Hello.
My
name
is
Marie
Gérus-Durand,
and
I'm
working
for
C
erba Research
Montpellier.
Today,
I
will
show
you
how
we
set
up
automatization
of
immunohistochemistry
data
analysis
for
protocol
validation
in
Montpellier
using
JMP
and
its
tools
like
dashboard
and
add-in
functions.
First,
some
words
about
Cerba Research,
it's
a
worldwide
company
with
capabilities
in
all
the
continents.
Here
I
highlighted
in
yellow
the
department
I'm
working
for.
It's
a
Histopathology
EHC
Department.
As
you
can
see,
I'm
based
in
Montpellier
in
France. W
e
have also
other
labs
in
US,
in
New
York,
and
in
Taiwan
in
Taipei.
First
of
all,
what
is
immunohisto
chemistry?
The
aim
of
the
technique
is
to
detect
targets protein
mainly
on
a
tissue
sample.
Here
you
have
a
slice
of
a
tissue,
for
example,
when
you
do
a
biopsy.
We
will
look
at
the
targets
of
interest
using antibodies,
which
will
detect
the
target.
This
antibody
is
recognized
by
another
one
which
is
combined
with
chromal
fluoroform
or
active
components
that
allow
the
detection
of
the
target.
Here
you
see,
for
example,
these
three
components
are
highlighted,
meaning
that
antibodies
bind
it
and
we
can
detect
it.
After
the
experiment,
we
can
can
look
at
the
slides
under
microscope
or
using
a
scanner,
which
allow
visualization
and
analysis
of
the
results.
On
the
next
slide,
I
just
zoom
in
so
you
can
see
better
what
it
looks
like.
Here
it's
a
skin
sample
and
you
have
a
cell
nuclear
in
blue
and
the
target
of
interest
in
red.
One
of
the challenges
within
immunohisto
chemistry
and
histopathology
is
that
you
have
many
possible
protocols,
colorations.
Here
on
the
left,
it's
two
different
histological
colorations.
It
doesn't
involve
antibodies
like
I
show
you,
but
it
just
reactive
with
the
different
components
of
the
tissues.
You
see
here
for
the
MOVAT,
we
have
five
colors.
For
the
HE,
we
have
only
one
color,
but
the
intensity
depends
on
the
type
of
structure
you
are
looking
at
in
the
tissue.
On
the
right,
you
have
two
immunohistochemistry
protocol.
One
simplex,
we
called
it,
because
we
detect
only
one
target,
and
it's
a
chromogenic
here.
It's
in
brown.
On
the
top
right,
you
have
a
multiplex.
H
ere
we
detect
many
components.
Here
it's
a
fourplex,
four
targets
on
the
same
slide,
and
each
is
revealed
by
different
flow
of
work.
You
have
different
color
for
each
of
the
targets.
Among
of
these
coloration
detection
possibilities,
then
you
have
a
multitude of
possible
analysis
method.
The
slides
can
be
analyzed
by
a
pathologist,
which
will
give
us
qualitative
or
semi-
quantitative
data,
or
by
image
analysis,
which
will
give
us
quantitative
data.
Another
layer
of
that
is
that
you
can
have
reportable
parameters
which
are
single.
For
example,
if
you
have
a
simlplex,
you
detect
the
targets,
only
one
target,
and
you
assess
only
one
parameter
like
percentage
of
positive
cells,
for
example,
or
you
can
have
many.
For
one
target,
you
can
have
the
percentage
of
positive
cells
and
a
specific
histology
score.
Or
if
you
have
a
multiplex,
then
you
can
multiply
this
for
all
targets
in
the
multiplex.
Each
report
level
parameter
is
target-
dependent.
You
can
imagine
that
we
have
a
lot
of
combination
that
we
can
have
to
access
during
our
validations.
In
Cerba Research
M
ontpellier,
in
2022,
we
have
a
small
part,
like
20
%
of
our
project
related
to
animals.
We
are
studying
animal
samples.
The
other
projects
were
on
human
samples.
Among
this,
most
of
them are
four
clinical
trials
of
the
project.
