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update singlecell_lung_adenocarcinoma example
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@josschavezf
josschavezf committed Feb 20, 2024
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181 changes: 107 additions & 74 deletions vignettes/singlecell_lung_adenocarcinoma.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -6,19 +6,22 @@ vignette: >
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---

# Dataset Explanation


[Maynard et al.](https://pubmed.ncbi.nlm.nih.gov/32822576/) Processed Illumina Single Cell RNAseq of metastatic lung cancer using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. The raw data can be found [here](https://www.ncbi.nlm.nih.gov/bioproject/591860).

To run this vignette, download the files from this [Google drive](https://drive.google.com/drive/folders/1sDzO0WOD4rnGC7QfTKwdcQTx3L36PFwX)


# Set up Giotto Environment

```{r, eval=FALSE}
# Ensure Giotto Suite is installed.
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
# Ensure GiottoData, a small, helper module for tutorials, is installed.
if(!"GiottoData" %in% installed.packages()) {
pak::pkg_install("drieslab/GiottoData")
}
# Ensure the Python environment for Giotto has been installed.
genv_exists = Giotto::checkGiottoEnvironment()
if(!genv_exists){
Expand All @@ -27,13 +30,9 @@ if(!genv_exists){
}
```


## Set up Giotto Environment

``` {r, eval=FALSE}
library(Giotto)
library(GiottoData)
# 1. set working directory
results_folder = 'path/to/result'
Expand All @@ -49,57 +48,67 @@ instrs = createGiottoInstructions(save_dir = results_folder,
python_path = my_python_path)
```

## Dataset Explanation

[Maynard et al.](https://pubmed.ncbi.nlm.nih.gov/32822576/) Processed Illumina Single Cell RNAseq of metastatic lung cancer using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. The raw data can be found [here](https://www.ncbi.nlm.nih.gov/bioproject/591860).

To run this vignette, download the files from this [Google drive](https://drive.google.com/drive/folders/1sDzO0WOD4rnGC7QfTKwdcQTx3L36PFwX)

## Part 1: Create Giotto object
# 1. Create the Giotto object

Load data

```{r, eval=FALSE}
raw.data <- read.csv("Data_input/csv_files/S01_datafinal.csv",
header=T, row.names = 1)
data_dir = "Data_input/csv_files/"
raw.data <- read.csv(paste0(data_dir, "S01_datafinal.csv"),
header = T,
row.names = 1)
```

Load metadata

```{r, eval=FALSE}
metadata <- read.csv("Data_input/csv_files/S01_metacells.csv",
row.names=1, header=T)
metadata <- read.csv(paste0(data_dir, "S01_metacells.csv"),
row.names = 1,
header = T)
```

Find ERCC's, compute the percent ERCC, and drop them from the raw data.

```{r, eval=FALSE}
erccs <- grep(pattern = "^ERCC-",
x = rownames(x = raw.data),
value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-",
x = rownames(x = raw.data),
value = FALSE)
raw.data <- raw.data[-ercc.index,]
```

Create Giotto object

``` {r, eval=FALSE}
giotto_SC <- createGiottoObject(expression = raw.data,
instructions = instrs)
```

Calculate percent ribosomal genes and add to metadata

```{r, eval=FALSE}
ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]",
x = rownames(raw.data), value = TRUE)
x = rownames(raw.data),
value = TRUE)
percent.ribo <- Matrix::colSums(raw.data[ribo.genes, ])/Matrix::colSums(raw.data)
giotto_SC <- addCellMetadata(giotto_SC,
new_metadata = data.frame(percent_ribo = percent.ribo))
```

## Part 2: Process Giotto Object
# 2. Process Giotto Object

``` {r, eval=FALSE}
## filter
giotto_SC <- filterGiotto(gobject = giotto_SC,
expression_threshold = 1,
feat_det_in_min_cells = 10,
Expand All @@ -108,39 +117,45 @@ giotto_SC <- filterGiotto(gobject = giotto_SC,
verbose = T)
## normalize
giotto_SC <- normalizeGiotto(gobject = giotto_SC, scalefactor = 6000)
giotto_SC <- normalizeGiotto(gobject = giotto_SC,
scalefactor = 6000)
## add gene & cell statistics
giotto_SC <- addStatistics(gobject = giotto_SC, expression_values = 'raw')
giotto_SC <- addStatistics(gobject = giotto_SC,
expression_values = 'raw')
```

## Part 3: Dimension Reduction
# 3. Dimension Reduction

``` {r, eval=FALSE}
## PCA ##
giotto_SC <- calculateHVF(gobject = giotto_SC)
giotto_SC <- runPCA(gobject = giotto_SC,
center = TRUE,
cale_unit = TRUE)
screePlot(giotto_SC,
ncp = 30,
save_param = list(save_name = '3_scree_plot'))
```

![](images/singlecell_lung_adenocarcinoma/3_scree_plot.png)

