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13-Functional-Analysis.Rmd
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13-Functional-Analysis.Rmd
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---
title: "13-Functional-Analysis"
output: html_document
---
# Functional Analysis
## Google Slides
<iframe src="https://docs.google.com/presentation/d/e/2PACX-1vT6PPJ9MLfG9Ri71Tn_BG0Ko8te2WPPf5pgeaMaopRwdfGqv3WkKWiuxXD86rXpR-5DSA62QvmOKJd4/embed?start=false&loop=false&delayms=3000" frameborder="0" width="760" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
## Gene sets and signatures
### Cell Cycle
```{r score_cycle, eval=FALSE}
marrow <- CellCycleScoring(object = marrow, s.genes = s.genes, g2m.genes = g2m.genes,
set.ident = TRUE)
# view cell cycle scores and phase assignments
head(x = [email protected])
```
```{r vis_cycle, eval=FALSE}
# Visualize the distribution of cell cycle markers across
RidgePlot(object = marrow, features.plot = c("PCNA", "TOP2A", "MCM6", "MKI67"), nCol = 2)
```
```{r pca_cycle, eval=FALSE}
# Running a PCA on cell cycle genes reveals, unsurprisingly, that cells
# separate entirely by phase
marrow <- RunPCA(object = marrow, pc.genes = c(s.genes, g2m.genes), do.print = FALSE)
PCAPlot(object = marrow)
```
## Pathway analysis
## inferCNV / honeybadger
[Github Page](https://github.com/broadinstitute/inferCNV)
### Create the InferCNV Object
Reading in the raw counts matrix and meta data, populating the infercnv object
```{r, eval=FALSE}
infercnv_obj = CreateInfercnvObject(
raw_counts_matrix="../example/oligodendroglioma_expression_downsampled.counts.matrix",
annotations_file="../example/oligodendroglioma_annotations_downsampled.txt",
delim="\t",
gene_order_file="../example/gencode_downsampled.txt",
ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)"))
```
### Filtering genes
Removing those genes that are very lowly expressed or present in very few cells
```{r, eval=FALSE}
# filter out low expressed genes
cutoff=1
infercnv_obj <- require_above_min_mean_expr_cutoff(infercnv_obj, cutoff)
# filter out bad cells
min_cells_per_gene=3
infercnv_obj <- require_above_min_cells_ref(infercnv_obj, min_cells_per_gene=min_cells_per_gene)
## for safe keeping
infercnv_orig_filtered = infercnv_obj
#plot_mean_chr_expr_lineplot(infercnv_obj)
save('infercnv_obj', file = '../example_output/infercnv_obj.orig_filtered')
```
### Normalize each cell's counts for sequencing depth
```{r, eval=FALSE}
infercnv_obj <- infercnv:::normalize_counts_by_seq_depth(infercnv_obj)
```
### Perform Anscombe normalization
Suggested by Matan for removing noisy variation at low counts
```{r, eval=FALSE}
infercnv_obj <- infercnv:::anscombe_transform(infercnv_obj)
```
<!--save('infercnv_obj', file='../example_output/infercnv_obj.anscombe')
```-->
### Log transform the normalized counts:
```{r, eval=FALSE}
infercnv_obj <- log2xplus1(infercnv_obj)
```
<!--save('infercnv_obj', file='../example_output/infercnv_obj.log_transformed')
```-->
### Apply maximum bounds to the expression data to reduce outlier effects
```{r, eval=FALSE}
threshold = mean(abs(get_average_bounds(infercnv_obj)))
infercnv_obj <- apply_max_threshold_bounds(infercnv_obj, threshold=threshold)
```
### Initial view, before inferCNV operations:
```{r, results="hide", eval=FALSE}
plot_cnv(infercnv_obj,
out_dir='../example_output/',
output_filename='infercnv.logtransf',
x.range="auto",
title = "Before InferCNV (filtered & log2 transformed)",
color_safe_pal = FALSE,
x.center = mean([email protected]))
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.logtransf.png")
```
### Perform smoothing across chromosomes
```{r, eval=FALSE}
infercnv_obj = smooth_by_chromosome(infercnv_obj, window_length=101, smooth_ends=TRUE)
```
<!--# save('infercnv_obj', file='../example_output/infercnv_obj.smooth_by_chr')-->
```{r, eval=FALSE}
# re-center each cell
infercnv_obj <- center_cell_expr_across_chromosome(infercnv_obj, method = "median")
```
<!--# save('infercnv_obj', file='../example_output/infercnv_obj.cells_recentered')-->
```{r, results='hide', eval=FALSE }
plot_cnv(infercnv_obj,
out_dir='../example_output/',
output_filename='infercnv.chr_smoothed',
x.range="auto",
title = "chr smoothed and cells re-centered",
color_safe_pal = FALSE)
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.chr_smoothed.png")
```
### Subtract the reference values from observations, now have log(fold change) values
```{r, eval=FALSE}
infercnv_obj <- subtract_ref_expr_from_obs(infercnv_obj, inv_log=TRUE)
```
<!--
save('infercnv_obj', file='../example_output/infercnv_obj.ref_subtracted')
```-->
```{r, results="hide", eval=FALSE}
plot_cnv(infercnv_obj,
out_dir='../example_output/',
output_filename='infercnv.ref_subtracted',
x.range="auto",
title="ref subtracted",
color_safe_pal = FALSE)
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.ref_subtracted.png")
```
### Invert log values
Converting the log(FC) values to regular fold change values, centered at 1 (no fold change)
This is important because we want (1/2)x to be symmetrical to 1.5x, representing loss/gain of one chromosome region.
