Supplementary
information
Figure
S1:
Lineage
trees
generated
based
on
a
competence
progression
model
of
development.
A-H.
8
random
trees
sampled
from
the
simulated
datasets.
I.
Tree
size
distribution.
The
median
sized
tree
contains
5
cells.
Figure
S2:
Motifs
reveal
committed
progenitors
in
a
binary
fate
model
of
development.
A.
Lineage
trees
were
simulated
using
a
binary
fate
model
of
development.
The
upstream
progenitors
(‘i’,
‘j’,
and
‘k’)
can
either
self-renew
with
20%
probability
or
differentiate
into
two
committed
progenitors
with
40%
probability
each.
Downstream
progenitors
(‘l’,
‘m’,
‘n’,
and
‘o’)
can
either
self-renew
with
20%
probability
or
differentiate
into
two
terminal
fates
with
40%
probability
each.
B.
Cell
type
proportions
in
500
simulated
lineage
trees.
C.
Deviation
score
for
top
17
most
significant
doublet
patterns,
calculated
using
the
mean
and
standard
deviation
of
counts
across
10000
resamples.
Null
z-scores
were
calculated
by
comparing
a
random
resample
dataset
to
the
rest
of
the
resample
datasets.
10
datasets
containing
50000
simulated
trees
each
were
used,
with
the
standard
deviation
across
the
datasets
plotted
as
error
bars.
D.
Deviation
score
for
top
17
most
significant
quartet
patterns
with
at
least
3
cell
types.
E.
Deviation
score
for
top
17
most
significant
octet
patterns
with
at
least
7
cell
types.
F.
Deviation
score
for
select
patterns
that
reflect
sequential
differentiation
of
cell
fates
using
datasets
of
varying
size.
Shading
indicates
95%
confidence
interval
across
10
datasets
for
each
point.
Figure
S3:
Lineage
trees
generated
based
on
a
binary
fate
model
of
development.
A-H.
8
random
trees
sampled
from
the
simulated
datasets.
I.
Tree
size
distribution.
The
median
sized
tree
contains
16
cells.
Figure
S4:
No
triplet
patterns
show
significant
deviation
from
the
null
expectation
in
the
zebrafish
retina
dataset.
A.
Counts
for
triplet
patterns
in
the
observed
zebrafish
retina
trees
from
He
et
al.
35
in
the
temporal
region
and
across
10000
resamples.
All
10000
resamples
are
represented
in
the
violin
plots,
but
a
random
subset
of
only
100
resamples
are
shown
as
overlaying
dot
plots.
The
top
15
significant
triplets
across
progenitors
from
all
spatial
regions
are
shown.
The
expected
count
was
calculated
analytically
(
Methods
).
B.
Counts
for
triplet
patterns
in
the
middle
region
of
zebrafish
retina
and
across
10000
resamples.
C.
Counts
for
triplet
patterns
in
the
nasal
region
of
zebrafish
retina
and
across
10000
resamples.
D.
Deviation
score
for
triplet
patterns
in
the
temporal,
middle,
and
nasal
region,
calculated
using
the
mean
and
standard
deviation
of
counts
across
10000
resamples.
Triplet
patterns
with
an
observed
and
expected
count
of
0
were
omitted
from
analysis.
Figure
S5:
Doublet
lineage
motif
analysis
in
mouse
blastocyst
development
suggests
weaker
fate
commitment
for
inside
progenitors
at
the
last
cell
division,
compared
to
outside
progenitors.
A.
Counts
for
all
doublet
patterns
in
the
observed
mouse
blastocyst
trees
from
Morris
et
al.
38
in
the
outside
progenitors
and
across
10000
resamples
(*
=
adjusted
p-value
<
0.05;
**
=
adjusted
p-value
<
0.005,
***
=
adjusted
p-value
<
0.0005).
All
10000
resamples
are
represented
in
the
violin
plots,
but
a
random
subset
of
only
100
resamples
are
shown
as
overlaying
dot
plots.
The
expected
count
was
calculated
analytically
(
Methods
).
B.
Counts
for
all
doublet
patterns
in
the
observed
mouse
blastocyst
trees
in
the
inside
progenitors
and
across
10000
resamples.
C.
Deviation
score
for
all
doublet
patterns
in
the
outside
and
inside
progenitors,
calculated
using
the
mean
and
standard
deviation
of
counts
across
10000
resamples.
Doublet
patterns
with
an
observed
and
expected
count
of
0
were
omitted
from
analysis.
Figure
S6:
No
quartet
patterns
show
significant
deviation
from
the
null
expectation
in
the
mouse
blastocyst
dataset.
A.
Counts
for
quartet
patterns
in
the
observed
mouse
blastocyst
trees
from
Morris
et
al.
38
in
the
outside
progenitors
and
across
10000
resamples.
All
10000
resamples
are
represented
in
the
violin
plots,
but
a
random
subset
of
only
100
resamples
are
shown
as
overlaying
dot
plots.
The
top
15
significant
quartets
across
both
sets
of
progenitors
are
shown.
The
expected
count
was
calculated
analytically
(
Methods
).
B.
Counts
for
quartet
patterns
in
the
observed
mouse
blastocyst
trees
in
the
inside
progenitors
and
across
10000
resamples.
C.
Deviation
score
for
quartet
patterns
in
the
outside
and
inside
progenitors,
calculated
using
the
mean
and
standard
deviation
of
counts
across
10000
resamples.
Quartet
patterns
with
an
observed
and
expected
count
of
0
were
omitted
from
analysis.
Figure
S7:
The
loss
of
the
(A,A)
motif
reduces
the
subspace
of
accessible
cell
type
proportions.
A.
The
matrix
equation
describes
the
linear
transformation
from
푧
(
푠
)
=
푋
*
푦
(
푠
)
+
푒
(
푠
)
motif
frequencies
to
cell
type
distributions.
The
empirically
determined
values
for
each
term,
based
on
the
rat
retina
dataset,
are
shown
as
.
푧
3
=
푋
3
*
푦
3
+
푒
3
B.
The
(A,A)
doublet
motif
was
omitted
from
the
motif
matrix
and
all
A
cells
born
through
푋
3
an
(A,A)
doublet
were
counted
as
part
of
the
non-motif
vector
.
The
perturbed
model
is
푒
3
represented
as
.
푧
4
=
푋
4
*
푦
4
+
푒
4
C.
Cell
type
distributions
were
simulated
using
a
null
model
(
)
or
the
empirical
motif
푋
0
matrix
based
on
the
rat
retina
motifs,
excluding
the
(A,A)
motif,
which
is
represented
as
.
The
lower
bounds
were
set
at
,
the
counts
for
all
cell
types
in
the
experimental
rat
푋
4
푒
4
retina
dataset
that
were
born
outside
of
a
motif,
including
all
A
cells
born
through
an
(A,A)
doublet.
The
upper
bound
was
set
by
constraining
the
total
number
of
cells
to
be
the
same
as
in
the
rat
retina
dataset,
.
The
data
was
plotted
as
a
ternary
푖
∑
(
푧
4
)
푖
=
푐
표
푛
푠
푡
plot
where
each
axis
corresponds
to
the
proportion
of
one
cell
type,
with
the
cell
type
proportions
of
mouse,
rabbit,
monkey,
and
chick
retina
from
Masland
2
and
Yamagata
et
al.
39
overlaid.