Hi,
this
is
Sam
Gardner
with
JMP.
I'm
a
Product
Manager
at
JMP.
We're
here
to
talk
today
about
introducing
JMP Pro
for
Genomics,
pushing
the
boundaries
of
JMP Pro
to
enable
data
science
on
the
desktop.
I
am
one
of
the
presenters.
I'll
be
doing
the
introduction
to
this
topic.
I'm
S
enior
Product
Manager
for
Health
and
Life
Sciences
in the
Product
Management
team
at
JMP.
Our co-presenter
today
is
Russ
Wolfinger,
who's
a
Distinguished
Research
Fellow
and
our
Director
of
Scientific
Discovery
and
Genomics
at
JMP.
We'll
talk
a
little
bit
about
the
background
of
genetics
and
genomics,
functional
genomics,
and
then
talk
about
what
we're
doing
to
transition
from
our
former
product,
JMP
genomics,
to
using
JMP
Pro
for
genomics.
Russ
will
demonstrate
some
of
the
new
capabilities
in
the
product.
A
little
bit
about
classical
genetics.
This
is
where
a
lot
of
this
got
started.
People
have
been
doing
classical
genetics
for
a
long
time.
They've
been
breeding
plants
and
animals
to
get
desired
traits
for
those
plants
and
animals.
They've
seen
that
they
can
do
that
to
get,
stronger
animals,
better
plants,
plants with
desired
properties
and
so
on.
You
probably
studied
a
long
time
ago,
when
you
were
young
in
school,
about
Gregor
Mendel,
the
monk,
who
spent
many
years
studying
garden
peas.
He
actually
measured
seven
distinct
characteristics
of
these
peasâ
their
height,
their
pod
shape
and
color,
seed
shape
and
color,
flower
position
and
colorâ
and
observed
that
as
these
peas
were
crossbred
with
each
other,
that
the
traits
were
passed
on
from
the
parent
plants
to
the
progeny
plants
following
some
rather
specific
mathematical
ratios
who have made
it
probabilistically
possible
to
make
predictions
about
what
the
progeny
would
look
like
based
on
the
traits
of
the parents.
His and
later
work
established
the
principles
of
genetic
inheritance.
What
is
genomics?
Genomics is
more
than
just
classical
genetics.
Genomics
uses
a
combination
of
DNA
measurement
methods
and
recombinant
DNA
methods
to
sequence
and
assemble
and
analyze
the
structure
and
function
of
genomes.
It
differs
from
classical
genetics
in that
it
looks
at
the
organism's
full
complement
of
genetic
or
hereditary
material.
It
focuses
on
the
interactions
between
the
loci
or
the
location
of
different
genes
on
the
genome,
and
the
alleles,
the
variation
in
the
genes
in
the
genome,
so
that
you
can
understand
things
like
epistasis,
pleiotropic
heterosis,
which
are
things
like,
okay,
one
gene
affects
many
things.
That's
pleiotropy.
Epistasis is
that
sometimes,
one
gene
impacts
the
output
or
the
effect
of
another
gene.
Heterosis
is
sometimes
you
get
synergistic
effects
by
combining
the
genes
from
two
different
parents
or
two
different
organisms.
This
all
relies
upon
the
use
of
the
central
dogma
of
genomics.
That
dogma
is
that
DNA,
which
is
the
code
for
our
biological
systems,
is
transcribed
into
RNA,
which
is
the
code
that's
used
to
make
things
and
make
proteins
in
the
body.
The
proteins
are
the
little
chemical
engines
that
do
things
inside
the
body
and
give it
its
function.
From
that,
you
can
actually
then
measure
things
like
metabolites,
what
actually
happens,
what
do
those
proteins
actually
do
inside
the
cells
and
inside
the
body.
The
path
is
DNA
creates
RNA
creates
protein,
and
the
protein
regulates
how
things
function
in
the
body,
and
that
produces
metabolites.
Data
is
really
enabling
a
genomics
revolution.
Modern
measurement
techniques
are
really
helping
us
understand
the
structure
and
function
of
the
genome
and
how
it
works
inside
the
cells
in
biological
system.
We
can
sequence
the
genome now.
We've
got
next-
generation
sequencing.
