Briefings in Bioinformatics,
Journal Year:
2023,
Volume and Issue:
24(4)
Published: July 1, 2023
The
rapid
growth
of
omics-based
data
has
revolutionized
biomedical
research
and
precision
medicine,
allowing
machine
learning
models
to
be
developed
for
cutting-edge
performance.
However,
despite
the
wealth
high-throughput
available,
performance
these
is
hindered
by
lack
sufficient
training
data,
particularly
in
clinical
(in
vivo
experiments).
As
a
result,
translating
this
knowledge
into
practice,
such
as
predicting
drug
responses,
remains
challenging
task.
Transfer
promising
tool
that
bridges
gap
between
domains
transferring
from
source
target
domain.
Researchers
have
proposed
transfer
predict
outcomes
leveraging
pre-clinical
(mouse,
zebrafish),
highlighting
its
vast
potential.
In
work,
we
present
comprehensive
literature
review
deep
methods
health
informatics
decision-making,
focusing
on
molecular
data.
Previous
reviews
mostly
covered
image-based
works,
while
more
detailed
analysis
papers.
Furthermore,
evaluated
original
studies
based
different
evaluation
settings
across
cross-validations,
splits
model
architectures.
result
shows
those
great
potential;
sequencing
state-of-the-art
lead
significant
insights
conclusions.
Additionally,
explored
various
datasets
papers
with
statistics
visualization.
International Journal of Molecular Sciences,
Journal Year:
2023,
Volume and Issue:
24(3), P. 2943 - 2943
Published: Feb. 2, 2023
As
an
emerging
sequencing
technology,
single-cell
RNA
(scRNA-Seq)
has
become
a
powerful
tool
for
describing
cell
subpopulation
classification
and
heterogeneity
by
achieving
high-throughput
multidimensional
analysis
of
individual
cells
circumventing
the
shortcomings
traditional
detecting
average
transcript
level
populations.
It
been
applied
to
life
science
medicine
research
fields
such
as
tracking
dynamic
differentiation,
revealing
sensitive
effector
cells,
key
molecular
events
diseases.
This
review
focuses
on
recent
technological
innovations
in
scRNA-Seq,
highlighting
latest
results
with
scRNA-Seq
core
technology
frontier
areas
embryology,
histology,
oncology,
immunology.
In
addition,
this
outlines
prospects
its
innovative
application
Chinese
(TCM)
discusses
issues
currently
being
addressed
great
potential
exploring
disease
diagnostic
targets
uncovering
drug
therapeutic
combination
multiomics
technologies.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: Jan. 18, 2023
Spatially
resolved
transcriptomics
involves
a
set
of
emerging
technologies
that
enable
the
transcriptomic
profiling
tissues
with
physical
location
expressions.
Although
variety
methods
have
been
developed
for
data
integration,
most
them
are
single-cell
RNA-seq
datasets
without
consideration
spatial
information.
Thus,
can
integrate
from
multiple
tissue
slides,
possibly
individuals,
needed.
Here,
we
present
PRECAST,
integration
method
complex
batch
effects
and/or
biological
between
slides.
PRECAST
unifies
factor
analysis
simultaneously
clustering
and
embedding
alignment,
while
requiring
only
partially
shared
cell/domain
clusters
across
datasets.
Using
both
simulated
four
real
datasets,
show
improved
detection
outstanding
visualization,
estimated
aligned
embeddings
labels
facilitate
many
downstream
analyses.
We
demonstrate
is
computationally
scalable
applicable
to
different
platforms.
Biomedicine & Pharmacotherapy,
Journal Year:
2023,
Volume and Issue:
165, P. 115077 - 115077
Published: July 1, 2023
Traditional
bulk
sequencing
methods
are
limited
to
measuring
the
average
signal
in
a
group
of
cells,
potentially
masking
heterogeneity,
and
rare
populations.
The
single-cell
resolution,
however,
enhances
our
understanding
complex
biological
systems
diseases,
such
as
cancer,
immune
system,
chronic
diseases.
However,
technologies
generate
massive
amounts
data
that
often
high-dimensional,
sparse,
complex,
thus
making
analysis
with
traditional
computational
approaches
difficult
unfeasible.
To
tackle
these
challenges,
many
turning
deep
learning
(DL)
potential
alternatives
conventional
machine
(ML)
algorithms
for
studies.
DL
is
branch
ML
capable
extracting
high-level
features
from
raw
inputs
multiple
stages.
Compared
ML,
models
have
provided
significant
improvements
across
domains
applications.
In
this
work,
we
examine
applications
genomics,
transcriptomics,
spatial
multi-omics
integration,
address
whether
techniques
will
prove
be
advantageous
or
if
omics
domain
poses
unique
challenges.
Through
systematic
literature
review,
found
has
not
yet
revolutionized
most
pressing
challenges
field.
using
shown
promising
results
(in
cases
outperforming
previous
state-of-the-art
models)
preprocessing
downstream
analysis.
Although
developments
generally
been
gradual,
recent
advances
reveal
can
offer
valuable
resources
fast-tracking
advancing
research
single-cell.
