Today
we're
going
to
be
talking
about
using
JMP,
our
favorite
software
and
applying
it
to
a
real-
world
problem
in
our
analytical
sciences
department
at
Amyris
which
is
a
symbio
company.
Before
we
jump
into
that,
I
wanted
to
introduce
myself
as
well
as
Scott,
who
helped
me
along
this
journey.
I'm
Stefan,
I'm
an
associate
director
of
R&
D
data
analytics A
myris.
I
have
twelve
years
of
industry
experience,
a
lot
of
diverse
background,
I've
worked
in
various
labs
from
analytical
chemistry
to
fermentation
science
and
in
more
recent
years
focused
more
on
the
quality
and
data
science
side
of
things.
Scott
has
helped
me in
a
lot
of
the
content
here
and
has
been
working
with
Amyris
for
a
number
of
years
and
he
is
one
of
the
JMP
pros
working
for JMP.
I'd
like
to
start
off
by
just
saying
thank
you
to
Scott
for
helping
us
out
here.
We're
going
to
split
the
talk
today
into
three
parts.
I'm
going
to
give
a
bit
of
background
and
context
both
on
synthetic
biology,
if
you
haven't
heard
of
that
before,
and
analytical
chemistry.
The
main
part
of
the
talk
is
really
going
to
be
focused
on
then
applying
JMP
to
a
specific
question
we
had
and
then
finally
we'll
wrap
it
up
briefly
touching
on
automation
and
then
the
impact
of
the
analysis
and
this
case
study
we'll
look
at
together.
S
ome
of
you
may
not
be
familiar
with
synthetic
biology
or
analytical
chemistry
and
I
really
like
to
understand
context
and
background
and
is
going
to
be
relatively
important
for
the
case
study
we
look
at,
we'll
focus
on
that
today
and
start
there.
S
ynthetic
biology
really
leverages
microorganisms,
as
we
like
to
call
them,
as
living
factories.
We
use
mainly
yeast
in
the
case
of
Amyris
that
we
precision
engineer,
and
we
use
the
process
of
fermentation,
which
is
not
a
new
thing
it's
something
people
have
been
using
for
thousands
of
years,
mainly
to
make
alcohol
and
bread
in
a
lot
of
cases.
In
our
case,
we're
using
the
yeast
in
fermentation,
feeding
it
sugar
and
converting
that
sugar
into
a
variety
of
target
ingredients
and
chemicals.
Those
ingredients
and
chemicals
we
can
then
make
higher
purity
so
they
may
be
higher
performing
lower
costs
and
in
a
more
sustainable
fashion.
To
give
an
example,
this
isn't
just
a
fairy
tale.
This
is
reality,
it's
not
an
idea,
we
have
18
molecules
today
that
we
manufacture
at
scale
and
I'm
showing
a
subset
of
those
here.
There's
an
example
on
the
top
left
you
have
Artemisinin.
That's
an
antimalarial
drug,
it
was
our
first
molecule
and
that's
how
our
company
was
founded.
In
the
top
middle,
we
have
Biophene
which
is
actually
a
powerful
building
block
that
we
then
convert
into
other
chemicals
and
applications.
One
example
being
Squalene
which
is
a
very
popular
emollient
used
in
the
cosmetics
industry
and
traditionally
is
sourced
from
shark
livers
and
one
that
might
be
familiar
in
the
bottom
middle,
we
have
patchouli.
Some
people
associate
that
with
the
hippie
smell,
it's
a
fragrance,
but
it's
actually
really
ubiquitous
in
the
fragrance
industry
as
a
base
note,
so
it
goes
into
thousands
of
products.
Things
like
Tide
detergent
have
patchouli
in
it
and
we
can
manufacture
this,
which
is
traditionally
extracted
from
plants
with
our
synthetic
biology
platform.
I
work
in
the
R&D
function,
and
so
our
goal
is
really
to
identify
the
best
E
strains
that
we
can
then
use
at
manufacturing
scale,
and
that
requires
research
at
scale.
