fig:sce-structure
fig:scworkflow
fig:quick-start-umap
fig:qc-plot-pancreas
fig:qc-dist-416b
fig:qc-mito-zeisel
fig:qc-mito-spike-zeisel
fig:discardplot416b
fig:discardplotpbmc
fig:histlib
fig:deconv-zeisel
fig:norm-spike-t
fig:norm-effect-malat
fig:cellbench-lognorm-fail
fig:cellbench-lognorm-downsample
fig:trend-plot-pbmc
fig:trend-plot-seger-noweight
fig:cv2-pbmc
fig:spike-416b
fig:tech-pbmc
fig:blocked-fit
fig:zeisel-scree
fig:elbow
fig:cluster-pc-choice
fig:zeisel-parallel-pc-choice
fig:corral-sort
fig:zeisel-pca
fig:zeisel-pca-multi
fig:tsne-brain
fig:tsne-perplexity
fig:umap-brain
fig:tsne-clust-graph
fig:pbmc-force
fig:walktrap-v-others
fig:cluster-mod
fig:cluster-graph
fig:tsne-clust-kmeans
fig:kmeans-gap
fig:tsne-clust-kmeans-best
fig:kmeans-tree
fig:tsne-kmeans-graph-pbmc
fig:dend-416b
fig:dend-cluster
fig:tsne-416b
fig:silhouette416b
fig:pbmc-silhouette
fig:pbmc-box-purities
fig:walktrap-v-louvain-prop
fig:walktrap-res-clustree
fig:pbmc-rand-breakdown
fig:bootstrap-matrix
fig:ccr7-dist-memory
fig:cd48-memory-expression
fig:heat-basic-pbmc
fig:heat-focused-pbmc
fig:heat-wmw-pbmc
fig:comparative-markers-tw
fig:viol-de-binom
fig:viol-gcg-lawlor
fig:pval-dist
fig:singler-heat-pbmc
fig:singler-cluster
fig:singler-comp-pancreas
fig:auc-dist
fig:aucell-muraro-heat
fig:violin-milk
fig:lipid-synth-violin
fig:thrsp-violin
fig:tsne-pbmc-uncorrected
fig:tsne-pbmc-rescaled
fig:tsne-pbmc-residuals
fig:tsne-pbmc-corrected
fig:heat-after-mnn
fig:rand-after-mnn
fig:tsne-pbmc-corrected-markers
fig:pbmc-marker-blocked
fig:tsne-initial
fig:heat-cluster-label
fig:md-embryo
fig:mds-embryo
fig:bcvplot
fig:qlplot
fig:allantois-dispersion
fig:exprs-unique-de-allantois
fig:exprs-unique-de-allantois-more
fig:proxy-ambience
fig:abplotbcv
fig:abplotql
fig:rankplot
fig:ambientpvalhist
fig:qc-mito-pbmc
fig:ambient-removal-igkc
fig:ambient-removal-lyz
fig:barcode-rank-mix-genes
fig:barcode-rank-mix-hto
fig:hto-total-comp
fig:hto-1to2-hist
fig:hto-ambient
fig:hto-ambient2
fig:hto-mix-tsne
fig:mammary-swapped-barcode-rank
fig:mammary-swapped-barcode-rank-after
fig:heatclust
fig:markerexprs
fig:denstsne
fig:densclust
fig:hash-barcode-rank
fig:hto-2to3-hash
fig:tsne-hash
fig:doublet-prop-hash-dist
fig:heat-cyclin
fig:heat-cyclin-grun
fig:dist-lef1
fig:phaseplot416b
fig:cell-cycle-regression
fig:cell-cycle-regression2
fig:cell-cycle-regression3
fig:leng-nocycle
fig:discard-416b
fig:cell-cycle-contrastive
fig:tscan-nest-tsne
fig:tscan-nest-pseudo
fig:tscan-nest-omega
fig:tscan-nest-mnn
fig:traj-princurve-tsne-nest
fig:traj-princurve-clustered-nest
fig:traj-princurve-omag-nest
fig:nest-1-simple-down
fig:nest-1-simple-up
fig:nest-1-simple-up-heat
fig:nest-pseudo-reordered
fig:nest-3-versus
fig:entropy-nest
fig:tsne-hermann-velocity
fig:tscan-sperm-velocity
fig:tcell-pseudotime
fig:nuclei-qc
fig:nuclei-tsne
fig:nuclei-tsne-merged
fig:nuclei-contamination
fig:cite-detected-ab-hist
fig:cite-total-con-hist
fig:cite-total-all-hist
fig:pbmc-adt-ambient-profile
fig:comp-bias-norm
fig:control-bias-norm
fig:tsne-tags
fig:heat-tags
fig:subcluster-stats
fig:gzmh-cd8-t
fig:subcluster-tag-dist
fig:combined-umap
fig:tsne-naive
fig:heat-pd-1
fig:unnamed-chunk-1
fig:tcr-prop-cluster-prod
fig:tcr-prop-most-abundant
fig:iSEE-default
fig:iSEE-landing
fig:iSEE-qc
fig:iSEE-anno
fig:iSEE-hvg
fig:iSEE-rowcol
fig:iSEE-tour
fig:elsie-fail
fig:vegeta-fail
fig:unref-416b-qc-dist
fig:unref-416b-qc-comp
fig:unref-416b-norm
fig:unref-416b-variance
fig:unref-416b-tsne
fig:unref-416b-silhouette
fig:unref-416b-markers
fig:unref-zeisel-qc-dist
fig:unref-zeisel-qc-comp
fig:unref-zeisel-norm
fig:unref-zeisel-var
fig:unref-zeisel-tsne
fig:unref-zeisel-heat-cell
fig:unref-zeisel-heat-lfc
fig:unref-unfiltered-pbmc-qc
fig:unref-unfiltered-pbmc-mito
fig:unref-unfiltered-pbmc-norm
fig:unref-unfiltered-pbmc-var
fig:unref-unfiltered-pbmc-tsne
