Abundant Transcripts

 As long as we’re in heatmap mode, we thought we’d throw some fairly “conventional” data into R’s “heatmap.2” function. This time, it’s simply the most abundant transcripts found in 61 different tissues. All the data, of course, is found in our database; if your data strongly overlaps with a particular tissue type and you run it through our apps, you’ll certainly know. You can find the underlying data (and a lot more) here: https://www.proteinatlas.org/about/download .

The presence of abundance data in our database is also useful in another respect. If your data is biased with abundant transcripts/protein, as opposed to a more typical mix of abundant and rare entities, and you apply our Fisher app to the data, the app’s output will be overloaded with abundance studies. This is a bit of a warning. It’s possible that you simply need to adjust the “background” for your data (e.g. a typical proteomic study contains around 4,000 proteins…our default background of 20,000 is not appropriate in this case, and you’ll need to tweak it). There may be other, more problematic reasons for the bias in your data. Alternatively, there may be a silly mistake…your data was sorted according to abundance. We’ve noted problems in our own data entries this way (and then fixed them!). We also note issues in external datasets. Our favorite is probably the GO “ESTABLISHMENT OF PROTEIN LOCALIZATION TO ENDOPLASMIC RETICULUM” group, which is a wonderful proxy for the most abundant proteins in human tissue. Of course, a final possibility is this: your data is legitimately tweaked toward or against abundance. One phenomenon we note is that cancer tissues often lose, to some extent, the underlying tissue identity; stomach cancer tissue, for example, will become less stomach-y, and you may note this via a cancer-related decrease in the most abundant stomach entities.

One final technical point…if you’re using our co-expression app, these abundance lists will not be examined unless you manually tweak the “regulation” feature to “ANY.” In other words, only lists involving up/down-regulated entities will be examined unless you overrule the “regulation” feature.

Here’s the heatmap, with “values” being –log(P-values), as generated by Fisher’s exact test, applied to all combinations of cell types:

It shouldn’t be surprising that some extreme P-values were generated. The basic components for metabolism and structure (etc.) are both plentiful and don’t vary much from cell to cell.

The image probably aligns with your own ideas about similarities between various cell types. I was surprised how cleanly, however, the cell types clustered into various groups (see the dendrogram on top). The left-most columns contain cell types that don’t seem to overlap with other cell types with great significance: testis, liver, parathyroid, and placenta, in particular, followed by cerebellum, granulocytes, skeletal muscle, thymus, heart, and intestines. Next, there’s a square of red/orange/yellow color. That’s all brain tissue: basal ganglia, pons/medulla, spinal cord, olfactory gland, hypothalamus, amygdala, midbrain, cerebral cortex, corpus callosum, thalamus, hippocampus. Perhaps it’s interesting that the cerebellum was not found in this group. Next is a grouping of cell types that don’t overlap any other types with extreme significance, with a few exceptions (total pbmcs/monocytes, duodenum/colon, monocytes/dendritic cells). The next strong red/orange patch consists of gall bladder, vagina, skeletal muscle, cervix, prostate, fallopian tube, endometrium, and bladder. The next red/orange patch contains t-cells, NK cells, b-cells, pbmcs, and the spleen. Next, the appendix, lymph nodes, and tonsils group together strongly.

Examining the underlying data, the weakest overlap belongs to the cerebellum/liver pairing, with a P-value that doesn't even reach 0.05.

Oddly, the midbrain and the amygdala match up with the rectum fairly significantly!

*Note to self and anybody who 1) doesn’t think my heatmap is utter garbage and 2) would like to do something similar. Here’s the code that generates the colors:

col = c("navy","blue","dodgerblue","lightskyblue","palegreen","yellow","orange", "red")

breaks <- c(0, 2, 12, 25, 50, 100, 150, 200, 325)

heatmap.2(blah, blah, col = col, breaks = breaks, blah, blah)

I like this approach because it’s easy. Just make sure you’ve got one more break than colors (above there are 9 breaks and 8 colors). Of course, if you must make a gradient, you can’t use this easy method. In any case, here’s a nice color “cheatsheet”: https://www.nceas.ucsb.edu/sites/default/files/2020-04/colorPaletteCheatsheet.pdf . The bottom of the sheet contains names for something like 600 colors that you can plug in as above.

