From be3b4c7f2d3881df1c2058a861a7e4f6637e93fd Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 2 Sep 2020 16:28:05 -0400 Subject: [PATCH 01/15] Ignore more --- .gitignore | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index b3311ea..e29ed7c 100644 --- a/.gitignore +++ b/.gitignore @@ -32,6 +32,14 @@ override.tf.json work *.nextflow* +# Rstudio server files +rstudio-server.json +.rstudio + +# Cache files +.cache + +# intermediate reference and analysis files workflows/rnaseq-ref-index/annotation/ workflows/rnaseq-ref-index/fasta/ - +workflows/alevin-quant/data/ \ No newline at end of file From 3f1bf5f381b84d4542a05862728efe527759446f Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 2 Sep 2020 16:28:14 -0400 Subject: [PATCH 02/15] Update docker file --- workflows/images/scpca_r/Dockerfile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/workflows/images/scpca_r/Dockerfile b/workflows/images/scpca_r/Dockerfile index ef6f446..01d2e22 100644 --- a/workflows/images/scpca_r/Dockerfile +++ b/workflows/images/scpca_r/Dockerfile @@ -7,10 +7,12 @@ RUN apt-get update && apt-get install -y --no-install-recommends apt-utils dialo # Add curl, bzip2 and dev libs RUN apt-get update -qq && apt-get -y --no-install-recommends install \ + awscli \ bzip2 \ curl \ libbz2-dev \ libgdal-dev \ + libglpk-dev \ liblzma-dev \ libreadline-dev \ libudunits2-dev @@ -29,6 +31,7 @@ RUN install2.r --error --deps TRUE \ RUN R -e "BiocManager::install(c( \ 'AnnotationHub', \ 'ensembldb', \ + 'fishpond', \ 'scran', \ 'scater', \ 'tximport'), \ From 8ddb3b1e6ab7291880986a7e719fdbd1d3e5e05d Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 2 Sep 2020 16:28:42 -0400 Subject: [PATCH 03/15] Start of instructions for using docker w/ AWS --- workflows/alevin-quant/README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 workflows/alevin-quant/README.md diff --git a/workflows/alevin-quant/README.md b/workflows/alevin-quant/README.md new file mode 100644 index 0000000..2dc0b8d --- /dev/null +++ b/workflows/alevin-quant/README.md @@ -0,0 +1,11 @@ +## Docker + +Running the benchmark analysis notebook should be done via docker, with the following command to mount the local directory as the home direcory and pass in the current user's AWS credentials and launch RStudio. + +``` +docker run \ + --mount type=bind,target=/home/rstudio,source=$PWD \ + --mount type=bind,target=/home/rstudio/.aws,source=$HOME/.aws \ + -e PASSWORD= -p 8787:8787 \ + ccdl/scpca_r +``` From c82b5f40fa02d6dbed42f9140fb70b72dedbefec Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 2 Sep 2020 16:29:03 -0400 Subject: [PATCH 04/15] Initial benchmark analysis notebook --- workflows/alevin-quant/benchmark-analysis.Rmd | 138 + .../alevin-quant/benchmark-analysis.nb.html | 5798 +++++++++++++++++ 2 files changed, 5936 insertions(+) create mode 100644 workflows/alevin-quant/benchmark-analysis.Rmd create mode 100644 workflows/alevin-quant/benchmark-analysis.nb.html diff --git a/workflows/alevin-quant/benchmark-analysis.Rmd b/workflows/alevin-quant/benchmark-analysis.Rmd new file mode 100644 index 0000000..6d75bbe --- /dev/null +++ b/workflows/alevin-quant/benchmark-analysis.Rmd @@ -0,0 +1,138 @@ +--- +title: "Alevin Index comparisons" +author: "Joshua Shapiro for CCDL" +output: html_notebook +--- + +This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates. + +### Load Libraries +```{r setup} +library(ggplot2) +library(magrittr) +library(SingleCellExperiment) +library(tximport) +``` + +### File and directory setup +```{r} +data_dir <- file.path("data", "alevin-quant") +dir.create(data_dir, recursive = TRUE, showWarnings = FALSE) + +quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant" + +``` + + + + +## Sync S3 files + +```{r} +sync_call <- paste("aws s3 sync", quant_s3, data_dir) +system(sync_call) + +``` + +Get the sample list and make a data frame with sample info + +```{r} +quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE) + +# split ids into components for later processing +quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>% + tidyr::separate(info, sep = "[-]", + into = c("sample", "index_type")) %>% + tidyr::separate(index_type, + into = c("index_content", "kmer", "sa"), + extra = "drop") %>% + dplyr::mutate(kmer = stringr::str_remove(kmer, "k")) + +``` + +## Get Annotations from AnnotationHub + +```{r} +hub = AnnotationHub::AnnotationHub(ask = FALSE) +# Ensembl v100 Homo Sapiens is AH79689 +ensdb = hub[["AH79689"]] +ensg <- genes(ensdb) +``` + +Create vectors of mitochondiral genes and coding genes for later. + +```{r} +# create the mitochondrial gene list +mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id + +coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id +``` + + + +# Process Alevin quantifications + +## tximport and make SCEs + +```{r} +sces <- quant_dirs %>% + purrr::map( + ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"), + type = "alevin")) %>% + purrr::map( + ~ SingleCellExperiment(list(counts = .x$counts))) + +# add names +names(sces) <- quant_dirs +``` + + +Calculate cell and feature QC statistics for each sample. + +```{r} +sces <- sces %>% + purrr::map( + ~ scater::addPerCellQC( + .x, + subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)], + ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] ) + ) + ) %>% + purrr::map(scater::addPerFeatureQC) +``` + +Combine all cell QC stats into a single data frame +```{r} +sce_cell_qc <- purrr::map_df(sces, + ~ as.data.frame(colData(.x)) %>% + tibble::rownames_to_column(var = "cell_id"), + .id = "quant_id") %>% + dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir")) +``` + + +Combine all feature QC stats into a single data frame. +```{r} +sce_feature_qc <- purrr::map_df(sces, + ~ as.data.frame(rowData(.x)) %>% + tibble::rownames_to_column(var = "gene_id"), + .id = "quant_id") %>% + dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir")) +``` + +## Plotting the QC stats + +Plot cell QC gene accumulation curves & mito fraction +```{r} +ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), + aes(x = sum, y = detected, color = subsets_mito_percent)) + + geom_point() + + facet_grid(sample ~ paste(index_content, sa) ) + + scale_color_viridis_c() + + scale_x_log10() + + scale_y_log10() + +``` + + + diff --git a/workflows/alevin-quant/benchmark-analysis.nb.html b/workflows/alevin-quant/benchmark-analysis.nb.html new file mode 100644 index 0000000..cc85e33 --- /dev/null +++ b/workflows/alevin-quant/benchmark-analysis.nb.html @@ -0,0 +1,5798 @@ + + + + + + + + + + + + + + +Alevin Index comparisons + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + +

