numbers of differentially expressed genes by timepoint #5

raynamharris · 2019-03-19T21:30:37Z

Given the large amount of gene expression variance due to tissue type, I examined differences between points in each tissue separately. These are the counts:

Summary of differentially expressed genes (DEG) between timepoints.

Contrast	Hyp Female	Hyp Male	Pit Female	Pit Male	Gon Female	Gon Male
Cont - Bldg	14674	117	14593	144	14859	4
Bldg - Lay	14742	0	14593	0	14836	0
Lay - Inc3	0	0	0	0	19	0
Inc3 - Inc9	0	1	0	3	2	0
Inc9 - Inc 17	0	2	57	37	9	0
Inc17 - Hatch	0	0	3	1	3	0
Hatch - Nest5	1	0	3	0	0	0
Nest5 - Nest9	0	0	0	0	24	0
Nest9 - Cont	79	220	44	147	310	14
Nest9 - Bldg	14704	0	14671	0	14844	0

See:

raynamharris · 2019-04-06T01:30:31Z

I find the above limma results to be very confusing... how can 14,000 genes be differentially expressed between 2 given timepoints??? That seems like too many to me. So, I decided to recalculate the number of differentially expressed genes between all time points (but within each sex and each tissue) using DESeq2.

To do this, I wrote a function that returns the number of differentially expressed genes (where FDR < 0.1). Tne, I wrote a nested for loop that stores which times points are being compared and the number of DEGs. Then I widen the table and print. I also use geom_tile to print a visual version of the table.

numDEGs <- function(group1, group2){
  res <- results(dds, contrast = c("treatment", group1, group2), independentFiltering = T)
  sumpadj <- sum(res$padj < 0.1, na.rm = TRUE)
  return(sumpadj)
}

a <- levels(colData$treatment)
b <- levels(colData$treatment)

dat=data.frame()
for (i in a){
  for (j in b){
    if (i != j) {
      k <- paste(i,j, sep = "") #assigns usique rownames
      dat[k,1]<-i               
      dat[k,2]<-j
      dat[k,3]<- numDEGs(i,j) #caluculates number of DEGs
    }
  }
}

data_wide <- spread(dat, V2, V3)

ggplot(dat, aes(V1, V2)) +
  geom_tile(aes(fill = V3)) +
  scale_fill_viridis(na.value="#FFFFFF00") + 
  xlab("Treatment") + ylab("Treatment") +
  labs(fill = "# of DEGs")

raynamharris · 2019-04-06T01:31:00Z

female hyp

##        V1 bldg control hatch inc.d17 inc.d3 inc.d9  lay   n5   n9
## 1    bldg   NA    7436  1709    5025   2039   1165  778 1295   55
## 2 control 7436      NA  3519    2284   5303   5523 7164 4885 7191
## 3   hatch 1709    3519    NA       2     82      6 2132    3  723
## 4 inc.d17 5025    2284     2      NA    125    305 4090   17 3952
## 5  inc.d3 2039    5303    82     125     NA     13 2193  482 1867
## 6  inc.d9 1165    5523     6     305     13     NA 1456   65 1031
## 7     lay  778    7164  2132    4090   2193   1456   NA 1198 1039
## 8      n5 1295    4885     3      17    482     65 1198   NA  271
## 9      n9   55    7191   723    3952   1867   1031 1039  271   NA

male hyp

##        V1 bldg control hatch inc.d17 inc.d3 inc.d9  lay   n5   n9
## 1    bldg   NA    4352     0       0      0      0  126    0    0
## 2 control 4352      NA  4315    3474   3827   4251 5500 4878 5284
## 3   hatch    0    4315    NA       0      0      2   34    0    0
## 4 inc.d17    0    3474     0      NA      0      0  373    1    0
## 5  inc.d3    0    3827     0       0     NA      0  127    1    2
## 6  inc.d9    0    4251     2       0      0     NA 2527  409   16
## 7     lay  126    5500    34     373    127   2527   NA    0    1
## 8      n5    0    4878     0       1      1    409    0   NA    0
## 9      n9    0    5284     0       0      2     16    1    0   NA

See https://github.com/macmanes-lab/DoveParentsRNAseq/blob/master/analysis/02_DESeq2_hyp.md

raynamharris · 2019-04-06T01:37:18Z

female gonad

##        V1 bldg control hatch inc.d17 inc.d3 inc.d9  lay   n5   n9
## 1    bldg   NA    7436  1709    5025   2039   1165  778 1295   55
## 2 control 7436      NA  3519    2284   5303   5523 7164 4885 7191
## 3   hatch 1709    3519    NA       2     82      6 2132    3  723
## 4 inc.d17 5025    2284     2      NA    125    305 4090   17 3952
## 5  inc.d3 2039    5303    82     125     NA     13 2193  482 1867
## 6  inc.d9 1165    5523     6     305     13     NA 1456   65 1031
## 7     lay  778    7164  2132    4090   2193   1456   NA 1198 1039
## 8      n5 1295    4885     3      17    482     65 1198   NA  271
## 9      n9   55    7191   723    3952   1867   1031 1039  271   NA

male gonad

##        V1 bldg control hatch inc.d17 inc.d3 inc.d9  lay   n5   n9
## 1    bldg   NA    4352     0       0      0      0  126    0    0
## 2 control 4352      NA  4315    3474   3827   4251 5500 4878 5284
## 3   hatch    0    4315    NA       0      0      2   34    0    0
## 4 inc.d17    0    3474     0      NA      0      0  373    1    0
## 5  inc.d3    0    3827     0       0     NA      0  127    1    2
## 6  inc.d9    0    4251     2       0      0     NA 2527  409   16
## 7     lay  126    5500    34     373    127   2527   NA    0    1
## 8      n5    0    4878     0       1      1    409    0   NA    0
## 9      n9    0    5284     0       0      2     16    1    0   NA