Many
years
ago,
when
JMP
first moved
into
this
area,
helping
customers to be able
to
analyze
this
type
of
data,
the
way
to
measure
it
was
microwaves,
which
was
much
more
focused
on
very
specific
parts
of
the
genome,
and
oftentimes
a
very
limited
set
of
genes
in
the
genome.
Now,
you
can
sequence
the
whole
genome
of
an
organism.
Also,
you
can
look
at
things
like
expression
and
regulation.
We're
talking
about
the
metabolites.
What
is the
output
into
the
biological
system
that
you
can
measure?
You
can
look
at
how
the
proteins
are
produced
or
what
those
proteins
are
doing.
You
can
also
look
at
how
the
structure
of
the
DNA
itself,
what's
called
epigenetics,
impacts
the
function
of how
DNA
works
and
how
the
genes
work
inside
the
body.
There
are
typically
three
main
stages
of
analysis
that
happen
when
you're
doing
this
type
of
work.
One
is
you
just
generate
the
raw
data.
You
do
the
sequencing
work,
generate
the
genome-
sequencing
data,
or
measure
the
metabolites
or
the
protein
expression
or
the
RNA
expression.
And
then
that
generates
pretty
large
data
sets
that
have
to
be
filtered
and de-
multiplexed
and
trimmed
and
scored
and
cleaned
up.
This
is
typically
handled
in
a
automated
or
semiautomated
workflow
on
computer
systems
that
can
process
very
large
data
files.
Then
it
typically
goes
into
a
second
stage
where
you
start
to
do
sequence
alignment
and
basically
lining
things
up,
and
being
able
to
do
things
like
counts.
How
many
times
did
I
see
the
expression
of
a
particular
RNA
fragment
or
RNA
sequence?
Or
how
many
times
did
I
see
a
particular
protein?
Or
all
this
raw
data,
how
does
it
line
up
to
actually
make
a
picture
of
what
the
structure
of
the
whole
genome
is?
That's
a
pretty
big
mathematical
computational
process.
That
typically
also
gets
done
on
pretty
large
computational
systems
with
a
lot
of
computational
resources.
And
then
the
third stage,
which
is
the
stage
where
JMP
really
has
played
in,
and
where JMP
Pro
will
continue
to
play
in,
is
the
determining
genotype
associations
and
genotype-to-
phenotype
relationships.
A
phenotype
is
just
a
trait
of
organisms,
the
relationship
between
the
genes
and
the
traits.
And
also
looking
at
correlations
and
associations
of
the
different
genetic
markers
inside
the
genome,
or
the
variance
of
the
genetic
markers.
Oftentimes,
what you
want to
do
is
you
want
to
characterize
those
and
then
correlate
them
to
physical,
biological,
or
maybe
disease
state
characteristics.
All
of
this
can
actually
be
done
with
desktop
software.
JMP
Pro
is
our
solution
to
do
that
going
forward
in
the
future.
We've
had
a
product
called
JMP
Genomics
for
14
years,
up
until
this
year,
that
we
were
providing
the
customers.
It
was
a
combination
product
of
JMP
and
SAS.
SAS was
really
needed
back
early
when
we
first put
this
out
to
do
a
lot
of
the
data
processing,
because
the
size
and
the
types
of
data
we
looked
at
was
very
difficult
to
do
with
a
desktop
software
package
like
JMP.
SAS did
the
data
processing,
some
of
the
statistical
methods,
but
JMP
was
used
for
further
statistical
analysis
and
visualizing
the
results
of
those
analysis.
JMP
Genomics
has
been
used in research
and
industry
for
a
wide
variety
of
genomics
problems
for
many
years.
But
we
made
a
strategic
decision
this
year
to
discontinue
selling
products
that
contain
SAS with
them.
That's
part
of
the
decision
that
was
made
for
JMP
to
become
an independent
company.
We're
a
wholly-owned
subsidiary
of
SAS
now,
and
are
moving
down
that
road
of
independence.
We
are
not
going
to
be
selling
anything
but
JMP
products
going
forward.
Because
of
that,
we
have
looked
now
to
move
the
functions
for
genomic
data
analysis
into
JMP
Pro.
In
JMP
Pro
17,
which
will
be
available
this
fall
in
2022,
has
been
and
will
be
optimized
for
big
and
wide
data
problems.
It's
going
to
have
capabilities
to
meet
the
needs
of
genomic
data
science
and
genomic
data
scientists.
