Supplemental Information Automatic Segmentation of … Information Automatic Segmentation of...

Current Biology, Volume 26

Supplemental Information

Automatic Segmentation of Drosophila

Neural Compartments Using GAL4 Expression

Data Reveals Novel Visual Pathways

Karin Panser, Laszlo Tirian, Florian Schulze, Santiago Villalba, Gregory S.X.E.Jefferis, Katja Bühler, and Andrew D. Straw

Figure S1. (data related to Figure 1): Evaluation of k-‐medoids clustering for automatically segmenting brain regions into anatomical structures. (A) Repeatability scores across multiple runs of the k-‐medoids algorithm. The adjusted Rand index, a measure of repeatability, was calculated based on 10 repeated runs of the k-‐medoids algorithm for both datasets and several brain regions. (B) Colocalization similarity (measured as Dice coefficient s on the set of voxels in the manually annotated region and the set in the clustering result) between the Janelia FlyLight dataset and manual assignments using the same 3D template brain. Manual assignments were based on a manually segmented neuropil image. Glomeruli that could not be unambiguously identified were labeled “glomerulus”. (Janelia FlyLight data for the right antennal lobe region, run 1, 6502 voxels, 3462 driver lines, k equal 60.) (C) Automatic segmentation of central complex (CX). 3D axes scale 30 µm. (D) Individual singleton clusters (left) and average image of strongly expressing driver lines in each cluster with broad driver lines removed (right). Scale bars 20 µm. (E) Average images from agglomerated clusters (top) and dendrogram of agglomerated hierarchy. Scale bars 20 µm. (F) As in E, but from the Vienna dataset, k=60. Scale bars 20 µm. Panels C-‐E: Janelia FlyLight data for CX, run 1, 27598 voxels, 3462 driver lines, k=60.

A

0 20 40 60 80 100 120 140 160number of clusters (k)

0.0

0.2

0.4

0.6

0.8

1.0

Adju

sted

Ran

d In

dex

Janelia ALJanelia CXJanelia MBJanelia oVLNPJanelia SEZVienna ALVienna CXVienna MBVienna oVLNPmean

automatic singleton cluster assignment

man

ual a

nnot

atio

n

co-localization similarity (s)(Dice coefficient)

B

C43 C13

C23C46

singletoncluster average image

singletoncluster average image

C D

0.0 0.64

E F

C116C110

C43

C13

C23

C46

Janelia dataset, k=60, run 1

C110 C116 C117

C117

Vienna dataset, k=60, run 1

C88

C108C110

C88 C108 C110

agglomerated cluster average images agglomerated cluster average images

C51

C39

C29

C48

C55

C04

C03

C14

C58

C40

C01

C02

C45

C06

C21

C33

C26

C19

C22

C53

C47

C36

C10

C05

C07

C28

C49

C13

C41

C34

C38

C12

C09

C25

C23

C08

C35

C42

C32

C11

C15

C16

C17

C18

C20

C24

C27

C30

C31

C37

C43

C44

C46

C50

C52

C54

C56

C57

C59

C60

VVA3DL3

glomerulusVM2DM6DM3

glomerulusglomerulus

VL1DA1

VL2pVM3VA2VA5

glomerulusDM5,DM2,DM1


DL5D

VL2aDP1lDL2dDA4?

glomerulusVM7

DP1mDC1VM2

glomerulusVM1

glomerulusDC2VA7

glomerulusVA1dVA1v

glomerulusDC3


Figure S2. (data related to Figures 2-‐3): Clustering quality for oVLNP in both datasets. (A) Quantification of similarity between clusters as measured by voxel-‐to-‐voxel co-‐expression distance ( , where s is the Dice coefficient between the two sets of enhancer expression) for each medoid of every cluster of run 1 in the oVLNP region using the Janelia dataset. (B) Dendrogram of agglomerative hierarchical clustering using average linkage showing a representation of co-‐expression distance between medoids in the oVLNP region of the Janelia dataset. (C) Quantification of similarity between clusters as measured by voxel-‐to-‐voxel co-‐expression distance for each medoid of every cluster in the oVLNP region of run 1 the Vienna dataset. (D) Dendrogram as in B using the Vienna dataset.

