10k A375 Cells Transduced with (1) Non-Target and (1) Target sgRNA#

Dataset: 10k A375 Cells Transduced with (1) Non-Target and (1) Target sgRNA, Dual Indexed

The detailed description of this dataset can be found here.


Preparation#

Download fastq files.

$ wget https://cg.10xgenomics.com/samples/cell-exp/4.0.0/SC3_v3_NextGem_DI_CRISPR_10K/SC3_v3_NextGem_DI_CRISPR_10K_fastqs.tar

$ tar xvf SC3_v3_NextGem_DI_PBMC_CSP_1K/SC3_v3_NextGem_DI_PBMC_CSP_1K_fastqs.tar

Combine reads of different lanes.

$ cat SC3_v3_NextGem_DI_CRISPR_10K_fastqs/SC3_v3_NextGem_DI_CRISPR_10K_crispr_fastqs/SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_L00?_R1_001.fastq.gz > SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_combined_R1_001.fastq.gz

$ cat SC3_v3_NextGem_DI_CRISPR_10K_fastqs/SC3_v3_NextGem_DI_CRISPR_10K_crispr_fastqs/SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_L00?_R2_001.fastq.gz > SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_combined_R2_001.fastq.gz

Download cell barcode info. These are the cell-associated barcodes in this single cell RNA-Seq library.

$ wget https://cf.10xgenomics.com/samples/cell-exp/4.0.0/SC3_v3_NextGem_DI_CRISPR_10K/SC3_v3_NextGem_DI_CRISPR_10K_filtered_feature_bc_matrix.tar.gz

$ tar zxvf SC3_v3_NextGem_DI_CRISPR_10K_filtered_feature_bc_matrix.tar.gz

Inspect cell barcodes.

$ gzip -dc filtered_feature_bc_matrix/barcodes.tsv.gz | head

AAACCCACACATAGCT-1
AAACCCACATCATGAC-1
AAACCCAGTCATCGGC-1
AAACCCAGTCGCACAC-1
AAACCCAGTGCAACGA-1
AAACCCATCAAGCGTT-1
AAACCCATCAGATGCT-1
AAACCCATCATCTACT-1
AAACCCATCCTTATGT-1
AAACCCATCTCGGCTT-1

Prepare feature barcodes.

$ wget https://cf.10xgenomics.com/samples/cell-exp/4.0.0/SC3_v3_NextGem_DI_CRISPR_10K/SC3_v3_NextGem_DI_CRISPR_10K_feature_ref.csv

Inspect feature barcode info.

$ cat SC3_v3_NextGem_DI_CRISPR_10K_feature_ref.csv

id,name,read,pattern,sequence,feature_type,target_gene_id,target_gene_name
RAB1A-2,RAB1A-2,R2,(BC)GTTTAAGAGCTAAGCTGGAA,GCCGGCGAACCAGGAAATA,CRISPR Guide Capture,ENSG00000138069,RAB1A
NON_TARGET-1,NON_TARGET-1,R2,(BC)GTTTAAGAGCTAAGCTGGAA,AACGTGCTGACGATGCGGGC,CRISPR Guide Capture,Non-Targeting,Non-Targeting

Clean up.

$ cut -d ',' -f1,5 SC3_v3_NextGem_DI_CRISPR_10K_feature_ref.csv | sed 's/,/\t/g' | tail -2 > SC3_v3_NextGem_DI_CRISPR_10K_feature_ref.tsv

$ cat SC3_v3_NextGem_DI_CRISPR_10K_feature_ref.tsv

RAB1A-2 GCCGGCGAACCAGGAAATA
NON_TARGET-1    AACGTGCTGACGATGCGGGC

QC#

The first 20,000 read pairs are sampled (set by -n, default 100,000) for quality control. The -t option can be used to set the number of threads. By default, diagnostic results and plots are generated in the qc directory (set by --output_directory), and the full length of read 1 and read 2 are searched against reference cell and feature barcodes, respectively. The per base content of both read pairs and the distribution of matched barcode positions are summarized. Use -r1_c and/or -r2_c to limit the search range, and -cb_n and/or -fb_n to set the mismatch tolerance for cell and/or feature barcode matching (default 3).

