If desired, SeqPlots will save plot sets as binary R files, allowing you to quickly load the pre-calculated set for replotting. Saved plot sets can also be shared with other SeqPlots users.
Controls available on the "Load or save plotset" panel:
Load saved plot set
- drop-down list to select a plotset.
Once the Rdata binary file is selected the plot grid will be displayed.
Selecting the file reveals two additional buttons:Remove dataset
- this button deletes the selected saved plot set from user data.Download plotset
- this button saves a copy of the plotset in selected location.Save current plot set
- Enter desired name and press the Save
button (appears after input of name). It is also possible to save a copy of loaded plot sets. The plot set binary files can be renamed simply by loading them, saving a copy and deleting original source file.All saved dataset can be found in data location
/publicFiles. Any SeqPlots Rdata binaries put in the folder will become available for loading in Load saved plot set
control.
The plot sets files can be also directly loaded in R. This allows further processing and customization of the plots. Data structure is a nested list, which elements be accessed by [[
R operator. The nesting goes as follow:
feature
- R listtrack
- R listmeans
- numeric vector giving mean signal value for each (binned) genomic positionstderror
- numeric vector giving standard error for each (binned) genomic positionconint
- numeric vector giving 95% confidence interval for each (binned) genomic positionall_ind
- numeric vector giving the genomic position in base pairse
- character string giveing numeric vector giving the indicates of anchored distance, NULL for
point features plotsdesc
- auto generated title of the plotheatmap
- numeric matrix, (binned) signal values for each genomic position (columns) and each feature (rows)The example structure:
```{r eval=FALSE} List of 2 $ HTZ1_Differential_genes_TOP100_v2.gff:List of 2 ..$ HTZ1_JA00001_IL1andIL2_F_N2_L3_NORM_linear_1bp_IL010andIL009_averaged.bw :List of 7 .. ..$ means : num [1:501] 2.52 2.52 2.52 2.53 2.54 ... .. ..$ stderror: num [1:501] 0.114 0.112 0.111 0.11 0.109 ... .. ..$ conint : num [1:501] 0.226 0.223 0.221 0.218 0.217 ... .. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ... .. ..$ e : NULL .. ..$ desc : chr "HTZ1_JA00001_IL1andIL2...\n@HTZ1_Differential_genes_TOP100_v2" .. ..$ heatmap : num [1:100, 1:501] 2.36 5.25 2.2 3.48 4.32 ... ..$ HTZ1_JA00001_IL3andIIL5_F_lin35_L3_NORM_linear_1bp_IL008andIL011_averaged.bw:List of 7 .. ..$ means : num [1:501] 2.36 2.35 2.35 2.36 2.38 ... .. ..$ stderror: num [1:501] 0.126 0.125 0.125 0.126 0.125 ... .. ..$ conint : num [1:501] 0.249 0.249 0.247 0.251 0.249 ... .. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ... .. ..$ e : NULL .. ..$ desc : chr "HTZ1_JA00001_IL3andIIL5...\n@HTZ1_Differential_genes_TOP100_v2" .. ..$ heatmap : num [1:100, 1:501] 2.61 3.17 1.42 2.46 4.26 ... $ HTZ1_Differential_genes_BOTTOM100.gff:List of 2 ..$ HTZ1_JA00001_IL1andIL2_F_N2_L3_NORM_linear_1bp_IL010andIL009_averaged.bw :List of 7 .. ..$ means : num [1:501] 1.57 1.57 1.58 1.6 1.62 ... .. ..$ stderror: num [1:501] 0.0996 0.0985 0.1003 0.1022 0.1018 ... .. ..$ conint : num [1:501] 0.198 0.195 0.199 0.203 0.202 ... .. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ... .. ..$ e : NULL .. ..$ desc : chr "HTZ1_JA00001_IL1andIL2...n@HTZ1_Differential_genes_BOTTOM100" .. ..$ heatmap : num [1:100, 1:501] 1.64 1.37 1.61 1.77 1.86 ... ..$ HTZ1_JA00001_IL3andIIL5_F_lin35_L3_NORM_linear_1bp_IL008andIL011_averaged.bw:List of 7 .. ..$ means : num [1:501] 1.94 1.94 1.95 1.96 1.97 ... .. ..$ stderror: num [1:501] 0.123 0.123 0.124 0.126 0.128 ... .. ..$ conint : num [1:501] 0.244 0.245 0.246 0.251 0.253 ... .. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ... .. ..$ e : NULL .. ..$ desc : chr "HTZ1_JA00001_IL3andIIL5...\n@HTZ1_Differential_genes_BOTTOM100" .. ..$ heatmap : num [1:100, 1:501] 1.61 1.37 1.29 3.04 3.77 ...
```
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