## Part 4: Cluster
# 4. Cluster

``` {r, eval=FALSE}
## cluster and run UMAP ##
# sNN network (default)
giotto_SC <- createNearestNetwork(gobject = giotto_SC,
dim_reduction_to_use = 'pca',
dim_reduction_name = 'pca',
dimensions_to_use = 1:10, k = 15)
dimensions_to_use = 1:10,
k = 15)
# UMAP
giotto_SC = runUMAP(giotto_SC, dimensions_to_use = 1:10)
giotto_SC <- runUMAP(giotto_SC,
dimensions_to_use = 1:10)
# Leiden clustering
giotto_SC <- doLeidenCluster(gobject = giotto_SC,
Expand All @@ -156,15 +171,16 @@ plotUMAP(gobject = giotto_SC,

![](images/singlecell_lung_adenocarcinoma/4_Cluster.png)

## Part 5: Differential Expression
# 5. Differential Expression

``` {r, eval=FALSE}
markers_scran = findMarkers_one_vs_all(gobject=giotto_SC,
method="scran",
expression_values="normalized",
cluster_column='leiden_clus',
min_feats=3)
markergenes_scran = unique(markers_scran[, head(.SD, 2), by="cluster"][["feats"]])
markers_scran = findMarkers_one_vs_all(gobject = giotto_SC,
method = "scran",
expression_values = "normalized",
cluster_column = 'leiden_clus',
min_feats = 3)
markergenes_scran = unique(markers_scran[, head(.SD, 2), by = "cluster"][["feats"]])
plotMetaDataHeatmap(giotto_SC,
expression_values = "normalized",
Expand All @@ -177,7 +193,7 @@ plotMetaDataHeatmap(giotto_SC,

![](images/singlecell_lung_adenocarcinoma/5_metaheatmap.png)

## Part 6: FeaturePlot
# 6. FeaturePlot

``` {r, eval=FALSE}
# Plot known marker genes across different cell types. e.g. EPCAM for epithelial cells
Expand All @@ -189,7 +205,7 @@ dimFeatPlot2D(giotto_SC,

![](images/singlecell_lung_adenocarcinoma/6_featureplot.png)

## Part 7: Cell type Annotation
# 7. Cell type Annotation

```{r}
marker_genes = list(
Expand All @@ -211,6 +227,7 @@ marker_genes = list(
```{r, eval=FALSE}
library(dplyr)
library(ComplexHeatmap)
heatmap_table <- calculateMetaTable(gobject = giotto_SC,
expression_values = 'normalized',
metadata_cols = 'leiden_clus',
Expand Down Expand Up @@ -239,9 +256,11 @@ heatmap_table <- heatmap_table %>%
))
heatmap_matrix <- heatmap_table[,c("leiden_clus", "variable","zscores_rescaled_per_feat")]
heatmap_matrix <- tidyr::pivot_wider(heatmap_matrix,
names_from = "leiden_clus",
values_from = "zscores_rescaled_per_feat")
rownames_matrix <- heatmap_matrix$variable
colnames_matrix <- colnames(heatmap_matrix)
Expand All @@ -261,7 +280,7 @@ panel_fun = function(index, nm) {
}
## heatmap z-score per leiden cluster
png(filename = "results/6_heatmap_all_clusters_cell_types.png",
png(filename = paste0(results_folder, "6_heatmap_all_clusters_cell_types.png"),
width = 2000,
height = 1500,
res = 300)
Expand Down Expand Up @@ -334,10 +353,12 @@ lung_labels<-c("carcinoma_cells",#1
)
names(lung_labels) <- 1:32
giotto_SC <- annotateGiotto(gobject = giotto_SC,
annotation_vector = lung_labels ,
cluster_column = 'leiden_clus',
name = 'lung_labels')
dimPlot2D(gobject = giotto_SC,
dim_reduction_name = 'umap',
cell_color = "lung_labels",
Expand All @@ -347,19 +368,20 @@ dimPlot2D(gobject = giotto_SC,
```