$ fba qc \
    -1 SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_combined_R1_001.fastq.gz \
    -2 SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_combined_R2_001.fastq.gz \
    -w filtered_feature_bc_matrix/barcodes.tsv.gz \
    -f SC3_v3_NextGem_DI_CRISPR_10K_feature_ref.tsv \
    -r1_c 0,16 \
    -n 20000

This library was constructed using the Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (Dual Index) with Feature Barcode technology for CRISPR Screening and sequenced on an Illumina NovaSeq 6000. The first 16 bases of read 1 represent cell barcodes, and the following 12 bases represent UMIs. The base content plot indicates that the GC content of cell barcodes is evenly distributed. However, there is a slight T-enrichment in the UMIs.

../../../_images/Pyplot_read1_barcodes_starting_ending4.webp ../../../_images/Pyplot_read1_per_base_seq_content6.webp

Regarding read 2, the per base content analysis indicates that the first 31 bases are consistent and easily readable. These bases correspond to the Template Switch Oligo (TSO) sequence used in library construction. From base 32 onward, we have observed two distinct genotypes in the sampled reads.

../../../_images/Pyplot_read2_per_base_seq_content6.webp ../../../_images/Pyplot_read2_barcodes_starting_ending6.webp

The detailed qc results are stored in the feature_barcoding_output.tsv.gz file. The matching_pos columns indicate the matched positions on reads, while the matching_description columns indicate mismatches in the format of substitutions:insertions:deletions.

$ gzip -dc qc/feature_barcoding_output.tsv.gz | head

read1_seq       cell_barcode    cb_matching_pos cb_matching_description read2_seq       feature_barcode fb_matching_pos fb_matching_description
CNCCACACACGTGTTAatgagtactagc    CCTCACACACGTAGTT        0:15    2:0:1   AAGCAGTGGTATCAACGCAGAGTACATGGGATAGGTTTGGTCCTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCC    no_match        NA      NA
GNCGCGATCAGCATTActtttgtcaccc    GTCGCGAAGAGCATTA        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGACTGTTGCTGGTGTGTACTTGCTAAGGTTTATGTCAGTTCAAGATTATAAGCCCCCCAG    no_match        NA      NA
TNGGAAGGTAAGTGTAatcgagggaaca    TGGGAAGCAAAGTGTA        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGGCCGGCGAACCAGGAAATAGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAT    RAB1A-2_GCCGGCGAACCAGGAAATAG    31:51   0:0:0
CNCCCAAGTCGATAGGgagcgcaagcat    CCCAACTCACGATAGG        2:16    1:0:2   AAGCAGTGGTATCAACGCAGAGTACATGGGGGCCGGCGAACCAGGAAATAGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAT    RAB1A-2_GCCGGCGAACCAGGAAATAG    31:51   0:0:0
CNCACTGCAAACGGTGggcgtaaatgag    CTCACTGGTAACGGTG        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGGCCGGCGAACCAGGAAATAGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAT    RAB1A-2_GCCGGCGAACCAGGAAATAG    31:51   0:0:0
ANCATCACAGGCGCTTgtcccactatat    AGCATCAGTGGCGCTT        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGGCCGGCGAACCAGGAAATAGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAT    RAB1A-2_GCCGGCGAACCAGGAAATAG    31:51   0:0:0
ANACGAACACTTTCATccaaaagaagtt    AAACGAAGTCTTTCAT        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGGCCGGCGAACCAGGAAATAGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAT    RAB1A-2_GCCGGCGAACCAGGAAATAG    31:51   0:0:0
ANCAACCAGTATCGTTgaaatcctggta    AACAACCTCTATCGTT        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGAACGTGCTGACGATGCGGGCGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAA    NON_TARGET-1_AACGTGCTGACGATGCGGGC       31:51   0:0:0
GNAGCCCGTACCACATgggcccagtatg    GAAGCCCCAACCACAT        0:16    3:0:0   AAGCAGTGGTATCAACGCAGAGTACATGGGGGCCGGCGAACCAGGAAATAGTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAT    RAB1A-2_GCCGGCGAACCAGGAAATAG    31:51   0:0:0

Barcode extraction#

Although the length of the RAB1A-1 and RAB1A-2 feature barcodes differ by one base, they both start at the same position on read 2. To enable accurate feature barcode identification, we will include an extra downstream base (G) for the RAB1A-2 feature barcode to make their lengths equal.

