diff --git a/graphics_bioinf.R b/graphics_bioinf.R index 7de8b1a7f09e7c53e1e4698d2c225569ba62cb73..c53951551f1f28c06962a781e120c64b9d7c2035 100644 --- a/graphics_bioinf.R +++ b/graphics_bioinf.R @@ -36,6 +36,9 @@ library("limma") library("Single.mTEC.Transcriptomes") library("DESeq2") library("tibble") +library("broom") +library("scran") +library("locfit") theme_set(theme_gray(base_size = 18)) @@ -138,6 +141,36 @@ scatter_tra + lm_tra <- lm(tra ~ total_detected, data = tra_detected) lm_tra +## ----loessExampleLinFit, dependson="fit_model"--------------------------- + +# create_data +y <- seq(from=1, to=10, length.out=100) +a <- y^3 +y^2 + rnorm(100,mean=0, sd=30) +dataL <- data.frame(a=a, y=y) +qplot(y, a, data = dataL) + +# linear fit +linreg <- lm(a~y, data = dataL) + + +(qplot(y, a, data = dataL) + + geom_abline(slope = tidy(linreg, quick = TRUE)[2,2], + intercept = tidy(linreg, quick = TRUE)[1,2])) +tidy(linreg) + +dataL$LinReg <- predict(linreg) + +## ----loessExampleFit, dependson="loessExampleLinFit"--------------------- + +dataL$locFit <- predict(locfit(y~lp(a, nn=0.5, deg=1), data=dataL), + newdata = dataL$a) + + + (qplot(a, y, data = dataL, main = "Linear vs. local regression") + + geom_line(aes(x = a, y = locFit), color = "dodgerblue3") + + geom_line(aes(x = a, y = LinReg), color = "coral3")) + + ## ----session_info, cache = FALSE----------------------------------------- sessionInfo() diff --git a/graphics_bioinf.Rmd b/graphics_bioinf.Rmd index af33a8b4a0b5a7a7f7415c7ad7bbf4bac5e7ce54..b06e6c715ba48bb163e4386c47ece2f3a8abec8a 100755 --- a/graphics_bioinf.Rmd +++ b/graphics_bioinf.Rmd @@ -64,6 +64,9 @@ library("limma") library("Single.mTEC.Transcriptomes") library("DESeq2") library("tibble") +library("broom") +library("scran") +library("locfit") theme_set(theme_gray(base_size = 18)) @@ -311,13 +314,89 @@ We can of course always add more predictors to the linear function. The coeffici \(b\) is called the __slope__ and \(a\) is called the __intercept__ . We can fit a linear regression via a call to the function `lm()`. The regression -model is specified using R's formula notation. +model is specified using R's formula notation. ```{r regresssion_tra} lm_tra <- lm(tra ~ total_detected, data = tra_detected) lm_tra ``` +As we can see, the estimated +slope is ~ `r tidy(lm_tra, quick = TRUE)[2, "estimate"]`, indicating +that we have a proportion of `r tidy(lm_tra, quick = TRUE)[2, "estimate"]` +tra expressing genes on average per cell for the highly variable genes. +This is in line with the supplementary figure 1 of the original publication, +which uses the full set of expressed genes, not just the highly variable +ones. + +# Local regression (LOESS) + + + +Local regression is a commonly used approach for fitting flexible non--linear +functions, which involves computing many local linear regression fits and combining +them. Local regression is a very useful technique both for data visualization and +trend fitting. Fitting many local models requires quite some computational power, +but it usually feasible with today's hardware. We illustrate the local regression +using the `r CRANpkg("locfit") ` package on simulated data. + +We first fit a linear regression line to simulated +data that follows a polynomial trend and see that it does not really +fit well. + +```{r loessExampleLinFit, dependson="fit_model"} + +# create_data +y <- seq(from=1, to=10, length.out=100) +a <- y^3 +y^2 + rnorm(100,mean=0, sd=30) +dataL <- data.frame(a=a, y=y) +qplot(y, a, data = dataL) + +# linear fit +linreg <- lm(a~y, data = dataL) + + +(qplot(y, a, data = dataL) + + geom_abline(slope = tidy(linreg, quick = TRUE)[2,2], + intercept = tidy(linreg, quick = TRUE)[1,2])) +tidy(linreg) + +dataL$LinReg <- predict(linreg) +``` + +We now use the function `locfit` to perform a local regression on the +data. It takes the predictors wrapped in a call to `lp()`. Within this +function we can also set tunning parameters. An important one is the `nn` +one, which set the proportion of nearest--neighbors to be used for the local fits. + +The lower this percentage, the more closely the line will follow the data points. + +```{r loessExampleFit, dependson="loessExampleLinFit"} + +dataL$locFit <- predict(locfit(y~lp(a, nn=0.5, deg=1), data=dataL), + newdata = dataL$a) + + + (qplot(a, y, data = dataL, main = "Linear vs. local regression") + + geom_line(aes(x = a, y = locFit), color = "dodgerblue3") + + geom_line(aes(x = a, y = LinReg), color = "coral3")) + +``` + + + +# Normalization and confounding factors. + + +## Normalization of single cell data + +* use scran size factors + +## Confounding factors + +* ZINBA Wave + + # Session Info diff --git a/graphics_bioinf.html b/graphics_bioinf.html index 020488ad70565e53da22f35d0edd922e08c21c6f..8cd0dc0df951065ed8d204d27e4f4e4ed9da0f0e 100644 --- a/graphics_bioinf.html +++ b/graphics_bioinf.html @@ -11,7 +11,7 @@ - + Visual exploration for bioinformatics @@ -164,7 +164,7 @@ $(document).ready(function () {

