This repository and the associated manuscript are currently under review. Expect revisions and alterations to the code here to make it more streamlined, reusable, and address reviewer comments.
The TEA-seq manuscript is currently available on bioRxiv at:
https://www.biorxiv.org/content/10.1101/2020.09.04.283887v2
Citation information:
Elliott Swanson, Cara Lord, Julian Reading, Alexander T. Heubeck, Adam K. Savage, Richard Green, Xiao-jun Li, Troy R. Torgerson, Thomas F. Bumol, Lucas T. Graybuck, Peter J. Skene. TEA-seq: a trimodal assay for integrated single cell measurement of transcripts, epitopes, and chromatin accessibility (2020). bioRxiv 2020.09.04.283887; doi: https://doi.org/10.1101/2020.09.04.283887
Coming soon to eLife!
A detailed bench protocol for TEA-seq written by Elliott Swanson is available on protocols.io.
Data from Swanson, et al. will be available on GEO at Series GSE158013
The Rmarkdown Notebook at data/00_geo_data_retrieval.Rmd provides methods for quickly retrieving supplementary files from GEO.
See method-specific dataset descriptions below for details on specific GEO samples.
Raw (FASTQ) data from Swanson, et al. will be available on dbGaP at Study accession phs002316.v1.p1
Genome Builds
All samples used in Swanson, et al. were from human donors. We use GRCHg38/hg38 genome builds throughout our processing and analysis.
Transcriptome Builds and gene annotations
For analysis, we utilized transcriptome annotations from multiple sources:
- For preprocessing of scATAC-seq, we utilized ENSEMBLv93 annotations provided by the 10x Genomics 3' RNA-seq reference v3.1.0 (used in-house for RNA-seq pipelines). These annotations were used to assess FRITSS and for gene body counts in our preprocessing pipeline.
- For downstream analysis, we utilized the annotations stored in the BioConductor
BSgenome.Hsapiens.UCSC.hg38package, which is imported by ArchR for scATAC-seq analysis.
Computing Environment
Preprocessing and analysis scripts were generated and run on Linux/Unix-like platforms (Debian/Ubuntu) with R >= v3.6.3 and 4.0.2 .
Software Dependencies
These software tools are used in various parts of our analysis and processing scripts:
command-line tools
bcl2fastq
cellranger count >= v5.0.0
cellranger-atac count >= v1.0.0
cellranger-arc count >= v1.0.0
bedtools2
bowtie2
fastp
GATK >= v3.7
GNU Parallel
HTSlib and SAMtools
Language requirements
python >= 3.7
R >= 3.6.3
Github R packages
ArchR
Custom Seurat for 3-way WNN
scrattch.vis
Other R packages
See the file common/R_dependencies.R for additional dependencies from CRAN and BioConductor.
Reference Region Sets
For ATAC-seq data analysis, we made use of several reference hg38 region sets from multiple sources. Retrieval and assembly of these regions for use in downstream analysis are available in the R script reference/get_reference_datasets.R.
The references used for analysis are:
- ENSEMBLv93 Gene bodies, filtered for genes used in 10x Genomics reference
refdata-cellranger-GRCh38-3.0.0. - ENSEMBLv93 TSS regions (TSS +/- 2kb) filtered as for 1.
- scATAC-seq PBMC peaks from Lareau, et al. at GEO Accession GSE123577.
- ENCODE/Altius DNase-seq Index regions from Mouleman, et al. at ENCODE File ID ENCFF503GCK.
Barcode quantification was performed using the C program BarCounter developed by Elliott Swanson.
We utilize BarCounter for the processing of ADT counts for both the ICICLE-seq and TEA-seq analyses, below.
For convenience, a version of BarCounter is linked to this repository, and will be cloned along with other repository code.
The main repository for Barcounter releases is at https://github.com/AllenInstitute/BarCounter-release.
In its current implementation, BarCounter expects reads to conform to the cell and UMI barcode positions used by 10x Genomics for 3' scRNA-seq and the CellRanger Multiome RNA + ATAC kits. In addition, standard Illumina-like FASTQ naming conventions are required.