We
run
highly
automated
a
lot
of
high-
throughput
workflows
at
Amyris
in
Emeryville,
and
so
from
the
left
there
we
start
screening
our
yeast
strains
at
a
capacity
of
about
600,000
strains
per
month.
We
take
those
top
performers
and
we
promote
them
to
what
we
call
our
bench-
scale
bioreactor
fermentations,
which
you
can
see
pictured
on
the
right
there.
Throughout
all
of
this,
we're
creating
a
lot
of
strains,
which
means
we
also
need
to
understand
what's
happening
in
those
strains,
what
are
they
producing,
how
much,
and
that's
really
where
analytics
come
in.
Those
analytics
need
to
be
run
at
a
scale
to
match
that
so
we
can
really
get
the
data
to
understand
what's
happening.
With
this
scale
of
research,
there's
a
lot
of
opportunities,
and
a
lot
of
those
opportunities
come
from
looking
at
conventional
approaches
and
reconsidering
how
to
do
those.
I will talk
a
little
bit
about
analytical
chemistry.
Again,
that's
not
anything
that's
unique
to
synthetic
biology.
It's
pervasive
in
a
lot
of
industries,
petroleum
industry,
environmental
sciences,
pharma,
very
common
way
just
to
measure
things.
I'll
talk
here
really
about
chromatography,
and
as
an
example,
I'll
take
fermentation
that
we
do
on
the
bioreactive
scale.
From
this
fermentation,
we're
going
to
sample
that
while
it's
running.
We're
going
to
get
a
dirty
sample
from
that
which
we
then
can
further
prepare
and
dilute.
We
have
this
mixture
of
components
in
this
final
form.
We'll
then
take
this
mixture
of
components,
we'll
run
it
across
some
separation
techniques.
That's
a
chromatograph.
What
that's
going
to
do
is
based
on
the
property
of
those
components,
might
be
size,
it
might
be
polarity,
it'll
allow
us
to
separate
those
out.
We
then
feed
that
into
some
detection
mechanism.
There's
a
variety
that
you
can
use
and
what
that
gives
you
is
a
separation
of
these
components
over
time
and
then
some
intensity
of
response.
The
last
piece
and
where
we're
going
to
focus
today
is
intensity
isn't
really
a
useful
thing
for
you
or
me
to
make
decisions
on.
We
need
to
translate
that
into
something
useful
like
a
concentration.
The
calibration
curve
allows
us
to
translate
that
intensity
into
a
concentration,
and
of
course,
you
can
imagine
if
you
get
that
translation
wrong,
your
data
is
going
to
be
wrong
and
it's
going
to
mislead
you.
Calibration
curves
is
where
we'll
focus
today,
and
that's
the
heart
of
the
question.
A
calibration
curve
is
created,
by
running
standards
with
varying
levels
of
your
known
component.
The
example
I'm
showing
here,
we
have
a
low
mid-
high,
so
a
three
-evel
calibration.
We
know
what
the
concentration
is
in
those
because
we
prepared
them,
and
we
measure
the
response
on
these
instruments.
From
there,
we
can
fit
some
calibration
curve.
In
this
example,
I'm
showing
just
a
simple
linear
fit,
and
then
we
can
run
unknown
samples,
read
the
response
off
our
instrument
and
do
an
inverse
prediction.
We're
taking
our
response
from
the
Y
and
predicting
what
the
quantity is
in
that
sample.
It's
a
very
common
way
to
be
able
to
quantify
things
in
unknown
samples.
That's
our
background.
We're
going
to
jump
into
the
case
study
looking
at
this
key
question
we
had
around
optimizing
a
part
of
our
process
in
our
analytics.
A bit
more
background
here
is
that
when
we
do
calibration
in
our
labs,
there's
a
cost
associated
maintenance
of
these
calibrations
and
calibration
curves
and
calibration
standards
is
expensive,
both
due
to
people's
time,
but
also
materials.
These
materials
can
often
cost
thousands,
even
tens
of
thousands
of
dollars
per
gram.
With
the
scale
that
we're
doing
our
research
at,
it
really
pushes
us
again
to
reconsider
those
conventional
approaches.