See https://github.com/macmanes-lab/DoveParentsRNAseq/blob/master/analysis/04_DESeq2_gon.md

raynamharris · 2019-04-07T03:10:53Z

female pit

##        V1 bldg control hatch inc.d17 inc.d3 inc.d9  lay   n5   n9
## 1    bldg   NA    6448  2682    3427     37    103  280  494  294
## 2 control 6448      NA  5966    5341   5550   6244 6315 6140 6141
## 3   hatch 2682    5966    NA       6   1083   1671 3348 1089 2319
## 4 inc.d17 3427    5341     6      NA   1132   2017 3995 1711 2883
## 5  inc.d3   37    5550  1083    1132     NA      1  333  198  377
## 6  inc.d9  103    6244  1671    2017      1     NA  107   87  277
## 7     lay  280    6315  3348    3995    333    107   NA  460  570
## 8      n5  494    6140  1089    1711    198     87  460   NA   23
## 9      n9  294    6141  2319    2883    377    277  570   23   NA

male pit

##        V1 bldg control hatch inc.d17 inc.d3 inc.d9  lay   n5   n9
## 1    bldg   NA    7026  1953     880     14      0    0  612   99
## 2 control 7026      NA  7026    6724   7028   6997 7304 7304 7114
## 3   hatch 1953    7026    NA      56   1880   1157 2231  155 1207
## 4 inc.d17  880    6724    56      NA    660    433  951  142  486
## 5  inc.d3   14    7028  1880     660     NA      1    5  703  183
## 6  inc.d9    0    6997  1157     433      1     NA    5  541   35
## 7     lay    0    7304  2231     951      5      5   NA  677   75
## 8      n5  612    7304   155     142    703    541  677   NA   63
## 9      n9   99    7114  1207     486    183     35   75   63   NA

https://github.com/macmanes-lab/DoveParentsRNAseq/blob/master/analysis/03_DESeq2_pit.md

raynamharris · 2019-04-07T03:15:43Z

combined figure showing the total number of DEGs between all the timepoints for females (top) and males (bottom)

raynamharris · 2019-04-07T03:22:48Z

here's the same image without the control groups as a reference.

raynamharris · 2019-07-29T17:54:01Z

I'm working on a new analysis to examine specific hypotheses regarding timepoints. Thes two images show the number of changes between each time point through the normal parental stage.

raynamharris · 2019-07-29T17:58:25Z

These two images show how manipulation changes expression by comparing to a specific manipulation to a specific time point in parental care (Eg. Day 3 remove eggs versus normal Day 3 incubation). I have also included "normal transitions" (e.g. lay versus day 3 incubation).

Here's an image for all the egg or chick removal stages.

And here's focusing on manipulations around egg hatching.

raynamharris · 2019-09-07T05:39:40Z

Back to only the characterization study, I like this viewing comparing the two normalization/ differential equation methods edgeR and DESeq2.

Table 1: EdgeR comparison of total DEGs determined by edgeR and DESeq2

Method	Contrast	Hyp Female	Hyp Male	Pit Female	Pit Male	Gon Female	Gon Male
EdgeR	Cont - Bldg	14674	117	14593	144	14859	4
EdgeR	Bldg - Lay	14742	0	14593	0	14836	0
EdgeR	Lay - Inc3	0	0	0	0	19	0
EdgeR	Inc3 - Inc9	0	1	0	3	2	0
EdgeR	Inc9 - Inc 17	0	2	57	37	9	0
EdgeR	Inc17 - Hatch	0	0	3	1	3	0
EdgeR	Hatch - Nest5	1	0	3	0	0	0
EdgeR	Nest5 - Nest9	0	0	0	0	24	0
DESeq2	Cont - Bldg	3452	4499	3950	4680	4597	2135
DESeq2	Bldg - Lay	0	0	101	0	104	1
DESeq2	Lay - Inc3	0	0	18	1	440	1
DESeq2	Inc3 - Inc9	1	0	0	1	8	0
DESeq2	Inc9 - Inc 17	1	0	640	186	1	0
DESeq2	Inc17 - Hatch	2	0	3	18	1	0
DESeq2	Hatch - Nest5	166	0	340	3	1	0
DESeq2	Nest5 - Nest9	0	0	1	23	19	0

raynamharris · 2019-11-23T02:52:53Z

here's the latest of this version, pituitary only

from the very bottom of https://github.com/macmanes-lab/DoveParentsRNAseq/blob/master/analysis/03_DESeq2_characterization.md

raynamharris · 2019-11-23T04:16:32Z

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numbers of differentially expressed genes by timepoint #5

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numbers of differentially expressed genes by timepoint #5

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female hyp

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