Sequencing Data
R1: 28 nt - First 16 nt are 10x Cell Barcodes; next 12 nt are UMIsR2: 15 nt (or longer) - first 15 nt are Antibody/ADI barcodes. Reads may be longer if co-sequenced with RNA/ATAC data
BarCounter requires both a cell barcode list and a list of ADT tags for counting antibody UMI abundance. The cell barcode list can be either the full list of available barcodes or a filtered list of barcodes that pass QC criteria (e.g. outs/filtered_feature_bc_matrix/barcodes.tsv from CellRanger outputs).
BarCounter has the following parameters:
-wpath to a barcode "whitelist".txtortxt.gzfile-tpath to an ADT "taglist".csvorcsv.gzfile-1path tosummary.csvfromcellranger-atac count-2aSampleSheet.csvfile, described below-opath to an output directory
Full barcode lists for -w are provided by 10x Genomics in the cellranger files.
They can be found in these locations relative to the base cellranger directory:
CellRanger ARC v1.0.0: cellranger-arc-1.0.0/lib/python/cellranger/barcodes/
CellRanger v3.1.0: cellranger-3.1.0/cellranger-cs/3.1.0/lib/python/cellranger/barcodes/
CellRanger v4.0.0: cellranger-4.0.0/lib/python/cellranger/barcodes/
CellRanger v5.0.0: cellranger-5.0.0/lib/python/cellranger/barcodes/
The ADT taglist for -t should be a 2-column .csv file without a header, for example:
CAGCCATTCATTAGG,CD10
GACAAGTGATCTGCA,CD11b
TACGCCTATAACTTG,CD11c
CTTCACTCTGTCAGG,CD123
GTGTGTTGTCCTATG,CD127
TCTCAGACCTCCGTA,CD14
Multiple R1 and R2 files can be supplied to the -1 and -2 arguments. These should be comma-separated without a space, e.g. for two lanes:
-1 S001_S1_L001_R1_001.fastq.gz,S001_S1_L002_R1_001.fastq.gz
-2 S001_S1_L001_R2_001.fastq.gz,S001_S1_L002_R2_001.fastq.gz
An example BarCounter run:
/shared/apps/barcounter \
-w /shared/apps/cellranger-arc-1.0.0/lib/python/cellranger/barcodes/737K-arc-v1.txt.gz \
-t X061_barcounter_taglist_18OCT2020.csv \
-1 /mnt/x060-multiome/fastq_downsampled/ADT_125M/X061-AP0C1W1-C_S1_L001_R1_001.fastq.gz \
-2 /mnt/x060-multiome/fastq_downsampled/ADT_125M/X061-AP0C1W1-C_S1_L001_R2_001.fastq.gz \
-o /mnt/x060-multiome/barcounter_downsampled/X061-AP0C1W1
The single-cell assay for transposase-accessible chromatin (scATAC-seq) is a method that utilizes the Tn5 transposase to insert short sequence tags into the genome - ideally without perturbing the native chromatin state, so that measured fragments reflect the true state of nuclear structure. In Swanson, et al., we compare nuclear isolation protocols and cell permeabilization to assess the quantitative and qualitative differences between scATAC-seq protocols, and to enable multimodal measurement of scATAC-seq with transcription and/or cell surface epitopes.
Sequencing Data
The scATAC-seq preprocessing and analysis pipelines are built around libraries with the 10x Genomics scATAC-seq read structure:
I1: 8 nt, i7 Well/sample IndexesI2: 16 nt, 10x Cell BarcodesR1: 50 nt, ATAC-seq fragment insertionR2: 50 nt, ATAC-seq fragment insertion
Because our samples all originate from human donors, raw data (FASTQ files) used for Swanson, et al. are in the process of submission to dbGAP for controlled access.