We're
running
millions
of
samples
per
year,
and
we
have
a
really
diverse
set
of
analytical
methods
so
we
have
currently
in
our
lab
in
Emeryville,
over
100
different
analytical
methods
measuring
all
components.
One
place
we
looked
at
is
conventionally.
We
see
this
with
most
people
we
hire,
this
is
where
people
start.
Conventionally
calibration
curves
often
have
five
to
seven
levels,
whether
they're
linear
or
not.
We
think
about
they
say,
okay,
five
to
seven
levels,
linear
fit.
In
theory,
the
most
you
might
need
is
or
the
minimum
you
might
need
is
two
and
there's
a
cost
to
each
additional
level,
both
in
materials
and
preparation
costs
and
maintenance.
This
is
where
we
wanted
to
look
and
ask
the
question,
look,
can
we
actually
reduce
this
number
for
an
existing
method
without
significant
impact
on
our
actual
data
quality?
The
way
we
quantify
our
unknown
samples.
This
is
where
JMP
comes
in,
we're
going
to
use
Jump
here
to
simulate
some
alternative
calibration
schemes,
in
this
case,
reducing
the
number
of
levels
of
calibrations
and
to
reiterate
what
we've
walked
through
our
problem
ultimately
is
that
calibration
maintenance
is
costly.
That's
exasperated
by
the
scale
we
do
it
at.
Our
general
approach
is
really
going
to
be
to
look
at
how
can
we
optimize
this.
Let's
look
at
reducing
the
levels
of
those
calibrations,
and
then
our
specific
solution
is
using JMP
here
to
ask
the
question,
look,
if
we
went
back
in
time
and if
theoretically,
we
had
run
two
calibrators
or
three
calibrators
instead
of
six
or
seven,
how
would
that
have
impacted
our
data?
Our
case
here,
we're
going
to
focus
on
a
single
method
today.
This
is
a
real
method
we've
been
running
for
about
six
months.
We
have
22
batches
of
samples
we've
run
on
this
method,
so
it's
about
1000
samples.
Our
existing
calibration
I
show
here
on
the
right
is
a
linear
calibration.
It
has
six
levels
and
we've
estimated
if
we
can
reduce
this
to
the
minimum
of
two
levels,
we
could
save
an
estimate
of
$15,000
a
year.
There's
a
real
measurable
motivation
to
understand
if
we
can
pursue
this.
Showing
here
the
general
workflow
that
we
came
up
with.
I'm
going
to
go
through
this
really
quickly
right
now
but
no
worries.
We're
going
to
walk
through
it
step
by
step
together.
We're
really
going
to
just
pull
the
historical
data.
We're
going
to
recreate
our
historical
calibration
in
JMP
to
validate
what
we're
doing
in
JMP
matches
what
we've
done
historically,
and
then
we're
going
to
say,
okay,
let's
eliminate
some
of
these
levels,
recreate
the
calibration
with
those
reduced
levels,
and
then
evaluate
what
impact
that
has
on
our
targets.
Now,
I
think
in
this
case
it's
also
really
important
to
emphasize
you
see,
we
have
two
pass-
fail
forks
in
the
road.
Often
when
we're
doing
analysis
on
data
in
hand,
we're
looking
for
statistical
significance
with
studies
like
this,
it's
really
important
to
determine
what
your
practical
requirements
are.
In
this
case,
what
does
that
mean?
We're
talking
about
impact
on
the
measurement
of
unknown
samples.
Ultimately,
we
want
to
make
sure
that
reducing
the
calibration
is
not
going
to
bias
the
measurement
in
one
way
or
the
other.
We
want
the
measurement
to
be
the
same.
As
many
people
will
tell
you,
the
same
is
not
really
a
quantifiable
thing,
it
depends
on
your
sample
size,
the
noise
in
your
process.
We
need
to
define
what
is
no
different,
same
or
no
impact
mean.