$ cat SC3_v3_NextGem_DI_CRISPR_10K_feature_ref_edited.tsv

RAB1A-2 GCCGGCGAACCAGGAAATAG
NON_TARGET-1    AACGTGCTGACGATGCGGGC

The search ranges for barcode matching are set to 0,16 on read 1 and 31,51 on read 2. We allow for two mismatches for both cell and feature barcodes using the parameters -cb_m and -cf_m.

$ fba extract \
    -1 SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_combined_R1_001.fastq.gz \
    -2 SC3_v3_NextGem_DI_CRISPR_10K_crispr_S1_combined_R2_001.fastq.gz \
    -w filtered_feature_bc_matrix/barcodes.tsv.gz \
    -f SC3_v3_NextGem_DI_CRISPR_10K_feature_ref_edited.tsv \
    -o feature_barcoding_output.tsv.gz \
    -r1_c 0,16 \
    -r2_c 31,51 \
    -cb_m 2 \
    -fb_m 2

Preview of result.

$ gzip -dc feature_barcoding_output.tsv.gz  | head

read1_seq       cell_barcode    cb_num_mismatches       read2_seq       feature_barcode fb_num_mismatches
GGCAGTCTCCGTTACTtatccagccttc    GGCAGTCTCGGTAACT        2       aagcagtggtatcaacgcagagtacatggggGCCGGCGAACCAGGAAATAGtttaagagctaagctggaaacagcatagcaagtttaaat    RAB1A-2_GCCGGCGAACCAGGAAATAG     0
TTACGTTGTGAATCGGgtggggctcttc    TTACGTTCAGAATCGG        2       aagcagtggtatcaacgcagagtacatggggAACGTGCTGACGATGCGGGCgtttaagagctaagctggaaacagcatagcaagtttaaa    NON_TARGET-1_AACGTGCTGACGATGCGGGC        0
TCGGGCAAGGATTGGTttctactcggaa    TCGGGCATCGATTGGT        2       aagcagtggtatcaacgcagagtacatgggaACGTGCTGACGATGCGGGCGtttaagagctaagctggaaacagcatagcaagtttaaat    NON_TARGET-1_AACGTGCTGACGATGCGGGC        2
ACAACCACACATCTAGcggcatcatact    ACAACCAGTCATCTAG        2       aagcagtggtatcaacgcagagtacatggggCCGGCGAACCAGGAAATAGTttaagagctaagctggaaacagcatagcaagtttaaata    RAB1A-2_GCCGGCGAACCAGGAAATAG     2
AGACTCAAGTGCTAGAacagaactggtg    AGACTCATCTGCTAGA        2       aagcagtggtatcaacgcagagtacatggggAACGTGCTGACGATGCGGGCgtttaagagctaagctggaaacagcatagcaagtttaaa    NON_TARGET-1_AACGTGCTGACGATGCGGGC        0
GAGTTGTTCGAACATTctgcccgacgtc    GAGTTGTAGGAACATT        2       aagcagtggtatcaacgcagagtacatggggAACGTGCTGACGATGCGGGCgtttaagagctaagctggaaacagcatagcaagtttaaa    NON_TARGET-1_AACGTGCTGACGATGCGGGC        0
AGACTCAGTGGCACAAtgtcagaattca    AGACTCACAGGCACAA        2       aagcagtggtatcaacgcagagtacatggggGCCGGCGAACCAGGAAATAGtttaagagctaagctggaaacagcatagcaagtttaaat    RAB1A-2_GCCGGCGAACCAGGAAATAG     0
TGCACGGAGGATAACCcgtgcacgtaca    TGCACGGTCGATAACC        2       aagcagtggtatcaacgcagagtacatggggGCCGGCGAACCAGGAAATAGtttaagagctaagctggaaacagcatagcaagtttaaat    RAB1A-2_GCCGGCGAACCAGGAAATAG     0
CGTAGTAGTAACACGGaagagggaactg    CGTAGTAGTAACGCGA        2       aagcagtggtatcaacgcagagtacatggggAACGTGCTGACGATGCGGGCgtttaagagctaagctggaaacagcatagcaagtttaaa    NON_TARGET-1_AACGTGCTGACGATGCGGGC        0

Result summary.