That's
a
very
[inaudible 00:04:56]
.
W
e
have
some
others
that
are
outside
clinical
trials,
a
quarter
of
them,
and
a
small
portion,
3
%
that
are
CAP
compliant.
CAP
is
a
specific
regulation
for
US.
It is
to
know
that
before
being
used
in
a
clinical
trial,
we
should
demonstrate
that
our
protocol
that
we
developed
in
Montpellier
show
consistency
in
results
for
section
of
the
same
sample.
If
we
analyze
the
sample
at
different
type points,
for
example,
the
samples
of
patients
involved
in
the
study,
the
first
year
should
be
the
same
than
in
the
five
years
after.
On
the
different
automatons,
we
have
at
our different
sites,
and
when
the
samples
are
analyzed
by
different
operators
or
pathologists.
This
applies
a
rigorous
validation
according
to
the
health
agency,
and
this
validation
is
mainly
based
on
statistical
criterion.
The
implication
for
the
company
is
that
we
need
to
increase
the
team
members
to
support
the
increasing
number
of
projects
we
have
each
year,
and
we
need
a
normal
genus
statistical
analysis
pipeline
to
be
sure
that
we
will
give
all
our
clients
the
same
type
of
results.
Obviously,
we
need
statistical
analysis
tool,
and
it's
when
we
choose
JMP
to
support
our
validation
of
the
protocol.
Today,
I
will
show
you
only
a
part
of
what
we
are
doing
because
I
don't
have
time
to
show
you
everything.
I
chose
a
quite
simple
example.
It's
a kind of
experiment
we
do
to
validate
the
precision
of
the
protocol,
meaning
that
we
check
the
intra-run
,
which
is
called
repeatability
precision
over
three
slides.
Here
are
the
three
ones
highlighted
in
purple
in
the
same
cycle.
The
three
slides
I
run
at
the
same
time,
they
come
from
the
same
sample,
and
we
just
check
that
we
have
the
same
data.
The
inter-run,
reproducibility,
test
over
two
slides
highlighted
in
blue
in
each
cycle.
In
total,
we
have
six
slides
over
three
cycles.
For
reportable
parameters,
I
will
use
an
image
analysis
dataset,
which
is
quantitative
data
and
usually
easier
to
analyze.
We
will
have
two
reportable
parameters
for
one
target.
How
do
you
start?
First,
we
need
to
import
the
data.
I
don't
know
if
you
are
familiar
with
that.
But
in
our
case,
we
have
data
either
from
Word
documents,
so
we
use
the
Word
Import
Tool
available
on
JMP
community
website
.
I
put
the
link
here
so
you
can
go
and
find
it
again.
Or
we
import
data
from
Excel
either
directly
by
opening
the
file
in
JMP
or
by
using
the
JMP
tool
in
Excel.
For
this
presentation,
just
to
be
faster,
I
create
some
script
to
help
me
to
focus
on
the
dashboard
creation
after
that,
which
will
take
more
time.
But
this
is
just
some
script
and
just
to
be
faster,
but
I
will
not
develop
into
it
then.
Here
I
will
open
a
data
table
from
Project
X
here.
As
you
can
see,
it's
a
quite
simple
table.
I
have
four
columns
with
the
validation,
the
slide
ID,
which
are
internal
slide
numbers,
and
the
dataset
for
my
two
reportable
parameters.
This
data
continues
because
they
come
from
image
analysis.
Once
I
have
this,
I
will
need
to
prepare
my
data.
It's
a
most
time
consuming
part
of
analysis
data.
Again,
it's
why
I
have
a
script.
You
see
the
five
columns
where
I
did.
The
sample
to
be
able
to
correlate
each
data
to
the
same
sample,
which
is
a
part
of
the
slide
ID
we
have
internally.
I
just
get
a
formula
here
to
help
me
to
do
that.
The
slide
number,
which
are
the
last
digits
of
the
slide
ID,
and
the
slide
order,
the
1,
2,
3,
4,
5,
6,
7
for
each
sample.