Processed data from scATAC-seq datasets used in Swanson, et al. will be available for download from these GEO Samples:
| Accession | PBMC Type | Perm/Nuc | Prep | Purification | Pur. Method | WellID |
|---|---|---|---|---|---|---|
| GSM4784064 | leukapheresis | Perm | 0.01% Dig. | none | none | B003-W7 |
| GSM4784065 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | B003-W8 |
| GSM4784066 | ficoll | Nuc | 1x 10xNIB | none | none | X024-W1 |
| GSM4784067 | ficoll | Nuc | 0.25x 10xNIB | none | none | X024-W2 |
| GSM4784068 | ficoll | Nuc | 0.1x 10xNIB | none | none | X024-W3 |
| GSM4784069 | ficoll | Nuc | 1x ANIB | none | none | X024-W4 |
| GSM4784070 | ficoll | Perm | 0.01% Dig. | none | none | X025-W1 |
| GSM4784071 | ficoll | Perm | 0.05% Dig. | none | none | X025-W2 |
| GSM4784072 | ficoll | Perm | 0.1% Dig. | none | none | X025-W3 |
| GSM4784073 | ficoll | Perm | 0.2% Dig. | none | none | X025-W4 |
| GSM4784074 | ficoll | Perm | 0.01% Dig. | none | none | X027-W1 |
| GSM4784075 | ficoll | Perm | 0.01% Dig. | FACS | D/D-Neu. | X027-W3 |
| GSM4784076 | ficoll | Perm | 0.01% Dig. | Mag. Bead | Negative | X032-W1 |
| GSM4784077 | ficoll | Perm | 0.01% Dig. | Mag. Bead | anti-CD15 | X032-W2 |
| GSM4784078 | leukapheresis | Perm | 0.01% Dig. | Mag. Bead | Negative | X032-W3 |
| GSM4784079 | leukapheresis | Perm | 0.01% Dig. | Mag. Bead | anti-CD15 | X032-W4 |
| GSM4784080 | ficoll | Nuc | 1x 10xNIB | none | none | X041-W1 |
| GSM4784081 | ficoll | Nuc | 1x 10xNIB | FACS | D/D | X041-W2 |
| GSM4784082 | ficoll | Nuc | 1x 10xNIB | FACS | D/D-Neu. | X041-W3 |
| GSM4784083 | ficoll | Perm | 0.01% Dig. | none | none | X041-W4 |
| GSM4784084 | ficoll | Perm | 0.01% Dig. | FACS | D/D | X041-W5 |
| GSM4784085 | ficoll | Perm | 0.01% Dig. | FACS | D/D-Neu. | X041-W6 |
1. FASTQ Downsampling
To perform comparisons between wells, we downsampled the raw sequenced FASTQ reads to either 125 million read pairs or 200 million, based on the number of available reads in the compared samples.
Downsampling was performed simply using head, as the reads in FASTQ files are not sorted. Note that FASTQ files have 4 lines per read, so the number of reads to select must be 4 * , i.e. 500,000,000 lines for 125 million reads.
Parallelized code used for these downsampling steps is available in scatac_preprocessing/00_fastq_downsampling.sh.
2. cellranger-atac count
After downsampling, reads were aligned using cellranger-atac count with default parameters for each well.
3. Results processing
After cellranger-atac count alignment, we performed custom window counts and reference region counts for each cell barcode using a paralellized shell script built around bedtools2. This script uses the singlecell.csv and fragments.tsv.gz files generated by cellranger-atac count as inputs, and generates a variety of outputs for downstreat processing. The script is available in scatac_preprocessing/01_atac_preprocessing.sh.
This script has the following parameters:
-bPath to bedtools binary location-gGenome (hg19 or hg38)-iInput fragments.tsv.gz-jN Parallel Jobs-oOutput Directory-sInput singlecell.csv-tTemporary Directory-wWell ID
For example:
bash 01_atac_preprocessing.sh \
-b bedtools \
-i X041-AP0C1W6/outs/fragments.tsv.gz \
-g hg38 \
-j 30 \
-o X041-AP0C1W6/preprocessed \
-s X041-AP0C1W6/outs/singlecell.csv \
-t preprocessing_temp \
-w X041-AP0C1W6
It outputs the following files:
- Altius Index sparse matrix: [WellID]_altius_sparse_matrix.tsv.gz
- Reference Peaks sparse matrix: [WellID]_peaks_sparse_matrix.tsv.gz
- TSS Region sparse matrix: [WellID]_tss_sparse_matrix.tsv.gz
- Gene Bodies sparse matrix: [WellID]_gene_bodies_sparse_matrix.tsv.gz
- Altius Index total counts: [WellID]_altius_total_counts.tsv.gz
- Reference Peaks total counts: [WellID]_peaks_total_counts.tsv.gz
- TSS Region total counts: [WellID]_tss_total_counts.tsv.gz
- Gene Bodies total counts: [WellID]_gene_bodies_total_counts.tsv.gz
- 5kb Window counts: [WellID]_window_5k_counts.tsv.gz
- 20kb Window counts: [WellID]_window_20k_counts.tsv.gz
- 100kb Window counts: [WellID]_window_100k_counts.tsv.gz
- UMI count frequencies (saturation curve): [WellID]_saturation_curve.tsv.gz
- Fragment width counts: [WellID]_fragment_widths.tsv.gz
4. Custom QC filtering and reporting
With the results from preprocessing, we perform QC analysis and barcode filtering using and Rmarkdown file, which generates a report along with output files. The script at scatac_preprocessing/02_run_tenx_atac_qc.R is a parameterized wrapper around the markdown file at scatac_preprocessing/rmarkdown/tenx_atac_qc.Rmd.