It's
going
to
utilize
the
strength
of
JMP Pro's
predictive
analytics
and
interactive
visualization
to
help
enable
discoveries
in
this
area
of
work.
Some
of
the
enhancements
that
we've
made
to
push
the
boundaries
of
JMP
Pro
include
just
removing
barriers
and
bottlenecks
in
the
software.
It's
one
thing
to
do
analysis
on
tens
or
hundreds
or
even
thousands
of
columns
in
a
data
table.
But
when
you
have
a
data
table
which
maybe
has
many
thousands
or
hundreds
of
thousands
of
columns,
you
start
to
reveal
limitations
sometimes
in
your
software.
By
doing
this
work,
we've
uncovered
places
where
we
just
need
to
streamline
how
operations
happen
inside
the
program.
We've
done
that.
An
example
would
be
if
I
wanted
to
do
a
transformation
on
hundreds
of
thousands
of
columns,
we've
significantly
improved
that
process.
It happens
much
faster
on
the
data
tables.
Also
being
able
to
do
very
fast
and
efficient
multivariate
analysis
methods
like
principal
component
analysis
and
clustering,
when
you
have
these
really
wide
genomic
data
tables.
And
then
being
able
to
do
models
over
and
over
again
on
thousands
and
thousands
of
response
columns,
and
to
do
that
efficiently
and
effectively.
The
second
goal
that
we
have
in
this
transition
is
that
bring
in
some
capabilities
in
the
JMP
Pro
that
are
very
specific
for
genetic
and
genomic
analysis.
For
instance,
being
able
to
import
different
formats
that
are
commonly
used
in
this
area.
Also,
being
able
to
do
genetic
marker
analysis
and
simulation,
as
well
as
bringing
in
some
newer
popular
data
reduction
methods
such
as
t-SNE
and
Unimap.
Overall,
what
we're
getting
to
is
a
product
that's
going
to
be
lean.
It installs
very
quickly.
You
can
use
it
on
your
desktop,
but
you
can
use
it
to
do
this
very
powerful
analysis
on
these
large,
complex,
wide
data
tables.
To
illustrate
that,
I'm
going
to
turn
it
over
to
Russ.
Russ
is
going
to
show
us
actually
how
you
can
do
some
realistic
analysis
and
some
real
study
analysis
here
on
some
genomic
and
genetic
data.
Well,
thank
you,
Sam.
It's
a
real
exciting
time
for
us.
I
know
I've
actually
been
with
the
genomics
analysis
revolution
within
SAS
for
over
20
years
now.
We
actually
[inaudible 00:11:46]
in
the
early
2000s
called
Scientific
Solutions,
where
we
were
starting
to
look
at
some
of
the
early
micro
array
data.
It's
been
a
really
fun
20
years.
Now,
I
would
say,
almost
one
of
the
most
exciting
times
ever
for
us,
where
we're
now
able
to
code
some
of
these
routines
directly
in
JMP
pro
using
C++.
A
lot
of
them
are
running
much
faster
than
we
had
in
the
previous
JMP
Genomics
product.
I
want
to
give
you
a
little f
lavor
of
that
today
with
an
example.
This
is
a
data
set
on
loblolly pines,
which
for
those
of
you
from
the
Southeast
might
know
it
as
probably
one
of
the
most
popular
species
of
pine.
Typically,
if
you
go
into
Home Depot
or Lowe's
and
buy
some
two-
by-
fours
or
plywood,
it's
going
to
be
made
of
l
oblolly.
When
you
fly
into
the
area,
you
happen to see
a
lot
of
tree
cover.
Many
of
those,
I'd
say
a
good
chunk
of
those
trees,
especially
towards
the
Eastern
part
of
North
Carolina,
are
lobl
ollies.
It's
a
very
important
species,
one
that
we
really
want
to
understand
well.
It's
been
studied
very
thoroughly,
and
even
more
so
now
that
we've
got
some
crunches
going
on
with
home
building
and
what
have
you,
it's
critical
to
understand
it
inside
and
out.
Genomic
technology
is
fantastic
for
revealing
some
things
that
we
just
never
knew
before.
This
data
is
actually
still
10
years
old.
It
was
from
a
paper
in
the
Journal
of
Genetics
by
Resende
et
al.
This
is
a
group
of
researchers
from
the
University
of
Florida
and
Embrapa
in
Brazil
and
University
of
Iowa,
I
believe,
if I
recall
correctly.
