scRNA-seq Concepts & Data Formats

Before writing any code, understand what makes single-cell data fundamentally different from bulk — the dropout problem, sparse matrices, AnnData and Seurat object structures, and which public datasets to use for practice.

Week 31Timeline

~7 hrsStudy time

FREEAlways

Start Lesson 1 → View in curriculum →

Curriculum

10 lessons in this module

1What is Single-Cell RNA-seq?Bulk vs single-cell, why scRNA-seq, biological questions it answers, cell heterogeneityLive

2How scRNA-seq Works: The Wet LabDissociation, droplet/microfluidics, barcoding, UMIs, library prep overviewSoon

3The Count MatrixCells × genes matrix, what counts mean, sparsity, why zeros dominateSoon

4Raw Data Formats: FASTQ & BAMscRNA-seq FASTQ structure, barcode+UMI reads, CB/UB BAM tagsSoon

5Cell Ranger & Alevin: QuantificationRunning Cell Ranger count, STARsolo basics, output folder structureSoon

6The MEX Format (matrix.mtx, barcodes, features)Market Exchange Format, reading with Python/R, sparse matricesSoon

7The H5AD Format (AnnData)AnnData object anatomy, .obs, .var, .X, .obsm, reading/writing h5adSoon

8The RDS / SingleCellExperiment FormatSCE object in R, colData, rowData, assays, converting to SeuratSoon

9Quality Metrics & Ambient RNAnFeature, nCount, % mitochondrial, doublets, EmptyDrops conceptSoon

10Capstone: Inspect a Real DatasetDownload 10x PBMC 3k, load MEX → AnnData, basic QC plot, save h5adSoon

📋 Not sure where this fits? Module 24 is part of the full bioinformatics curriculum — a structured 42-week learning path from Bash to single-cell RNA-seq.

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Lesson 1 is live. The rest are on their way.

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Module 24 of 31

scRNA-seq Concepts & Data Formats

Topics covered in this module

10 lessons in this module

Lesson 1 is live. The rest are on their way.