The wrapper has the following parameters:
-ppath to preprocessed directory from the previous step-spath tosinglecell.csvfromcellranger-atac count-upath tosummary.csvfromcellranger-atac count-kaSampleSheet.csvfile, described below-wWell ID-gGenome (hg38)-doutput files directory-opath for the output HTML report file
For example:
mkdir X041-AP0C1W6/atac_qc/
Rscript 02_run_tenx_atac_qc.R \
-p X041-AP0C1W6/preprocessed \
-s X041-AP0C1W6/outs/singlecell.csv \
-u X041-AP0C1W6/outs/summary.csv \
-k X041_SampleSheet.csv \
-w X041-AP0C1W6 \
-g hg38 \
-d X041-AP0C1W6/atac_qc/ \
-d X041-AP0C1W6/X041-AP0C1W6_qc_report.html
The SampleSheet.csv file provides the relationship between Well IDs and Sample Names that are used for outputs. An example is shown below:
SampleID,BatchID,WellID,PoolID
nuc-none,X041,X041-AP0C1W1,X041-P0
nuc-dd,X041,X041-AP0C1W2,X041-P0
nuc-ddn,X041,X041-AP0C1W3,X041-P0
perm-none,X041,X041-AP0C1W4,X041-P0
perm-dd,X041,X041-AP0C1W5,X041-P0
perm-ddn,X041,X041-AP0C1W6,X041-P0
The script generates the following outputs in addition to the QC report:
- Altius Index .h5 file: [BatchID]_[SampleID]_altius.h5
- Reference Peak .h5 file: [BatchID]_[SampleID]_peaks.h5
- Gene TSS .h5 file: [BatchID]_[SampleID]_tss.h5
- Gene Bodies .h5 file: [BatchID]_[SampleID]_gene_bodies.h5
- 5kb Window .h5 file: [BatchID]_[SampleID]_window_5k.h5
- 20kb Window .h5 file: [BatchID]_[SampleID]_window_20k.h5
- 100kb Window .h5 file: [BatchID]_[SampleID]_window_100k.h5
- All barcode metadata: [BatchID]_[SampleID]_all_metadata.csv.gz
- QC filtered barcode metadata: [BatchID]_[SampleID]_filtered_metadata.csv.gz
- Saturation projection: [BatchID]_[SampleID]_saturation_projection.csv.gz
- Fragment width summary: [BatchID]_[SampleID]_fragment_width_summary.csv.gz
- JSON file: [BatchID]_[SampleID]_atac_qc_metrics.json
5. Fragment filtering
After QC analysis, we filter the fragments.tsv.gz file to retain only cell barcodes that pass QC. This is performed using the script at scatac_preprocessing/03_filter_fragments.sh.
There are 6 parameters for this script:
-i: fragments.tsv.gz file from cellranger-atac outs/-m: filtered_metadata.csv.gz file generated in the previous step-j: N Parallel Jobs-o: Path to an output directory-t: Path to a temporary directory-s: The sample name, e.g. [BatchID]_[SampleID]
For example:
bash 03_filter_fragments.sh \
-i X041-AP0C1W6/outs/fragments.tsv.gz \
-m X041-AP0C1W6/atac_qc/X041_perm-ddn_filtered_metadata.csv.gz \
-j 30 \
-o X041-AP0C1W6/atac_qc \
-t filtering_temp \
-s X041_perm-ddn
6. Cell type labeling
Once filtered, the fragments file can be used as an input for ArchR-based cell type label transfer. As for step 4, this is implemented using a paramaterizing R script, scatac_preprocessing/04_run_archr_atac_analysis.R, and an Rmarkdown report, scatac_preprocessing/rmarkdown/tenx_atac_archr_analysis.Rmd.
