Briefings in Bioinformatics,
Journal Year:
2022,
Volume and Issue:
23(5)
Published: July 17, 2022
Abstract
The
rapid
development
of
spatial
transcriptomics
allows
the
measurement
RNA
abundance
at
a
high
resolution,
making
it
possible
to
simultaneously
profile
gene
expression,
locations
cells
or
spots,
and
corresponding
hematoxylin
eosin-stained
histology
images.
It
turns
promising
predict
expression
from
images
that
are
relatively
easy
cheap
obtain.
For
this
purpose,
several
methods
devised,
but
they
have
not
fully
captured
internal
relations
2D
vision
features
dependency
between
spots.
Here,
we
developed
Hist2ST,
deep
learning-based
model
RNA-seq
Around
each
sequenced
spot,
image
is
cropped
into
an
patch
fed
convolutional
module
extract
features.
Meanwhile,
with
whole
neighbored
patches
through
Transformer
graph
neural
network
modules,
respectively.
These
learned
then
used
by
following
zero-inflated
negative
binomial
distribution.
To
alleviate
impact
small
data,
self-distillation
mechanism
employed
for
efficient
learning
model.
By
comprehensive
tests
on
cancer
normal
datasets,
Hist2ST
was
shown
outperform
existing
in
terms
both
prediction
region
identification.
Further
pathway
analyses
indicated
our
could
reserve
biological
information.
Thus,
enables
generating
data
elucidating
molecular
signatures
tissues.
Nature Methods,
Journal Year:
2022,
Volume and Issue:
19(2), P. 171 - 178
Published: Jan. 31, 2022
Spatial
omics
data
are
advancing
the
study
of
tissue
organization
and
cellular
communication
at
an
unprecedented
scale.
Flexible
tools
required
to
store,
integrate
visualize
large
diversity
spatial
data.
Here,
we
present
Squidpy,
a
Python
framework
that
brings
together
from
image
analysis
enable
scalable
description
molecular
data,
such
as
transcriptome
or
multivariate
proteins.
Squidpy
provides
efficient
infrastructure
numerous
methods
allow
efficiently
manipulate
interactively
is
extensible
can
be
interfaced
with
variety
already
existing
libraries
for
Genome Medicine,
Journal Year:
2022,
Volume and Issue:
14(1)
Published: June 27, 2022
Abstract
Single-cell
transcriptomics
(scRNA-seq)
has
become
essential
for
biomedical
research
over
the
past
decade,
particularly
in
developmental
biology,
cancer,
immunology,
and
neuroscience.
Most
commercially
available
scRNA-seq
protocols
require
cells
to
be
recovered
intact
viable
from
tissue.
This
precluded
many
cell
types
study
largely
destroys
spatial
context
that
could
otherwise
inform
analyses
of
identity
function.
An
increasing
number
platforms
now
facilitate
spatially
resolved,
high-dimensional
assessment
gene
transcription,
known
as
‘spatial
transcriptomics’.
Here,
we
introduce
different
classes
method,
which
either
record
locations
hybridized
mRNA
molecules
tissue,
image
positions
themselves
prior
assessment,
or
employ
arrays
probes
pre-determined
location.
We
review
sizes
tissue
area
can
assessed,
their
resolution,
genes
profiled.
discuss
if
preservation
influences
choice
platform,
provide
guidance
on
whether
specific
may
better
suited
discovery
screens
hypothesis
testing.
Finally,
bioinformatic
methods
analysing
transcriptomic
data,
including
pre-processing,
integration
with
existing
inference
cell-cell
interactions.
Spatial
-omics
are
already
improving
our
understanding
human
tissues
research,
diagnostic,
therapeutic
settings.
To
build
upon
these
recent
advancements,
entry-level
those
seeking
own
research.
Nature Communications,
Journal Year:
2022,
Volume and Issue:
13(1)
Published: April 1, 2022
Recent
advances
in
spatially
resolved
transcriptomics
have
enabled
comprehensive
measurements
of
gene
expression
patterns
while
retaining
the
spatial
context
tissue
microenvironment.
Deciphering
spots
a
needs
to
use
their
information
carefully.
To
this
end,
we
develop
graph
attention
auto-encoder
framework
STAGATE
accurately
identify
domains
by
learning
low-dimensional
latent
embeddings
via
integrating
and
profiles.
better
characterize
similarity
at
boundary
domains,
adopts
an
mechanism
adaptively
learn
neighboring
spots,
optional
cell
type-aware
module
through
pre-clustering
expressions.
We
validate
on
diverse
datasets
generated
different
platforms
with
resolutions.
could
substantially
improve
identification
accuracy
denoise
data
preserving
patterns.
Importantly,
be
extended
multiple
consecutive
sections
reduce
batch
effects
between
extracting
three-dimensional
(3D)
from
reconstructed
3D
effectively.