Here's
the
reference
if
you
want
to
look
it
up.
The
data
are
also
freely
available.
I've
got
them.
I
went
ahead
and
downloaded
them
from
the
supplemental
information
and
loaded
them
into
a
JMP
table
that
you
see
here.
As
Sam
was
mentioning,
the format
in JMP
Pro
is
what
we
typically
like
to
call
a
wide
format,
where
we've
got
everything
in
one
table.
Here,
we've
got
some
genotype
indicator
numbers
indicating
the
lines
as
well
as
the
mother
and
father
that
the
trees
came
from.
And
then
this
specific
data
set
that
I've
got
here,
we've
got
six
traits
that
we've
measured.
I
believe
actually
there's
more.
I think
there's
17,
if
you
want
to
see
the
reference.
Our
key
focus
of
interest
are
these
genetic
markers.
This
data
set's
small
by
today's
standards.
We've
only
got
4,800.
I
say
"Only
4,800"
but that's
still
quite
a
few.
As
you
can
see,
I'm
scrolling
through
here,
they're
all
coded
as
either
zero,
one,
or two.
These
are
so-called
SNP
markers,
single
nucleotide
polymorphisms,
where
we'll
have
either...
The
number
here
indicates
the
number
of
the
major
allele
that
we
have
in
the
data.
Zero
would
be
the
little A, little A,
if you're
familiar
with
the
old
genetics
notation.
The
twos
would
be
the
big
A, big A.
The
ones
would
be
all
the
heterozygotes.
So 4,500
of
these
markers.
The
basic
goal
in
the
end,
typically...
In
fact,
that
was
what
the
paper
that
this
was
from
was
about.
They
were
comparing
several
of
the
popular
predictive
methods.
But
before
we
get
to
prediction,
there's
a
lot
of
really
good
things
that
you
want
to
do
just
to
make
sure
the
data
are
as
expected,
and
also
to
learn
and
discover
structure
and
other
interesting
characteristics.
Let's
dive
in
and
see
what we can do
with a
typical
workflow
here
in
JMP Pro.
I
would
typically
just
like
to
look
at
the
data
in
JMP.
We
can
use
just
basic
platforms.
For
example,
here,
let
me
bring
up
the
multi-
area
platform
and
just
check
out
basic
plots
of
the
data
against
one
another.
You
can
see,
for
example,
here,
rootnum
and
root
numbin
are
fairly
highly
correlated
with
each
other.
Other ones,
not
so
much.
You
can
do
distributions.
For
example, w
e
can
do
it
here
with
the
distribution
platform.
These
traits
have
actually
already
been
centered,
I
think.
I believe
all
of
them
have
a
mean
of
around
zero.
They've
gone
through
a
little
bit
of
pre-processing
that
we
won't
go
into
today.
That's the
way
they
came
from
the
paper.
Our
basic
goal
is
to
use
the
genetic
information
to
predict
these
traits.
They
represent
various
characteristics
of
the
loblolly
trees.
For
example,
C
WAC,
I
believe
that's
crowned
with
across
the
plant
beddings.
It's
a
measure
of
the
tree
size.
We've
got
other
measurements
of
density
and
characteristic
of
the
roots,
etc.
All
important
things
to
know
about
and
when
studying
these
trees.
Let
me
walk
you
through
what
we
might
consider
a
a
basic
workflow
once
you
have
your
data
set
up
like
this.
Now,
before
doing
that, though,
I
do
want
to
mention
too
that
we
have
put
in
a
fair
bit
of
work
to
helping
and
aiding
with
importing
such
data.
This
particular
data
came
as
just
standard
comma
separated
value
files,
so
no
big
deal
to
import
it.
But
often,
genetic
data
like
this
come
in
so-called
VCF
files.
We now
have
new
routines
to
be
able
to
import
those
directly,
as
well
as
import
files
from
the
popular
database,
and
then
a
few
other
formats,
IDAT
and
what
have
you.
Trying
to
make
it
really
easy
to
get
your
data
into
JMP.
As you know,
once
you've
got
your
data
set
up
in
a
JMP
table,
there's
just
all
kinds
of
great
things
you
can
do.
Many
of
the
things
that
you
hear
about...