64.7% (93,795,979 out of 145,032,428) of total read pairs have valid cell and feature barcodes. Majority of fragments in this library have correct structure.

2021-02-15 01:51:59,262 - fba.__main__ - INFO - fba version: 0.0.7
2021-02-15 01:51:59,262 - fba.__main__ - INFO - Initiating logging ...
2021-02-15 01:51:59,262 - fba.__main__ - INFO - Python version: 3.7
2021-02-15 01:51:59,262 - fba.__main__ - INFO - Using extract subcommand ...
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Number of reference cell barcodes: 11,791
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Number of reference feature barcodes: 2
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Read 1 coordinates to search: [0, 16)
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Read 2 coordinates to search: [31, 51)
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Cell barcode maximum number of mismatches: 2
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Feature barcode maximum number of mismatches: 2
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Read 1 maximum number of N allowed: 3
2021-02-15 01:51:59,276 - fba.levenshtein - INFO - Read 2 maximum number of N allowed: 3
2021-02-15 01:52:02,510 - fba.levenshtein - INFO - Matching ...
2021-02-15 02:20:39,807 - fba.levenshtein - INFO - Read pairs processed: 10,000,000
2021-02-15 02:49:04,142 - fba.levenshtein - INFO - Read pairs processed: 20,000,000
2021-02-15 03:17:27,422 - fba.levenshtein - INFO - Read pairs processed: 30,000,000
2021-02-15 03:45:54,615 - fba.levenshtein - INFO - Read pairs processed: 40,000,000
2021-02-15 04:14:23,049 - fba.levenshtein - INFO - Read pairs processed: 50,000,000
2021-02-15 04:42:49,377 - fba.levenshtein - INFO - Read pairs processed: 60,000,000
2021-02-15 05:11:15,736 - fba.levenshtein - INFO - Read pairs processed: 70,000,000
2021-02-15 05:39:43,011 - fba.levenshtein - INFO - Read pairs processed: 80,000,000
2021-02-15 06:08:09,940 - fba.levenshtein - INFO - Read pairs processed: 90,000,000
2021-02-15 06:36:39,658 - fba.levenshtein - INFO - Read pairs processed: 100,000,000
2021-02-15 07:05:08,115 - fba.levenshtein - INFO - Read pairs processed: 110,000,000
2021-02-15 07:33:32,101 - fba.levenshtein - INFO - Read pairs processed: 120,000,000
2021-02-15 08:02:01,233 - fba.levenshtein - INFO - Read pairs processed: 130,000,000
2021-02-15 08:30:29,660 - fba.levenshtein - INFO - Read pairs processed: 140,000,000
2021-02-15 08:44:47,038 - fba.levenshtein - INFO - Number of read pairs processed: 145,032,428
2021-02-15 08:44:47,038 - fba.levenshtein - INFO - Number of read pairs w/ valid barcodes: 93,795,979
2021-02-15 08:44:47,153 - fba.__main__ - INFO - Done.

Matrix generation#

Only fragments with correctly matched cell and feature barcodes are included, while fragments with UMI lengths less than the specified value are discarded. UMI removal is performed using UMI-tools (Smith, T., et al. 2017. Genome Res. 27, 491–499.), with the starting position on read 1 set by -us (default 16) and the length set by -ul (default 12). The UMI deduplication method can be set using -ud (default directional), and the UMI deduplication mismatch threshold can be specified using -um (default 1).

The generated feature count matrix can be easily imported into well-established single cell analysis packages: Seurat and Scanpy.

$ fba count \
    -i feature_barcoding_output.tsv.gz \
    -o matrix_featurecount.csv.gz \
    -us 16 \
    -ul 12 \
    -um 1 \
    -ud directional

Result summary.

