Just out: Molecular conservation of marsupial and eutherian placentation and lactation

September 2017: Eutherian mammals (e.g. human, mouse) are often referred to as “placental” mammals, yet marsupials (e.g. kangaroo, koala) also have a fully functioning placenta, made out of the yolk sac. The evolutionary relationship between eutherian and marsupial placentas had not been investigated at the molecular level, so we performed RNA-Seq on a prized collection of wallaby placentas, provided by the Renfree lab! This was a collaboration with Michael Guernsey of Julie Baker’s lab (Stanford) and Marilyn Renfree (Univ of Melbourne).
eLIFE | Commentary | Stanford press release

Prettier graphs using Excel

Excel is commonly used for processing data, but the default graphs lack a little something when it comes to scientific presentation (I guess perceptual accuracy and aesthetics are not a key criterion for business graphs, so we get stuck with ugly graphs).

I made these notes a few years ago using Office 2007, but the process is similar with the more recent versions.

Plotting graphs using Excel / Office 2007

First get your data: here is some I prepared earlier… we want to plot the data in the column under “y” with the sem under SEM

In this example I will do a column chart so I have put in a set of labels for the groups I am potting – this makes it easier to get the labels on the x-axis quickly. I have selected the labels and y data

Change to the “insert” Tab and select the graph type – here I will select a column plot

… and choose a simple plot type suitable for the data.

The graph is created with labels but no error bars:






I have made the plot narrower by dragging the right border to the left.

I have selected the data by clicking on one of the columns



Under the “Layout” tab choose “analysis and select “error Bars” and under this choose “more error bars”

Use the menu under “Error amount” to pick “custom”, and select the error data for both the plus error bar and the minus error bar. Your graph now should have error bars showing the SEM.

Now you can tidy up the graph (Layout tab), add axis labels (Layout::Axis titles) and graph title (Layout::Chart Title), remove or adjust the legend (Layout::Legend), format the axis numbering (Layout::Axis), add lines of best fit (Layout::Trendline), format/remove gridlines, etc etc.

With a bit of fiddling you can make the graph look reasonably good






You can even add adornments like markers to indicate statistical significances etc… (Insert::Shapes gives you lines, boxes etc – the usual suite of office drawing tools)

Alternatively you can paste the graph into PowerPoint  — if you use Paste::Special::Enhanced Metafile, then ungroup (twice) you convert the image of the graph into Microsoft office drawing objects which you can edit using the normal PowerPoint tools. I often find this approach makes it easier to set line thicknesses, colours, text fonts etc etc than doing the equivalent in Excel graph mode. It also allows you to lay out multiple graphs or other images onto a page, with easy resizing etc to make things fit together aesthetically. Of course doing it that way loses the ability for automatic recalculation of the graph in the presentation by changing the data etc that you get if you paste the graph in as an excel object (the default paste mode). Have a play and you can choose which ever approach is most appropriate for your needs.

Once you have a graph format in excel you can re-use it with different data too. Just copy and paste into a new location to get a duplicate, then under the Design tab choose Select data to feed in the new numbers. The formats, colours, fonts etc that you so laboriously chose will stay with the graph, so once you have changed the data used by the graph you should have a nice graph of your other data (you will probably need to change the graph axis ranges if you set these manually; you may need to redraw any adornments you added – P values, text, lines etc, and perhaps move the legend to a new location).

Reproduction Down Under 2017

We are organising a conference. For details see http://rdu.uom.org.au/.

Australia is home to a unique spectrum of mammalian species. Our animals are our national mbr-and-furry-pyheritage. Our Coat of Arms is held up by two of them – significantly, both are animals that cannot walk backwards! Internationally we are renowned for our iconic mammals, in particular the kangaroo, koala and platypus. Yet sadly, research on our native species is not a national priority. Marilyn Renfree has spent most of her research life uncovering the secrets of marsupial reproduction and development, comparing them to eutherian mammals, and discovered the many novel ways that make marsupials ideal biomedical models but also how to enhance their management and conservation. What would Australia be without our monotremes and marsupials? This conference is to acknowledge the contribution of Marilyn Renfree to Australian science in the fields of reproduction and development in the year of her 70th birthday.

This conference will cover a diverse set of topics in the field of reproduction and development, divided into five main themes over two and a half days. These themes are: Contraception and Conservation; Development and Diapause; Sex and Reproduction, Genomic Imprinting and Marsupial and Monotreme Genomics

Colourblindness and graphics

About one in ten males (including myself) have some degree of red-green colour blindness (and 0.5% of females), so you should bear that in mind when you are making graphics to display to others. Red-green colour blindness comes in varying degrees – it isn’t necessarily that red and green cannot be distinguished, but that are clearly different to people with normal colour vision may be hard (or impossible) to discriminate for others. Here are some hints.

colour-blind-examples-01Thin lines are difficult. Thin lines don’t activate many colour sensors (cones) in the retina, so it may be difficult to discriminate the colours of thin lines. On the graph to the right I am hard pressed to tell which line is which colour. In fact, I am not certain these lines are coloured – they could be grey.


colour-blind-examples-02Making the lines thicker aids clarity and makes the colour MUCH easier to discriminate. In this version of the graph I can work out which line is red and which is green without too much effort, at least at the size this shows on screen in this page. If one is sitting at the back of a theatre looking at a tiny version on a screen … then we are back to the too few cones activated problem.

colour-blind-examples-03Using solid symbols adds extra colour area to activate more cones. This helps. But for those on the weaker end of the colour blindness spectrum it still makes for difficulties.



colour-blind-examples-04Here we have a secondary means of discriminating the lines – hollow vs solid symbol and dashed vs continuous line. Now the lines are clearly distinct, even for those who are totally colour blind.



colour-blind-examples-05Alternatively, to accommodate red-green colour-blindness, use a colour palette which is friendly. The blue line is clearly distinct from the red ??? or is it green or grey??? Whatever, the two lines are distinctly different because I can see blue well.


colour-blind-examples-06Another approach is to use different degrees of brightness, as well as different colours, for the lines and symbols. One can see differences in shade clearly on this version even if you cannot discriminate any colours.



Loading sets of images into powerpoint slides

Sometimes one may want to generate a PowerPoint slide with a set of images – for example a set of micrograph images to discuss with your colleagues (or a lovely set of your holiday photos to make your friends jealous). You can do this by manually adding each image, one by one, then resizing, repositioning, formatting …. , but there is a much quicker way. Here is a guide to automating the process. Continue reading

Fluorescence Images: Merging and optimising

If you are using fluorescence microscopy you may need to merge images taken with different filter sets – for example DAPI to pick out nuclei together with fluorescent staining with or or more specific antibodies. Commonly you will want to merge these images into a composite. Image optimisation and merging can be achieved easily using the free Fiji package with ImageJ. Continue reading

Uterine flushing proteome of the tammar wallaby

Uterine flushing proteome of the tammar wallaby after reactivatiCynthia Martin’s manuscript “Uterine flushing proteome of the tammar wallaby after reactivation from diapause.” was just accepted by the international journal Reproduction. The pre-press manuscript is available at http://www.reproduction-online.org/content/early/2016/08/01/REP-16-0154. The work is a collaboration between Cynthia, Chin Seng Ang, a proteomics specialist at Bio21, and her supervisors Geoff, David and Marilyn. This is the first study to investigate changes in uterine secretion profiles using extremely sensitive, cutting-edge LC-MS/MS approaches, and highlights some of the factors that may be involved in the regulation of embryonic diapause.

Pubmed Record: http://www.ncbi.nlm.nih.gov/pubmed/27486272