This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates.

+
+

Load Libraries

+ + + +
library(ggplot2)
+library(magrittr)
+library(SingleCellExperiment)
+ + +
Loading required package: SummarizedExperiment
+Loading required package: GenomicRanges
+Loading required package: stats4
+Loading required package: BiocGenerics
+Loading required package: parallel
+
+Attaching package: ‘BiocGenerics’
+
+The following objects are masked from ‘package:parallel’:
+
+    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport,
+    clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply,
+    parSapply, parSapplyLB
+
+The following objects are masked from ‘package:stats’:
+
+    IQR, mad, sd, var, xtabs
+
+The following objects are masked from ‘package:base’:
+
+    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
+    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
+    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order,
+    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
+    rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit,
+    which, which.max, which.min
+
+Loading required package: S4Vectors
+
+Attaching package: ‘S4Vectors’
+
+The following object is masked from ‘package:base’:
+
+    expand.grid
+
+Loading required package: IRanges
+Loading required package: GenomeInfoDb
+Loading required package: Biobase
+Welcome to Bioconductor
+
+    Vignettes contain introductory material; view with 'browseVignettes()'.
+    To cite Bioconductor, see 'citation("Biobase")', and for packages
+    'citation("pkgname")'.
+
+Loading required package: DelayedArray
+Loading required package: matrixStats
+
+Attaching package: ‘matrixStats’
+
+The following objects are masked from ‘package:Biobase’:
+
+    anyMissing, rowMedians
+
+
+Attaching package: ‘DelayedArray’
+
+The following objects are masked from ‘package:matrixStats’:
+
+    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
+
+The following objects are masked from ‘package:base’:
+
+    aperm, apply, rowsum
+ + +
library(tximport)
+ + + +
+
+

File and directory setup

+ + + +
data_dir <- file.path("data", "alevin-quant")
+dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)
+
+s3_path = "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"
+ + + +
+
+

Sync S3 files

+ + + +
sync_call <- paste("aws s3 sync", s3_path, data_dir)
+system(sync_call)
+ + +
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+download: s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant/905_3-txome_k31_no_sa/alevin/quants_mat_cols.txt to data/alevin-quant/905_3-txome_k31_no_sa/alevin/quants_mat_cols.txt
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+download: s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant/905_3-txome_k31_no_sa/alevin/quants_mat.gz to data/alevin-quant/905_3-txome_k31_no_sa/alevin/quants_mat.gz
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+download: s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant/905_3-txome_k31_no_sa/alevin/raw_cb_frequency.txt to data/alevin-quant/905_3-txome_k31_no_sa/alevin/raw_cb_frequency.txt
+ + + +

Get the sample list and make a data frame with sample info

+ + + +
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)
+
+quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
+  tidyr::separate(info, sep = "[-]", 
+                  into = c("sample", "index_type")) %>%
+  tidyr::separate(index_type, 
+                  into = c("index_content", "kmer", "sa"), 
+                  extra = "drop") %>%
+  dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
+  
+ + + +
+
+

Get Annotations from AnnotationHub

+ + + +
hub = AnnotationHub::AnnotationHub(ask = FALSE)
+# Ensembl v100 Homo Sapiens is AH79689
+ensdb = hub[["AH79689"]]
+ensg <- genes(ensdb)
+ + + +

Create vectors of mitochondiral genes and coding genes for later.

+ + + +
# create the mitochondrial gene list
+mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id
+
+coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id
+ + + +
+
+

Process Alevin quantifications

+
+

tximport and make SCEs

+ + + +
sces <- quant_dirs %>%
+  purrr::map(
+    ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"),
+               type = "alevin")) %>%
+  purrr::map(
+    ~ SingleCellExperiment(list(counts = .x$counts))) 
+ + +
reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+reading in alevin gene-level counts across cells with fishpond
+ + +
# add names
+names(sces) <- quant_dirs
+ + + +

Calculate cell and feature QC statistics for each sample.

+ + + +
sces <- sces %>% 
+  purrr::map(
+    ~ scater::addPerCellQC(
+      .x,
+      subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)],
+                     ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] )
+    ) 
+  ) %>%
+  purrr::map(scater::addPerFeatureQC)
+ + + +

Combine all cell QC stats into a single data frame

+ + + +
sce_cell_qc <- purrr::map_df(sces, 
+                        ~ as.data.frame(colData(.x)) %>%
+                          tibble::rownames_to_column(var = "cell_id"), 
+                        .id = "quant_id") %>%
+  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
+ + + +

Combine all feature QC stats into a single data frame.

