celltypes as tsv file 
+    Rscript scripts/03-save-coldata.R \
+      --sce_file $sce_file \
+      --output_file ${celltype_tsv_dir}/${library_id}_celltype-assignments.tsv
+
+done 
+
+echo "Combining TSVs and adding consensus labels"
+# run script to combine all tsv files and assign consensus cell types
+Rscript scripts/04-combine-celltype-tables.R \
+  --celltype_tsv_dir $celltype_tsv_dir \
+  --panglao_ref_file $panglao_ref_file \
+  --consensus_ref_file $consensus_ref_file \
+  --output_file $scpca_consensus_assignments_file
diff --git a/analyses/cell-type-consensus/scripts/03-save-coldata.R b/analyses/cell-type-consensus/scripts/03-save-coldata.R
new file mode 100644
index 000000000..84d431b11
--- /dev/null
+++ b/analyses/cell-type-consensus/scripts/03-save-coldata.R
@@ -0,0 +1,76 @@
+#!/usr/bin/env Rscript
+
+# This script is used to grab the colData from a SCE object and save it as a TSV file
+
+library(optparse)
+
+option_list <- list(
+  make_option(
+    opt_str = c("--sce_file"),
+    type = "character",
+    help = "Path to RDS file containing a processed SingleCellExperiment object from scpca-nf"
+  ),
+  make_option(
+    opt_str = c("--output_file"),
+    type = "character",
+    help = "Path to file where colData will be saved, must end in `.tsv`"
+  )
+)
+
+# Parse options
+opt <- parse_args(OptionParser(option_list = option_list))
+
+# Set up -----------------------------------------------------------------------
+
+# make sure input files exist
+stopifnot(
+  "sce file does not exist" = file.exists(opt$sce_file)
+)
+
+# load SCE
+suppressPackageStartupMessages({
+  library(SingleCellExperiment)
+})
+
+# Extract colData --------------------------------------------------------------
+
+# read in sce 
+sce <- readr::read_rds(opt$sce_file)
+
+# extract ids 
+library_id <- metadata(sce)$library_id
+# account for multiplexed libraries that have multiple samples 
+# for now just combine sample ids into a single string and don't worry about demultiplexing 
+sample_id <- metadata(sce)$sample_id |> 
+  paste0(collapse = ";")
+project_id <- metadata(sce)$project_id
+
+# check if cell line since cell lines don't have any cell type assignments 
+# account for having more than one sample and a list of sample types
+# all sample types should be the same theoretically 
+is_cell_line <- all(metadata(sce)$sample_type == "cell line")
+
+# only create and write table for non-cell line samples
+if(!is_cell_line){
+  
+  # get df with ids, barcodes, and cell type assignments
+  celltype_df <- colData(sce) |> 
+    as.data.frame() |> 
+    dplyr::mutate(
+      project_id = project_id,
+      sample_id = sample_id,
+      library_id = library_id
+    ) |> 
+    dplyr::select(
+      project_id,
+      sample_id,
+      library_id,
+      barcodes, 
+      contains("celltype") # get both singler and cellassign with ontology 
+    )
+  
+  # save tsv 
+  readr::write_tsv(celltype_df, opt$output_file)
+  
+}
+
diff --git a/analyses/cell-type-consensus/scripts/04-combine-celltype-tables.R b/analyses/cell-type-consensus/scripts/04-combine-celltype-tables.R
new file mode 100644
index 000000000..453f2d4a9
--- /dev/null
+++ b/analyses/cell-type-consensus/scripts/04-combine-celltype-tables.R
@@ -0,0 +1,108 @@
+#!/usr/bin/env Rscript
+
+# This script is used to combine all TSV files containing cell types into a single TSV file 
+# The output TSV file will include the following added columns: 
+# panglao_ontology: CL term assigned to panglao term
+# panglao_annotation: human readable value associated with the CL term for panglao term 
+# blueprint_annotation_fine: Fine-grained annotation from blueprint associated with singler_celltype_ontology 
+# consensus_annotation: human readable name associated with the consensus label 
+# consensus_ontology: CL ontology term for the consensus cell type 
+
+project_root <- rprojroot::find_root(rprojroot::has_dir(".github"))
+
+library(optparse)
+
+option_list <- list(
+  make_option(
+    opt_str = c("--celltype_tsv_dir"),
+    type = "character",
+    help = "Path to directory containing TSV files with cell type annotations from single samples.
+      All TSV files in this directory will be combined into a single file."