Here
we're
going
to
set
our
acceptance
criteria
ahead
of
time
for
this
first
step
as
accuracy
within
half
the
percent,
and
for
the
second
step
as
accuracy
within
1%.
We'll
see
these
come
back
as
we
walk
through
this.
Our
first
step,
and
every
page
here,
I'm
going
to
show
in
the
top
right
what
step
we
are
in
the
process,
as
well
as
highlighting
what
JMP
platforms
we're
using.
For
our
first
step,
we're
going
to
be
pulling
our
historical
data
from
a
database,
in
our
case,
we
have
a
database.
We
have
a
Lin
system
that
already
has
the
data
in
a
structured
format.
You
could
also
import
this
from
CSV,
however,
you
can
access
the
data.
We're
pulling
it
in
our
case
using
raw
SQL
and
JSL
and
it
pulls
in
a
structured
format
showing
a
subset
of
the
columns
we
have,
but
what
you'll
notice
is
in
this
case
we
have
our
six
calibrators
as
well
as
a
number
of
unknown
samples.
We're
pulling
in
the
historical
data
as
the
core
data
set
we're
working
with.
The
first
step
is
recreating
and
validating
the
same
calibration
curve
so
that
same
six-
point
calibration
in
JMP.
Now,
you
might
ask
why
we
have
to
do
this.
There are
two
main
reasons.
One
is
calibration
curves
can
have
a
lot
of
caveats.
They
can
have
weighting,
they
can
have
anchor
points,
they
could
be
forced
through
zero,
they
could
be
nonlinear.
This
is
a
good
way
to
validate
that
you're
using
the
right
parameters
and
JMP
to
recreate
this.
The
other
reason
is
that
we
don't
expect
these
values
to
be
exactly
the
same.
The
reason
being
that
a
lot
of
these
analytical
software
uses
some
proprietary
regression
that
is
not
exactly
like,
let's
say,
ordinary
least
squares
regression.
To
do
this,
we're
going
to
use
a
specialized
modeling
fit
curve
parameter
and
really
just
recreating
our
calibration
curve.
Just
like
I
showed
earlier,
where
we
have
our
known
quantity
of
our
six
standards
on
the
X
and
our
raw
intensity
or
signal
response
on
the
Y.
In
our
case,
we
have
22
batches,
I'm
not
showing
all
of
them
here,
but
we're
reproducing
this
for
22
different
sequences
in
essentially
one
click
and
what
I
call
the
power
of
the
Control
key
if
you
don't
know
this
trick.
Will
save
you
a
ton
of
time,
if
you
hold
down
the
Control
key,
click
on
the
red
button,
whatever
you
do
is
going
to
apply
to
every
analysis
in
that
window.
Recently
learned
that's
apparently
called
broadcasting,
so
you
could
use
that
as
well.
We're
recreating
a
calibration
curve
for
each
of
our
batches
and
then
in
the
same
specialized
modeling
platform,
we're
then
saving
the
inverse
prediction
formula.
Because
we're
predicting
from
Y
to
X,
if
you
remember
back
to
our
calibration
intro,
to
be
able
to
save
the
predicted
values
back
to
our
data
table.
This
then
looks
like
this
where
on
our
data
table
we
have
first
our
historical
quantity,
what
we
pull
from
the
database,
and
now
we
have
our
raw
quantity
that
we
generated
from
these
newly
created
calibration
curves
and
JMP.
We
have
a
multiplier
we
have
to
apply,
do
the
sample
prep
we
do
that
we
pull
from
the
database
so
that's
already
there
and
it's
going
to
stay
constant.
We
simply
need
to
just
apply
a
calculated
column
here
to
have
a
comparative
value
to
our
historical
data.
If
you
look
in
this
first
raw,
our
value
is
very
close
to
but
not
exactly
the
same
as
our
historical
data.
Next
up,
we're
going
to
visually
do
a
comparison,
plotting
our
historical
against
the
JMP
recreation
of
that
calibration,
and
this
is
a
good
check
again,
to
look
through
your
data.
What
you
would
expect
or
hope
for
is
a
line
that
essentially
looks
like
Y
equals
X.