+ + + +
sce_feature_qc <- purrr::map_df(sces, 
+                        ~ as.data.frame(rowData(.x)) %>%
+                          tibble::rownames_to_column(var = "gene_id"), 
+                        .id = "quant_id") %>%
+  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
+ + + +
+
+

Plotting the QC stats

+

Plot cell QC gene accumulation curves & mito fraction

+ + + +
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
+       aes(x = sum, y = detected, color = subsets_mito_percent)) +
+  geom_point() +
+  facet_grid(sample ~ paste(index_content, sa) ) +
+  scale_color_viridis_c() +
+  scale_x_log10() +
+  scale_y_log10()
+ + +

+ + +
NA
+ + + + +
+
+ +
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+ + + +
+ + + + + + + + + + + + + + + + From d9456d35310defbcfe8118ba064f3430dfc9a66f Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Thu, 3 Sep 2020 13:24:39 -0400 Subject: [PATCH 05/15] More analysis of cdna vs ncRNA --- workflows/alevin-quant/benchmark-analysis.Rmd | 106 ++++++++++++++ .../alevin-quant/benchmark-analysis.nb.html | 134 +++++++++++++++++- 2 files changed, 239 insertions(+), 1 deletion(-) diff --git a/workflows/alevin-quant/benchmark-analysis.Rmd b/workflows/alevin-quant/benchmark-analysis.Rmd index 6d75bbe..a0aad41 100644 --- a/workflows/alevin-quant/benchmark-analysis.Rmd +++ b/workflows/alevin-quant/benchmark-analysis.Rmd @@ -134,5 +134,111 @@ ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), ``` +# Find features that are reliably detected + +To start, will select features that are detected in > 5% of cells in at least one sample/index. + +```{r} +detected_features <- sce_feature_qc %>% + dplyr::filter(detected >= 5.0) %>% + dplyr::pull(gene_id) %>% + unique() +# How many? +length(detected_features) +``` + +How many of these are not protein coding? + +```{r} +detected_nc <- detected_features[!detected_features %in% coding_genes] + +length(detected_nc) +``` + +Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only. +```{r} +# select noncoding detected features +nc_feature_qc <- sce_feature_qc %>% + dplyr::filter(gene_id %in% detected_nc) + +# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome + +nc_txome <- nc_feature_qc %>% + dplyr::filter(quant_id == "834-txome_k31_no_sa") %>% + dplyr::arrange(desc(mean)) + +nc_cdna <- nc_feature_qc %>% + dplyr::filter(quant_id == "834-cdna_k31_no_sa") + +# Join the tables +nc_feature_comparison <- nc_txome %>% + dplyr::left_join(nc_cdna, + by = "gene_id", + suffix = c("_txome", "_cdna")) %>% + dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna") + +nc_feature_comparison +``` + +As expected, most of the features that are not listed as coding do not appear in the cdna only set! +Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file. + +Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. +MALAT1 is potentially interesting, and it does seem that we should probably keep it! + +Lets make a vector of the noncoding cDNAs for future work: +```{r} +nc_cdna_genes <- sce_feature_qc %>% + dplyr::filter(quant_id == "834-cdna_k31_no_sa", + ! gene_id %in% coding_genes) %>% + dplyr::pull(gene_id) +``` + + +## Comparing coding and noncoding txomes + +First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding. + +```{r} +sce_feature_filtered <- sce_feature_qc %>% + dplyr::filter(kmer == "31", sa == "no") %>% + dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding", + gene_id %in% nc_cdna_genes ~ "noncoding cDNA", + TRUE ~ "ncRNA")) + +ggplot(sce_feature_filtered, + aes (x = mean, color = class)) + + geom_density() + + scale_x_log10() + + scale_color_brewer(palette = "Dark2") + + facet_grid(index_content ~ sample) +``` + +The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. + +Just to check, lets look at the correlation of coding genes within a sample: + + +```{r} +# pick a random set of cells to look at +cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100) + +features <- rownames(sces[["834-cdna_k31_no_sa"]]) +coding_features <- features[features %in% coding_genes] + +compare_expression <- data.frame( + cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])), + txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample])) +) + +cor(compare_expression, method = "spearman") +``` +```{r} +qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome)) +``` +Correlation is nearly perfect. + +## Conclusion +I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project. diff --git a/workflows/alevin-quant/benchmark-analysis.nb.html b/workflows/alevin-quant/benchmark-analysis.nb.html index cc85e33..f528a0b 100644 --- a/workflows/alevin-quant/benchmark-analysis.nb.html +++ b/workflows/alevin-quant/benchmark-analysis.nb.html @@ -5727,11 +5727,143 @@

Plotting the QC stats

+ + +
+

Find features that are reliably detected

+

To start, will select features that are detected in > 5% of cells in at least one sample/index.

+ + + +
detected_features <- sce_feature_qc %>%
+  dplyr::filter(detected >= 5.0) %>%
+  dplyr::pull(gene_id) %>%
+  unique()
+length(detected_features)
+ + +
[1] 15202
+ + + +

How many of these are not protein coding?

+ + + +
detected_nc <- detected_features[!detected_features %in% coding_genes]
+
+length(detected_nc)
+ + +
[1] 1689
+ + + +

Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only.

+ + + +
# select noncoding detected features
+nc_feature_qc <- sce_feature_qc %>% 
+  dplyr::filter(gene_id %in% detected_nc) 
+
+# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome 
+
+nc_txome <-  nc_feature_qc %>%
+  dplyr::filter(quant_id == "834-txome_k31_no_sa") %>%
+  dplyr::arrange(desc(mean))
+
+nc_cdna <-  nc_feature_qc %>%
+  dplyr::filter(quant_id == "834-cdna_k31_no_sa")
+
+# Join the tables
+nc_feature_comparison <- nc_txome %>%
+  dplyr::left_join(nc_cdna, 
+                   by = "gene_id", 
+                   suffix = c("_txome", "_cdna")) %>%
+  dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna")
+
+nc_feature_comparison
+ + +
+ +
+ + + +

As expected, most of the features that are not listed as coding do not appear in the cdna only set! Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file.

+

Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. MALAT1 is potentially interesting, and it does seem that we should probably keep it!

+

Lets make a vector of the noncoding cDNAs for future work:

+ + + +
nc_cdna_genes <- sce_feature_qc %>%
+  dplyr::filter(quant_id == "834-cdna_k23_no_sa",
+                ! gene_id %in% coding_genes) %>%
+  dplyr::pull(gene_id)
+ + + +
+

Comparing coding and noncoding txomes

+

First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding.