fig:unref-mono-pbmc-markers
fig:unref-pbmc-filtered-var
fig:unref-filtered-pbmc-variance
fig:unref-filtered-pbmc-tsne
fig:unref-filtered-pbmc-merged-tsne
fig:unref-pbmc-adt-qc
fig:unref-pbmc-adt-qc-mito
fig:unref-norm-pbmc-adt
fig:unref-clustmod-pbmc-adt
fig:unref-tsne-pbmc-adt
fig:unref-grun-qc-dist
fig:unref-grun-norm
fig:unref-grun-tsne
fig:unref-muraro-qc-dist
fig:unref-muraro-norm
fig:unref-muraro-variance
fig:unref-seger-heat
fig:unref-muraro-tsne
fig:unref-lawlor-qc-dist
fig:unref-lawlor-qc-comp
fig:unref-lawlor-norm
fig:unnamed-chunk-4
fig:unref-seger-qc-dist
fig:unref-seger-norm
fig:unref-seger-variance
fig:unref-seger-heat-1
fig:unref-seger-tsne
fig:unref-seger-tsne-correct
fig:unref-ma-plots
fig:unref-voom-plots
fig:tsne-pancreas-rescaled
fig:tsne-pancreas-mnn
fig:tsne-pancreas-mnn-many
fig:tsne-pancreas-mnn-donor
fig:tsne-pancreas-mnn-donor-all
fig:unref-hgrun-qc-dist
fig:unref-hgrun-norm
fig:unref-hgrun-var
fig:unref-hgrun-tsne
fig:unref-heat-hgrun-markers
fig:unref-nest-qc-dist
fig:unref-nest-norm
fig:unref-nest-var
fig:unref-nest-tsne
fig:unref-heat-nest-markers
fig:unref-assignments-nest
fig:unref-nest-facs
fig:unref-paul-qc-dist
fig:unref-paul-norm
fig:unref-paul-var
fig:unref-paul-heat
fig:unref-paul-tsne
fig:unref-paul-tsne2
fig:unref-umap-merged-hsc
fig:unref-umap-traj-hsc
fig:unref-umap-traj-hsc2
fig:unref-pijuan-var-1
fig:unref-pijuan-var-2
fig:unref-pijuan-var-3
fig:unref-pijuan-tsne
fig:unref-bach-qc-dist
fig:unref-bach-qc-comp
fig:unref-bach-norm
fig:unref-bach-var
fig:unref-bach-tsne
fig:unref-messmer-hesc-qc
fig:unref-messmer-hesc-norm
fig:unref-messmer-hesc-cyclone
fig:unref-messmer-hesc-var
fig:unref-messmer-hesc-tsne
fig:unref-messmer-hesc-cpca-tsne
fig:unref-hca-bone-qc
fig:unref-hca-bone-mito
fig:unref-hca-bone-var
fig:unref-hca-bone-ab
fig:unref-hca-bone-umap
fig:unref-hca-bone-dotplot
introduction
what-you-will-learn
preliminaries
workflows
what-you-wont-learn
who-we-wrote-this-for
why-we-wrote-this
acknowledgements
learning-r-and-more
the-benefits-of-r-and-bioconductor
getting-started-with-r
running-r-locally
installing-r
for-macoslinux-users
installing-r-bioconductor-packages
getting-help-in-and-out-of-r
bioconductor-documentation
bioconductor-packages
biocviews
bioconductor-forums
beyond-r-basics
becoming-an-r-expert
becoming-a-bioconductor-developer
nice-companions-for-r
shellbash
git
other-languages
data-infrastructure
background
storing-primary-experimental-data
filling-the-assays-slot
adding-more-assays
handling-metadata
on-the-columns
on-the-rows
other-metadata
single-cell-specific-fields
background-1
dimensionality-reduction-results
alternative-experiments
size-factors
column-labels
conclusion
overview
introduction-1
experimental-design
obtaining-a-count-matrix
data-processing-and-downstream-analysis
quick-start
quality-control
quality-control-motivation
choice-of-qc-metrics
identifying-low-quality-cells
fixed-qc
quality-control-outlier
identifying-outliers
outlier-assumptions
qc-batch
other-approaches
quality-control-plots
quality-control-discarded
marking-qc
normalization
motivation
library-size-normalization
normalization-by-deconvolution
spike-norm
applying-the-size-factors
normalization-transformation
downsampling-and-log-transforming
other-options
feature-selection
motivation-1
quantifying-per-gene-variation
variance-of-the-log-counts
coefficient-of-variation
sec:spikeins
accounting-for-blocking-factors
variance-batch
using-a-design-matrix
hvg-selection
overview-1
based-on-the-largest-metrics
based-on-significance
feature-selection-positive
apriori-hvgs
feature-selection-subsetting
dimensionality-reduction
overview-2
principal-components-analysis
choosing-the-number-of-pcs
motivation-2
using-the-elbow-point
using-the-technical-noise
based-on-population-structure
using-random-matrix-theory