Visual exploration for bioinformatics

Bernd Klaus

-

11 August 2017

+

15 August 2017

@@ -178,7 +178,12 @@ $(document).ready(function () {
  • 4.1 Exercise: Geometries and aspect ratios
  • 5 Regression models
    • -
  • 6 Session Info
    • +
  • 6 Local regression (LOESS)
    • +
  • 7 Normalization and confounding factors.
    • +
  • 8 Session Info
    • @@ -187,7 +192,7 @@ To compile this document graphics.off();rm(list=ls());rmarkdown::render('graphics_bioinf.Rmd');purl('graphics_bioinf.Rmd') -->

    LAST UPDATE AT

    -
       [1] "Fri Aug 11 18:45:13 2017"
    +
       [1] "Tue Aug 15 16:38:33 2017"

    1 Required packages and other preparations

    library("readxl")
@@ -216,8 +221,11 @@ graphics.off();rm(list=ls());rmarkdown::render('graphics_bioinf.Rmd');purl('grap
 library("Single.mTEC.Transcriptomes")
 library("DESeq2")
 library("tibble")
-
-theme_set(theme_gray(base_size = 18))
+library("broom")
+library("scran")
+library("locfit")
    +
       locfit 1.5-9.1    2013-03-22
    +
    theme_set(theme_gray(base_size = 18))
 
 
 
@@ -378,9 +386,59 @@ lm_tra
    Coefficients: (Intercept) total_detected 36.944 0.218 +

    As we can see, the estimated slope is ~ 0.218, indicating that we have a proportion of 0.218 tra expressing genes on average per cell for the highly variable genes. This is in line with the supplementary figure 1 of the original publication, which uses the full set of expressed genes, not just the highly variable ones.

    +
    +
    +

    6 Local regression (LOESS)

    +

    Local regression is a commonly used approach for fitting flexible non–linear functions, which involves computing many local linear regression fits and combining them. Local regression is a very useful technique both for data visualization and trend fitting. Fitting many local models requires quite some computational power, but it usually feasible with today’s hardware. We illustrate the local regression using the locfit package on simulated data.

    +

    We first fit a linear regression line to simulated data that follows a polynomial trend and see that it does not really fit well.

    +
    # create_data
+y <- seq(from=1, to=10, length.out=100)
+a <- y^3 +y^2  + rnorm(100,mean=0, sd=30)
+dataL <- data.frame(a=a, y=y)
+qplot(y, a, data = dataL)
    +

    +
    # linear fit
+linreg <- lm(a~y, data = dataL)
+
+
+(qplot(y, a, data = dataL) +
+  geom_abline(slope = tidy(linreg, quick = TRUE)[2,2], 
+              intercept = tidy(linreg, quick = TRUE)[1,2]))
    +

    +
    tidy(linreg)
    +
                term estimate std.error statistic  p.value
+   1 (Intercept)     -306     26.73     -11.4 9.82e-20
+   2           y      114      4.39      25.9 1.36e-45
    +
    dataL$LinReg <- predict(linreg)
    +

    We now use the function locfit to perform a local regression on the data. It takes the predictors wrapped in a call to lp(). Within this function we can also set tunning parameters. An important one is the nn one, which set the proportion of nearest–neighbors to be used for the local fits.

    +

    The lower this percentage, the more closely the line will follow the data points.

    +
    dataL$locFit  <- predict(locfit(y~lp(a, nn=0.5, deg=1), data=dataL),
+                         newdata = dataL$a)
+
+
+ (qplot(a, y, data = dataL, main = "Linear vs. local regression")
+ +  geom_line(aes(x = a, y = locFit), color = "dodgerblue3")
+ +  geom_line(aes(x = a, y = LinReg), color = "coral3"))
    +