Now
we
don't
want
to
stop
at
a
visual
analysis.
We
of
course,
want
to
bring
some
statistics
into
it.
This
is
where
we
introduced
the
passing
Bablock
regression.
It's
actually
something
that
was
just
added
into
the
base
jump
functionality.
I
think
with
JMP
17
used
to
be
an
add-
on
for
a
long
time.
I'm
glad
it's
there
now.
This
is
a
specialized
regression
that's
non-
parametric
and
robust
outliers,
that's
really
designed
specifically
for
comparing
analytical
methods.
For
many
of
you,
probably
irrelevant
you're
never
going
to
have
to
use
it,
but
we
need
to
use
it
in
the
world
we're
working
in.
What
this
regression
does,
it
gives
you
two
hypothesis
tests
to
test
for
constant
bias
as
well
as
proportional
bias.
S
tarting
with
a
constant
bias,
where
we're
seeing
if
there's
bias.
Imagine
the
line
moving
up
and
down
the
same
across
the
range.
We're
evaluating
if
the
confidence
interval
of
our
intercept
does
or
does
not
include
zero.
For
proportional
bias,
where
the
bias
would
change
based
on
the
response.
We're
evaluating
if
the
confidence
interval
of
our
slope
does
or
does
not
include
one.
Now
in
our
case,
we
reject
the
null
hypotheses
in
both
of
these
cases,
which
tells
us
that
we
do
have
statistically
significant
bias,
both
constant
and
proportional
in
our
data
set.
From
here
you
might
say,
okay,
we're
done
there's
bias
we
can't
move
on,
but
thinking
back,
this
is
why
it's
really
important
to
define
what
the
practical
significance
is
because
any
statistician
will
tell
you
in
our
data
set
we
have
1000
samples,
you
have
1,000
samples
you're
going
to
be
looking
at
very
tight
confidence
intervals.
You're
going
to
be
able
to
detect
very
small
differences.
We
have
a
statistically
significant
difference
but
does
it
matter?
That
brings
us
to
our
last
step
we're
going
to
calculate,
again
using
the
column
formula,
the
relative
difference
between
the
two
methods
and
I'm
showing
a
distribution
of
that
below
and
that
distribution
then
gives
us
access
to
this
test
equivalence.
This
allows
you
to
test
a
distribution
of
values
against
the
constant
that
you
define
within
some
confidence.
Here
in
this
window,
we'll
enter
our
target
mean
is
zero
because
we
hypothesized
that
they're
going
to
be
the
same
so
no
difference.
Now
we
get
to
enter
our
acceptance
criteria,
which
was
0.5%.
This
gives
us
this
very
nice
output
with
our
final
two
hypotheses
tests
where
if
we
reject
these,
we
can
determine
essentially
that
the
mean
of
this
data
set
is
equivalent
to
zero
within
plus
or
-0.5%.
This
one
you
might
say,
hey
Stefan,
this
is
doing
a
t-
test,
your
distribution
is
not
exactly
normal
and
I
think
you'd
be
right
and
if
I
went
back
I
might
actually
use
the
test
mean
platform
because
that
gives
you
access
to
non-
parametric
equivalence
tests.
Regardless,
this
is
a
really
useful
and
direct
way
to
test
for
practical
significance.
We've
pulled
our
historical
data
from
the
database,
we've
recreated
and
evaluated
the
calibration
curve
and
we've
established
that
it
passes
our
acceptance
criteria.
If
it
had
failed,
it
could
be
an issue
with
the
data
set.
You
might
not
be
using
the
right
calibration
parameters.
There are
a
number
of
reasons,
we
generally
would
pretty
much
always
expect
this
to
pass.
It
usually
just
requires
some
investigation
to
what's
going on
in
the
way
you
recreated
this
calibration.
Our
next
step
is
down-
sampling
or
reducing
the
number
of
levels
of
our
calibration.
Now,
if
we
try
to
do
this
without
JMP,
we
have
to
go
into
every
single
sequence
in
our
analytical
software
manually
remove
calibrators,
recalculate
things.