Give
you
some
more
ideas,
as
well
as
some
new
things
that
we've
put
into
place.
To
start
out,
we've
got
a
brand
new
couple
of
platforms
under
the
Analyze
menu
here
at
the
bottom.
Genetics.
Analyze,
Genetics.
We've
got
Marker
Statistics
and
Marker
Simulation.
Let's
run
the
first one,
Marker
Statistics.
This
is
just
a
basic
platform
for
looking
at
characteristics
of
a
set
of
markers.
You
can
see
here,
I'm
loading.
We've
got
4,853
SNPs
organized
in
a
group
here
in
the
JMP
table.
I
just
move
them
over
into
the
markers.
If everything else
is
okay,
we'll just
click
OK.
It
runs
quite
quickly.
What
this
basically
does
is
it takes
each
marker
and
computes
a
variety
of
standard
statistical
genetic
statistics
that
you
can
look
across
here
and
see
what's
going
on.
A
key
thing
to
check
for
a
so-called
Hardy-
Weinberg
Equilibrium.
You
can
do
a
statistical
test
of
that
and
get
p-
values
from
it,
and
even
plot
these
along
in
a
graph
like
this.
On
the
Y
axis,
we
actually
use
the
log
10
p-value,
which
we
also
call
the
log
worth.
To
go
once
step
further,
you
can
make
a
false
discovery
rate
adjustment
to
avoid
the
multiple
testing
problem.
You
can
see
here,
we've
actually
plotted
both:
the
raw
p-value,
the
raw
log
worth,
as
well
as
their
FDR
adjusted
p-value.
They
tend
to
be
quite
similar,
especially
for
the
large
ones.
These
markers
up
here
are
ones
that
would
be
out
of
equilibrium,
very
likely
due
to
the
cloning
of
the
trees.
These would be
markers
that
might
tend
to
drift
or
stabilize
over
time
with
future
crosses.
It would be good
to
check
these
out
and
make
sure
the
distributions
of
the
alleles
are
as
expected.
Arcing
all
the
way
back
to
the Gregor
Mendel
days,
things
that
we
learned
about
how
alleles
like this
should
behave.
That's
a
good
place
to
start,
just
to
get
an
idea
for
the
markers.
Let's
move
next
and
do
some
pattern
discovery.
Here,
there's
several
nice
things
we
can
try.
A
very
basic
one
that's
also
been
popular
for
decades
with
gene
expression
data
is
just
to
do
hierarchical
clustering.
Again,
I'm
just
going
to
put
the
SNPs
in
here.
You
typically
will
want
to
use
one
of
these
faster
methods.
Let's
use
fast
ward.
We
do
have
some
missing
values,
so
let's
do
imputation.
We'll
go
ahead
and
cluster it
two
ways.
Let's
click
OK
here.
I'm
going
to
go ahead.
I'm running
everything
live
today.
A
few
of
these
things
will
take
seconds
to
run.
A
nalyses
I've
got
that
actually
will
take
a
few
minutes
that
I
won't
run
live
just
for
sake
of
time.
But
you
can
see
here,
this
scale
of
data,
JMP
Pro
can handle
fairly
readily.
This
one,
you
can
see
that
the
progress
bar
here
will
take
probably
30 seconds
to
a
minute
to
finish.
But
not
too
bad
for
a
medium-
sized
data
set
like
this.
Again,
we're
clustering
around
926
rows
and
4,800
columns.
But
before
actually
the
performance
enhancements,
this
kind of analysis
would
take
several
minutes.
In
many
cases,
we've
been
able
to
achieve
orders of
magnitude
speed
up.
I'm
able,
basically,
to
enable
you
to
do
analyses
like
this
close
to
real
time.
A
little
bit
of
waiting
might
be
required
as
here,
but
in
general,
it's pretty
nice
to
be
able
to
quickly
get
answers
to
fairly
difficult
questions.
For
example,
here,
we're
trying
to
see
how
other
rows
of
our
data
cluster
with
each
other.
Now
here,
a
very
interesting
thing
occurs.
You
can
see
I've
got
colorings
that
I
did
to
the
data.
I
colored
the
mother
and
father,
or maternal
and
paternal
alleles.
If
we
look
at
this
variable
here,
there's
around
71
unique
levels.
And
then
within
each
cross,
there's
up
to
17
or
20
individuals.
The
data
have
very
nice,
tight
clusters.
