Single Cell Sequencing Analysis

Single Cell Sequencing Data Review

● Sequencing depth = (# of cells) x (required depth):○ RNA - 50k paired end reads / cell for cell type classification○ RNA - .25M-1M paired reads / cell for transcriptome coverage○ DNA - 30-100x per cell

● e.g. 1000 cell scRNA-Seq = 250M-1B reads per sample!○ Bulk mRNA-Seq: 30M-80M per sample

● Sequences in one PE fastq file are entirely barcodes● Read length > 50bp for annotated genome

Rizzetto, et al. 2017. “Impact of Sequencing Depth and Read Length on Single Cell RNA Sequencing Data of T Cells.” Scientific Reports 7 (1): 12781.

The Trees: Cells

● What cell types are in a sample?● What are their relative proportions?● How do their transcriptomes differ?● Which/how do cells respond to stimulus?● How do cells change over time?● What is the level of mosaicism in tissues?

Analysis Overview: scRNA-Seq

1. Sequence QCa. Demultiplexb. UMI Collapsing

2. Alignment+QC3. Quantification4. Normalization5. DE, Clustering, etc

https://hemberg-lab.github.io/scRNA.seq.course/introduction-to-single-cell-rna-seq.html

Sequence QC

● One sample is 100s-10,000s of cells○ i.e. ~1,000 fastq files per sample○ May or may not be already demultiplexed by core

● Paired end:○ Read 1: molecule sequence○ Read 2: barcode - used for demultiplexing and UMI collapsing

● Normal fastq processing and QC:○ Adapter and quality trimming of both reads

(barcode read can still have adapter)○ fastqc, multiqc

Unique Molecular Identifiers

● Enable detection of qPCR amp. artefacts● Not required, but often used● Reads deduplicated or collapsed by cell

barcode+UMI sequence prior to analysis● Barcodes/UMIs designed to tolerate

sequencing errors○ i.e. >2 edit distance between any two sequences

DropletDroplet

Unique Molecular Identifiers

Pool of Cell BarcodesACATAGAAGGTAGATACCCATTAG...

Pool of UMI BarcodesAAATAATAATAA...

Sequenced BarcodeACATAGAAAAATCCCATTAGAAAT*ACATAGAAAATA*GGTAGATAAAATGGTAGATAATAAACATAGAAAATA*ACATAGAAAATA*ACATAGAAATAAGGTAGATAAATAACATAGAAAATA*CCCATTAGAAAT*CCCATTAGATAA*CCCATTAGATAA*

Droplet Cell

One BC per droplet

All UMIs per droplet

Ampl

ify,

Pool, a

nd Seque

nce

* PCR duplicates

Red cell: 6 reads,3 original fragments

Orange cell: 4 reads, 2 original fragments

Blue cell: 3 reads,3 original fragments

(conceptual)

Alignment

● Use either UMI collapsed or original reads

● UMI-tools: toolkit for working with UMIs

● Standard tools and QC, i.e.:○ Alignment: STAR, bwa, bowtie, etc

○ QC: RSeQC, multiqc, etc

● NB: Some aligners have single cell mode○ e.g. STARsolo - STAR aligner scRNA-Seq mode

QC: Mitochondria and spike-in controls

● High % reads mapping to mitochondrial genes = indicates low sample quality

● Spike-in (synthetic) RNA is sometimes used as an alternative control

● Idea: if mito/spike-in reads make up high proportion of reads, mRNA concentration was low

Quantification

● STAR+htseq-count, kallisto, salmon, etc● Each sample has a different # of cells● Each cell has the same number of

measurements (e.g. genes)○ = (# of samples) x (# of cells) x (# of genes)○ Sparse: most will be zero!

● We consider only single sample case below

Count Matrix Normalization

● Normalization needed to make counts comparable between cells

● Two possible levels of normalization:○ Within cell (e.g. divide by column sum, “library size”)

○ Within dataset (e.g. divide by total number of reads)● All methods from bulk apply, i.e.

○ CPM, FPKM, DESeq2 etc…

The Counts Matrixcell1 cell2 cell3 cell4 cell5 cell6 ... cellM

gene1 93 25 0 0 3335 0 82

gene2 5 2 0 3 1252 0 12

gene3 0 0 0 0 0 0 0

gene4 98 21 1 1 5318 0 75

gene5 0 0 513 0 0 325 135

gene6 0 0 113 0 1 497 255

gene7 3 0 0 0 6 0 0

...

geneN 68 52 0 2 4313 63

● Counts matrix contains either:○ Read counts or○ UMI counts if used

● Each cell has:○ Total number of counts

(col. sum, “library size”)○ Number of non-zero genes

● Each gene has:○ # of non-zero cells○ Non-zero mean/variance

● Matrix is sparse: many zeros● Zeros may be:

○ Cell lacks gene○ A “drop-out”: gene present

but was missed by qPCR

Examining the Counts Matrixcell1 cell2 cell3 cell4 cell5 cell6 ... cellM

gene1 93 25 0 0 3335 0 82

gene2 5 2 0 3 1252 0 12

gene3 0 0 0 0 0 0 0

gene4 98 21 1 1 5318 0 75

gene5 0 0 513 0 0 325 135

gene6 0 0 113 0 1 497 255

gene7 3 0 0 0 6 0 0

...

geneN 68 52 0 2 4313 63

Each cell type has a signature, i.e. a pattern of gene expression

Consistent pattern of expression suggests same cell type:

● Cells 1, 2, and 5 (M?)● Cells 3, 6 (M?)