7.6% (7,145,799 out of 93,795,979) of read pairs with valid cell and feature barcodes are unique fragments. 4.9% (7,143,943 out of 145,032,428) of total sequenced read pairs contribute to the final matrix.

2020-10-20 04:47:32,738 - fba.__main__ - INFO - fba version: 0.0.7
2020-10-20 04:47:32,738 - fba.__main__ - INFO - Initiating logging ...
2020-10-20 04:47:32,738 - fba.__main__ - INFO - Python version: 3.7
2020-10-20 04:47:32,738 - fba.__main__ - INFO - Using count subcommand ...
2020-10-20 04:47:32,738 - fba.count - INFO - UMI-tools version: 1.0.1
2020-10-20 04:47:32,795 - fba.count - INFO - UMI starting position on read 1: 16
2020-10-20 04:47:32,795 - fba.count - INFO - UMI length: 12
2020-10-20 04:47:32,795 - fba.count - INFO - UMI-tools deduplication threshold: 1
2020-10-20 04:47:32,795 - fba.count - INFO - UMI-tools deduplication method: directional
2020-10-20 04:47:32,795 - fba.count - INFO - Header line: read1_seq cell_barcode cb_num_mismatches read2_seq feature_barcode fb_num_mismatches
2020-10-20 04:51:50,886 - fba.count - INFO - Number of lines processed: 93,795,979
2020-10-20 04:51:50,893 - fba.count - INFO - Number of cell barcodes detected: 11,758
2020-10-20 04:51:50,894 - fba.count - INFO - Number of features detected: 2
2020-10-20 05:00:42,298 - fba.count - INFO - Total UMIs after deduplication: 7,145,799
2020-10-20 05:00:42,320 - fba.count - INFO - Median number of UMIs per cell: 477.0
2020-10-20 05:00:42,434 - fba.__main__ - INFO - Done.

Demultiplexing#

Negative binomial distribution#

Cells are demultiplexed based on the feature count matrix using demultiplexing method 1 (set by -dm), which is implemented based on the method described by Stoeckius, M., et al. (2018) with some modifications. The output directory for demultiplexing is set by --output_directory (default demultiplexed). A cell identity matrix is generated, where 0 indicates negative and 1 indicates positive. To adjust the quantile threshold for demultiplexing, use -q (default 0.9999). To generate visualization plots, set -v.

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    --output_directory demultiplexed \
    -dm 1 \
    -q 0.75 \
    -v

Heatmap of the relative abundance of features (sgRNAs) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (sgRNAs, no transcriptome information used). Colors indicate the sgRNA status for each cell, as called by FBA.

t-SNE embedding

Gaussian mixture model#

The implementation of demultiplexing method 2 (set by -dm) is inspired by the method described on the 10x Genomics’ website. To set the probability threshold for demultiplexing, use -p (default 0.9).

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    -dm 2 \
    -v

2021-10-04 14:14:15,659 - fba.__main__ - INFO - fba version: 0.0.x
2021-10-04 14:14:15,659 - fba.__main__ - INFO - Initiating logging ...
2021-10-04 14:14:15,659 - fba.__main__ - INFO - Python version: 3.8
2021-10-04 14:14:15,659 - fba.__main__ - INFO - Using demultiplex subcommand ...
2021-10-04 14:14:36,166 - fba.__main__ - INFO - Skipping arguments: "-q/--quantile", "-cm/--clustering_method"
2021-10-04 14:14:36,166 - fba.demultiplex - INFO - Output directory: demultiplexed
2021-10-04 14:14:36,166 - fba.demultiplex - INFO - Demultiplexing method: 2
2021-10-04 14:14:36,166 - fba.demultiplex - INFO - UMI normalization method: clr
2021-10-04 14:14:36,167 - fba.demultiplex - INFO - Visualization: On
2021-10-04 14:14:36,167 - fba.demultiplex - INFO - Visualization method: tsne
2021-10-04 14:14:36,167 - fba.demultiplex - INFO - Loading feature count matrix: matrix_featurecount.csv.gz ...
2021-10-04 14:14:37,875 - fba.demultiplex - INFO - Number of cells: 11,758
2021-10-04 14:14:37,875 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2021-10-04 14:14:37,920 - fba.demultiplex - INFO - Number of features: 2 / 2 (after filtering / original in the matrix)
2021-10-04 14:14:37,920 - fba.demultiplex - INFO - Features: NON_TARGET-1 RAB1A-2
2021-10-04 14:14:37,920 - fba.demultiplex - INFO - Total UMIs: 7,145,799 / 7,145,799
2021-10-04 14:14:37,942 - fba.demultiplex - INFO - Median number of UMIs per cell: 477.0 / 477.0
2021-10-04 14:14:37,942 - fba.demultiplex - INFO - Demultiplexing ...
2021-10-04 14:14:38,418 - fba.demultiplex - INFO - Generating heatmap ...
2021-10-04 14:14:42,078 - fba.demultiplex - INFO - Embedding ...
2021-10-04 14:15:24,288 - fba.__main__ - INFO - Done.