The
clustering
algorithm
actually
found
those.
You can
see
the
colors
indicate
the
coloring.
This
color
theme
is
a
bit
jarring.
Let's
move
it
to black
and
white.
We
can
see
the
structure
a
little
more
cleanly.
Here,
we
can
see
the
areas
of
white
or
where
we've
got
some
of
those
minor
alleles
starting
to
cluster
and
identifying
the
key
places
in
the
genome
that
distinguish
these
unique
crosses.
This
is
a
nice
plot
just
to
get
an
overall
feel
for
the
various
lines
and
how
they
compare
with
one
another.
But
the
main
lesson
are
these
tight
clusters
that
are
mapping
up
exactly
like
we
would
expect
with
the
initial
crosses,
basically
like
very
close
siblings
to
one
another
compared
to
cousins,
or
second
cousins,
third cousins,
etc
.
Now,
another
way
to
go
about
this
would
be
more
of
a
dimension
reduction
type
approach.
Here,
the
number
one
analysis
is
principal
components.
Let's
try
that
on
our
steps
and
see
what
that
reveals.
Here,
let's
just
use
the
defaults.
Sorry,
actually,
I
wanted
to
show
off...
There's
a
brand
new
method
for
wide
data
that's
called
fast
approximate.
It's
a
nice
addition
in
software.
It
actually
uses,
if
you're
familiar
with
the
method
called
a
randomized
SVD
approach.
You
can
see
a little
message.
Let's
see
what's
in
the
log.
It
turned
out
this
was
actually
one
case
where
an
error
message
was
quite
beneficial.
The
software
actually
indicated
which
markers...
There
were
some
markers,
they
were
non-numeric
or
constant.
It
turned
out
that
a
handful
of
these
markers
in
the
table
were
constant.
This
would
be
a
case
where
we
could
go
back
and
actually
clean
those
out,
since
they're
not
really
contributing
much
to
the
analysis,
they're
just
constant.
But
the
PCA
platform
found
them
as
a
byproduct.
But
if
you
look
at
the
scores,
first two
principal
components,
we
again
have
this
nice
clustering
of
families.
As
usual
with
JMP,
all
these
plots
are
interactive
and
connected
to
one
another.
We
can,
for
example,
click
on
one
of
the
branches
of
the
tree
over
here,
and
it
will
highlight
that
cluster
in
the
PCA.
We
can
map
these
two
graphs
to
one
another.
In
fact,
well,
let's
do
that.
We
can
add
a
third one.
This
is
another
brand
new
platform
that's
just
coming
out
in
JMP
17,
called
Multivariate
Embedding.
Here,
we're
going
to
compute
the
popular
t-SNE
algorithm,
which
stands
for
T
multivariate
embedding.
This
has
actually
been
quite
popular
in
the
machine
learning
world,
and
it has
trickled
its
way
into
the
genomics
field,
especially
with
single-
cell
RNA.
It
does
a
little
bit
different
dimensional
projection
than
PCA.
It
tries
to
identify
local
structure,
whereas
PCA
is
looking
for
dimensions
of
largest
variability
across
all
markers.
T-SNE's
trying
to
find
tight
local
clusters.
It's
actually
perfect
for this kind of data,
just
to
reveal
these
families.
You
can
see
the
nice
little
groups
of
clusters,
and
maybe
more
importantly,
which
clusters
themselves
are
near
each
other.
You
can
take
a
picture
here.
Kind
of
looks
like
a
butterfly,
something
t-SNE will
often
have
.
I'd
encourage
you
to
try
it
on
your
data
once
you
get
your
hands
on
JMP
17.0.
That's
revealing
some
nice
structure
in
the
data.
Let's
move
on
now
to
a
dd some
more
statistically-
oriented
modeling.
For
it,
the
basic
thing
to
usually
start
out
with
is
what
we
would
call
a
genome-
wide a
ssociation
study,
where
w
e'll
basically
take
our
trait,
or
our
traits,
in
this
case,
and
screen
them
against
all
the
markers.
The
workhorse
platform
here
is
Response
Screening.
I'm
going
to
Analyze,
Screening,
Response
Screening.
We've
done
quite
a
bit
of
work
on
this
thanks
especially to
John
Saul
,
who has
implemented
some
nice
performance
improvements.
What
this
does
is
basically
a
big
Y
by
X
analysis.