Janelia FlyLight cluster

A B

C D

Jane

lia F

lyLi

ght c

lust

er

0.0 1.0co-expression distance(metric Dice distance)

Vienna Tiles cluster

Vien

na T

iles

clus

ter

0.0 1.0co-expression distance(metric Dice distance)

0.0 0.5 1.0average linkage distance

0.0 0.5 1.0average linkage distance

441833252115626523129235855204130446163735711514540287492412321460484250275938475834174322919525613533954363110

282353572554161225630523124411374219509546432672139361413494027445118358383561176059201541478553222453348341029

1− s

Figure S3. (data related to Figures 2-‐3): Automatically assigned oVLNP singleton clusters colocalize with manually segmented optic glomeruli, repeated clustering of the same dataset gives similar results, and clustering of different datasets gives similar results. (A-‐B) Colocalization similarity (measured as Dice coefficient s on the set of voxels in the manually annotated region and the set in the clustering result) between the Janelia FlyLight dataset and manual assignments using the same 3D template brain. (Janelia FlyLight data for oVLNP, 42317 voxels, 3462 driver lines, k equal 60.) (C-‐D) Colocalization similarity between the Vienna Tiles dataset and manual assignments using the same 3D template brain. (Vienna Tiles data for oVLNP, 13458 voxels, 6022 driver lines, k equal 60.)

B

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0.00

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C13

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C21

C22

C23

C24

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C28

C29

C31

C34

C35

C36

C37

C38

C39

C40

C41

C42

C43

C44

C46

C50

C51

C53

C54

C55

C56

C58

C60

LC04LC06LC09LC10LC11LC12LC13LC15LC16LC17LC18LC20LC21

LC22/LPLC4LC24LPC1

LPLC1LPLC2LPLC3MC61MC62MC63

automatic singleton cluster assignment (run 2, Vienna Tiles dataset)

man

ual a

nnot

atio

n

C24

C42

C02

C33

C16

C13

C58

C40

C49

C38

C44

C18

C19

C14

C22

C59

C30

C06

C28

C17

C41

C55

C01

C03

C04

C05

C07

C08

C09

C10 C11

C12

C15

C20

C21

C23

C25

C26

C27

C29

C31

C32

C34

C35

C36

C37

C39

C43

C45

C46

C47

C48

C50

C51

C52

C53

C54

C56

C57

C60


LC22/LPLC4LC24LPC1


A

C

automatic singleton cluster assignment (run 1, Janelia FlyLight dataset)

C33

C57

C32

C22

C07

C05

C46

C28

C37

C23

C29

C43

C40

C16

C53

C30

C18

C44

C35

C56

C48

C50

C01

C02

C03

C04

C06

C08

C09

C10 C11

C12

C13

C14

C15

C17

C19

C20

C21

C24

C25

C26

C27

C31

C34

C36

C38

C39

C41

C42

C45

C47

C49

C51

C52

C54

C55

C58

C59

C60


LC22/LPLC4LC24LPC1


man

ual a

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n

0.00

0.32

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co-lo

caliz

atio

n si

mila

rity (s)

(Dic

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)

automatic singleton cluster assignment (run 1, Vienna Tiles dataset)

man

ual a

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C26

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C60

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C18

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C35

C36

C37

C39

C41

C43

C45

C47

C48

C49

C50

C52

C53

C54

C58

C59


LC22/LPLC4LC24LPC1


Table S1. (data related to Figure 4): Table with VPN, Clusters, Driver lines, Flycircuit IDs. Note: MC63 may be synonymous with VPN-‐MB1 [S3], which was published while this study was under review.