Heatmap of the relative abundance of features (sgRNAs) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (sgRNAs, no transcriptome information used). Colors indicate the sgRNA status for each cell, as called by FBA.

t-SNE embedding

UMI distribution and model fitting threshold:

UMI distribution UMI distribution

Poisson-Gaussian mixture model#

The implementation of demultiplexing method 3 (set by -dm) is inspired by Replogle, M., et al. (2021). The probability threshold for demultiplexing can be set using -p (default 0.5).

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    -dm 3 \
    -v

2021-12-20 00:13:17,443 - fba.__main__ - INFO - fba version: 0.0.x
2021-12-20 00:13:17,443 - fba.__main__ - INFO - Initiating logging ...
2021-12-20 00:13:17,443 - fba.__main__ - INFO - Python version: 3.9
2021-12-20 00:13:17,443 - fba.__main__ - INFO - Using demultiplex subcommand ...
2021-12-20 00:13:19,774 - fba.__main__ - INFO - Skipping arguments: "-q/--quantile", "-cm/--clustering_method"
2021-12-20 00:13:19,774 - fba.demultiplex - INFO - Output directory: demultiplexed
2021-12-20 00:13:19,774 - fba.demultiplex - INFO - Demultiplexing method: 3
2021-12-20 00:13:19,774 - fba.demultiplex - INFO - UMI normalization method: clr
2021-12-20 00:13:19,774 - fba.demultiplex - INFO - Visualization: On
2021-12-20 00:13:19,774 - fba.demultiplex - INFO - Visualization method: tsne
2021-12-20 00:13:19,774 - fba.demultiplex - INFO - Loading feature count matrix: matrix_featurecount.csv.gz ...
2021-12-20 00:13:20,479 - fba.demultiplex - INFO - Number of cells: 11,758
2021-12-20 00:13:20,479 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2021-12-20 00:13:20,497 - fba.demultiplex - INFO - Number of features: 2 / 2 (after filtering / original in the matrix)
2021-12-20 00:13:20,497 - fba.demultiplex - INFO - Features: NON_TARGET-1 RAB1A-2
2021-12-20 00:13:20,497 - fba.demultiplex - INFO - Total UMIs: 7,145,799 / 7,145,799
2021-12-20 00:13:20,506 - fba.demultiplex - INFO - Median number of UMIs per cell: 477.0 / 477.0
2021-12-20 00:13:20,506 - fba.demultiplex - INFO - Demultiplexing ...
2021-12-20 00:13:21,930 - fba.demultiplex - INFO - Generating heatmap ...
2021-12-20 00:13:23,070 - fba.demultiplex - INFO - Embedding ...
2021-12-20 00:13:41,271 - fba.__main__ - INFO - Done.

Heatmap of the relative abundance of features (sgRNAs) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (sgRNAs, no transcriptome information used). Colors indicate the sgRNA status for each cell, as called by FBA.

t-SNE embedding

UMI distribution and model fitting threshold:

UMI distribution UMI distribution

Kernel density estimation#

CRISPR perturbations are demultiplexed based on the abundance of features using demultiplexing method 4, which is implemented with modifications to the method described in McGinnis, C., et al. (2019).