    +
    +
    +

    7 Normalization and confounding factors.

    +
    +

    7.1 Normalization of single cell data

    + +
    +
    +

    7.2 Confounding factors

    + +
    -

    6 Session Info

    +

    8 Session Info

    sessionInfo()
       R version 3.4.1 (2017-06-30)
    Platform: x86_64-pc-linux-gnu (64-bit)
@@ -403,61 +461,68 @@ lm_tra
    [8] methods base other attached packages: - [1] bindrcpp_0.2 sva_3.24.4 - [3] genefilter_1.58.1 mgcv_1.8-18 - [5] nlme_3.1-131 BiocParallel_1.10.1 - [7] DESeq2_1.16.1 SummarizedExperiment_1.6.3 - [9] DelayedArray_0.2.7 Biobase_2.36.2 - [11] GenomicRanges_1.28.4 GenomeInfoDb_1.12.2 - [13] IRanges_2.10.2 S4Vectors_0.14.3 - [15] BiocGenerics_0.22.0 Single.mTEC.Transcriptomes_1.4.0 - [17] limma_3.32.5 openxlsx_4.0.17 - [19] car_2.1-5 corrplot_0.77 - [21] plotly_4.7.1 forcats_0.2.0 - [23] entropy_1.2.1 magrittr_1.5 - [25] factoextra_1.0.4 gtools_3.5.0 - [27] fdrtool_1.2.15 matrixStats_0.52.2 - [29] pheatmap_1.0.8 stringr_1.2.0 - [31] RColorBrewer_1.1-2 dplyr_0.7.2 - [33] purrr_0.2.3 readr_1.1.1 - [35] tidyr_0.6.3 tibble_1.3.3 - [37] ggplot2_2.2.1 tidyverse_1.1.1 - [39] BiocStyle_2.4.1 readxl_1.0.0 - [41] knitr_1.16 + [1] sva_3.24.4 genefilter_1.58.1 + [3] mgcv_1.8-18 nlme_3.1-131 + [5] locfit_1.5-9.1 scran_1.4.5 + [7] scater_1.4.0 broom_0.4.2 + [9] bindrcpp_0.2 BiocParallel_1.10.1 + [11] DESeq2_1.16.1 SummarizedExperiment_1.6.3 + [13] DelayedArray_0.2.7 Biobase_2.36.2 + [15] GenomicRanges_1.28.4 GenomeInfoDb_1.12.2 + [17] IRanges_2.10.2 S4Vectors_0.14.3 + [19] BiocGenerics_0.22.0 Single.mTEC.Transcriptomes_1.4.0 + [21] limma_3.32.5 openxlsx_4.0.17 + [23] car_2.1-5 corrplot_0.77 + [25] plotly_4.7.1 forcats_0.2.0 + [27] entropy_1.2.1 magrittr_1.5 + [29] factoextra_1.0.4 gtools_3.5.0 + [31] fdrtool_1.2.15 matrixStats_0.52.2 + [33] pheatmap_1.0.8 stringr_1.2.0 + [35] RColorBrewer_1.1-2 dplyr_0.7.2 + [37] purrr_0.2.3 readr_1.1.1 + [39] tidyr_0.6.3 tibble_1.3.3 + [41] ggplot2_2.2.1 tidyverse_1.1.1 + [43] BiocStyle_2.4.1 readxl_1.0.0 + [45] knitr_1.16 loaded via a namespace (and not attached): - [1] minqa_1.2.4 colorspace_1.3-2 rprojroot_1.2 - [4] htmlTable_1.9 XVector_0.16.0 base64enc_0.1-3 - [7] MatrixModels_0.4-1 bit64_0.9-7 ggrepel_0.6.5 - [10] AnnotationDbi_1.38.2 lubridate_1.6.0 xml2_1.1.1 - [13] codetools_0.2-15 splines_3.4.1 mnormt_1.5-5 - [16] geneplotter_1.54.0 Formula_1.2-2 jsonlite_1.5 - [19] nloptr_1.0.4 pbkrtest_0.4-7 annotate_1.54.0 - [22] broom_0.4.2 cluster_2.0.6 compiler_3.4.1 - [25] httr_1.2.1 backports_1.1.0 assertthat_0.2.0 - [28] Matrix_1.2-10 lazyeval_0.2.0 acepack_1.4.1 - [31] htmltools_0.3.6 quantreg_5.33 tools_3.4.1 - [34] gtable_0.2.0 glue_1.1.1 GenomeInfoDbData_0.99.0 - [37] reshape2_1.4.2 Rcpp_0.12.12 cellranger_1.1.0 - [40] psych_1.7.5 lme4_1.1-13 rvest_0.3.2 - [43] XML_3.98-1.9 MASS_7.3-47 zlibbioc_1.22.0 - [46] scales_0.4.1 hms_0.3 SparseM_1.77 - [49] yaml_2.1.14 memoise_1.1.0 gridExtra_2.2.1 - [52] rpart_4.1-11 RSQLite_2.0 latticeExtra_0.6-28 - [55] stringi_1.1.5 highr_0.6 checkmate_1.8.3 - [58] rlang_0.1.1 pkgconfig_2.0.1 bitops_1.0-6 - [61] evaluate_0.10.1 lattice_0.20-35 bindr_0.1 - [64] labeling_0.3 htmlwidgets_0.9 bit_1.1-12 - [67] bookdown_0.4 plyr_1.8.4 R6_2.2.2 - [70] Hmisc_4.0-3 DBI_0.7 haven_1.1.0 - [73] foreign_0.8-69 survival_2.41-3 RCurl_1.95-4.8 - [76] nnet_7.3-12 modelr_0.1.1 rmarkdown_1.6 - [79] locfit_1.5-9.1 grid_3.4.1 data.table_1.10.4 - [82] blob_1.1.0 digest_0.6.12 xtable_1.8-2 - [85] munsell_0.4.3 viridisLite_0.2.0 + [1] backports_1.1.0 Hmisc_4.0-3 igraph_1.1.2 + [4] plyr_1.8.4 lazyeval_0.2.0 shinydashboard_0.6.1 + [7] splines_3.4.1 digest_0.6.12 htmltools_0.3.6 + [10] viridis_0.4.0 checkmate_1.8.3 memoise_1.1.0 + [13] cluster_2.0.6 annotate_1.54.0 modelr_0.1.1 + [16] colorspace_1.3-2 blob_1.1.0 rvest_0.3.2 + [19] ggrepel_0.6.5 haven_1.1.0 tximport_1.4.0 + [22] RCurl_1.95-4.8 jsonlite_1.5 lme4_1.1-13 + [25] bindr_0.1 zoo_1.8-0 survival_2.41-3 + [28] glue_1.1.1 gtable_0.2.0 zlibbioc_1.22.0 + [31] XVector_0.16.0 MatrixModels_0.4-1 SparseM_1.77 + [34] scales_0.4.1 DBI_0.7 edgeR_3.18.1 + [37] Rcpp_0.12.12 viridisLite_0.2.0 xtable_1.8-2 + [40] htmlTable_1.9 foreign_0.8-69 bit_1.1-12 + [43] Formula_1.2-2 DT_0.2 htmlwidgets_0.9 + [46] httr_1.2.1 FNN_1.1 acepack_1.4.1 + [49] pkgconfig_2.0.1 XML_3.98-1.9 nnet_7.3-12 + [52] dynamicTreeCut_1.63-1 labeling_0.3 rlang_0.1.1 + [55] reshape2_1.4.2 AnnotationDbi_1.38.2 munsell_0.4.3 + [58] cellranger_1.1.0 tools_3.4.1 RSQLite_2.0 + [61] evaluate_0.10.1 yaml_2.1.14 bit64_0.9-7 + [64] mime_0.5 quantreg_5.33 xml2_1.1.1 + [67] biomaRt_2.32.1 compiler_3.4.1 pbkrtest_0.4-7 + [70] beeswarm_0.2.3 statmod_1.4.30 geneplotter_1.54.0 + [73] stringi_1.1.5 lattice_0.20-35 Matrix_1.2-10 + [76] psych_1.7.5 nloptr_1.0.4 data.table_1.10.4 + [79] bitops_1.0-6 httpuv_1.3.5 R6_2.2.2 + [82] latticeExtra_0.6-28 bookdown_0.4 gridExtra_2.2.1 + [85] vipor_0.4.5 codetools_0.2-15 MASS_7.3-47 + [88] assertthat_0.2.0 rhdf5_2.20.0 rjson_0.2.15 + [91] rprojroot_1.2 mnormt_1.5-5 GenomeInfoDbData_0.99.0 + [94] hms_0.3 grid_3.4.1 rpart_4.1-11 + [97] minqa_1.2.4 rmarkdown_1.6 shiny_1.0.3 + [100] lubridate_1.6.0 base64enc_0.1-3 ggbeeswarm_0.6.0 -
    ---
title: "Visual exploration for bioinformatics"
author: "Bernd Klaus"
date: "`r doc_date()`"
output: 
    BiocStyle::html_document2:
        toc: true
        highlight: tango
        self_contained: true
        toc_float: false
        code_download: true
---