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Supplemental Experimental Procedures

Thresholding, Dice similarity, k-‐Medoids, and Hierarchical Agglomeration

GAL4 expression patterns were transformed into a binary representation in two steps.

First, the image is thresholded and second, morphological opening (dilation of the erosion

by a 3x3x3 structuring kernel) is applied to reduce clutter. The threshold was chosen so

that the resulting mask yielded 1% stained voxels. This simple heuristic was more reliable

for the datasets tested compared to other standard automatic thresholding methods.

From the binarized images, the set of expressing lines was assembled for each voxel.

Similarity between voxels based on the respective expression set from voxel A and the set

from voxel B is computed using Dice’s coefficient as where ∩ denotes

intersection and ∣x∣ denotes the number of elements in set x. To decrease the effects of

registration error and image acquisition noise and to increase the speed of subsequent

processing steps, we binned the original image voxel data into larger voxels, using a 3x3x3

nearest-‐neighbor downsampling. Analysis was performed on specific brain regions (e.g.

antennal lobe or oVLNP) defined by a 3D brain atlas of neuropils (included in the

supplemental data). Voxels in the bounding cube but not in the defined neuropil were

excluded. The k-‐medoids algorithm [S1] was run in Julia 0.4.0 using JuliaStats Clustering

0.5.0 (see Supplementary file 1). The k-‐medoids was performed on Dice dissimilarity (1-‐s).

To agglomerate the medoids, we used the fastcluster package [S2] with Python 2.7.10

using average linkage with metric distance between medoids.

Initial clustering was performed on a distance matrix found as follows. For each voxel

within the analyzed brain region (e.g. antennal lobe or lateral protocerebrum), we

calculated the set of driver lines for which GFP expression was higher than a threshold.

We used the Dice coefficient (a measure of overlap, see above) to quantify expression

similarity between each possible pair of n voxels. This n x n distance matrix was used to

group voxels into clusters of similar expression using k-‐medoids clustering, a standard

clustering technique (Figure 1A, see Experimental Procedures for details). Clustering with

other standard algorithms such as mini-‐batch k-‐means gave qualitatively similar results,

and we focus here on k-‐medoids only for convenience. As typical for clustering algorithms,

one parameter controls the number of clusters, and in our case we chose several different

values for k and evaluated results for different choices and in each of the two independent

datasets. Every voxel in the analysis is assigned to exactly one cluster. Neither manual

inspection nor calculation of a metric designed to measure clustering repeatability,

adjusted Rand index (Figure S1A), showed an obvious optimal value for k. Therefore, we

chose a value of k equal 60 as a number which appeared to provide sufficiently many

s =2 A∩BA + B

1− s

clusters to capture important structures at a small scale without producing an

overwhelming number. The result of the initial clustering algorithm is the assignment of

each voxel in the input brain region to one of k clusters. The second major step,

hierarchical clustering, took the cluster centers from the first step and agglomerated these

‘singletons’ into 2k-‐1 clusters.

Evaluating repeatability of clustering

As discussed above, automatic calculation of a measure of repeatability (adjusted Rand

index, Figure S1A) found no obvious optimum value of k used in the initial clustering step.

Therefore, we sought to gain a more biologically meaningful sense of consistency across

multiple runs of the algorithm for k=60 by comparing visually the results of manual and

automatic segmentations. We did this for the oVLNP with each of four different clustering

runs, two from each dataset (Figure S3). The results show that, despite different random

number initialization seeds, most optic glomeruli have a strong correspondence with a

singleton cluster across repeated runs of the algorithm within and across the two datasets

(Vienna Tiles and Janelia FlyLight). This indicates substantial biologically meaningful

repeatability within and between datasets at the first clustering step, which agglomeration

then structures hierarchically.

Supplemental References

S1. Kaufman, L., and Rousseeuw, P. J. (1987). Clustering by Means of Medoids. In Statistical Data Analysis Based on the L1 Norm and Related Methods.

S2. fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python Journal of Statistical Software https://www.jstatsoft.org/article/view/v053i09.

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