bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 28, 2024
Abstract
Missing
values
are
a
major
challenge
in
the
analysis
of
mass
spectrometry
proteomics
data.
hinder
reproducibility,
decrease
statistical
power
for
identifying
differentially
expressed
(DE)
proteins
and
make
it
challenging
to
analyze
low-abundance
proteins.
We
present
Lupine,
deep
learning-based
method
imputing,
or
estimating,
missing
tandem
tag
(TMT)
Lupine
is,
our
knowledge,
first
imputation
that
is
designed
learn
jointly
from
many
datasets,
we
provide
evidence
this
approach
leads
more
accurate
predictions.
validated
by
applying
TMT
data
>
1,000
cancer
patient
samples
spanning
ten
types
Clinical
Proteomics
Tumor
Atlas
Consortium
(CPTAC).
outperforms
state
art
imputation,
identifies
DE
than
other
methods,
corrects
batch
effects,
learns
meaningful
representation
samples.
implemented
as
an
open
source
Python
package.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 29, 2024
Abstract
Introduction
African
Americans
(AA)
are
widely
underrepresented
in
plasma
biomarker
studies
for
Alzheimer’s
disease
(AD)
and
current
diagnostic
candidates
do
not
reflect
the
heterogeneity
of
AD.
Methods
Untargeted
proteome
measurements
were
obtained
using
SomaScan
7k
platform
to
identify
novel
biomarkers
AD
a
cohort
AA
clinically
diagnosed
as
dementia
(n=183)
or
cognitively
unimpaired
(CU,
n=145).
Machine
learning
approaches
implemented
set
proteins
that
yields
best
classification
accuracy.
Results
A
protein
panel
achieved
an
area
under
curve
(AUC)
0.91
classify
vs
CU.
The
reproducibility
this
finding
was
observed
ANMerge
AMP-AD
Diversity
brain
datasets
(AUC=0.83;
AUC=0.94).
Discussion
This
study
demonstrates
potential
discovery
through
untargeted
proteomics
machine
approaches.
Our
findings
also
highlight
importance
matrisome
cerebrovascular
dysfunction
pathophysiology.
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 13, 2024
Advances
have
led
to
a
greater
understanding
of
the
genetics
Alzheimer's
Disease
(AD).
However,
gap
between
predicted
and
observed
genetic
heritability
estimates
when
using
single
nucleotide
polymorphisms
(SNPs)
small
indel
data
remains.
Large
genomic
rearrangements,
known
as
structural
variants
(SVs),
potential
account
for
this
missing
heritability.
By
leveraging
from
two
ongoing
cohort
studies
aging
dementia,
Religious
Orders
Study
Rush
Memory
Aging
Project
(ROS/MAP),
we
performed
genome-wide
association
analysis
testing
around
20,000
common
SVs
1,088
participants
with
whole
genome
sequencing
(WGS)
data.
A
range
Related
Disorders
(AD/ADRD)
clinical
pathologic
traits
were
examined.
Given
limited
sample
size,
no
significant
was
found,
but
mapped
across
81
AD
risk
loci
discovered
22
in
linkage
disequilibrium
(LD)
GWAS
lead
directly
associated
AD/ADRD
phenotypes
(nominal
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 28, 2024
Abstract
Missing
values
are
a
major
challenge
in
the
analysis
of
mass
spectrometry
proteomics
data.
hinder
reproducibility,
decrease
statistical
power
for
identifying
differentially
expressed
(DE)
proteins
and
make
it
challenging
to
analyze
low-abundance
proteins.
We
present
Lupine,
deep
learning-based
method
imputing,
or
estimating,
missing
tandem
tag
(TMT)
Lupine
is,
our
knowledge,
first
imputation
that
is
designed
learn
jointly
from
many
datasets,
we
provide
evidence
this
approach
leads
more
accurate
predictions.
validated
by
applying
TMT
data
>
1,000
cancer
patient
samples
spanning
ten
types
Clinical
Proteomics
Tumor
Atlas
Consortium
(CPTAC).
outperforms
state
art
imputation,
identifies
DE
than
other
methods,
corrects
batch
effects,
learns
meaningful
representation
samples.
implemented
as
an
open
source
Python
package.