<!--<
To compile this document
graphics.off();rm(list=ls());rmarkdown::render('graphics_bioinf.Rmd');purl('graphics_bioinf.Rmd')
-->

```{r options, include=FALSE}
library(knitr)
options(digits=3, width=80)
golden_ratio <- (1 + sqrt(5)) / 2
opts_chunk$set(echo=TRUE,tidy=FALSE,include=TRUE,
               dev=c('png', 'pdf', 'svg'), fig.height = 5, fig.width = 4 * golden_ratio, comment = '  ', dpi = 300,
cache = TRUE)
```

 **LAST UPDATE AT**

```{r, echo=FALSE, cache=FALSE}
print(date())
```



# Required packages and other preparations


```{r required_packages_and_data, echo = TRUE, cache=FALSE}
library("readxl")
library("BiocStyle")
library("knitr")
library("tidyverse")
library("RColorBrewer")
library("stringr")
library("pheatmap")
library("matrixStats")
library("purrr")
library("fdrtool")
library("readr")
library("gtools")
library("factoextra")
library("magrittr")
library("entropy")
library("forcats")
library("plotly")
library("corrplot")
library("car")
library("forcats")
library("openxlsx")
library("readxl")
library("limma")
library("Single.mTEC.Transcriptomes")
library("DESeq2")
library("tibble")

theme_set(theme_gray(base_size = 18))



library(BiocParallel)
multicoreParam <- MulticoreParam(workers = round(parallel::detectCores() * 0.5))
register(multicoreParam)

data_dir <- file.path("data/")