The wrapper requires these parameters:
-f: The filtered_fragments.tsv.gz file from the previous step-s: The sample name, e.g. [BatchID]_[SampleID]-g: The genome (hg38)-t: The number of threads to use in parallel-d: Path to an output directory-o: Path for the output HTML report file
For example:
mkdir X041-AP0C1W6/archr
Rscript 04_run_archr_atac_analysis.R \
-f X041-AP0C1W6/atac_qc/X041_perm-ddn_filtered_fragments.tsv.gz \
-s X041_perm-ddn \
-g hg38 \
-t 8 \
-d X041-AP0C1W6/archr \
-o X041-AP0C1W6/X041_perm-ddn_labeling_report.html
Figure 1
scatac_analysis/Figure-1_analysis.Rmd
Figure 1 - Figure Supplement 2
scatac_analysis/Figure-1-FS-2_analysis.Rmd
Figure 1 - Figure supplement 5
scatac_analysis/Figure-1-FS-5_analysis.Rmd
Figure 2
scatac_analysis/Figure-2_analysis.Rmd
Table 1
scatac_analysis/Table-1_analysis.Rmd
Integrated cellular indexing of chromatin landscapes and epitopes (ICICLE-seq) is a proof-of-concept method for the co-capture of scATAC-seq data and cell surface epitopes, intended to be analogous to cellular indexing of transcription and epitopes (CITE-seq). This assay consists of barcoded surface antibody tagging (as in CITE-seq) on permeabilized single cells treated with custom Tn5 complexes that have polyadenylated tag sequences for capture on the 10x 3' scRNA-seq kit.
Sequencing Data
ICICLE-seq data consists of two sequencing libraries from each well - one for the ATAC data, and the other of ADT data. These can be sequenced separately or together. If together, there will be excess bases at the 3' end of the ADT library R2 read.
ATAC data:
R1: 28 nt, 10x Cell Barcodes and UMIsI2: 8 nt, i7 Well/Method IndexesR2: 100 nt, ATAC-seq fragment insertion
ADT data:
R1: 28 nt, 10x Cell Barcodes and UMIsI2: 8 nt, i7 Well/Method IndexesR2: 100 nt, the first 15nt of which are the ADT epitope barcodes.
Because our samples all originate from human donors, raw data (FASTQ files) used for Swanson, et al. are in the process of submission to dbGAP for controlled access.
Processed data from ICICLE-seq datasets used in Swanson, et al. will be available for download from these GEO Samples:
| Accession | PBMC Type | Perm/Nuc | Prep | Purification | Pur. Method | WellID |
|---|---|---|---|---|---|---|
| GSM4784086 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X044-W1 |
| GSM4784087 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X044-W2 |
| GSM4784088 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X044-W3 |
icicle_preprocessing/ICICLE-seq_preprocessing.py
Figure 3
icicle_analysis/Figure-3_analysis.Rmd
Figure 3 - Figure Supplement 2
also in icicle_analysis/Figure-3_analysis.Rmd
TEA-seq is a 3-modality single cell assay that simultaneously measures transcription (using 3' scRNA-seq), epitopes (using oligo-tagged antibodies), and accessibility (using scATAC-seq) on the 10x Genomics Multiome RNA + ATAC kit.
Sequencing Data
TEA-seq data consists of three sequencing libraries from each well - one for RNA-seq data, one for ATAC data, and the third for ADT data. All 3 can be sequenced simultaneously with these read lengths:
R1: 50 ntI1: 10 ntI2: 16 ntR2: 90 nt
If working with a sequencing vendor, you may need to demultiplex these samples yourself using bcl2fastq to convert to FASTQ files with appropriate read lengths (see teaseq_preprocessing/00_teaseq_bcl2fastq.sh).