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The Perturbome

When focusing on cell types, we could make a list of the most abundant transcripts in particular cell types. We could also focus on the proteome. We could even ask, “what are the common transcripts/proteins that are rarely seen in a particular cell type?” We could search for cell type markers that are rarely or never seen in other cell types, even if these markers are not particularly abundant in the cell type of interest. Our database is chock-full of the above sorts of lists.

There’s another sort of list we are able to prepare, largely because the sheer size of our database affords the opportunity. The largest portion of the database falls into the category of “perturbation studies” wherein cells are perturbed via drug, knockout, heat, whatever. We can thus ask the question, “what transcripts/proteins are most commonly perturbed in a particular cell type?” We can also ask which entities are least frequently perturbed in a particular cell type. This is not a question of abundance or of “uniqueness” to a particular cell type. Rather, we’re focusing on the entities that fluctuate when you tweak a particular sort of cell.

Pulling data from about 10,000 studies, we’ve constructed these lists for 20 different cell types: brain, liver, skin, muscle, lymphocytes, stem, kidney, breast, colon, prostate, heart, lung, intestines, glands, pancreas, dendritic, ovaries, adipose, fibroblasts, epithelial. Why not other cell types in our database? That’s primarily because the above 20 designations are the most common in our database; we required at least 100 studies for each cell type. We could have also included “blood” as a category, but we chose to break it down further into two common subtypes: lymphocytes and dendritic cells. Some other choices were somewhat arbitrary (we have a lot of macrophage studies…why didn’t we include them?) Note also that some of these cell types can overlap….skin and liver are different tissues, but skin can contain stem cells, and breast cells can be epithelial. For this initial stab at the “perturbome”, this isn’t a problem.

With the 20 cell types, we generated 40 lists, as each cell contains entities that are frequently perturbed, as well as entities that are rarely/never perturbed; two lists per cell type. Entities that are “rarely perturbed” most likely are simply never expressed in the particular cell type, though it is possible that they are indeed expressed, but it’s difficult to tweak them; we don’t discriminate between these two cases.

If you’re interested, the dirty details are as follows: We first generated a list of all genes found in the above studies. We then simply counted their occurrences in the above 20 cell types. We then used the binomial distribution to calculate how significantly a particular gene may be over/under-represented in a particular cell type. The “probability” input for the binomial distribution (which is .5 if you’re talking about coin flips) is calculated by dividing the total genes perturbed in a tissue (e.g. brain) by the total genes perturbed over all 20 tissues. Liver, for example, constitutes 7% (.07) of all genes in our database’s perturbome. Thus, if you know that gene ABC is found 100 times in our liver studies, and 500 times over all studies, you’re equipped to perform a probability calculation. In the final step, we simply rank genes according to these probabilities, making sure to discriminate between significance generated from an excess, versus depletion, of a particular gene.

So…what did we find? First, what is the most commonly perturbed gene over all tissue types? The answer is EGR1, perturbed 1581 times, followed by SERPINA3, IFIT1, GDF15, and FOS. What is the most common gene which was never perturbed in a particular tissue? That distinction belongs to LUM (lumican), which was never perturbed in dendritic cells, despite being altered a total of 758 times over the other 19 tissues. Perhaps dendritic cells are adamant that they not be confused with other cell types that express lumican, which is largely an extracellular protein.

Of note, GAPDH, commonly used as a housekeeping control gene, was only perturbed 358 times. Actin-B was seen 610 times. Our gene lists primarily reflect perturbations, not abundance.

Below is one of the more obscure gene tables you’ll ever stumble across. It details the top genes that were never perturbed in particular cell types. The table is ordered by the count over all other tissues; thus, the cell types at the bottom of the table express a large array of transcripts/proteins.

CELL TYPE	GENE	COUNT OVER OTHER TISSUES
dendritic	lum	758
adipose	hopx	603
intestines	nav2	591
ovary	lam1	481
heart	jdp1	469
prostate	pck1	456
gland	hpp1	435
pancreas	cd38	422
muscle	slc6a14	405
colon	kcnk2	339
fibroblast	c1orf116	329
kidney	scca1	312
skin	sizn	230
brain	ugt2b15	205
lung	gpr37l1	183
breast	miat	175
stem	cyp2c9	164
liver	blcap	149
epithelial	ces1g	121

 

Another question: what are the genes that were uniquely perturbed in particular tissues? The champion is probably GM1818, a mouse gene that was perturbed 21 times in the brain, but never elsewhere. For human genes, we have FAM90A7P, which was perturbed 16 times in the brain, and never elsewhere. The brain, in fact, seems to have the largest number of uniquely perturbed genes by a large margin; the first case of a non-brain gene that was uniquely perturbed was the mouse gene AI132709 (liver), which was tweaked in 8 studies in our database…201 brain-unique genes are tweaked at least as frequently. The lncRNA Lnc-CHSY1-3 was uniquely expressed in lymphocytes, albeit with a mere 4 occurrences.