Be
really
long
and
tedious
thing.
JMP
this
is
as
easy
as
just
using
the
data
filter.
In
our
case
with
this
six-
point
calibration
we
have
a
linear
one.
We
know
that
the
minimum
number
of
points
we
need
for
linear
fix is
two.
We're
picking
the
highest
and
the
lowest
calibrators
and
just
filtering
down
to
those.
From
here
I'm
going
to
go
pretty
quickly,
but
really
all
we're
going
to
do
is
recreate
this
calibration
with
two
points
in
JMP.
Again,
we're
using
the
specialized
modeling
platform,
doing
a
fit
line.
The
only
difference
now
is
we
have
two
points
instead
of
six
and
we're
applying
that
inverse
prediction
formula
back
to
the
data
table,
which
again
is
going
to
give
us
our
inverse
prediction
and
then
we
apply
the
multiplier
and
because
I
know
I'm
going
to
do
the
practical
significance,
I'm
just
going
to
preemptively
calculate
a
relative
difference
between
the
two-
point
calibration
and
then
the
historical
difference.
Again,
we
go
through
the
Passing
Babock
and
not
so
surprisingly,
again
considering
the
size
of
our
data
set,
we're
going
to
reject
the
null
hypothesis
here
and
establish
that
we
have
statistically
significant
bias,
both
proportional
and
constant.
We
move
on
to
test
our
acceptance
criteria.
Remember
back
now
our
threshold
is
1%
instead
of
0.5%
and
that's
working
with
the
stakeholders
of
the
data
to
establish
what
is
an
acceptable
equivalency.
That's
always
important
pre-
work
to
do
and
we're
going
to
test
that
equivalency.
Here
we
find
that
these
two
methods
are
equivalent
within
plus
or
minus 1%.
On
the
unknown
samples
and
that's
really
important.
We're
using
those
historical
real-
world
samples
to
really
ask
the
question
what
if
we
went
back
in
time
and
reran
all
these
calibrations
with
two
points
and
reported
the
data
of
these
unknown
samples.
How
would
those
values
change?
On
average
we
see
that
they
change
very
little,
and
so
it
gives
a
lot
of
credence
to
considering
inducing
those
calibration
levels.
We've
essentially
demonstrated
this
now
and
so
this
calibration
on
the
left
and
the
calibration
on
the
right
we're
saying
are
equivalent
,
aren't
going
to
provide
equivalent
quantitation
within
1%
and
so
we
have
essentially
the
evidence
we
need
to
push
for
this
change.
We
passed
our
first
check,
we
reran
the
evaluation
with
the
two,
we
passed
that
and
now
we're
at
our
final
step
of
implementing
those
changes
in
our
process.
Now
it's
arguably
the
most
important
part,
if
you
do
an
analysis,
we
just
leave
it
sitting
there,
doesn't
do
much
good.
This
can
sometimes
be
the
hardest
part.
You
have
to
go
out,
you
have
to
convince
people,
especially
in
cases
like
this
and
you
have
to
take
consideration
of
maybe
are
there
additional
things
that
this
analysis
didn't
consider.
I'm happy
to
talk
to
anyone
about
that,
but
we're
not
going
to
go
in
depth
of
what
the
other
considerations
we
have
to
think
about
before
putting
this
into
action,
but
with
this
example,
we
did
actually
end
up
reducing
calibration
levels
from
six
to
two
and
that
reduced
the
annual
cost
of
running
that
method
by
about
$15,000.
From
there
we
might
say,
okay,
what
now?
Are
we
done?
Of
course
not.
Right
now
we
need
to
look
at
we
did
it
for
one
method,
we
have
a
suite
of
another
100
plus
methods
that
may
also
have
these
many
level
calibrations
that
might
be
overkill
for
what
we
need.
We
want
to
look
at
repeating
the
analysis
for
other
methods.
That's
where
I
think
automation
comes
in.
It
is
a
really
great
way
to
scale
this
one-
off
analyzes
for
ourselves,
but
also
for
others.
