Mtx format

The mtx, matrix market, format is a sparse format for matrices. It only stores non zero values and is becoming popular in single-cell softwares.

The main advantage is that it requires less space than a dense matrix and that you can easily add different feature names within the same object.

For CITE-seq-Count, the output looks like this:

OUTFOLDER/
-- umi_count/
-- -- matrix.mtx.gz
-- -- features.tsv.gz
-- -- barcodes.tsv.gz
-- read_count/
-- -- matrix.mtx.gz
-- -- features.tsv.gz
-- -- barcodes.tsv.gz
-- unmapped.csv
-- run_report.yaml

File descriptions

• features.tsv.gz contains the feature names, in this context our tags.
• barcodes.tsv.gz contains the cell barcodes.
• matrix.mtx.gz contains the actual values. read_count and umi_count contain respectively the read counts and the collapsed umi counts. For analysis you should use the umi data. The read_count can be used to check if you have an overamplification or oversequencing issue with your protocol.
• unmapped.csv contains the top N tags that haven't been mapped.
• run_report.yaml contains the parameters used for the run as well as some statistics. here is an example:

Date: 2019-10-01
Running time: 13.86 seconds
CITE-seq-Count Version: 1.4.3
Reads processed: 1000000
Percentage mapped: 33
Percentage unmapped: 67
Uncorrected cells: 0
Correction:
    Cell barcodes collapsing threshold: 1
    Cell barcodes corrected: 57
    UMI collapsing threshold: 2
    UMIs corrected: 329
Run parameters:
    Read1_filename: fastq/test_R1.fastq.gz,fastq/test2_R1.fastq.gz
    Read2_filename: fastq/test_R2.fastq.gz,fastq/test2_R2.fastq.gz
    Cell barcode:
        First position: 1
        Last position: 16
    UMI barcode:
        First position: 17
        Last position: 26
    Expected cells: 100
    Tags max errors: 1
    Start trim: 0

Packages to read MTX

R

I recommend using Seurat and their Read10x function to read the results.

With Seurat V3:

Read10x('OUTFOLDER/umi_count/', gene.column=1)

With Matrix:

library(Matrix)
matrix_dir = "/path_to_your_directory/out_cite_seq_count/umi_count/"
barcode.path <- paste0(matrix_dir, "barcodes.tsv.gz")
features.path <- paste0(matrix_dir, "features.tsv.gz")
matrix.path <- paste0(matrix_dir, "matrix.mtx.gz")
mat <- readMM(file = matrix.path)
feature.names = read.delim(features.path, header = FALSE, stringsAsFactors = FALSE)
barcode.names = read.delim(barcode.path, header = FALSE, stringsAsFactors = FALSE)
colnames(mat) = barcode.names$V1
rownames(mat) = feature.names$V1

Python

I recommend using scanpy and their read_mtx function to read the results.

Example:

import scanpy
import pandas as pd
import os
path = 'umi_cell_corrected'
data = scanpy.read_mtx(os.path.join(path,'umi_count/matrix.mtx.gz'))
data = data.T
features = pd.read_csv(os.path.join(path, 'umi_count/features.tsv.gz'), header=None)
barcodes = pd.read_csv(os.path.join(path, 'umi_count/barcodes.tsv.gz'), header=None)
data.var_names = features[0]
data.obs_names = barcodes[0]