count-based-dimensionality-reduction
dimensionality-reduction-for-visualization
motivation-3
visualizing-with-pca
t-stochastic-neighbor-embedding
uniform-manifold-approximation-and-projection
visualization-interpretation
clustering
motivation-4
what-is-the-true-clustering
clustering-graph
background-2
implementation
other-parameters
assessing-cluster-separation
k-means-clustering
background-3
base-implementation
assessing-cluster-separation-1
in-two-step-procedures
hierarchical-clustering
background-4
implementation-1
silhouette-width
general-purpose-cluster-diagnostics
cluster-separation-redux
comparing-different-clusterings
cluster-bootstrapping
subclustering
marker-detection
motivation-5
pairwise-tests-between-clusters
motivation-6
combining-pairwise-statistics-per-cluster
looking-for-any-differences
finding-cluster-specific-markers
balancing-stringency-and-generality
using-the-log-fold-change
marker-tests
using-the-wilcoxon-rank-sum-test
using-a-binomial-test
using-custom-de-methods
combining-multiple-marker-statistics
marker-batch
using-the-block-argument
using-the-design-argument
p-value-invalidity
from-data-snooping
false-replicates
further-comments
cell-type-annotation
motivation-7
assigning-cell-labels-from-reference-data
overview-3
using-existing-references
using-custom-references
assigning-cell-labels-from-gene-sets
assigning-cluster-labels-from-markers
computing-gene-set-activities
integrating-datasets
motivation-8
setting-up-the-data
batch-diagnosis
linear-regression
performing-mnn-correction
correction-diagnostics
mixing-between-batches
preserving-biological-heterogeneity
integration-with-markers
using-corrected-values
multi-sample-comparisons
motivation-9
setting-up-the-data-1
differential-expression-between-conditions
creating-pseudo-bulk-samples
performing-the-de-analysis
introduction-2
pre-processing
statistical-modelling
putting-it-all-together
looping-across-labels
cross-label-meta-analyses
ambient-problems
motivation-10
finding-affected-degs
by-estimating-ambient-contamination
with-prior-knowledge
without-an-ambient-profile
subtracting-ambient-counts
differential-abundance
overview-4
performing-the-da-analysis
composition-effects
background-5
assuming-most-labels-do-not-change
removing-the-offending-labels
testing-against-a-log-fold-change-threshold
comments-on-interpretation
de-da-duality
sacrificing-differences
droplet-processing
motivation-11
qc-droplets
background-6
testing-for-empty-droplets
relationship-with-other-qc-metrics
removing-ambient-contamination
cell-hashing
background-7
cell-calling-options
demultiplexing-on-hto-abundance
further-comments-1
removing-swapped-molecules
doublet-detection
overview-5
doublet-detection-with-clusters
doublet-simulation
doublet-detection-in-multiplexed-experiments
background-8
identifying-inter-sample-doublets
guilt-by-association-for-unmarked-doublets
further-comments-2
cell-cycle-assignment
motivation-12
using-the-cyclins
using-reference-profiles
using-the-cyclone-classifier
removing-cell-cycle-effects
comments
with-linear-regression-and-friends
removing-cell-cycle-related-genes
using-contrastive-pca
trajectory-analysis
overview-6
obtaining-pseudotime-orderings
overview-7
cluster-based-minimum-spanning-tree
basic-steps
tweaking-the-mst
further-comments-3
principal-curves
characterizing-trajectories
overview-8
changes-along-a-trajectory
changes-between-paths
further-comments-4
finding-the-root
overview-9
entropy-based-methods
rna-velocity
real-timepoints
single-nuclei-rna-seq-processing
introduction-3
quality-control-for-stripped-nuclei
comments-on-downstream-analyses
nuclei-ambient-tricks
integrating-with-protein-abundance
motivation-13
setting-up-the-data-2
quality-control-1
normalization-1
overview-10
library-size-normalization-1
cite-seq-median-norm
control-based-normalization
computing-log-normalized-values