Thanks
to
this
slide
ordering,
I
would
say,
I
implement
the
repeater.
The
three
first
slides
which
were
staying
in
the
same
cycle
for
repeater,
and
two
first
slides
of
each
cycle
for
reproductivity
test.
Here
I
have
all
the
information
needed
to
do
my
analysis.
I
go
back
to
my
journal,
and
we
will
want
to
do
the
dashboard
question.
I
still
have
some
steps
to
do
before
that
because
I
would
like
to
have
all
the
analysis
I
want
to
put
in
the
dashboard.
Here
are
the
two
little
table
you
see
where
we
are
required
to
analyze
the
CV
of
our
protocol
for
each
sample
for
repeatability.
I
selected
only
the
three
first
slides
thanks
to
the
local
data
filter.
The
same
for
reproducibility,
where
you
see
I
have
the
slides
from
the
reproducibility
column.
Here
are
the
data
that
I
need.
This
data
I
updated
them
in
here.
You
see
I
have
much
more
columns
now.
It's
easier
to
find
the
name
here.
I
have
the
sample
CV
for
repeatability,
for
reportable
parameter
1
and
2,
and
then
the
same
for
reproducibility
for
the
two
parameters,
and
then
I
do
the
mean
of
both
samples
for
each
of
these
columns.
Here,
all
the
data
I
will
need
to
implement
in
my
dashboard.
I
can
cross
this
table.
I
don't
need
them
anymore.
I
will
now
do
the
graphs
and
tables
that
will
really
fit
in
the
dashboard.
Again,
what
I
want
to
show
to
the
client
is
the
distribution
of
the
data
for
repeatability.
I
put
as
well
the
standard
deviation
and
the
mean.
Usually,
it's
pretty
good
here and
for
reproducibility
where
I
have
all
my
six
slides.
Then
I
would
put
a
table
with
a
CV
for
each
sample
for
repeatability
and
the
reproducibility
on
the
left
for
the
first
parameter
and
on
the
right
for
the
second
one.
The
same
outline
for
the
mean
of
the
two
samples.
These
are
the
four
part
I
want
to
show
on
my
dashboard.
I
will
show
you
how
it
looks
like.
I
want
to
obtain
something
like
that.
This
is
often
I
did
that
I
can
show
it,
the
two
graphs
and
the
two
table.
It's
what
I
would
show
you
how
to
do
now.
You
see
that
all
the
graphs
are
to
the
same
data
table,
sorry,
and
it's
much
easier
to
do
the
dashboard
after.
I
saved
as
well
all
the
scripts
so
I
can
redo
them
whenever
I
want.
I
will
create
a
new
file,
new
dashboard.
You
have
many
templates. I
usually
start
from
blank
and
just
you
have
to
put
in
what
you
want
to
see.
Sometimes
it's
a
bit
difficult
because
it's
small,
but
we
always
manage
to
find
our
way.
My
table
at
the
bottom,
you
just
drop
them
where
you
want
to
have
them.
It's
pretty
simple.
You
can
change
the
names of each
part,
so
I
will
not
do
it
just.
But
you
can
see
that
you
can
edit
all
the
parts.
You
can
run
your
script
and
then
give
you
the
dashboard.
It's
pretty
similar
to
what
I
showed
you
before.
I
have
my
two
tables
at
the
bottom
and
my
two
graphs
at
the
top
here.
I
have
inverted
the
two,
so
I
usually
prefer
to
start
with
repeat
that.
I
will
just
switch
them.
If
I
put
it
where
something
was
already,
they
just
switch.
Here
we
are.
This
is
the
layout
I
want,
so
it's
good.
After,
you
can
play
to
see
better,
more
or
less,
of
the
table,
et
cetera.
But
this
is
just
for
visual
[inaudible 00:15:00]
process.
Then,
okay,
now
we
have
dashboard,
but
it
would
be
more
interested
if
we
can
directly
go
for
the
dashboard
when
you
have
all
your
data
you
want
on
the
dashboard
and
click
and
you
have
a
dashboard.