+ + + +
sce_feature_filtered <- sce_feature_qc %>% 
+  dplyr::filter(kmer == "31", sa == "no") %>%
+  dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding",
+                                         gene_id %in% nc_cdna_genes ~ "noncoding cDNA",
+                                          TRUE ~ "ncRNA"))
+
+ggplot(sce_feature_filtered, 
+       aes (x = mean, color = class)) +
+  geom_density() + 
+  scale_x_log10() + 
+  scale_color_brewer(palette = "Dark2") +
+  facet_grid(index_content ~ sample)
+ + +

+ + + +

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can’t see any particular reason to exclude them.

+

Just to check, lets look at the correlation of coding genes within a sample:

+ + + +
cor(compare_expression, method = "spearman")
+
+ + +
          cdna    txome
+cdna  1.000000 0.994648
+txome 0.994648 1.000000
+ + + + +
qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome))
+ + +

+ + + +

Correlation is nearly perfect.

+
+
+

Conclusion

+

I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.

-
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
+
---
title: "Alevin Index comparisons"
author: "Joshua Shapiro for CCDL"
output: html_notebook
---

This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates.

### Load Libraries
```{r setup}
library(ggplot2)
library(magrittr)
library(SingleCellExperiment)
library(tximport)
```

### File and directory setup
```{r}
data_dir <- file.path("data", "alevin-quant")
dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)

quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"

```




## Sync S3 files

```{r}
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
system(sync_call)

```

Get the sample list and make a data frame with sample info

```{r}
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)

# split ids into components for later processing
quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
  tidyr::separate(info, sep = "[-]", 
                  into = c("sample", "index_type")) %>%
  tidyr::separate(index_type, 
                  into = c("index_content", "kmer", "sa"), 
                  extra = "drop") %>%
  dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
  
```

## Get Annotations from AnnotationHub

```{r}
hub = AnnotationHub::AnnotationHub(ask = FALSE)
# Ensembl v100 Homo Sapiens is AH79689
ensdb = hub[["AH79689"]]
ensg <- genes(ensdb)
```

Create vectors of mitochondiral genes and coding genes for later.

```{r}
# create the mitochondrial gene list
mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id

coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id
```



# Process Alevin quantifications

## tximport and make SCEs

```{r}
sces <- quant_dirs %>%
  purrr::map(
    ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"),
               type = "alevin")) %>%
  purrr::map(
    ~ SingleCellExperiment(list(counts = .x$counts))) 

# add names
names(sces) <- quant_dirs
```


Calculate cell and feature QC statistics for each sample.

```{r}
sces <- sces %>% 
  purrr::map(
    ~ scater::addPerCellQC(
      .x,
      subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)],
                     ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] )
    ) 
  ) %>%
  purrr::map(scater::addPerFeatureQC)
```

Combine all cell QC stats into a single data frame
```{r}
sce_cell_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(colData(.x)) %>%
                          tibble::rownames_to_column(var = "cell_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```


Combine all feature QC stats into a single data frame.
```{r}
sce_feature_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(rowData(.x)) %>%
                          tibble::rownames_to_column(var = "gene_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```

## Plotting the QC stats

Plot cell QC gene accumulation curves & mito fraction
```{r}
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
       aes(x = sum, y = detected, color = subsets_mito_percent)) +
  geom_point() +
  facet_grid(sample ~ paste(index_content, sa) ) +
  scale_color_viridis_c() +
  scale_x_log10() +
  scale_y_log10()
  
```

# Find features that are reliably detected

To start, will select features that are detected in > 5% of cells in at least one sample/index. 

```{r}
detected_features <- sce_feature_qc %>%
  dplyr::filter(detected >= 5.0) %>%
  dplyr::pull(gene_id) %>%
  unique()
# How many?
length(detected_features)
```

How many of these are not protein coding?

```{r}
detected_nc <- detected_features[!detected_features %in% coding_genes]

length(detected_nc)
```

Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only.
```{r}
# select noncoding detected features
nc_feature_qc <- sce_feature_qc %>% 
  dplyr::filter(gene_id %in% detected_nc) 

# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome 

nc_txome <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-txome_k31_no_sa") %>%
  dplyr::arrange(desc(mean))

nc_cdna <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa")

# Join the tables
nc_feature_comparison <- nc_txome %>%
  dplyr::left_join(nc_cdna, 
                   by = "gene_id", 
                   suffix = c("_txome", "_cdna")) %>%
  dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna")

nc_feature_comparison
```

As expected, most of the features that are not listed as coding do not appear in the cdna only set!
Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file.

Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. 
MALAT1 is potentially interesting, and it does seem that we should probably keep it!

Lets make a vector of the noncoding cDNAs for future work:
```{r}
nc_cdna_genes <- sce_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa",
                ! gene_id %in% coding_genes) %>%
  dplyr::pull(gene_id)
```


## Comparing coding and noncoding txomes

First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding.

```{r}
sce_feature_filtered <- sce_feature_qc %>% 
  dplyr::filter(kmer == "31", sa == "no") %>%
  dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding",
                                         gene_id %in% nc_cdna_genes ~ "noncoding cDNA",
                                          TRUE ~ "ncRNA"))

ggplot(sce_feature_filtered, 
       aes (x = mean, color = class)) +
  geom_density() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Dark2") +
  facet_grid(index_content ~ sample)
```

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them.

Just to check, lets look at the correlation of coding genes within a sample:


```{r}
# pick a random set of cells to look at
cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100)

features <- rownames(sces[["834-cdna_k31_no_sa"]])
coding_features <- features[features %in% coding_genes]

compare_expression <- data.frame(
  cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])),
  txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample]))
)

cor(compare_expression, method = "spearman")
```
```{r}
qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome))
```
Correlation is nearly perfect.