clustering-and-interpretation
integration-with-gene-expression-data
by-subclustering
by-combined-clustering
by-differential-testing
repertoire-seq
motivation-14
loading-the-tcr-repertoire
leveraging-list-semantics
converting-back-to-dataframes
case-study-for-clonotype-analyses
repeating-for-immunoglobulins
interactive-sharing
motivation-15
interactive-quickstart
isee-examples
quality-control-2
annotation-of-cell-populations
querying-features-of-interest
reproducible-visualizations
dissemination
additional-resources
dealing-with-big-data
motivation-16
fast-approximations
nearest-neighbor-searching
big-data-svd
parallelization
out-of-memory-representations
interoperability
motivation-17
interchanging-with-seurat
interchanging-with-scanpy
lun-416b-cell-line-smart-seq2
introduction-4
data-loading
quality-control-3
normalization-2
variance-modelling
batch-correction
dimensionality-reduction-1
clustering-1
interpretation
zeisel-mouse-brain-strt-seq
introduction-5
data-loading-1
quality-control-4
normalization-3
variance-modelling-1
dimensionality-reduction-2
clustering-2
interpretation-1
unfiltered-human-pbmcs-10x-genomics
introduction-6
data-loading-2
quality-control-5
normalization-4
variance-modelling-2
dimensionality-reduction-3
clustering-3
interpretation-2
filtered-human-pbmcs-10x-genomics
introduction-7
data-loading-3
quality-control-6
normalization-5
variance-modelling-3
dimensionality-reduction-4
clustering-4
data-integration
human-pbmc-with-surface-proteins-10x-genomics
introduction-8
data-loading-4
quality-control-7
normalization-6
dimensionality-reduction-5
clustering-5
grun-human-pancreas-cel-seq2
introduction-9
data-loading-5
quality-control-8
normalization-7
variance-modelling-4
data-integration-1
dimensionality-reduction-6
clustering-6
muraro-human-pancreas-cel-seq
introduction-10
data-loading-6
quality-control-9
normalization-8
variance-modelling-5
data-integration-2
dimensionality-reduction-7
clustering-7
lawlor-human-pancreas-smarter
introduction-11
data-loading-7
quality-control-10
normalization-9
variance-modelling-6
dimensionality-reduction-8
clustering-8
segerstolpe-human-pancreas-smart-seq2
introduction-12
data-loading-8
quality-control-11
normalization-10
variance-modelling-7
dimensionality-reduction-9
clustering-9
data-integration-3
segerstolpe-comparison
merged-pancreas
introduction-13
the-good
the-bad
the-ugly
grun-mouse-hsc-cel-seq
introduction-14
data-loading-9
quality-control-12
normalization-11
variance-modelling-8
dimensionality-reduction-10
clustering-10
marker-gene-detection
nestorowa-mouse-hsc-smart-seq2
introduction-15
data-loading-10
quality-control-13
normalization-12
variance-modelling-9
dimensionality-reduction-11
clustering-11
marker-gene-detection-1
cell-type-annotation-1
miscellaneous-analyses
paul-mouse-hsc-mars-seq
introduction-16
data-loading-11
quality-control-14
normalization-13
variance-modelling-10
dimensionality-reduction-12
clustering-12
merged-hsc
introduction-17
data-loading-12
setting-up-the-merge
merging-the-datasets
combined-analyses
pijuan-sala-chimeric-mouse-embryo-10x-genomics
introduction-18
data-loading-13
quality-control-15
normalization-14
variance-modelling-11
merging
clustering-13
dimensionality-reduction-13
bach-mouse-mammary-gland-10x-genomics
introduction-19
data-loading-14
quality-control-16
normalization-15
variance-modelling-12
dimensionality-reduction-14
clustering-14
messmer-hesc
introduction-20
data-loading-15
quality-control-17
normalization-16
cell-cycle-phase-assignment
feature-selection-1
batch-correction-1
dimensionality-reduction-15
hca-human-bone-marrow-10x-genomics
introduction-21
data-loading-16
quality-control-18
normalization-17
variance-modeling
data-integration-4
dimensionality-reduction-16
clustering-15
differential-expression
cell-type-classification
contributors
bibliography