Filtering Cells and Genes● Many measurements

○ e.g. 30k genes x 1ks of cells

● Some cells are uninformative, e.g.:○ Very few reads, few genes detected○ Two cells sequenced together (i.e. doublets)

● Some genes are uninformative:○ Low # reads, low variance across all cells○ Too few cells express gene (e.g. < 10 of 10,000 cells

nonzero)

● Must filter genes and cells to reduce noise

Filtering the Counts Matrixcell1 cell2 cell3 cell4 cell5 cell6 ... cellM

gene1 93 25 0 0 3335 0 82

gene2 5 2 0 3 1252 0 12

gene3 0 0 0 0 0 0 0

gene4 98 21 1 1 5318 0 75

gene5 0 0 513 0 0 325 135

gene6 0 0 113 0 1 497 255

gene7 3 0 0 0 6 0 0

...

geneN 68 52 0 2 4313 63

Genes likely not expressed and should be filtered:

● Very few non-zero counts AND

● Low non-zero count mean

NB: Genes with few non-zero counts and HIGH non-zero count mean suggests rare cell type!

Filtering the Counts Matrixcell1 cell2 cell3 cell4 cell5 cell6 ... cellM

gene1 93 25 0 0 3335 0 82

gene2 5 2 0 3 1252 0 12

gene3 0 0 0 0 0 0 0

gene4 98 21 1 1 5318 0 75

gene5 0 0 513 0 0 325 135

gene6 0 0 113 0 1 497 255

gene7 3 0 0 0 6 0 0

...

geneN 68 52 0 2 4313 63

Cells might also be filtered:

● Very few or zero counts (cell4)

● Very many counts (cell5)○ Possible “doublet” of

same cell type● Inconsistent expression

pattern (cellM)○ Possible “doublet” of

different cell types

Doublet: two cells with same cell barcode

Filtering the Counts Matrix: Quality

● Filtering thresholds are subjective!● Must consider protocol, biological system,

and study design● Examples:

○ remove cells with median sum count < 3 median absolute deviations from median

○ Remove genes with more than 90% zeros AND non-zero mean < 10

Filtering the Counts Matrix: Variance

● Some genes are shared by all cells● Normalization assumes most genes are not

differentially expressed● Genes with low variance across cells are

uninformative● Filtering threshold is subjective!

Typical Analysis Paths

Counts matrix

Dimensionality Reduction

Unsupervised Clustering

Projection(tSNE, UMAP, etc) Visualization

Marker Gene Analysis

Differential Expression

Differences in Proportion

Unsupervised Clustering● Wish to identify subpopulations of cells using

similarity of transcript abundance● Clustering methods discover patterns in the

data● A priori no knowledge of number of clusters● Many available methods and metrics:

● PCA/Spectral analysis● Hierarchical or Ward agglomerative clustering● K-nearest neighbor clustering● Jaccard similarity

● Louvain community detection● K-means● Graph based clustering● Many, many more...

Analysis: Differential Expression

● Goal: identify gene expression differences between cell types (i.e. clusters)

● Simple solution: DESeq2 of each cluster vs all the others

● Significant genes drove the clustering● Examine for marker genes

Marker Gene Analysis

● Goal: Label each cluster to known cell type● Biological domain experts know which

genes are expressed by each cell type● Some clusters may be difficult to label

(novel cell types?)● NB: cells of one cell type may cluster by

state, e.g. cell cycle phase G1

Dimensionality Reduction

● Counts matrix may have many dimensions○ 1000s of genes x 1000s of cells

● Reduces # dimensions while preserving variance● May be necessary for large datasets (>1M cells) to

make downstream analysis algorithms tractable● Many methods available, including:

○ PCA○ Multidimensional Scaling (MDS)○ Downsampling

Projection + Visualization● Goal: accurately visualize cell clusters in two dimensions● Projection: embed samples of high dimensional space

into lower dimensional space, retaining structure of data○ e.g. map from (1000s genes x 1000s cells) to 2 (i.e. <x,y>)

● Ideally preserve both local and global structure● NB: this is challenging to do efficiently+accurately!● Available methods:

○ t-SNE: t-statistic Stochastic Neighbor Embedding○ UMAP: Uniform Manifold Approx. and Mapping○ PCA

t-SNE, UMAP, et ceteraConceptual idea:

1. Compute distance between all pairs of cells in high dimensional (i.e. all genes) space

2. Find a function that maps samples into 2D space s.t. cells that are close in original space are also close in embedded space

Can compute locally accurate mapping quickly:

● Cells near each other are similar● BUT cells far away from each other are not

necessarily proportionately far from each other!

● Local structure is preserved, global structure is not! t-SNE projections from human cortex single nuclear RNA-Seq

https://portal.brain-map.org/atlases-and-data/rnaseq

Visualizing Projections

● Wish to use clustering to interpret data● Projection methods produce an embedding● Strategy: map cell metadata onto

embedding and visualize● Following slides:

○ Single nuclear RNA-Seq from human cortex○ 6 regions sampled○ Source: Brain-Map Cell Type Database

https://portal.brain-map.org/atlases-and-data/rnaseq

Visualizing Projections

No color Colored by unsupervised clustering

Visualizing Projections

Colored by brain regionNon-neuronal (glia)

Glutamatergic neuronsGABAergic neurons

Colored by cell class, labeled by known

marker genes

The Many Subjects We Didn’t Cover

● Supervised clustering of cells by known markers/cell states (e.g. cell cycle)

● Comparing different samples● Pseudotime Analysis

○ Inferring cellular development/change over time● Imputation

○ Infer expression values for “dropout” genes● Many more...

Current Software

● Bioconductor

○ Seurat - One of the first analysis software packages

○ SingleCellExperiment - official Bioconductor class

○ scater - Single Cell Analysis Toolkit

● scanpy - single cell analysis in python● Many others now● Millions of others soon...