My
rule
of
thumb
is
if
I
find
myself
doing
an
analysis
more
than
two
or
three
times.
Let's
build
that
out
in
the
automation,
say
future
me
a
lot
of
time,
spend
a
little
time
now.
I'll
just
touch
on
this
very
briefly
and
I
want
to
shout
out
Scott
here
for
helping
me
with
a
lot
of
the
workflow
builder
work
and
the
scripting
but
these
native
automation
tools
in
JMP
are
really
powerful
and
they're
very
user-
friendly,
there's
a
lot
of
code-
free
options
and
so
there's
really
different
ways
you
can
do
this.
You
can
do
it
on
the
left
side,
right
in
a
classic
way
doing
all
the
scripting
even
allows
you
to
save
these
global
variables
so
it
could
give
us
place
for
you
to
have
users
enter
their
acceptance
criteria
which
might
change
and
or
you
can
leverage
the
workflow
builder
which
is
a
bit
of
a
newer
feature
but
really
lets
you
build
out
this
automation.
Even
if
you
just
want
to
script
it
raw,
you
can
build
the
framework
that
you
can
then
flesh
out.
The
two
things
I
will
say
about
this
is
how
much
you
can
automate
or
maybe
how
much
effort
you
have
to
put
into
it
is
going
to
be
limited
to
some
extent
by
how
rigid
that
workflow
is.
If
users
need
it
to
be
really
flexible,
need
to
interact
with
it,
it
could
become
very
challenging
to
automate,
and
of
course
the
data
consistency
is
key
as
well.
This
is
really
a
great
tool
to
help
others
reproduce
the
analysis,
but
you
really
do
have
to
also
train
them
and
document
the
work,
make
sure
they
know
what
it's
actually
doing.
As
we
all
know,
every
analysis
has
its
caveats.
You
need
people
not
just
to
click
and
have
a
report,
but
also
understand
a
little
bit
like
what
are
some
potential
things
that
could
come
up,
especially
if
you're
trying
to
future-
proof of
work.
I
like
to
bring
it
back
together
and
wrap
it
up
there
and
hope
today
that
I've
showed
you
that
JMP
you
don't
have
to
do
like
crazy
complex
or
sophisticated
things
in
JMP
you
could
piece
together
a
lot
of
simple
functionality
to
create
really
impactful
workflows.
Whether
you're
working
in
a
lab
at
your
organization,
wherever
it
is,
look
to
identify
these
improvements
in
existing
workflows.
I
like
to
think
about
if
you
all
are
in
the
experience
most
of
us
are
in,
there's
more
data
than
what
we
know
what
to
do
with.
Look
at
the
data
that
no
one
is
looking
at
and
then
challenge
the
conventional
thinking.
The
way
we're
working
is
always
changing,
ask
why
do
we
do
it
this
way.
In
our
case,
for
a
long
time,
this
is
the
way
we
do
it.
Five,
six-
point
calibration.
Ask
why,
what
if
we
didn't,
what
would
the
impact
be.
Of
course,
don't
have
to
tell
anyone
listening
here.
Use
JMP
for
the
scalable
analysis
and
then
use
automation
to
make
it
easy,
and
it
really
doesn't
have
to
be
fancy.
It
just
has
to
work
for
what
you
need
it
to
do.
Finally,
you
can
use
that
to
impact
these
impactful,
implement
impactful
change
and
use
data
to
drive
those
decisions.
It's
probably
one
of
the
most
convincing
tools
that
we
have
today.
If
you're
talking
to
management,
do
it
in
units
of
dollars
because
they
love
that.
I'll
wrap
it
up
there
I
think
last
thing
I'd
like
to
say
is
just
a
thank
you
to
the
JMP
Discovery
Summit
Committee,
all
the
people
organizing
special
thank
you
to
Scott
for
all
the
help
he
gave
me
in
the
past
with
Amyris,
but
also
with
this
talk
and
this
analysis,
and
then
a
number
of
people
at A
myris
who
were
involved in
with
this
and
with
that,
I
will
wrap
it
up.
Thank
you.
Bye.
