This package offers utilities that enable fast and accurate pre-processing of Feature Barcoding experiments, a common datatype in single-cell genomics. In Feature Barcoding assays, cellular data are recorded as short DNA sequences using procedures adapted from single-cell RNA-seq.
The kite ("kallisto indexing and tag extraction") package is used to prepare input files for Feature Barcoding experiments prior to running the kallisto | bustools scRNA-seq pipeline. Starting with a Python dictionary of Feature Barcode names and Feature Barcode sequences, the function kite_mismatch_maps produces a "mismatch map" and outputs "mismatch" fasta and "mismatch" transcript-to-gene (t2g) files. The mismatch files, containing the Feature Barcode sequences and their Hamming distance = 1 mismatches, are used to run kallisto | bustools on Feature Barcoding data.
The mismatch fasta file is used by kallisto index with a k-mer length -k set to the length of the Feature Barcode.
The mismatch t2g file is used by bustools count to generate a Features x Cells matrix.
In this way, kallisto | bustools will effectively search the sequencing data for the Feature Barcodes and their Hamming distance = 1 neighbors. We find that for Feature Barcodes of moderate length (6-15bp) pre-processing is remarkably fast and the results equivalent to or better than those from traditional alignment.
The docs directory contains an example Python notebook that uses kite, kallisto, bustools, and ScanPy to perform a complete Feature Barcode analysis, and the results are compared with CellRanger.
Clone the GitHub repo and use pip to install the kite package
!mkdir ./FeatureBarcoding
!cd ./FeatureBarcoding
!git clone https://github.com/pachterlab/kite
!pip install -e ./kite
Feature Barcode pre-processing requires kite as well as up-to-date versions of kallisto and bustools
kite 0.0.1
kallisto 0.46 or higher
bustools 0.39.0 or higher
For downstream analysis, we use ScanPy (Wolf et. al, Genome Biology 2018) and the LeidenAlg clustering package (Traag et. al, arXiv 2018).
This wrapper function is the easiest way to use kite. "Mismatch" t2g and fasta files are saved and can be used by kallisto | bustools to complete pre-processing(see below and Vignettes).
FeatureDict: a Python dictionary with Feature Barcode name : Feature Barcode sequence as key:value pairs
mismatch_t2g_path: filepath for a new "mismatch" t2g file
mismatch_fasta_path: filepath for a new "mismatch" fasta file
returns mismatch t2g and fasta files to the specified directories
This function returns all sample tags and and their single base mismatches (hamming distance 1) as an OrderedDict object. The number of elements in the object is (k=the length of the Feature Barocdes)(3=altnerative base pairs for each base)(N=number of Feature Barocdes) + (N=number of Feature Barcode sequences). For the 10x example dataset, 17 Feature Barcodes of length k=15 are used. These yield 15x3x17+17=782 entries in the OrderedDict object.
FeatureDict: a Python dictionary with Feature Barcode name : Feature Barcode sequence as key:value pairs
returns an OrderedDict object including correct and mismatch sequences for each whitelist Feature Barcode
Saves the OrderedDict generated by make_mismatch_map to file in fasta and t2g formats for building the kallisto index and running bustools count, respectively.
tag_map: OrderedDict object produced by make_mismatch_map
mismatch_t2g_path: filepath for a new "mismatch" t2g file
mismatch_fasta_path: filepath for a new "mismatch" fasta file
returns mismatch t2g and fasta files to the specified directories
To avoid potential pseudoalignment errors arising from inverted repeats, kallisto only accepts odd values for the k-mer >length
-k. If your Feature Barcodes have an even length, just add an appropriate constant base on one side and follow the >protocol as suggested. Adding constant bases in this way increases specificity and may be useful for experiments with low >sequencing quality or very short Feature Barcodes.
The docs folder contains a complete analysis (10x_kiteVignette.ipynb) for a 10x dataset collected on ~730 peripheral blood mononuclear cells (PBMCs) labeled with 17 unique Feature Barcoded antibodies. The dataset can be found here.
The following is an abbreviated walk-through showing key steps.
Navigate to a new directory and download 10x data with wget. Unzip files as shown.
The 10x 3M-february 2018 cell barcode whitelist is included with the kite GitHub repo here. Place it in the same directory.
$wget http://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_protein_v3/pbmc_1k_protein_v3_fastqs.tar
$tar -xvf ./pbmc_1k_protein_v3_fastqs.tar
$wget http://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_protein_v3/pbmc_1k_protein_v3_feature_ref.csv
$wget http://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_protein_v3/pbmc_1k_protein_v3_filtered_feature_bc_matrix.tar.gz
$!tar xvzf ./pbmc_1k_protein_v3_filtered_feature_bc_matrix.tar.gz
The directory now includes the raw fastqs, the Feature Barcode whitelist reference, the 10x 3M-february-2018 cell barcode whitelist, and the CellRanger Features x Cells matrix. See notebook for example.
pbmc_1k_protein_v3_fastqs/
pbmc_1k_protein_v3_feature_ref.csv
3M-february-2018.txt
filtered_feature_bc_matrix/
We start by making a Python dictionary containing Feature Barcode names and Feature Barcode sequences as key:value pairs. Code to generate this dictionary from the 10x pbmc_1k_protein_v3_feature_ref.csv is provided with the iPython notebook in the docs folder.