![](images/singlecell_lung_adenocarcinoma/7_Annotation.png)
# 8. Session Info

```{r, eval=FALSE}
sessionInfo()
```

```{r, eval=FALSE}
R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.2.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
Expand All @@ -368,42 +390,53 @@ time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] Giotto_4.0.2 GiottoClass_0.1.3
[1] ComplexHeatmap_2.18.0 dplyr_1.1.4 Giotto_4.0.3 GiottoClass_0.1.3
loaded via a namespace (and not attached):
[1] generics_0.1.3 utf8_1.2.4
[3] SparseArray_1.2.3 bitops_1.0-7
[5] gtools_3.9.5 lattice_0.21-9
[7] magrittr_2.0.3 grid_4.3.2
[9] Matrix_1.6-5 GenomeInfoDb_1.38.5
[11] fansi_1.0.6 SingleCellExperiment_1.24.0
[13] scales_1.3.0 codetools_0.2-19
[15] abind_1.4-5 cli_3.6.2
[17] rlang_1.1.3 crayon_1.5.2
[19] XVector_0.42.0 Biobase_2.62.0
[21] munsell_0.5.0 colorRamp2_0.1.0
[23] DelayedArray_0.28.0 S4Arrays_1.2.0
[25] parallel_4.3.2 tools_4.3.2
[27] GiottoUtils_0.1.3 dplyr_1.1.4
[29] colorspace_2.1-0 ggplot2_3.4.4
[31] SpatialExperiment_1.12.0 GenomeInfoDbData_1.2.11
[33] SummarizedExperiment_1.32.0 BiocGenerics_0.48.1
[35] vctrs_0.6.5 R6_2.5.1
[37] matrixStats_1.2.0 stats4_4.3.2
[39] lifecycle_1.0.4 magick_2.8.2
[41] zlibbioc_1.48.0 GiottoVisuals_0.1.2
[43] S4Vectors_0.40.2 IRanges_2.36.0
[45] pkgconfig_2.0.3 terra_1.7-65
[47] pillar_1.9.0 gtable_0.3.4
[49] data.table_1.14.10 glue_1.7.0
[51] Rcpp_1.0.12 tidyselect_1.2.0
[53] tibble_3.2.1 GenomicRanges_1.54.1
[55] rstudioapi_0.15.0 MatrixGenerics_1.14.0
[57] rjson_0.2.21 compiler_4.3.2
[59] RCurl_1.98-1.14
[1] colorRamp2_0.1.0 bitops_1.0-7 rlang_1.1.3
[4] magrittr_2.0.3 clue_0.3-65 GetoptLong_1.0.5
[7] RcppAnnoy_0.0.22 GiottoUtils_0.1.5 matrixStats_1.2.0
[10] compiler_4.3.2 DelayedMatrixStats_1.24.0 png_0.1-8
[13] systemfonts_1.0.5 vctrs_0.6.5 shape_1.4.6
[16] pkgconfig_2.0.3 SpatialExperiment_1.12.0 crayon_1.5.2
[19] fastmap_1.1.1 backports_1.4.1 magick_2.8.3
[22] XVector_0.42.0 scuttle_1.12.0 labeling_0.4.3
[25] utf8_1.2.4 rmarkdown_2.25 ragg_1.2.7
[28] purrr_1.0.2 xfun_0.42 bluster_1.12.0
[31] zlibbioc_1.48.0 beachmat_2.18.1 GenomeInfoDb_1.38.6
[34] jsonlite_1.8.8 DelayedArray_0.28.0 BiocParallel_1.36.0
[37] terra_1.7-71 irlba_2.3.5.1 parallel_4.3.2
[40] cluster_2.1.6 R6_2.5.1 RColorBrewer_1.1-3
[43] limma_3.58.1 reticulate_1.35.0 parallelly_1.37.0
[46] GenomicRanges_1.54.1 iterators_1.0.14 Rcpp_1.0.12
[49] SummarizedExperiment_1.32.0 knitr_1.45 future.apply_1.11.1
[52] usethis_2.2.3 IRanges_2.36.0 Matrix_1.6-5
[55] igraph_2.0.2 tidyselect_1.2.0 rstudioapi_0.15.0
[58] abind_1.4-5 yaml_2.3.8 doParallel_1.0.17
[61] codetools_0.2-19 listenv_0.9.1 lattice_0.22-5
[64] tibble_3.2.1 Biobase_2.62.0 withr_3.0.0
[67] evaluate_0.23 future_1.33.1 circlize_0.4.16
[70] pillar_1.9.0 MatrixGenerics_1.14.0 foreach_1.5.2
[73] checkmate_2.3.1 stats4_4.3.2 generics_0.1.3
[76] dbscan_1.1-12 RCurl_1.98-1.14 S4Vectors_0.40.2
[79] ggplot2_3.4.4 sparseMatrixStats_1.14.0 munsell_0.5.0
[82] scales_1.3.0 gtools_3.9.5 globals_0.16.2
[85] glue_1.7.0 metapod_1.10.1 tools_4.3.2
[88] GiottoVisuals_0.1.4 BiocNeighbors_1.20.2 data.table_1.15.0
[91] ScaledMatrix_1.10.0 locfit_1.5-9.8 fs_1.6.3
[94] scran_1.30.2 Cairo_1.6-2 cowplot_1.1.3
[97] tidyr_1.3.1 edgeR_4.0.15 colorspace_2.1-0
[100] SingleCellExperiment_1.24.0 GenomeInfoDbData_1.2.11 BiocSingular_1.18.0
[103] cli_3.6.2 rsvd_1.0.5 textshaping_0.3.7
[106] fansi_1.0.6 S4Arrays_1.2.0 uwot_0.1.16
[109] gtable_0.3.4 digest_0.6.34 progressr_0.14.0
[112] BiocGenerics_0.48.1 dqrng_0.3.2 SparseArray_1.2.4
[115] ggrepel_0.9.5 rjson_0.2.21 farver_2.1.1
[118] htmltools_0.5.7 lifecycle_1.0.4 GlobalOptions_0.1.2
[121] statmod_1.5.0
```


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