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    -dm 4 \
    -v

2021-12-26 18:11:16,685 - fba.__main__ - INFO - fba version: 0.0.x
2021-12-26 18:11:16,685 - fba.__main__ - INFO - Initiating logging ...
2021-12-26 18:11:16,686 - fba.__main__ - INFO - Python version: 3.9
2021-12-26 18:11:16,686 - fba.__main__ - INFO - Using demultiplex subcommand ...
2021-12-26 18:11:19,633 - fba.__main__ - INFO - Skipping arguments: "-q/--quantile", "-cm/--clustering_method", "-p/--prob"
2021-12-26 18:11:19,633 - fba.demultiplex - INFO - Output directory: demultiplexed
2021-12-26 18:11:19,633 - fba.demultiplex - INFO - Demultiplexing method: 4
2021-12-26 18:11:19,633 - fba.demultiplex - INFO - UMI normalization method: clr
2021-12-26 18:11:19,633 - fba.demultiplex - INFO - Visualization: On
2021-12-26 18:11:19,633 - fba.demultiplex - INFO - Visualization method: tsne
2021-12-26 18:11:19,633 - fba.demultiplex - INFO - Loading feature count matrix: matrix_featurecount.csv.gz ...
2021-12-26 18:11:19,745 - fba.demultiplex - INFO - Number of cells: 11,758
2021-12-26 18:11:19,745 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2021-12-26 18:11:19,762 - fba.demultiplex - INFO - Number of features: 2 / 2 (after filtering / original in the matrix)
2021-12-26 18:11:19,762 - fba.demultiplex - INFO - Features: NON_TARGET-1 RAB1A-2
2021-12-26 18:11:19,762 - fba.demultiplex - INFO - Total UMIs: 7,145,799 / 7,145,799
2021-12-26 18:11:19,771 - fba.demultiplex - INFO - Median number of UMIs per cell: 477.0 / 477.0
2021-12-26 18:11:19,771 - fba.demultiplex - INFO - Demultiplexing ...
2021-12-26 18:11:22,049 - fba.demultiplex - INFO - Quantile cutoff: 18
2021-12-26 18:11:23,703 - fba.demultiplex - INFO - Generating heatmap ...
2021-12-26 18:11:24,911 - fba.demultiplex - INFO - Embedding ...
2021-12-26 18:11:44,219 - fba.__main__ - INFO - Done.

Heatmap of the relative abundance of features (sgRNAs) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (sgRNAs, no transcriptome information used). Colors indicate the sgRNA status for each cell, as called by FBA.

t-SNE embedding

UMI distribution and model fitting threshold:

UMI distribution UMI distribution

Knee point#

Method 1#

Cells are demultiplexed based on the abundance of features, specifically sgRNAs. Demultiplexing method 5-2019 is our previous implementation, which aims to identify perturbations in the cells by detecting an inflection point on the feature UMI saturation curve (Xie, S., et al. 2019).