```{r, eval=FALSE}
infercnv_obj <- invert_log2(infercnv_obj)
```
<!--save('infercnv_obj', file='../example_output/infercnv_obj.inverted_log')
```-->
```{r, results="hide", eval=FALSE}
plot_cnv(infercnv_obj,
out_dir='../example_output/',
output_filename='infercnv.inverted',
color_safe_pal = FALSE,
x.range="auto",
x.center=1,
title = "inverted log FC to FC")
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.inverted.png")
```
### Removing noise
```{r, eval=FALSE}
infercnv_obj <- clear_noise_via_ref_mean_sd(infercnv_obj, sd_amplifier = 1.5)
```
<!--save('infercnv_obj', file='../example_output/infercnv_obj.denoised')
```-->
```{r, results="hide", eval=FALSE}
plot_cnv(infercnv_obj,
out_dir='../example_output/',
output_filename='infercnv.denoised',
x.range="auto",
x.center=1,
title="denoised",
color_safe_pal = FALSE)
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.denoised.png")
```
### Remove outlier data points
This generally improves on the visualization
```{r, eval=FALSE}
infercnv_obj = remove_outliers_norm(infercnv_obj)
```
<!--save('infercnv_obj', file="../example_output/infercnv_obj.outliers_removed")
```-->
```{r, results="hide", eval=FALSE}
plot_cnv(infercnv_obj,
out_dir='../example_output/',
output_filename='infercnv.outliers_removed',
color_safe_pal = FALSE,
x.range="auto",
x.center=1,
title = "outliers removed")
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.outliers_removed.png")
```
### Find DE genes by comparing the mutant types to normal types, BASIC
Runs a t-Test comparing tumor/normal for each patient and normal sample, and masks out those genes that are not significantly DE.
```{r, eval=FALSE}
plot_data = [email protected]
high_threshold = max(abs(quantile(plot_data[plot_data != 0], c(0.05, 0.95))))
low_threshold = -1 * high_threshold
infercnv_obj2 <- infercnv:::mask_non_DE_genes_basic(infercnv_obj, test.use = 't', center_val=1)
```
```{r, results="hide", eval=FALSE}
plot_cnv(infercnv_obj2,
out_dir='../example_output/',
output_filename='infercnv.non-DE-genes-masked',
color_safe_pal = FALSE,
x.range=c(low_threshold, high_threshold),
x.center=1,
title = "non-DE-genes-masked")
```
```{r, echo=FALSE, eval=FALSE}
knitr::include_graphics("../example_output/infercnv.non-DE-genes-masked.png")
```
### Additional Information
#### Online Documentation
For additional explanations on files, usage, and a tutorial please visit the [wiki](https://github.com/broadinstitute/inferCNV/wiki).
#### TrinityCTAT
This tool is a part of the TrinityCTAT toolkit focused on leveraging the use of RNA-Seq to better understand cancer transcriptomes. To find out more please visit [TrinityCTAT](https://github.com/NCIP/Trinity_CTAT/wiki)
#### Applications
This methodology was used in:
[Anoop P. Patel et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014 Jun 20: 1396-1401](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4123637/)
[Tirosh I et al.Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016 Apr 8;352(6282):189-96](http://www.ncbi.nlm.nih.gov/pubmed/27124452)