Nature Communications,
Journal Year:
2022,
Volume and Issue:
13(1)
Published: Oct. 17, 2022
Abstract
Computational
tools
for
integrative
analyses
of
diverse
single-cell
experiments
are
facing
formidable
new
challenges
including
dramatic
increases
in
data
scale,
sample
heterogeneity,
and
the
need
to
informatively
cross-reference
with
foundational
datasets.
Here,
we
present
SCALEX,
a
deep-learning
method
that
integrates
by
projecting
cells
into
batch-invariant,
common
cell-embedding
space
truly
online
manner
(i.e.,
without
retraining
model).
SCALEX
substantially
outperforms
iNMF
other
state-of-the-art
non-online
integration
methods
on
benchmark
datasets
modalities,
(e.g.,
RNA
sequencing,
scRNA-seq,
assay
transposase-accessible
chromatin
use
scATAC-seq),
especially
partial
overlaps,
accurately
aligning
similar
cell
populations
while
retaining
true
biological
differences.
We
showcase
SCALEX’s
advantages
constructing
continuously
expandable
atlases
human,
mouse,
COVID-19
patients,
each
assembled
from
sources
growing
every
data.
The
capacity
superior
performance
makes
particularly
appropriate
large-scale
applications
build
upon
previous
scientific
insights.
Genome biology,
Journal Year:
2022,
Volume and Issue:
23(1)
Published: June 27, 2022
Abstract
It
is
a
challenging
task
to
integrate
scRNA-seq
and
scATAC-seq
data
obtained
from
different
batches.
Existing
methods
tend
use
pre-defined
gene
activity
matrix
convert
the
into
data.
The
often
of
low
quality
does
not
reflect
dataset-specific
relationship
between
two
modalities.
We
propose
scDART,
deep
learning
framework
that
integrates
learns
cross-modalities
relationships
simultaneously.
Specifically,
design
scDART
allows
it
preserve
cell
trajectories
in
continuous
populations
can
be
applied
trajectory
inference
on
integrated
Nature Communications,
Journal Year:
2022,
Volume and Issue:
13(1)
Published: Dec. 1, 2022
Abstract
Single-cell
data
integration
can
provide
a
comprehensive
molecular
view
of
cells.
However,
how
to
integrate
heterogeneous
single-cell
multi-omics
as
well
spatially
resolved
transcriptomic
remains
major
challenge.
Here
we
introduce
uniPort,
unified
framework
that
combines
coupled
variational
autoencoder
(coupled-VAE)
and
minibatch
unbalanced
optimal
transport
(Minibatch-UOT).
It
leverages
both
highly
variable
common
dataset-specific
genes
for
handle
the
heterogeneity
across
datasets,
it
is
scalable
large-scale
datasets.
uniPort
jointly
embeds
datasets
into
shared
latent
space.
further
construct
reference
atlas
gene
imputation
Meanwhile,
provides
flexible
label
transfer
deconvolute
spatial
using
an
plan,
instead
embedding
We
demonstrate
capability
by
applying
variety
including
transcriptomics,
chromatin
accessibility,
data.
Nature Biotechnology,
Journal Year:
2023,
Volume and Issue:
42(7), P. 1096 - 1106
Published: Sept. 7, 2023
Abstract
Although
single-cell
and
spatial
sequencing
methods
enable
simultaneous
measurement
of
more
than
one
biological
modality,
no
technology
can
capture
all
modalities
within
the
same
cell.
For
current
data
integration
methods,
feasibility
cross-modal
relies
on
existence
highly
correlated,
a
priori
‘linked’
features.
We
describe
matching
X-modality
via
fuzzy
smoothed
embedding
(MaxFuse),
method
that,
through
iterative
coembedding,
smoothing
cell
matching,
uses
information
in
each
modality
to
obtain
high-quality
even
when
features
are
weakly
linked.
MaxFuse
is
modality-agnostic
demonstrates
high
robustness
accuracy
weak
linkage
scenario,
achieving
20~70%
relative
improvement
over
existing
under
key
evaluation
metrics
benchmarking
datasets.
A
prototypical
example
proteomic
with
data.
On
two
analyses
this
type,
enabled
consolidation
proteomic,
transcriptomic
epigenomic
at
resolution
tissue
section.
Nature Genetics,
Journal Year:
2023,
Volume and Issue:
55(12), P. 2104 - 2116
Published: Nov. 30, 2023
Abstract
Conventional
methods
fall
short
in
unraveling
the
dynamics
of
rare
cell
types
related
to
aging
and
diseases.
Here
we
introduce
EasySci,
an
advanced
single-cell
combinatorial
indexing
strategy
for
exploring
age-dependent
cellular
mammalian
brain.
Profiling
approximately
1.5
million
transcriptomes
400,000
chromatin
accessibility
profiles
across
diverse
mouse
brains,
identified
over
300
subtypes,
uncovering
their
molecular
characteristics
spatial
locations.
This
comprehensive
view
elucidates
expanded
or
depleted
upon
aging.
We
also
investigated
cell-type-specific
responses
genetic
alterations
linked
Alzheimer’s
disease,
identifying
associated
types.
Additionally,
by
profiling
118,240
human
brain
transcriptomes,
discerned
cell-
region-specific
transcriptomic
changes
tied
pathogenesis.
In
conclusion,
this
research
offers
a
valuable
resource
probing
both
normal
pathological