## Conclusion
I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.



From 247776c01c318e39f2f779e966e150ef91775368 Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 9 Sep 2020 12:43:15 -0400 Subject: [PATCH 06/15] Move benchmarks --- .../{alevin-quant => benchmarks}/alevin-benchmark-indexes.nf | 0 .../{alevin-quant => benchmarks}/alevin-benchmark_2020-08-31.html | 0 .../{alevin-quant => benchmarks}/alevin-benchmark_2020-09-08.html | 0 workflows/{alevin-quant => benchmarks}/benchmark-analysis.Rmd | 0 workflows/{alevin-quant => benchmarks}/benchmark-analysis.nb.html | 0 workflows/{alevin-quant => benchmarks}/trace_2020-08-31.txt | 0 workflows/{alevin-quant => benchmarks}/trace_2020-09-08.txt | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename workflows/{alevin-quant => benchmarks}/alevin-benchmark-indexes.nf (100%) rename workflows/{alevin-quant => benchmarks}/alevin-benchmark_2020-08-31.html (100%) rename workflows/{alevin-quant => benchmarks}/alevin-benchmark_2020-09-08.html (100%) rename workflows/{alevin-quant => benchmarks}/benchmark-analysis.Rmd (100%) rename workflows/{alevin-quant => benchmarks}/benchmark-analysis.nb.html (100%) rename workflows/{alevin-quant => benchmarks}/trace_2020-08-31.txt (100%) rename workflows/{alevin-quant => benchmarks}/trace_2020-09-08.txt (100%) diff --git a/workflows/alevin-quant/alevin-benchmark-indexes.nf b/workflows/benchmarks/alevin-benchmark-indexes.nf similarity index 100% rename from workflows/alevin-quant/alevin-benchmark-indexes.nf rename to workflows/benchmarks/alevin-benchmark-indexes.nf diff --git a/workflows/alevin-quant/alevin-benchmark_2020-08-31.html b/workflows/benchmarks/alevin-benchmark_2020-08-31.html similarity index 100% rename from workflows/alevin-quant/alevin-benchmark_2020-08-31.html rename to workflows/benchmarks/alevin-benchmark_2020-08-31.html diff --git a/workflows/alevin-quant/alevin-benchmark_2020-09-08.html b/workflows/benchmarks/alevin-benchmark_2020-09-08.html similarity index 100% rename from workflows/alevin-quant/alevin-benchmark_2020-09-08.html rename to workflows/benchmarks/alevin-benchmark_2020-09-08.html diff --git a/workflows/alevin-quant/benchmark-analysis.Rmd b/workflows/benchmarks/benchmark-analysis.Rmd similarity index 100% rename from workflows/alevin-quant/benchmark-analysis.Rmd rename to workflows/benchmarks/benchmark-analysis.Rmd diff --git a/workflows/alevin-quant/benchmark-analysis.nb.html b/workflows/benchmarks/benchmark-analysis.nb.html similarity index 100% rename from workflows/alevin-quant/benchmark-analysis.nb.html rename to workflows/benchmarks/benchmark-analysis.nb.html diff --git a/workflows/alevin-quant/trace_2020-08-31.txt b/workflows/benchmarks/trace_2020-08-31.txt similarity index 100% rename from workflows/alevin-quant/trace_2020-08-31.txt rename to workflows/benchmarks/trace_2020-08-31.txt diff --git a/workflows/alevin-quant/trace_2020-09-08.txt b/workflows/benchmarks/trace_2020-09-08.txt similarity index 100% rename from workflows/alevin-quant/trace_2020-09-08.txt rename to workflows/benchmarks/trace_2020-09-08.txt From 0c1931b45c17320737d5e9a5a9ee55dc750c4755 Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 9 Sep 2020 13:47:07 -0400 Subject: [PATCH 07/15] Update dockerfile location and instructions --- .../images/{scpca_r => scpca-r}/Dockerfile | 0 workflows/images/scpca-r/README.md | 27 +++++++++++++++++++ 2 files changed, 27 insertions(+) rename workflows/images/{scpca_r => scpca-r}/Dockerfile (100%) create mode 100644 workflows/images/scpca-r/README.md diff --git a/workflows/images/scpca_r/Dockerfile b/workflows/images/scpca-r/Dockerfile similarity index 100% rename from workflows/images/scpca_r/Dockerfile rename to workflows/images/scpca-r/Dockerfile diff --git a/workflows/images/scpca-r/README.md b/workflows/images/scpca-r/README.md new file mode 100644 index 0000000..6f9a712 --- /dev/null +++ b/workflows/images/scpca-r/README.md @@ -0,0 +1,27 @@ +This folder contains a Dockerfile for the scpca-r image used for R analysis in this repository. + +The image is built from a versioned Rocker image, with additional R packages and other utilities added as needed. + +It can be built with the following command run in this directory: + +``` +docker build . -t ghcr.io/alexslemonade/scpca-r +``` + +Alternatively, the lastest version should be available via: + +``` +docker pull ghcr.io/alexslemonade/scpca-r +``` + +To use and launch the RStudio server, you can use the following example command, which uses the current directory for the internal home directory. +Note that this also mounts the current user's `.aws` credentials folder, which can be useful for easy S3 access, as most data files in this repository are stored on S3 in private buckets. + +``` +docker run \ + --mount type=bind,target=/home/rstudio,source=$PWD \ + --mount type=bind,target=/home/rstudio/.aws,source=$HOME/.aws \ + -e PASSWORD= \ + -p 8787:8787 \ + ghcr.io/alexslemonade/scpca-r +``` \ No newline at end of file From 5ac1b093fa7488c1cdb4c4b2bb638f04119ba580 Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Wed, 9 Sep 2020 14:32:27 -0400 Subject: [PATCH 08/15] Update benchmark analysis notebook --- .gitignore | 2 +- .../{alevin-quant => benchmarks}/README.md | 8 +- workflows/benchmarks/benchmark-analysis.Rmd | 13 +- .../benchmarks/benchmark-analysis.nb.html | 4545 +++-------------- 4 files changed, 793 insertions(+), 3775 deletions(-) rename workflows/{alevin-quant => benchmarks}/README.md (60%) diff --git a/.gitignore b/.gitignore index ec62541..3f5d753 100644 --- a/.gitignore +++ b/.gitignore @@ -44,4 +44,4 @@ rstudio-server.json # intermediate reference and analysis files workflows/rnaseq-ref-index/annotation/ workflows/rnaseq-ref-index/fasta/ -workflows/alevin-quant/data/ \ No newline at end of file +workflows/benchmarks/data/ \ No newline at end of file diff --git a/workflows/alevin-quant/README.md b/workflows/benchmarks/README.md similarity index 60% rename from workflows/alevin-quant/README.md rename to workflows/benchmarks/README.md index 2dc0b8d..7ab966e 100644 --- a/workflows/alevin-quant/README.md +++ b/workflows/benchmarks/README.md @@ -6,6 +6,10 @@ Running the benchmark analysis notebook should be done via docker, with the foll docker run \ --mount type=bind,target=/home/rstudio,source=$PWD \ --mount type=bind,target=/home/rstudio/.aws,source=$HOME/.aws \ - -e PASSWORD= -p 8787:8787 \ - ccdl/scpca_r + -e PASSWORD= \ + -p 8787:8787 \ + ghcr.io/alexslemonade/scpca-r ``` + +The rstudio server can then be logged into via the browser by navigating to http://localhost:8787. +Login with the username `rstudio` and the password chosen above. \ No newline at end of file diff --git a/workflows/benchmarks/benchmark-analysis.Rmd b/workflows/benchmarks/benchmark-analysis.Rmd index a0aad41..e7d1beb 100644 --- a/workflows/benchmarks/benchmark-analysis.Rmd +++ b/workflows/benchmarks/benchmark-analysis.Rmd @@ -30,8 +30,7 @@ quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant" ```{r} sync_call <- paste("aws s3 sync", quant_s3, data_dir) -system(sync_call) - +system(sync_call, ignore.stdout = TRUE) ``` Get the sample list and make a data frame with sample info @@ -214,7 +213,8 @@ ggplot(sce_feature_filtered, facet_grid(index_content ~ sample) ``` -The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. +The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. +If anything, there are ncRNA genes tend to have somewhat higher mean expression than the noncoding cDNAs which are in the cDNA dataset. Just to check, lets look at the correlation of coding genes within a sample: @@ -238,7 +238,12 @@ qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome)) ``` Correlation is nearly perfect. -## Conclusion +### Transcriptome Conclusion + I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project. +## Comparing decoy sequences (TBD) + + + diff --git a/workflows/benchmarks/benchmark-analysis.nb.html b/workflows/benchmarks/benchmark-analysis.nb.html index f528a0b..f0b986a 100644 --- a/workflows/benchmarks/benchmark-analysis.nb.html +++ b/workflows/benchmarks/benchmark-analysis.nb.html @@ -1836,11 +1836,12 @@