The special status of the brain is also seen in the heatmap below. We took our 40 perturbation lists and performed Fisher’s exact test on all combinations of lists, for a total of 760 P-values. 

If the image is too small, you could click on it to get a bigger view. The first row is labeled “LO_COL”, which means “genes that were least frequently perturbed in the colon.” Hopefully the other 39 labels are self-explanatory. The color key shows the –log(P-values). Combinations with very significant P-values tend to make sense…highly perturbed genes in ”breast” and “gland” overlap with extreme significance, as do non-perturbed genes in the lymphocyte/dendritic categories, and perturbed genes in the colon/intestine. There are, however, some very interesting overlaps that might not be so intuitively obvious. For example:

1) genes that are rarely perturbed in the brain are rarely perturbed in stem cells.

2) genes highly perturbed in glands are rarely perturbed in the brain.

3) genes highly tweaked in the breast are rarely tweaked in stem cells.

4) genes that are rarely perturbed in the brain are also rarely perturbed in muscle and lymphocytes.

5) looking at the “high_BR” (highly perturbed in the brain) group, the best matching “highly perturbed” cell type would be “stem”, with a –log(P-value) of about 4. This is a bit of a cheat, since stem cells and brain cells are not exclusive (i.e. some brain cells are stem cells). In truth, then, highly perturbed genes in brain cells do not overlap with the highly perturbed genes of “pure” cell types with any significance.

6) unlike the brain, the rarely perturbed genes in some tissues don’t overlap rarely perturbed genes in other tissues with great significance. For example, the rarely perturbed genes in the pancreas don’t overlap with rarely perturbed genes in other tissues with any amazing significance; the best match, in fact, would be to intestines, with a -log(P) of 7.

You can tinker with the data yourself at whatismygene.com. The table below gives you the dbase IDs that allow you to perform operations with our various apps.

DBASE ID	CELLS
132346123	most frequently perturbed in the brain
132346124	least frequently perturbed in brain
132346125	most frequently perturbed in the liver
132346126	least frequently perturbed in the liver
132346127	most frequently perturbed in skin
132346128	least frequently perturbed in skin
132346129	most frequently perturbed in muscle
132346130	least frequently perturbed in muscle
132346131	most frequently perturbed in lymphocytes
132346132	least frequently perturbed in lymphocytes
132346133	most frequently perturbed in stem cells
132346134	least frequently perturbed in stem cells
132346135	most frequently perturbed in the kidney
132346136	least frequently perturbed in the kidney
132346137	most frequently perturbed in the breast
132346138	least frequently perturbed in the breast
132346139	most frequently perturbed in the colon
132346140	least frequently perturbed in the colon
132346141	most frequently perturbed in the prostate
132346142	least frequently perturbed in the prostate
132346143	most frequently perturbed in the heart
132346144	least frequently perturbed in the heart
132346145	most frequently perturbed in the lung
132346146	least frequently perturbed in the lung
132346147	most frequently perturbed in the intestines
132346148	least frequently perturbed in the intestines
132346149	most frequently perturbed in glands
132346150	least frequently perturbed in glands
132346151	most frequently perturbed in the pancreas
132346152	least frequently perturbed in the pancreas
132346153	most frequently perturbed in dendritic cells
132346154	least frequently perturbed in dendritic cells
132346155	most frequently perturbed in ovaries
132346156	least frequently perturbed in ovaries
132346157	most frequently perturbed in adipose tissue
132346158	least frequently perturbed in adipose tissue
132346159	most frequently perturbed in fibroblasts
132346160	least frequently perturbed in fibroblasts
132346161	most frequently perturbed in epithelial cells
132346162	least frequently perturbed in epithelial cells

 

We’re not finished with our dissection of the perturbome. We’ll resume the discussion in a couple weeks.

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