```




```{r import_gene_expression, echo=FALSE, eval=FALSE}
# Here we import the gene expression data using only the subset of highly 
# variable genes, resave it 
load(file.path(data_dir, "deGenesNone.RData"))
load(file.path(data_dir, "mTECdxd.RData"))

mtec_counts <- counts(dxd)[deGenesNone, ]

mtec_counts <- as_tibble(rownames_to_column(as.data.frame(mtec_counts), 
                                  var = "ensembl_id"))

mtec_cell_anno <- as_tibble(as.data.frame(colData(dxd))) %>%
                  modify_if(.p = is.factor, as.character)

data("biotypes")
data("geneNames")

mtec_gene_anno <- tibble(biotype) %>%
                  add_column(ensembl_id = names(biotype),
                             gene_name = geneNames,
                             .before =  "biotype")

mtec_cell_anno <- colData(dxd)

save(mtec_counts, file = file.path(data_dir, "mtec_counts.RData"),
     compress = "xz")

save(mtec_cell_anno, file = file.path(data_dir, "mtec_cell_anno.RData"),
     compress = "xz")


save(mtec_gene_anno, file = file.path(data_dir, "mtec_gene_anno.RData"),
     compress = "xz")

tras <- as_tibble(tras)

save(tras, file = file.path(data_dir, "tras.RData"),
     compress = "xz")
```


# mTEC single cell-RNA-Seq data


We summarize single-cell transcriptomes in the form of count matrices in which each row
represents a gene and each column represents one cell. The matrix is filled
with the number of sequenced fragments whose genomic alignment overlaps with the
genomic coordinates of the genes. For this, we only use cells for which more 
than 40% of the sequenced fragments could be uniquely assigned to the Mouse 
reference genome. We also discarded 28 cells from the batch 4 (since they were 
from another cell type that was sequenced together with some of the mTECs). 

It is sensible to only use highly variable genes in the analysis of 
single cell data (HVG), the original publication identified  more than
9,000 as highly variable using a method published in Brennecke et. al. 2013.
We retain those genes for further analysis.

We also import a table with genes annotated as tissue restricted antigens (tras)

from Shinto et. al. 2013 and annotation for mouse genes from ENSEMBL.


```{r import_data}
load(file.path(data_dir, "mtec_counts.RData"))
load(file.path(data_dir, "mtec_cell_anno.RData"))
load(file.path(data_dir, "mtec_gene_anno.RData"))
load(file.path(data_dir, "tras.RData"))
```


# Graphics in R

# ggplot2{#gg}

`r CRANpkg("ggplot2")` is a package by Hadley Wickham  that implements the idea of 
*grammar of graphics* -- a concept created by Leland Wilkinson in his book of the same name. Comprehensive documentation for the package can
be found on <http://ggplot2.tidyverse.org/>.  The online documentation includes
example use cases for each of the graphic types that are introduced in this lab (and
many more) and is an invaluable resource when creating figures.

Single cell RNA-Seq data has many zero due to a limited capture efficiency 
and biases in the reverse transcription. It is thus interesting to see how
the total number of genes expressed (at least one count) relates to 
the number of TRA encoding genes expressed. 

The single cell data also contains mTEC cells select based on surface markers,
we exclude those and make sure to inspect only protein coding genes.
In order to do this, we first turn the count table:

```{r mtec_count_table}
mtec_counts
```

which contains the counts for each cell in separte columns into a tidy 
data frame. We `gather` all columns except for the first one and then
add a column telling us whether a specific genes is coding for a TRA
and whether it was detected or not.

Then, we join the annotation to the tidy table

```{r tidy_count}
mtec_counts_tidy <- gather(mtec_counts, key = "cell_id", value = "count",
                           -ensembl_id) %>%
                           mutate(is_tra = ensembl_id %in% tras$gene.ids,
                                  is_detected = count > 0) %>%
                           left_join(mtec_cell_anno, 
                                    by = c("cell_id" = "cellID"))
```

Now we can compute the number of TRA genes detected per cell and filter 
the non--protein coding genes as well as the cells that have been processed
using FACS based on a surface marker.

Finally, we obtain a tidy table with two columns per cell: the number of 
tras and non-tras expressed. There is a subtle change in  observations: 
we go from genes within a single cell as our unit to single cells.

```{r tra_per_cell}

tra_detected <- filter(mtec_counts_tidy, is_detected == TRUE, 
                       SurfaceMarker == "None") %>%
                mutate(is_tra = ifelse(is_tra, "tra", "not_a_tra")) %>%
                group_by(cell_id, is_tra) %>% 
                tally() %>%
                spread(key = is_tra, value = n) %>%
                mutate(total_detected = sum(tra, not_a_tra))

tra_detected
```


We visualize this relationship in a scatterplot with ggplot2.

```{r travsall, fig.cap="Total number of genes vs TRA"}
scatter_tra <- ggplot(tra_detected, aes(x = total_detected, y = tra))+ 
               geom_point() +
               coord_equal()

scatter_tra
```


We just wrote our first "sentence" using the grammar of graphics. 
Let us deconstruct this sentence.
First, we specified the dataframe that contains the data, `mtec_counts_tidy`.
Then we told `ggplot` via the `aes`. This stands for `aesthetic` argument 
(Aesthetics are visual property of the objects in your plot.)
and tells `r CRANpkg("ggplot2")` which variables 
we want on the \(x\)-- and \(y\)--axes, respectively. 

Finally, we stated that we want the plot to use points, by adding the result 
of calling the function `geom_point`. The plot would be good to go now, but
as both axes represent counts and thus have the same units, 
we want their aspect ratio to be equal. 