I'm
going
to
move
our
six
targets
or
responses
into
the
Y
field,
our
SNPs
into
X.
And
then
all
you
do
is
hit
Go.
What
this
will
do...
I
think
we
do
imputation.
I
think
it
might
do
that
automatically.
Let's
see.
Yeah.
This
one
runs
lightning
fast.
I
basically
just
did
six
times
4,800
quick
regressions
and
plotted
all.
This
is
a
plot
of
all
the
p-values
at
once.
Again,
focusing
on
false
discovery
rate.
It's
got
to
be
very
careful
about
overfishing
data
like
this.
You
want
to
make
sure
any
lead
that
you
chase
is
significant,
even
after
a
false
discovery
adjustment.
Here,
we
see
now
that
this
crown width
feature
is
the
one
that's
popping
out
with
the
most
hits.
Then
there's
one
for
rustbin.
These
are
sorted
by
significance,
and
then
some
of
the
other
traits
start
to
pop
in.
But
clearly,
it
looks
like
we've
got
the
most
genetic
action
with
this
crown width
trait.
Now,
to
go
a
little
further
and
illustrate
the
things
we
can
do.
This
is
very
JMP
Pro like.
Let's
save
the
table
out
of
p-values.
We've
got
everything
now
in
a
new
JMP
table,
which
is
effectively
all
the
results,
and
they're
nicely
colored
for
us.
Just
want
to
browse
the
table.
But
I'm
going
to
go
ahead
and
use
Graph B
uilder
now.
Let's
make
some
volcano
plots
by
hand.
For
these,
we
w
ant
to
put
the
slope
on
the X-
axis,
and
then
the
log
worth
on
the
Y.
Let's
go
ahead.
We'll
make
a
separate
one
for
each
of
our
traits.
I'm
dragging
that
onto
the
wrap.
You
can
see
here,
this
is
the
kind of
thing
that
JMP
is
really
interesting
at.
I
t often
will
find
outliers
of the
data.
Here's
one
that's
way
out
here.
We've
got
a
slope
estimate
of
nearly
negative
2,000.
It
turns
out
that
this
variable
is
nearly
constant.
The
regression
just
blows
up
with
an
almost
nearly
vertical,
or
nearly
negative,
highly
negative
slope.
It
turns
out
this
is
more
of
an
anomaly
than
an
actual
significant
hit.
It
would
actually
make
sense
just
to
ignore
it.
But
it's
actually
nice
to
find
that
it's
in
the
table
and
be
able
to
identify
it.
This
is
the
kind of thing
that
JMP
is
often
really
good
at,
finding
weird
patterns.
But
to
hone
in
on
the
key
results,
let's
go
ahead
and
narrow
our
axes
down.
I
just
hit
the
axis
button,
and
we're
going
to
just
zoom
in.
Let's
go
minus
10 to 10.
You
can
see
here,
you
get
this
characteristic
V
shape,
where
again,
we're
plotting
the
slope
of
the
regression
versus
its
negative
log
p-value.
For CWAC,
we
actually
got,
again,
as
we
expected
before,
more
hits
than
anywhere
else.
A
bunch
of
markers
for
positive
and
negative
slope,
which
would
indicate
a
additive
genetic
relationship
going
one
way
to
the
other.
For
the
other
traits,
these
are
also
V
shape,
and
many
of
them
are
just
really
a
lot
less
significant
and
often sq
uished
in
with
one
another.
The
slope
also
depends
on
the
scale
of
the
measurement.
It's maybe
not
quite
as
meaningful if
we
put
all
these
on
the
same
exact
scale.
But
I
just
wanted
to
show
this
for
illustration,
as
a
way
to
compare
everything
side
by
side.
That's a GWAS.
Moving
forward,
let's
get
to
probably
what
our
main
objective
would
be,
which
would
be
to
predict
these
traits
as
a
function
of
the
markers.
Here,
we
do
have
access
to
all
the
great
predictive
modeling
platforms
that
are
in
JMP.
Some
of
these,
you
have
to
be
a
little
careful
to
use.
With
missing
data,
you
may
need
to
do
the
imputation
first.
Some
might
become
quite
slow
given
the
size
of
the
problem.
For
today,
I
just
want
to
show
probably
my
favorite
one,
which
is
XG
Boost,
using
the
XGB
oost
platform.
