Count Matrix Ingestion

Basic CSV/TSV Loading

import pandas as pd

TSV with gene IDs as first column

counts = pd.read_csv('counts.tsv', sep='\t', index_col=0)

CSV with header

counts = pd.read_csv('counts.csv', index_col=0)

Skip comment lines

counts = pd.read_csv('counts.txt', sep='\t', index_col=0, comment='#')

featureCounts Output

import pandas as pd

featureCounts format has 6 metadata columns before counts

fc = pd.read_csv('featurecounts.txt', sep='\t', comment='#') counts = fc.set_index('Geneid').iloc[:, 5:] # Skip Chr, Start, End, Strand, Length counts.columns = [c.replace('.bam', '').split('/')[-1] for c in counts.columns]

Salmon Quant Files

import pandas as pd from pathlib import Path

def load_salmon_quants(quant_dirs, column='NumReads'): '''Load multiple Salmon quant.sf files into a count matrix.''' dfs = {} for qdir in quant_dirs: sample = Path(qdir).name sf = pd.read_csv(f'{qdir}/quant.sf', sep='\t', index_col=0) dfs[sample] = sf[column] return pd.DataFrame(dfs)

Usage

quant_dirs = ['salmon_out/sample1', 'salmon_out/sample2', 'salmon_out/sample3'] counts = load_salmon_quants(quant_dirs, column='NumReads') tpm = load_salmon_quants(quant_dirs, column='TPM')

kallisto Abundance Files

import pandas as pd from pathlib import Path

def load_kallisto_quants(abundance_files, column='est_counts'): '''Load multiple kallisto abundance.tsv files.''' dfs = {} for f in abundance_files: sample = Path(f).parent.name ab = pd.read_csv(f, sep='\t', index_col=0) dfs[sample] = ab[column] return pd.DataFrame(dfs)

Usage

files = ['kallisto_out/sample1/abundance.tsv', 'kallisto_out/sample2/abundance.tsv'] counts = load_kallisto_quants(files, column='est_counts') tpm = load_kallisto_quants(files, column='tpm')

10X Genomics Sparse Matrix

import scanpy as sc

Load 10X directory (contains matrix.mtx, genes.tsv/features.tsv, barcodes.tsv)

adata = sc.read_10x_mtx('filtered_feature_bc_matrix/')

Load 10X H5 file

adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

Convert to dense DataFrame if needed

counts = adata.to_df()

AnnData H5AD Files

import anndata as ad import scanpy as sc

Load h5ad

adata = sc.read_h5ad('data.h5ad')

Access count matrix

counts = adata.to_df() # Dense DataFrame sparse_counts = adata.X # Sparse matrix (if stored sparse)

Access raw counts if normalized data is in .X

raw_counts = adata.raw.to_adata().to_df()

RDS Files (from R)

import pyreadr

Read RDS file

result = pyreadr.read_r('counts.rds') counts = result[None] # Access the data

For Seurat objects, use anndata2ri or convert in R first

Combine Multiple Files

import pandas as pd from pathlib import Path

def combine_count_files(file_pattern, index_col=0, sep='\t'): '''Combine multiple count files into one matrix.''' files = sorted(Path('.').glob(file_pattern)) dfs = {} for f in files: sample = f.stem.replace('_counts', '') dfs[sample] = pd.read_csv(f, sep=sep, index_col=index_col).iloc[:, 0] return pd.DataFrame(dfs)

Usage

counts = combine_count_files('counts/*_counts.tsv')

Filter Low-Count Genes

Keep genes with at least 10 counts in at least 3 samples

min_counts, min_samples = 10, 3 expressed = (counts >= min_counts).sum(axis=1) >= min_samples counts_filtered = counts.loc[expressed]

Alternative: total counts threshold

counts_filtered = counts[counts.sum(axis=1) >= 50]

Handle Gene ID Versions

Remove Ensembl version numbers (ENSG00000123456.12 -> ENSG00000123456)

counts.index = counts.index.str.split('.').str[0]

Or keep as-is for compatibility

Save Count Matrix

Save as TSV

counts.to_csv('count_matrix.tsv', sep='\t')

Save as compressed

counts.to_csv('count_matrix.tsv.gz', sep='\t', compression='gzip')

Save as AnnData

import anndata as ad adata = ad.AnnData(counts) adata.write_h5ad('counts.h5ad')

R Loading Equivalents

Basic CSV/TSV

counts <- read.csv('counts.csv', row.names=1) counts <- read.delim('counts.tsv', row.names=1)

featureCounts

fc <- read.delim('featurecounts.txt', comment.char='#', row.names=1) counts <- fc[, 6:ncol(fc)]

tximport for Salmon/kallisto

library(tximport) files <- file.path('salmon_out', samples, 'quant.sf') txi <- tximport(files, type='salmon', txOut=TRUE) counts <- txi$counts

Related Skills

• rna-quantification/featurecounts-counting - Generate featureCounts output

• rna-quantification/alignment-free-quant - Generate Salmon/kallisto output

• expression-matrix/sparse-handling - Memory-efficient storage

• expression-matrix/gene-id-mapping - Convert gene identifiers