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    -dm 5-2019 \
    -v

2022-01-02 13:57:51,792 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-02 13:57:51,792 - fba.__main__ - INFO - Initiating logging ...
2022-01-02 13:57:51,792 - fba.__main__ - INFO - Python version: 3.9
2022-01-02 13:57:51,792 - fba.__main__ - INFO - Using demultiplex subcommand ...
2022-01-02 13:57:54,328 - fba.__main__ - INFO - Skipping arguments: "-q/--quantile", "-cm/--clustering_method", "-p/--prob"
2022-01-02 13:57:54,329 - fba.demultiplex - INFO - Output directory: demultiplexed
2022-01-02 13:57:54,329 - fba.demultiplex - INFO - Demultiplexing method: 5-2019
2022-01-02 13:57:54,329 - fba.demultiplex - INFO - UMI normalization method: clr
2022-01-02 13:57:54,329 - fba.demultiplex - INFO - Visualization: On
2022-01-02 13:57:54,329 - fba.demultiplex - INFO - Visualization method: tsne
2022-01-02 13:57:54,329 - fba.demultiplex - INFO - Loading feature count matrix: raw/m2_2020-10-20/matrix_featurecount.csv.gz ...
2022-01-02 13:57:54,444 - fba.demultiplex - INFO - Number of cells: 11,758
2022-01-02 13:57:54,444 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2022-01-02 13:57:54,464 - fba.demultiplex - INFO - Number of features: 2 / 2 (after filtering / original in the matrix)
2022-01-02 13:57:54,464 - fba.demultiplex - INFO - Features: NON_TARGET-1 RAB1A-2
2022-01-02 13:57:54,464 - fba.demultiplex - INFO - Total UMIs: 7,145,799 / 7,145,799
2022-01-02 13:57:54,474 - fba.demultiplex - INFO - Median number of UMIs per cell: 477.0 / 477.0
2022-01-02 13:57:54,474 - fba.demultiplex - INFO - Demultiplexing ...
2022-01-02 13:57:55,509 - fba.demultiplex - INFO - Generating heatmap ...
2022-01-02 13:57:56,717 - fba.demultiplex - INFO - Embedding ...
2022-01-02 13:58:12,014 - fba.__main__ - INFO - Done.

Heatmap of the relative abundance of features (sgRNAs) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (sgRNAs, no transcriptome information used). Colors indicate the sgRNA status for each cell, as called by FBA.

t-SNE embedding

UMI distribution and model fitting threshold:

UMI distribution UMI distribution

Method 2#

Cells are demultiplexed based on the abundance of features, specifically sgRNAs. Demultiplexing method 5 is implemented to use the local maxima on the difference curve to detemine the knee point on the UMI saturation curve.

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    -dm 5 \
    -v

2021-12-29 22:55:34,303 - fba.__main__ - INFO - fba version: 0.0.x
2021-12-29 22:55:34,303 - fba.__main__ - INFO - Initiating logging ...
2021-12-29 22:55:34,303 - fba.__main__ - INFO - Python version: 3.9
2021-12-29 22:55:34,303 - fba.__main__ - INFO - Using demultiplex subcommand ...
2021-12-29 22:55:36,774 - fba.__main__ - INFO - Skipping arguments: "-q/--quantile", "-cm/--clustering_method", "-p/--prob"
2021-12-29 22:55:36,774 - fba.demultiplex - INFO - Output directory: demultiplexed
2021-12-29 22:55:36,774 - fba.demultiplex - INFO - Demultiplexing method: 5
2021-12-29 22:55:36,774 - fba.demultiplex - INFO - UMI normalization method: clr
2021-12-29 22:55:36,774 - fba.demultiplex - INFO - Visualization: On
2021-12-29 22:55:36,774 - fba.demultiplex - INFO - Visualization method: tsne
2021-12-29 22:55:36,774 - fba.demultiplex - INFO - Loading feature count matrix: matrix_featurecount.csv.gz ...
2021-12-29 22:55:36,886 - fba.demultiplex - INFO - Number of cells: 11,758
2021-12-29 22:55:36,886 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2021-12-29 22:55:36,904 - fba.demultiplex - INFO - Number of features: 2 / 2 (after filtering / original in the matrix)
2021-12-29 22:55:36,904 - fba.demultiplex - INFO - Features: NON_TARGET-1 RAB1A-2
2021-12-29 22:55:36,904 - fba.demultiplex - INFO - Total UMIs: 7,145,799 / 7,145,799
2021-12-29 22:55:36,913 - fba.demultiplex - INFO - Median number of UMIs per cell: 477.0 / 477.0
2021-12-29 22:55:36,913 - fba.demultiplex - INFO - Demultiplexing ...
2021-12-29 22:55:37,415 - fba.demultiplex - INFO - Generating heatmap ...
2021-12-29 22:55:38,576 - fba.demultiplex - INFO - Embedding ...
2021-12-29 22:55:57,485 - fba.__main__ - INFO - Done.

Heatmap of the relative abundance of features (sgRNAs) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (sgRNAs, no transcriptome information used). Colors indicate the sgRNA status for each cell, as called by FBA.

t-SNE embedding

UMI distribution and model fitting threshold:

UMI distribution UMI distribution