For
that,
we
just
need
to
do
an
add-
in.
It's
quite
simple.
Thanks
to
the
magic
triangle,
I
call
it,
the
red
triangle
here.
You
click
Save
Script
and
just
To
Add-I
n.
Then
it
will
create
a
script
that
will
do
the
same
dashboard
again.
Let's
go
back
to
the
data
table.
I
will
close
this
one.
You
are
sure
that
the
one
that
you
will
see
is
not this
one.
Sorry,
I
shouldn't
have
closed
it
before
doing
the
dashboard.
I
will
just
use
this
to
be
faster
and
not
to
create
it
again.
Here,
if
you
click
on
the
red,
Save
Script,
To
Add
-In.
Then
this
is
the
name
you
will
have
in
the
add-
in
list,
but
it's
to
manage
your
add-
ins,
I
would
say,
but
it's
not
the
one
that
will
figure
out
in
the
add-in
tab
in
JMP.
The
name
that
you're
in
the
add-in
tab
is
this
one.
For
today,
I
would
just
call
it
Test
so
I
know
which
one
is
this.
Save.
You
see
here,
you
have
all
the
script
used
by
JMP
to
do
this
dashboard,
and
I
will
save
it
in
our
Project
X.
Here
you
see
it
take
the
name
on
the
first
tab
Dashboard
only.
I
save
it.
Here
I
have
this
both
tick
Install
after
save,
so
it
was
already
put
in
my
add-in
list.
If
the
box
was
not
ticked,
then
you
have
just
to
go
to
the
location.
You
save
your
file
and
click
on
it
and
it's
installed.
Now
I
can
close
this
dashboard
.
I
have
created
my
complement already.
Now,
how
to
use
it?
I
just
went
a
bit
faster.
If
you
open
it,
it
installs.
As
I
have
it
already,
I
will
not
start
it.
It's
just
under
it.
If
you
go
to
View,
Add-ins,
sorry,
Dashboard,
and
Unregister,
it's
not
erased.
I
will
find
it
again
when
I
go
to
my
project.
Here
you
see
I
have
it.
If
I
double
-click,
it
ask
me
if
I
want
to
install
it.
Sure,
I
want
to
install
it.
It's
back
here
again.
You
can
share
it.
You
just
have
to
copy
the
same
file
I
clicked
on
and
paste
it
in
a
shared
folder
or
send
it
to
a
JMP
user
colleague.
You
can
modify
it.
For
this
you
have
to
go
in
Open.
Again,
this
is
the
dashboard
but
then
the
black
click
this
time,
just
go
on
the
arrow
here
to
open
and
Open
using
Add-In
Builder.
Here
you
go
back
to
the
first
time
window
where
you
have
your
script
here,
and
you
can
either
edit
the
script
or
put
other
functions
that
I
don't
really
use,
to
be
honest.
But
you
have
many
functions.
I'm
sure
you
will
find
more
information
on
JMP
website
about
that.
This,
for
example,
will
allow
you
to
put
all
the
preparation
step
in
the
same
complement.
When
you
run,
everything
is
done
at
the
same
time.
This
is
it.
In
conclusion,
using
this
dashboard
and
add-in
functions
allow
us
to
have
reports
consistency
because
we
have
always
the
same
set
up
of
results
to
send
to
the
clients.
We
increase
the
traceability.
Thanks
as
well
for
the
use
of
the
scripts
because
we
are
sure
that
we
are
all
doing
the
same.
I
t's
a
great
time
saver
because
as
you
say,
I
just
have
to
click
on
one
button
and
I
have
my
dashboard.
If
you
combine
this
with
a
precision timing.
All
the
data
preparation,
then
you
have
your
table,
you
click
on
one
button,
and
you
have
everything
done.
It's
a
great
time
saver.
It's
all
I
wanted
to
show
you
today.
I
hope
you
enjoy
it.
If
you
have
any
question,
don't
hesitate
to
reach
out
to
me
either
by
emailâ
you
have
the
email
on
the
first
slide
hereâ
or
through
the
JMP
community.
Thank
you.