After dumultiplexing, reads for each assay will be as follows:
ADT data:
R1: 28 nt, 10x Cell Barcodes and UMIsI1: 8 nt, i7 Well/Method IndexesR2: 90 nt, the first 15nt of which are the ADT epitope barcodes.
ATAC data:
R1: 50 nt, ATAC-seq fragment insertionI1: 8 nt, i7 Well/Method IndexesI2: 16 nt, 10x Cell BarcodesR2: 100 nt, ATAC-seq fragment insertion
RNA data:
R1: 28 nt, 10x Cell Barcodes and UMIsI1: 8 nt, i7 Well/Method IndexesR2: 90 nt, cDNA fragment sequence
Because our samples all originate from human donors, raw data (FASTQ files) used for Swanson, et al. are in the process of submission to dbGAP for controlled access.
Processed data from TEA-seq datasets used in Swanson, et al. will be available for download from these GEO Samples:
| Accession | PBMC Type | Perm/Nuc | Prep | Purification | Pur. Method | WellID |
|---|---|---|---|---|---|---|
| GSM4949911 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X061-AP0C1W1 |
| GSM5123951 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X066-MP0C1W3 |
| GSM5123952 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X066-MP0C1W4 |
| GSM5123953 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X066-MP0C1W5 |
| GSM5123954 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X066-MP0C1W6 |
teaseq_preprocessing/TEA-seq_Joint_Flowcell_bcl2fastq.sh
cellranger-arc count
BarCounter
ATAC QC
Add RNA metadata
Figure 4
teaseq_analysis/Figure-4_analysis.Rmd
Figure 4 - Figure Supplement 1
also in teaseq_analysis/Figure-4_analysis.Rmd
Figure 4 - Figure Supplement 2
also in teaseq_analysis/Figure-4_analysis.Rmd
Integrated cellular indexing of chromatin landscapes and epitopes (ICICLE-seq) is a proof-of-concept method for the co-capture of scATAC-seq data and cell surface epitopes, intended to be analogous to cellular indexing of transcription and epitopes (CITE-seq). This assay consists of barcoded surface antibody tagging (as in CITE-seq) on permeabilized single cells treated with custom Tn5 complexes that have polyadenylated tag sequences for capture on the 10x 3' scRNA-seq kit.
Sequencing Data
10x Multiome ATAC + Gene Expression data consists of two sequencing libraries from each well - one for the ATAC data, and the other of gene expression data. These are usually sequenced on separate flowcells due to the different sequence length requirements for the two library types.
ATAC data:
R1: 50 nt, ATAC-seq fragment insertionI1: 8 nt, i7 Well/Method IndexesI2: 16 nt, 10x Cell BarcodesR2: 100 nt, ATAC-seq fragment insertion
Gene expression data:
R1: 28 nt, 10x Cell Barcodes and UMIsI2: 8 nt, i7 Well/Method IndexesR2: 100 nt, 3' transcript sequence
Because our samples all originate from human donors, raw data (FASTQ files) used for Swanson, et al. are in the process of submission to dbGAP for controlled access.
Processed data from 10x Multiome ATAC + Gene Expression datasets used in Swanson, et al. will be available for download from these GEO Samples:
| Accession | PBMC Type | Perm/Nuc | Prep | Purification | Pur. Method | WellID |
|---|---|---|---|---|---|---|
| GSM5123949 | leukapheresis | Nuc | 10x NIB | FACS | D/D-Neu. | X066-W1 |
| GSM5123950 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. | X066-W2 |
To Do
To Do
Cellular indexing of transcription and epitopes (CITE-seq) is an assay for co-measurement of single-cell transcriptomes and cell surface epitopes on the 10x Genomics 3' RNA-seq kit.
Sequencing Data
CITE-seq data consists of two sequencing libraries from each well - one for the antibody-derived tags (ADTs) and the other of gene expression data. These are sometimes sequenced together.
ADT data:
R1: 28 nt, 10x Cell Barcodes and UMIsI1: 8 nt, i7 Well/Method IndexesR2: 90 nt, the first 15nt of which are the ADT epitope barcodes.
Gene expression data:
R1: 28 nt, 10x Cell Barcodes and UMIsI2: 8 nt, i7 Well/Method IndexesR2: 90 nt, 3' transcript sequence
Because our samples all originate from human donors, raw data (FASTQ files) used for Swanson, et al. are in the process of submission to dbGAP for controlled access.