Load Libraries

File and directory setup

- +
data_dir <- file.path("data", "alevin-quant")
 dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)
 
-s3_path = "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"
+quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant" + @@ -1849,3751 +1850,20 @@

File and directory setup

Sync S3 files

- -
sync_call <- paste("aws s3 sync", s3_path, data_dir)
-system(sync_call)
+ +
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
+system(sync_call, ignore.stdout = TRUE)
+
- -
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-download: s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant/905_3-txome_k31_no_sa/alevin/raw_cb_frequency.txt to data/alevin-quant/905_3-txome_k31_no_sa/alevin/raw_cb_frequency.txt
-

Get the sample list and make a data frame with sample info

- +
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)
 
+# split ids into components for later processing
 quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
   tidyr::separate(info, sep = "[-]", 
                   into = c("sample", "index_type")) %>%
@@ -5610,11 +1880,734 @@ 

Sync S3 files

Get Annotations from AnnotationHub

- -
hub = AnnotationHub::AnnotationHub(ask = FALSE)
-# Ensembl v100 Homo Sapiens is AH79689
-ensdb = hub[["AH79689"]]
-ensg <- genes(ensdb)
+ +
hub = AnnotationHub::AnnotationHub(ask = FALSE)
+ + +

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+ + +
snapshotDate(): 2020-04-27
+ + +
# Ensembl v100 Homo Sapiens is AH79689
+ensdb = hub[["AH79689"]]
+ + +
downloading 1 resources
+retrieving 1 resource
+
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+ + +
loading from cache
+require(“ensembldb”)
+ + +
ensg <- genes(ensdb)
@@ -5644,7 +2637,7 @@

tximport and make SCEs

purrr::map( ~ SingleCellExperiment(list(counts = .x$counts)))
- +
reading in alevin gene-level counts across cells with fishpond
 reading in alevin gene-level counts across cells with fishpond
 reading in alevin gene-level counts across cells with fishpond
@@ -5656,6 +2649,8 @@ 

tximport and make SCEs

reading in alevin gene-level counts across cells with fishpond reading in alevin gene-level counts across cells with fishpond reading in alevin gene-level counts across cells with fishpond +reading in alevin gene-level counts across cells with fishpond +reading in alevin gene-level counts across cells with fishpond reading in alevin gene-level counts across cells with fishpond
@@ -5720,7 +2715,7 @@

Plotting the QC stats

scale_y_log10() -

+

NA
@@ -5734,15 +2729,16 @@

Find features that are reliably detected

To start, will select features that are detected in > 5% of cells in at least one sample/index.

- +
detected_features <- sce_feature_qc %>%
   dplyr::filter(detected >= 5.0) %>%
   dplyr::pull(gene_id) %>%
   unique()
+# How many?
 length(detected_features)
- -
[1] 15202
+ +
[1] 15203
@@ -5754,8 +2750,8 @@

Find features that are reliably detected

length(detected_nc) - -
[1] 1689
+ +
[1] 1690
@@ -5785,10 +2781,10 @@

Find features that are reliably detected

nc_feature_comparison - +
@@ -5799,9 +2795,9 @@

Find features that are reliably detected

Lets make a vector of the noncoding cDNAs for future work:

- +
nc_cdna_genes <- sce_feature_qc %>%
-  dplyr::filter(quant_id == "834-cdna_k23_no_sa",
+  dplyr::filter(quant_id == "834-cdna_k31_no_sa",
                 ! gene_id %in% coding_genes) %>%
   dplyr::pull(gene_id)
@@ -5831,18 +2827,28 @@

Comparing coding and noncoding txomes

-

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can’t see any particular reason to exclude them.