Note that in `r CRANpkg("ggplot2")`, we build a plot "layer by layer":
We first specify a mapping from the data to aesthetics and then use an
appropriate geometry to display it. We can always add new layers or modify
existing ones by literally adding them, here we add horizontal lines
indicating our marginal counts to the scatter plot. This is known 
as a "rug plot"

```{r more_layers}
scatter_tra + 
  geom_rug(alpha = I(0.2))
```

We set (instead of mapped) the alpha value to 0.2, this increaseses the 
transparancy of the rugs and alleviates overplotting.


## Exercise: Geometries and aspect ratios

1. What happens if you omit the calls to `geom_point()` and
   `coord_equal()` ?
   
2. Use the internet (e.g. <http://ggplot2.tidyverse.org/>) to find 
out how to add a smoothing curve to the plot. Can you change the smoother
to a regression line?


```{r ex_geom}
ggplot(tra_detected, aes(x = total_detected, y = tra))

scatter_tra +
  geom_smooth(color = "coral3") +
  geom_smooth(method = "lm")
```

As Figure \@ref(fig:travsall) shows the two numbers seem very correlated 
and seem to follow a linear relation, 
indicating that the number of TRA genes that are detected within a cell 
is proportional to the number of detected genes. Regression models 
allow for the systematic characterization of such relationships. 

# Regression models

In regression we use one variable to explain or predict the other. It is customary
to plot the predictor variable on the x--axis and the predicted variable on the
y--axis.
The predictor is also called the independent variable, the explanatory variable,
the covariate, or simply x. The predicted variable is called the dependent 
variable, or simply y.

In a regression problem the data are pairs \((x_i , y_i )\) for \(i = 1, \dotsc , n\).
For each  \(x_i \), \(y_i\) is a random variable whose distribution depends on  \(x_i \). 
The regression model is:

\[
y_i = g(x_i) + \varepsilon_i .
\]

The above  expresses \(y_i\) as a systematic part \(g(x_i)\)
and an unexplained part
$\varepsilon_i$. Or more informally: 

__response = signal + noise__

\(g\) is called the regression function, and regression models provide means
to compute \(g\). A common choice for \(g\) is a linear function, if we have only
single predictor $X$, the simple linear regression model is:

\[
y_i = a +  b x_{i} + \varepsilon_i; \quad
 \varepsilon_i \sim N(0, \sigma);
\]

We can of course always add more predictors to the linear function. The coefficient
\(b\) is called the __slope__ and \(a\) is called the __intercept__ .

We can fit a linear regression via a call to the function `lm()`. The regression
model is specified using R's formula notation.

```{r regresssion_tra}
lm_tra <- lm(tra ~ total_detected, data = tra_detected)
lm_tra
```


# Session Info

```{r session_info, cache = FALSE}
sessionInfo()
```





    +
    ---
title: "Visual exploration for bioinformatics"
author: "Bernd Klaus"
date: "`r doc_date()`"
output: 
    BiocStyle::html_document2:
        toc: true
        highlight: tango
        self_contained: true
        toc_float: false
        code_download: true
---


<!--<
To compile this document
graphics.off();rm(list=ls());rmarkdown::render('graphics_bioinf.Rmd');purl('graphics_bioinf.Rmd')
-->

```{r options, include=FALSE}
library(knitr)
options(digits=3, width=80)
golden_ratio <- (1 + sqrt(5)) / 2
opts_chunk$set(echo=TRUE,tidy=FALSE,include=TRUE,
               dev=c('png', 'pdf', 'svg'), fig.height = 5, fig.width = 4 * golden_ratio, comment = '  ', dpi = 300,
cache = TRUE)
```

 **LAST UPDATE AT**

```{r, echo=FALSE, cache=FALSE}
print(date())
```



# Required packages and other preparations


```{r required_packages_and_data, echo = TRUE, cache=FALSE}
library("readxl")
library("BiocStyle")
library("knitr")
library("tidyverse")
library("RColorBrewer")
library("stringr")
library("pheatmap")
library("matrixStats")
library("purrr")
library("fdrtool")
library("readr")
library("gtools")
library("factoextra")
library("magrittr")
library("entropy")
library("forcats")
library("plotly")
library("corrplot")
library("car")
library("forcats")
library("openxlsx")
library("readxl")
library("limma")
library("Single.mTEC.Transcriptomes")
library("DESeq2")
library("tibble")
library("broom")
library("scran")
library("locfit")

theme_set(theme_gray(base_size = 18))



library(BiocParallel)
multicoreParam <- MulticoreParam(workers = round(parallel::detectCores() * 0.5))
register(multicoreParam)

data_dir <- file.path("data/")