This
is
a
case
where
I
actually
ran
this
beforehand,
because,
and
it's
to
run..
.
But
I
l
oaded
all
six
traits
into
XGB
oost
and
did
ten-
fold
cost
validation.
I
automatically
left
out
each
of
the
ten
folds.
Here,
you
can
see
the
results
of
that
run,
where
we've
got
the
solid
lines
here
in
these
graphs,
are
the
validation
curves
over
the
iterations
and
the
dotted
lines
of
the
training.
You
can
see
with
these
wide
problems,
there's
a
severe
risk
of
overfitting,
especially
with
a
powerful
approach
like
XG
Boost.
You
have
to
be
very
careful.
As you
can
see,
I actually
[inaudible 00:32:18]
parameters.
I
could
tweak
them
down
for
one,
and
you
can
see
the
other
parameters
here.
Within
each
model
fit,
we've
got
both
the
training,
observed
versus
predicted,
and
the
validation.
You
can
see
here
for
C
WAC
we
got
a
correlation
of
around
0.43.
Correlation
is
a
typical
measure
used
to
assess
performance.
This
is
competitive
with
what
was
published
in
the
paper
before,
without
hardly
much
tuning
at
all.
But
then
there's
a
lot
of
other
interesting
things
you
can
dive
into,
the
most
important
features,
etc.
We
even
got
some
new
things
for
instance,
one
thing
called
Shapley
values
that
I'd
encourage
you
to
check
out.
There's
going
to
be
another
talk
on
this
topic
by
Peter
Hirsch,
Florian
Laura
Lancaster
and myself
on
that
here
at
the
conference,
I
would
encourage
you
to
check
that
out.
It's
a
way
to
break
down
predictions
into
their
components.
That
gets
another
level
you
can
go
into
with
predicting.
That's
just
one
example
of
some
nice
predictive
modeling
you
can
do.
To
wrap
up
the
demo,
I
wanted
to
return
back
where
we
started
here
in
this
Genetics
menu.
We've
got
a
marker,
a
brand
new
marker
simulation
platform.
This
is
some
pretty
advanced
genetic
modeling
carried
out
by
our
internal
expert,
Luciano
Silva.
What
this
does
is
it
actually
will
do
virtual
crossing
by
the
genotypes.
The
idea
is
you'd
load
the
markers
in.
The
really
interesting
thing
is
you
can
put
a
predictor
formula
here.
For
example,
I
save
the
predictor
formula
from
the
XGBoost
model
of
CWAC.
What
this
will
do
is
both
simulate
the
crosses
and
predict
their
performance.
This
is
what
modern
virtual
breeding
does.
You
can
actually
virtually
cross
different
loblolly
pine
trees
and
predict
what
will
happen
with
them
without
having
to
wait
10,
20,
30
years
to
grow
them
in
the
field.
Extremely
powerful,
interesting
approach
that
revolutionized
the
way
modern
breeding
is
done,
and
why
so-called
genomic
selection,
or
predictive
modeling
with
genetic
markers
is
so
popular.
I'll
go
ahead
and
conclude
there.
I
hope
that
whetted
your
appetite
with
some
of
the
new
things
we've
got
going.
A
lot
of
the
things
I
showed
today
would
also
work
with
gene
expression
data,
although
that's
a
little
bit
different
ballgame
in
terms
of
what
you're
trying
to
do.
But
for
sake
of
time,
I
thought
it
would
be
good
just
to
look
at
this
one
example
and
dive
somewhat
deep.
Thank
you
very
much
for
your
attention.
Let
us
know
if
you've
got
questions
as
you
have
them.
We're
really
e
xcited
about
the
new
things
coming
in
JMP
17
Pro.
We've
got
a
lot
more
things
coming
in
the
works.
Thank
you very much.
We
recognize
that
lot
of
people
that
come
to
discovery,
this
may
not
be
their
area
of
expertise.
But you
may
know
somebody
who's
doing
this
work,
and
we
would
love
to
get
them
connected
with
what
we're
doing
here
at
JMP
Pro,
because
we
are
going
to
continue
to
invest
in
adding
capabilities
and
improving
the
software
so
it
can
do
work
like
this
better
and
better
to
meet
the
needs
of
scientists
across
the
life
sciences
and
this
industry.
Thanks
for
listening
in.