feature_barcodes={'CD3_TotalSeqB': 'AACAAGACCCTTGAG',
'CD4_TotalSeqB': 'TACCCGTAATAGCGT',
'CD8a_TotalSeqB': 'ATTGGCACTCAGATG',
'CD14_TotalSeqB': 'GAAAGTCAAAGCACT',
'CD15_TotalSeqB': 'ACGAATCAATCTGTG',
'CD16_TotalSeqB': 'GTCTTTGTCAGTGCA',
'CD56_TotalSeqB': 'GTTGTCCGACAATAC',
'CD19_TotalSeqB': 'TCAACGCTTGGCTAG',
'CD25_TotalSeqB': 'GTGCATTCAACAGTA',
'CD45RA_TotalSeqB': 'GATGAGAACAGGTTT',
'CD45RO_TotalSeqB': 'TGCATGTCATCGGTG',
'PD-1_TotalSeqB': 'AAGTCGTGAGGCATG',
'TIGIT_TotalSeqB': 'TGAAGGCTCATTTGT',
'CD127_TotalSeqB': 'ACATTGACGCAACTA',
'IgG2a_control_TotalSeqB': 'CTCTATTCAGACCAG',
'IgG1_control_TotalSeqB': 'ACTCACTGGAGTCTC',
'IgG2b_control_TotalSeqB': 'ATCACATCGTTGCCA'}
The kite_mismatch_maps function takes the Python dictionary (featurebarcodes) and writes a mismatch t2g and mismatch fasta. In this way, the 17 original Feature Barcodes become a mismatch fasta file and a mismatch t2g file, each with 782 entries.
import kite
kite.kite_mismatch_maps(featurebarcodes, './10xFeatures_t2g.txt', './10xFeaturesMismatch.fa')
Processing Feature Barcodes is similar to processing transcripts except instead of looking for transcript fragments of length -k (the k-mer length) in the reads, a "mismatch" index is used to search the raw reads for the Feature Barcode whitelist and mismatch sequences. Please refer to the kallisto documentation for more information on the kallisto | bustools workflow.
Because Feature Barcodes are typically designed to be robust to some sequencing errors, each Feature Barcode and its mismatches are unique across an experiment, thus each Feature Barcode equivalence class has a one-to-one correspondence to a member of the Feature Barcode whitelist. This is reflected in the t2g file, where each mismatch Feature Barcode points to a unique parent Feature Barcode from the whitelist, analogous to the relationship between genes and transcripts in the case of cDNA processing.
!head -4 ./10xFeatures_t2g.txt
CD3_TotalSeqB CD3_TotalSeqB CD3_TotalSeqB
CD3_TotalSeqB-0-1 CD3_TotalSeqB CD3_TotalSeqB
CD3_TotalSeqB-0-2 CD3_TotalSeqB CD3_TotalSeqB
CD3_TotalSeqB-0-3 CD3_TotalSeqB CD3_TotalSeqB
!head -8 ./10xFeaturesMismatch.fa
>CD3_TotalSeqB
AACAAGACCCTTGAG
>CD3_TotalSeqB-0-1
TACAAGACCCTTGAG
>CD3_TotalSeqB-0-2
GACAAGACCCTTGAG
>CD3_TotalSeqB-0-3
CACAAGACCCTTGAG
The mismatch fasta is used to run kallisto index.
!kallisto index -i ./index_path.idx -k 15 ./fasta_path.fa
[build] loading fasta file /home/jgehring/scRNAseq/kITE/10xTest/10xFeaturesMismatch.fa
[build] k-mer length: 15
[build] counting k-mers ... done.
[build] building target de Bruijn graph ... done
[build] creating equivalence classes ... done
[build] target de Bruijn graph has 782 contigs and contains 782 k-mers
Next, kallisto bus and bustools are used without modifications.
!kallisto bus -i ./index_path.idx -o ./ -x 10xv3 -t 4 \
./pbmc_1k_protein_v3_fastqs/pbmc_1k_protein_v3_antibody_fastqs/pbmc_1k_protein_v3_antibody_S2_L001_R1_001.fastq.gz \
./pbmc_1k_protein_v3_fastqs/pbmc_1k_protein_v3_antibody_fastqs/pbmc_1k_protein_v3_antibody_S2_L001_R2_001.fastq.gz \
./pbmc_1k_protein_v3_fastqs/pbmc_1k_protein_v3_antibody_fastqs/pbmc_1k_protein_v3_antibody_S2_L002_R1_001.fastq.gz \
./pbmc_1k_protein_v3_fastqs/pbmc_1k_protein_v3_antibody_fastqs/pbmc_1k_protein_v3_antibody_S2_L002_R2_001.fastq.gz \
We now have a BUS file for this pseudoalignment.
!bustools correct -w ./3M-february-2018.txt ./output.bus -o ./output_corrected.bus
!bustools sort -t 4 -o ./output_sorted.bus ./output_corrected.bus
!bustools count -o ./ --genecounts -g ./t2g_path.t2g -e ./matrix.ec -t ./transcripts.txt ./output_sorted.bus
Bustools count outputs a .mtx-formatted Features x Cells matrix and vectors of gene names and cell barcodes (genes.txt and barcodes.txt). From here, standard analysis packages like ScanPy and Seurat can be used to continue the Feature Barcode analysis.