+

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can’t see any particular reason to exclude them. If anything, there are ncRNA genes tend to have somewhat higher mean expression than the noncoding cDNAs which are in the cDNA dataset.

Just to check, lets look at the correlation of coding genes within a sample:

- -
cor(compare_expression, method = "spearman")
-
+ +
# pick a random set of cells to look at
+cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100)
+
+features <- rownames(sces[["834-cdna_k31_no_sa"]])
+coding_features <- features[features %in% coding_genes]
+
+compare_expression <- data.frame(
+  cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])),
+  txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample]))
+)
+
+cor(compare_expression, method = "spearman")
- -
          cdna    txome
-cdna  1.000000 0.994648
-txome 0.994648 1.000000
+ +
           cdna     txome
+cdna  1.0000000 0.9945349
+txome 0.9945349 1.0000000
@@ -5850,20 +2856,23 @@

Comparing coding and noncoding txomes

qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome))
-

+

Correlation is nearly perfect.

- -
-

Conclusion

+
+

Transcriptome Conclusion

I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.

+
+
+
+

Comparing decoy sequences (TBD)

-
---
title: "Alevin Index comparisons"
author: "Joshua Shapiro for CCDL"
output: html_notebook
---

This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates.

### Load Libraries
```{r setup}
library(ggplot2)
library(magrittr)
library(SingleCellExperiment)
library(tximport)
```

### File and directory setup
```{r}
data_dir <- file.path("data", "alevin-quant")
dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)

quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"

```




## Sync S3 files

```{r}
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
system(sync_call)

```

Get the sample list and make a data frame with sample info

```{r}
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)

# split ids into components for later processing
quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
  tidyr::separate(info, sep = "[-]", 
                  into = c("sample", "index_type")) %>%
  tidyr::separate(index_type, 
                  into = c("index_content", "kmer", "sa"), 
                  extra = "drop") %>%
  dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
  
```

## Get Annotations from AnnotationHub

```{r}
hub = AnnotationHub::AnnotationHub(ask = FALSE)
# Ensembl v100 Homo Sapiens is AH79689
ensdb = hub[["AH79689"]]
ensg <- genes(ensdb)
```

Create vectors of mitochondiral genes and coding genes for later.

```{r}
# create the mitochondrial gene list
mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id

coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id
```



# Process Alevin quantifications

## tximport and make SCEs

```{r}
sces <- quant_dirs %>%
  purrr::map(
    ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"),
               type = "alevin")) %>%
  purrr::map(
    ~ SingleCellExperiment(list(counts = .x$counts))) 

# add names
names(sces) <- quant_dirs
```


Calculate cell and feature QC statistics for each sample.

```{r}
sces <- sces %>% 
  purrr::map(
    ~ scater::addPerCellQC(
      .x,
      subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)],
                     ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] )
    ) 
  ) %>%
  purrr::map(scater::addPerFeatureQC)
```

Combine all cell QC stats into a single data frame
```{r}
sce_cell_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(colData(.x)) %>%
                          tibble::rownames_to_column(var = "cell_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```


Combine all feature QC stats into a single data frame.
```{r}
sce_feature_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(rowData(.x)) %>%
                          tibble::rownames_to_column(var = "gene_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```

## Plotting the QC stats

Plot cell QC gene accumulation curves & mito fraction
```{r}
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
       aes(x = sum, y = detected, color = subsets_mito_percent)) +
  geom_point() +
  facet_grid(sample ~ paste(index_content, sa) ) +
  scale_color_viridis_c() +
  scale_x_log10() +
  scale_y_log10()
  
```

# Find features that are reliably detected

To start, will select features that are detected in > 5% of cells in at least one sample/index. 

```{r}
detected_features <- sce_feature_qc %>%
  dplyr::filter(detected >= 5.0) %>%
  dplyr::pull(gene_id) %>%
  unique()
# How many?
length(detected_features)
```

How many of these are not protein coding?

```{r}
detected_nc <- detected_features[!detected_features %in% coding_genes]

length(detected_nc)
```

Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only.
```{r}
# select noncoding detected features
nc_feature_qc <- sce_feature_qc %>% 
  dplyr::filter(gene_id %in% detected_nc) 

# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome 

nc_txome <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-txome_k31_no_sa") %>%
  dplyr::arrange(desc(mean))

nc_cdna <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa")

# Join the tables
nc_feature_comparison <- nc_txome %>%
  dplyr::left_join(nc_cdna, 
                   by = "gene_id", 
                   suffix = c("_txome", "_cdna")) %>%
  dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna")

nc_feature_comparison
```

As expected, most of the features that are not listed as coding do not appear in the cdna only set!
Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file.

Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. 
MALAT1 is potentially interesting, and it does seem that we should probably keep it!

Lets make a vector of the noncoding cDNAs for future work:
```{r}
nc_cdna_genes <- sce_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa",
                ! gene_id %in% coding_genes) %>%
  dplyr::pull(gene_id)
```


## Comparing coding and noncoding txomes

First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding.

```{r}
sce_feature_filtered <- sce_feature_qc %>% 
  dplyr::filter(kmer == "31", sa == "no") %>%
  dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding",
                                         gene_id %in% nc_cdna_genes ~ "noncoding cDNA",
                                          TRUE ~ "ncRNA"))

ggplot(sce_feature_filtered, 
       aes (x = mean, color = class)) +
  geom_density() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Dark2") +
  facet_grid(index_content ~ sample)
```

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them.

Just to check, lets look at the correlation of coding genes within a sample:


```{r}
# pick a random set of cells to look at
cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100)

features <- rownames(sces[["834-cdna_k31_no_sa"]])
coding_features <- features[features %in% coding_genes]

compare_expression <- data.frame(
  cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])),
  txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample]))
)

cor(compare_expression, method = "spearman")
```
```{r}
qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome))
```
Correlation is nearly perfect.

## Conclusion
I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.



+
---
title: "Alevin Index comparisons"
author: "Joshua Shapiro for CCDL"
output: html_notebook
---

This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates.