+  ),
+  make_option(
+    opt_str = c("--panglao_ref_file"),
+    default = file.path(project_root, "references", "panglao-cell-type-ontologies.tsv"),
+    type = "character", 
+    help = "Path to file with panglao assignments and associated cell ontology ids"
+  ),
+  make_option(
+    opt_str = c("--consensus_ref_file"),
+    default = file.path(project_root, "references", "consensus-cell-type-reference.tsv"),
+    type = "character",
+    help = "Path to file containing the reference for assigning consensus cell type labels"
+  ), 
+  make_option(
+    opt_str = c("--output_file"),
+    type = "character",
+    help = "Path to file where combined TSV file will be saved. 
+      File name must end in either `.tsv` or `.tsv.gz` to save a compressed TSV file"
+  )
+)
+
+# Parse options
+opt <- parse_args(OptionParser(option_list = option_list))
+
+# Prep ref files ---------------------------------------------------------------
+
+# make sure reference files exist 
+stopifnot(
+  "panglao reference file does not exist" = file.exists(opt$panglao_ref_file),
+  "cell type consensus reference file does not exist" = file.exists(opt$consensus_ref_file),
+  "output file must end in `.tsv` or `.tsv.gz`" = stringr::str_detect(opt$output_file, ".tsv|.tsv.gz")
+)
+
+# read in ref files 
+# change names for panglao ref to match what's in the consensus file 
+panglao_ref_df <- readr::read_tsv(opt$panglao_ref_file) |>
+  dplyr::rename(
+    panglao_ontology = ontology_id,
+    panglao_annotation = human_readable_value,
+    original_panglao_name = panglao_cell_type
+  )
+
+consensus_ref_df <- readr::read_tsv(opt$consensus_ref_file) |>
+  # select columns to use for joining and consensus assigmments 
+  dplyr::select(
+    panglao_ontology, 
+    original_panglao_name,
+    blueprint_ontology,
+    consensus_annotation,
+    consensus_ontology
+  )
+
+# grab singler ref from celldex
+blueprint_ref <- celldex::BlueprintEncodeData()
+
+# get ontologies and human readable name into data frame for blueprint
+# in scpca-nf we don't include the fine label so this lets us add it in 
+blueprint_df <- data.frame(
+  blueprint_ontology = blueprint_ref$label.ont,
+  blueprint_annotation_fine = blueprint_ref$label.fine
+) |>
+  unique() |> 
+  tidyr::drop_na()
+
+# get list of all TSV files 
+all_files <- list.files(path = opt$celltype_tsv_dir,
+                        pattern = "*.tsv", 
+                        full.names = TRUE) 
+
+# read in TSV files and combine into a single df 
+all_cells_df <- all_files |> 
+  purrr::map(readr::read_tsv) |> 
+  dplyr::bind_rows() |> 
+  # add columns for panglao ontology and consensus
+  # first add panglao ontology 
+  dplyr::left_join(panglao_ref_df, by = c("cellassign_celltype_annotation" = "original_panglao_name")) |>
+  # now add in all the blueprint columns
+  dplyr::left_join(blueprint_df, by = c("singler_celltype_ontology" = "blueprint_ontology")) |> 
+  # then add consensus labels
+  dplyr::left_join(consensus_ref_df, 
+                   by = c("singler_celltype_ontology" = "blueprint_ontology",
+                          "cellassign_celltype_annotation" = "original_panglao_name",
+                          "panglao_ontology")) |>
+  # use unknown for NA annotation but keep ontology ID as NA
+  dplyr::mutate(consensus_annotation = dplyr::if_else(is.na(consensus_annotation), "Unknown", consensus_annotation))
+
+# export file 
+readr::write_tsv(all_cells_df, opt$output_file)
diff --git a/analyses/cell-type-consensus/scripts/README.md b/analyses/cell-type-consensus/scripts/README.md
index e61b7e5cc..c30058410 100644
--- a/analyses/cell-type-consensus/scripts/README.md
+++ b/analyses/cell-type-consensus/scripts/README.md
@@ -13,3 +13,8 @@ Ontology terms and labels along with the `cell type` label from the reference fi
 3. `02-prepare-consensus-reference.R`: This script is used to create a table with all consensus cell types. 
 The output table will contain one row for each combination of cell types in `PanglaoDB` and `BlueprintEncodeData` from `celldex` where a consensus cell type was identified.  
 If the combination is not included in the reference file, then no consensus cell type is assigned and can be set to "Unknown". 
+
+4. `03-save-coldata.R`: This script is used to grab the cell type annotations from the `colData` of an individual processed SCE object and save the output to a TSV file. 
+
+5. `04-combine-celltype-tables.R`: This script is used to combine individual TSV files with cell type annotations (output by `03-save-coldata.R`) into a single TSV file. 
+The consensus cell type reference is used to assign consensus cell types to all cells in the combined data frame and saved in the output TSV file.