```




```{r import_gene_expression, echo=FALSE, eval=FALSE}
# Here we import the gene expression data using only the subset of highly 
# variable genes, resave it 
load(file.path(data_dir, "deGenesNone.RData"))
load(file.path(data_dir, "mTECdxd.RData"))

mtec_counts <- counts(dxd)[deGenesNone, ]

mtec_counts <- as_tibble(rownames_to_column(as.data.frame(mtec_counts), 
                                  var = "ensembl_id"))

mtec_cell_anno <- as_tibble(as.data.frame(colData(dxd))) %>%
                  modify_if(.p = is.factor, as.character)

data("biotypes")
data("geneNames")

mtec_gene_anno <- tibble(biotype) %>%
                  add_column(ensembl_id = names(biotype),
                             gene_name = geneNames,
                             .before =  "biotype")

mtec_cell_anno <- colData(dxd)

save(mtec_counts, file = file.path(data_dir, "mtec_counts.RData"),
     compress = "xz")

save(mtec_cell_anno, file = file.path(data_dir, "mtec_cell_anno.RData"),
     compress = "xz")


save(mtec_gene_anno, file = file.path(data_dir, "mtec_gene_anno.RData"),
     compress = "xz")

tras <- as_tibble(tras)

save(tras, file = file.path(data_dir, "tras.RData"),
     compress = "xz")
```


# mTEC single cell-RNA-Seq data


We summarize single-cell transcriptomes in the form of count matrices in which each row
represents a gene and each column represents one cell. The matrix is filled
with the number of sequenced fragments whose genomic alignment overlaps with the
genomic coordinates of the genes. For this, we only use cells for which more 
than 40% of the sequenced fragments could be uniquely assigned to the Mouse 
reference genome. We also discarded 28 cells from the batch 4 (since they were 
from another cell type that was sequenced together with some of the mTECs). 

It is sensible to only use highly variable genes in the analysis of 
single cell data (HVG), the original publication identified  more than
9,000 as highly variable using a method published in Brennecke et. al. 2013.
We retain those genes for further analysis.

We also import a table with genes annotated as tissue restricted antigens (tras)

from Shinto et. al. 2013 and annotation for mouse genes from ENSEMBL.


```{r import_data}
load(file.path(data_dir, "mtec_counts.RData"))
load(file.path(data_dir, "mtec_cell_anno.RData"))
load(file.path(data_dir, "mtec_gene_anno.RData"))
load(file.path(data_dir, "tras.RData"))
```


# Graphics in R

# ggplot2{#gg}

`r CRANpkg("ggplot2")` is a package by Hadley Wickham  that implements the idea of 
*grammar of graphics* -- a concept created by Leland Wilkinson in his book of the same name. Comprehensive documentation for the package can
be found on <http://ggplot2.tidyverse.org/>.  The online documentation includes
example use cases for each of the graphic types that are introduced in this lab (and
many more) and is an invaluable resource when creating figures.

Single cell RNA-Seq data has many zero due to a limited capture efficiency 
and biases in the reverse transcription. It is thus interesting to see how
the total number of genes expressed (at least one count) relates to 
the number of TRA encoding genes expressed. 

The single cell data also contains mTEC cells select based on surface markers,
we exclude those and make sure to inspect only protein coding genes.
In order to do this, we first turn the count table:

```{r mtec_count_table}
mtec_counts
```

which contains the counts for each cell in separte columns into a tidy 
data frame. We `gather` all columns except for the first one and then
add a column telling us whether a specific genes is coding for a TRA
and whether it was detected or not.

Then, we join the annotation to the tidy table

```{r tidy_count}
mtec_counts_tidy <- gather(mtec_counts, key = "cell_id", value = "count",
                           -ensembl_id) %>%
                           mutate(is_tra = ensembl_id %in% tras$gene.ids,
                                  is_detected = count > 0) %>%
                           left_join(mtec_cell_anno, 
                                    by = c("cell_id" = "cellID"))
```

Now we can compute the number of TRA genes detected per cell and filter 
the non--protein coding genes as well as the cells that have been processed
using FACS based on a surface marker.

Finally, we obtain a tidy table with two columns per cell: the number of 
tras and non-tras expressed. There is a subtle change in  observations: 
we go from genes within a single cell as our unit to single cells.

```{r tra_per_cell}

tra_detected <- filter(mtec_counts_tidy, is_detected == TRUE, 
                       SurfaceMarker == "None") %>%
                mutate(is_tra = ifelse(is_tra, "tra", "not_a_tra")) %>%
                group_by(cell_id, is_tra) %>% 
                tally() %>%
                spread(key = is_tra, value = n) %>%
                mutate(total_detected = sum(tra, not_a_tra))

tra_detected
```


We visualize this relationship in a scatterplot with ggplot2.

```{r travsall, fig.cap="Total number of genes vs TRA"}
scatter_tra <- ggplot(tra_detected, aes(x = total_detected, y = tra))+ 
               geom_point() +
               coord_equal()

scatter_tra
```


We just wrote our first "sentence" using the grammar of graphics. 
Let us deconstruct this sentence.
First, we specified the dataframe that contains the data, `mtec_counts_tidy`.
Then we told `ggplot` via the `aes`. This stands for `aesthetic` argument 
(Aesthetics are visual property of the objects in your plot.)
and tells `r CRANpkg("ggplot2")` which variables 
we want on the \(x\)-- and \(y\)--axes, respectively. 

Finally, we stated that we want the plot to use points, by adding the result 
of calling the function `geom_point`. The plot would be good to go now, but
as both axes represent counts and thus have the same units, 
we want their aspect ratio to be equal. 