Processed data from CITE-seq dataset used in Swanson, et al. will be available for download from this GEO Sample:
| Accession | PBMC Type | Perm/Nuc | Prep | Purification | Pur. Method | WellID |
|---|---|---|---|---|---|---|
| GSM5123955 | leukapheresis | Whole Cell | NA | FACS | D/D-Neu. | X066-RW1 |
To Do
To Do
For convenience, we list all datasets for all methods in Swanson, et al. below:
| Method | Accession | PBMC Type | Perm/Nuc | Prep | Purification | Pur. Method |
|---|---|---|---|---|---|---|
| scATAC-seq | GSM4784064 | leukapheresis | Perm | 0.01% Dig. | none | none |
| scATAC-seq | GSM4784065 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| scATAC-seq | GSM4784066 | ficoll | Nuc | 1x 10xNIB | none | none |
| scATAC-seq | GSM4784067 | ficoll | Nuc | 0.25x 10xNIB | none | none |
| scATAC-seq | GSM4784068 | ficoll | Nuc | 0.1x 10xNIB | none | none |
| scATAC-seq | GSM4784069 | ficoll | Nuc | 1x ANIB | none | none |
| scATAC-seq | GSM4784070 | ficoll | Perm | 0.01% Dig. | none | none |
| scATAC-seq | GSM4784071 | ficoll | Perm | 0.05% Dig. | none | none |
| scATAC-seq | GSM4784072 | ficoll | Perm | 0.1% Dig. | none | none |
| scATAC-seq | GSM4784073 | ficoll | Perm | 0.2% Dig. | none | none |
| scATAC-seq | GSM4784074 | ficoll | Perm | 0.01% Dig. | none | none |
| scATAC-seq | GSM4784075 | ficoll | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| scATAC-seq | GSM4784076 | ficoll | Perm | 0.01% Dig. | Mag. Bead | Negative |
| scATAC-seq | GSM4784077 | ficoll | Perm | 0.01% Dig. | Mag. Bead | anti-CD15 |
| scATAC-seq | GSM4784078 | leukapheresis | Perm | 0.01% Dig. | Mag. Bead | Negative |
| scATAC-seq | GSM4784079 | leukapheresis | Perm | 0.01% Dig. | Mag. Bead | anti-CD15 |
| scATAC-seq | GSM4784080 | ficoll | Nuc | 1x 10xNIB | none | none |
| scATAC-seq | GSM4784081 | ficoll | Nuc | 1x 10xNIB | FACS | D/D |
| scATAC-seq | GSM4784082 | ficoll | Nuc | 1x 10xNIB | FACS | D/D-Neu. |
| scATAC-seq | GSM4784083 | ficoll | Perm | 0.01% Dig. | none | none |
| scATAC-seq | GSM4784084 | ficoll | Perm | 0.01% Dig. | FACS | D/D |
| scATAC-seq | GSM4784085 | ficoll | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| ICICLE-seq | GSM4949911 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| ICICLE-seq | GSM5123951 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| ICICLE-seq | GSM5123952 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| ICICLE-seq | GSM5123953 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| ICICLE-seq | GSM5123954 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| TEA-seq | GSM4949911 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| TEA-seq | GSM5123951 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| TEA-seq | GSM5123952 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| TEA-seq | GSM5123953 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| TEA-seq | GSM5123954 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| Multiome | GSM5123949 | leukapheresis | Nuc | 10x NIB | FACS | D/D-Neu. |
| Multiome | GSM5123950 | leukapheresis | Perm | 0.01% Dig. | FACS | D/D-Neu. |
| CITE-seq | GSM5123955 | leukapheresis | Whole Cell | NA | FACS | D/D-Neu. |
The license for this package is available on Github in the file LICENSE in this repository
We are not currently supporting this code, but simply releasing it to the community AS IS but are not able to provide any guarantees of support. The community is welcome to submit issues, but you should not expect an active response.
If you contribute code to this repository through pull requests or other mechanisms, you are subject to the Allen Institute Contribution Agreement, which is available in the file CONTRIBUTING in this repository