-------- Original Message --------
Subject: [Fwd: Re: Morphometrics of small, variable specimens--embryos]
Date: Tue, 29 Mar 2011 12:06:03 -0400
From: [email protected]
To: [email protected]
---------------------------- Original Message ----------------------------
Subject: Re: Morphometrics of small, variable specimens--embryos
From: "P. David Polly" <[email protected]>
Date: Tue, March 29, 2011 10:02 am
To: [email protected]
--------------------------------------------------------------------------
You might get part of the error by removing one of the PCs, but that
approach is less precise than partitioning it out. The PC axes are simply
axes of greatest variation so they are not directly associated with any
causal process (including mounting error). Error due to mounting may be
concentrated on one PC axis, but it may be spread across more than one, plus
non-error may also contribute to the same PC axis (e.g., true bilatral
asymmetry might logically contribute to shape variation in exactly the same
way as error in the saggital section).
The ANOVA approach is equivalent to regressing out the error (ANOVA and
regression are in some senses the same thing, one for factor variables and
the other for continuous variables). With the ANOVA you know you are
removing only variation due to the mounting, and you also know you are
removing most of it (to the extent that two mountings per specimen are
representative of the amount of the total amount of error).
----- Original Message -----
From: <[email protected]>
To: "P. David Polly" <[email protected]>
Sent: Tuesday, March 29, 2011 11:55 AM
Subject: Re: Morphometrics of small, variable specimens--embryos
Thanks! What about regressing the data on a PC that appears to explain
mounting errors?
Eric
Hi Eric,
I think you're on the right track with mounting the same specimen more
than
once. If you do every specimen two or three times you can partition out
the
shape variation due to mis-alignment of the plane. To do this you add an
additional level to your ANOVA so that it has factors for between group,
between individual, and between mount variation. There are several
papers
you can cite for this method, but my favourite is Baily and Byrnes, 1990.
A
new, old method for assessing measurement error in both univariate and
multivariate morphometric studies. Syst. Zool. 39: 124-130.
Best wishes,
David
-----------------------
Dr. P. David Polly
Department of Geological Sciences
Indiana University
1001 E. 10th Street
Bloomington, IN 47405 USA
[email protected]
+1 812 855 7994
http://mypage.iu.edu/~pdpolly/
(Adjunct in Biology and Anthropology)
----- Original Message -----
From: "morphmet" <[email protected]>
To: "morphmet" <[email protected]>
Sent: Tuesday, March 29, 2011 11:36 AM
Subject: Morphometrics of small, variable specimens--embryos
-------- Original Message --------
Subject: Morphometrics of small, variable specimens--embryos
Date: Tue, 29 Mar 2011 10:28:14 -0400
From: [email protected]
To: [email protected]
Hello all,
I am currently doing 2D and 3D analyses of midgestational mouse
embryos.
My sample is variable owing to ontogenetic variation. Genetic variation
is
very minimal, as my strains are mostly congenics, having practically
identical genetic backgrounds but differing only at one or two loci.
The
specimens are also very small.
First, for the 2D analysis, I am photographing freshly harvested and
unfixed embryos in three different orientations (top, lateral, and
"frontal" = palatal view), mounting in a petri dish of cold saline and
photo'ing two separate times per view. Each set of images per specimen
is
landmarked twice, and all the data will be subject to an initial
procrustes ANOVA to assess the relative strengths of the different
effects
of mounting, landmarking, genotype, and specimen. However, I expect,
and
experience shows, that a significant portion of the variance in the
data
will be due to mounting errors. The embryos are small and difficult to
position. GPA will take care of rotational errors. But slight rotations
out of the plane (pitch and yaw) will produce variation in the data
that
will look like shape variation. My hope is that by mounting and
photo'ing
twice, I will reduce pitch/yaw errors.
Will the mean square of the mounting effect reflect the amount of those
types of errors?
If I can identify a PC that appears to capture pitch or yaw, can I
regress
the procrustes coordinates on that PC in order to remove those errors
from
the data?
Thanks.
Eric
[email protected]
University of Calgary
Faculty of Medicine