Note that in `r CRANpkg("ggplot2")`, we build a plot "layer by layer":
We first specify a mapping from the data to aesthetics and then use an
appropriate geometry to display it. We can always add new layers or modify
existing ones by literally adding them, here we add horizontal lines
indicating our marginal counts to the scatter plot. This is known 
as a "rug plot"

```{r more_layers}
scatter_tra + 
  geom_rug(alpha = I(0.2))
```

We set (instead of mapped) the alpha value to 0.2, this increaseses the 
transparancy of the rugs and alleviates overplotting.


## Exercise: Geometries and aspect ratios

1. What happens if you omit the calls to `geom_point()` and
   `coord_equal()` ?
   
2. Use the internet (e.g. <http://ggplot2.tidyverse.org/>) to find 
out how to add a smoothing curve to the plot. Can you change the smoother
to a regression line?


```{r ex_geom}
ggplot(tra_detected, aes(x = total_detected, y = tra))

scatter_tra +
  geom_smooth(color = "coral3") +
  geom_smooth(method = "lm")
```

As Figure \@ref(fig:travsall) shows the two numbers seem very correlated 
and seem to follow a linear relation, 
indicating that the number of TRA genes that are detected within a cell 
is proportional to the number of detected genes. Regression models 
allow for the systematic characterization of such relationships. 

# Regression models

In regression we use one variable to explain or predict the other. It is customary
to plot the predictor variable on the x--axis and the predicted variable on the
y--axis.
The predictor is also called the independent variable, the explanatory variable,
the covariate, or simply x. The predicted variable is called the dependent 
variable, or simply y.

In a regression problem the data are pairs \((x_i , y_i )\) for \(i = 1, \dotsc , n\).
For each  \(x_i \), \(y_i\) is a random variable whose distribution depends on  \(x_i \). 
The regression model is:

\[
y_i = g(x_i) + \varepsilon_i .
\]

The above  expresses \(y_i\) as a systematic part \(g(x_i)\)
and an unexplained part
$\varepsilon_i$. Or more informally: 

__response = signal + noise__

\(g\) is called the regression function, and regression models provide means
to compute \(g\). A common choice for \(g\) is a linear function, if we have only
single predictor $X$, the simple linear regression model is:

\[
y_i = a +  b x_{i} + \varepsilon_i; \quad
 \varepsilon_i \sim N(0, \sigma);
\]

We can of course always add more predictors to the linear function. The coefficient
\(b\) is called the __slope__ and \(a\) is called the __intercept__ .

We can fit a linear regression via a call to the function `lm()`. The regression
model is specified using R's formula notation. 

```{r regresssion_tra}
lm_tra <- lm(tra ~ total_detected, data = tra_detected)
lm_tra
```

As we can see, the estimated 
slope is ~ `r tidy(lm_tra, quick = TRUE)[2, "estimate"]`, indicating 
that we have a proportion of `r tidy(lm_tra, quick = TRUE)[2, "estimate"]` 
tra expressing genes on average per cell for the highly variable genes. 
This is in line with the supplementary figure 1 of the original publication,
which uses the full set of expressed genes, not just the highly variable 
ones.

# Local regression  (LOESS)



Local regression is a commonly used approach for fitting flexible non--linear
functions, which involves computing many local linear regression fits and combining
them. Local regression is a very useful technique both for  data visualization and
trend fitting. Fitting many local models requires quite some computational power,
but it usually feasible with today's hardware. We illustrate the local regression
using the `r CRANpkg("locfit") ` package on simulated data.

We first fit a linear regression line to simulated
data that follows a polynomial trend and see that it does not really
fit well.

```{r loessExampleLinFit, dependson="fit_model"}

# create_data
y <- seq(from=1, to=10, length.out=100)
a <- y^3 +y^2  + rnorm(100,mean=0, sd=30)
dataL <- data.frame(a=a, y=y)
qplot(y, a, data = dataL)

# linear fit
linreg <- lm(a~y, data = dataL)


(qplot(y, a, data = dataL) +
  geom_abline(slope = tidy(linreg, quick = TRUE)[2,2], 
              intercept = tidy(linreg, quick = TRUE)[1,2]))
tidy(linreg)

dataL$LinReg <- predict(linreg)
```

We now use the function `locfit` to perform a local regression on the
data. It takes the predictors wrapped in a call to `lp()`. Within this
function we can also set tunning parameters. An important one is the  `nn`
one, which set the proportion of nearest--neighbors to be used for the local fits.

The lower this percentage, the more closely the line will follow the data points.

```{r loessExampleFit, dependson="loessExampleLinFit"}

dataL$locFit  <- predict(locfit(y~lp(a, nn=0.5, deg=1), data=dataL),
                         newdata = dataL$a)


 (qplot(a, y, data = dataL, main = "Linear vs. local regression")
 +  geom_line(aes(x = a, y = locFit), color = "dodgerblue3")
 +  geom_line(aes(x = a, y = LinReg), color = "coral3"))

```



# Normalization and confounding factors.


## Normalization of single cell data

* use scran size factors

## Confounding factors

* ZINBA Wave



# Session Info

```{r session_info, cache = FALSE}
sessionInfo()
```