### Load Libraries
```{r setup}
library(ggplot2)
library(magrittr)
library(SingleCellExperiment)
library(tximport)
```

### File and directory setup
```{r}
data_dir <- file.path("data", "alevin-quant")
dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)

quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"

```




## Sync S3 files

```{r}
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
system(sync_call, ignore.stdout = TRUE)
```

Get the sample list and make a data frame with sample info

```{r}
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)

# split ids into components for later processing
quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
  tidyr::separate(info, sep = "[-]", 
                  into = c("sample", "index_type")) %>%
  tidyr::separate(index_type, 
                  into = c("index_content", "kmer", "sa"), 
                  extra = "drop") %>%
  dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
  
```

## Get Annotations from AnnotationHub

```{r}
hub = AnnotationHub::AnnotationHub(ask = FALSE)
# Ensembl v100 Homo Sapiens is AH79689
ensdb = hub[["AH79689"]]
ensg <- genes(ensdb)
```

Create vectors of mitochondiral genes and coding genes for later.

```{r}
# create the mitochondrial gene list
mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id

coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id
```



# Process Alevin quantifications

## tximport and make SCEs

```{r}
sces <- quant_dirs %>%
  purrr::map(
    ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"),
               type = "alevin")) %>%
  purrr::map(
    ~ SingleCellExperiment(list(counts = .x$counts))) 

# add names
names(sces) <- quant_dirs
```


Calculate cell and feature QC statistics for each sample.

```{r}
sces <- sces %>% 
  purrr::map(
    ~ scater::addPerCellQC(
      .x,
      subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)],
                     ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] )
    ) 
  ) %>%
  purrr::map(scater::addPerFeatureQC)
```

Combine all cell QC stats into a single data frame
```{r}
sce_cell_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(colData(.x)) %>%
                          tibble::rownames_to_column(var = "cell_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```


Combine all feature QC stats into a single data frame.
```{r}
sce_feature_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(rowData(.x)) %>%
                          tibble::rownames_to_column(var = "gene_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```

## Plotting the QC stats

Plot cell QC gene accumulation curves & mito fraction
```{r}
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
       aes(x = sum, y = detected, color = subsets_mito_percent)) +
  geom_point() +
  facet_grid(sample ~ paste(index_content, sa) ) +
  scale_color_viridis_c() +
  scale_x_log10() +
  scale_y_log10()
  
```

# Find features that are reliably detected

To start, will select features that are detected in > 5% of cells in at least one sample/index. 

```{r}
detected_features <- sce_feature_qc %>%
  dplyr::filter(detected >= 5.0) %>%
  dplyr::pull(gene_id) %>%
  unique()
# How many?
length(detected_features)
```

How many of these are not protein coding?

```{r}
detected_nc <- detected_features[!detected_features %in% coding_genes]

length(detected_nc)
```

Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only.
```{r}
# select noncoding detected features
nc_feature_qc <- sce_feature_qc %>% 
  dplyr::filter(gene_id %in% detected_nc) 

# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome 

nc_txome <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-txome_k31_no_sa") %>%
  dplyr::arrange(desc(mean))

nc_cdna <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa")

# Join the tables
nc_feature_comparison <- nc_txome %>%
  dplyr::left_join(nc_cdna, 
                   by = "gene_id", 
                   suffix = c("_txome", "_cdna")) %>%
  dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna")

nc_feature_comparison
```

As expected, most of the features that are not listed as coding do not appear in the cdna only set!
Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file.

Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. 
MALAT1 is potentially interesting, and it does seem that we should probably keep it!

Lets make a vector of the noncoding cDNAs for future work:
```{r}
nc_cdna_genes <- sce_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa",
                ! gene_id %in% coding_genes) %>%
  dplyr::pull(gene_id)
```


## Comparing coding and noncoding txomes

First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding.

```{r}
sce_feature_filtered <- sce_feature_qc %>% 
  dplyr::filter(kmer == "31", sa == "no") %>%
  dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding",
                                         gene_id %in% nc_cdna_genes ~ "noncoding cDNA",
                                          TRUE ~ "ncRNA"))

ggplot(sce_feature_filtered, 
       aes (x = mean, color = class)) +
  geom_density() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Dark2") +
  facet_grid(index_content ~ sample)
```

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. 
If anything, there are ncRNA genes tend to have somewhat higher mean expression than the noncoding cDNAs which are in the cDNA dataset.

Just to check, lets look at the correlation of coding genes within a sample:


```{r}
# pick a random set of cells to look at
cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100)

features <- rownames(sces[["834-cdna_k31_no_sa"]])
coding_features <- features[features %in% coding_genes]

compare_expression <- data.frame(
  cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])),
  txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample]))
)

cor(compare_expression, method = "spearman")
```
```{r}
qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome))
```
Correlation is nearly perfect.

### Transcriptome Conclusion

I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.

## Comparing decoy sequences (TBD)





From bfbe53afbdc030b3b60b49bbe14d45788e120068 Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Thu, 10 Sep 2020 14:35:31 -0400 Subject: [PATCH 09/15] Add decoy comparison --- workflows/benchmarks/benchmark-analysis.Rmd | 96 +- .../benchmarks/benchmark-analysis.nb.html | 959 +++--------------- 2 files changed, 234 insertions(+), 821 deletions(-) diff --git a/workflows/benchmarks/benchmark-analysis.Rmd b/workflows/benchmarks/benchmark-analysis.Rmd index e7d1beb..f428922 100644 --- a/workflows/benchmarks/benchmark-analysis.Rmd +++ b/workflows/benchmarks/benchmark-analysis.Rmd @@ -12,8 +12,13 @@ library(ggplot2) library(magrittr) library(SingleCellExperiment) library(tximport) + +# set seed +set.seed(2020) ``` + + ### File and directory setup ```{r} data_dir <- file.path("data", "alevin-quant") @@ -23,9 +28,6 @@ quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant" ``` - - - ## Sync S3 files ```{r} @@ -43,7 +45,7 @@ quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>% tidyr::separate(info, sep = "[-]", into = c("sample", "index_type")) %>% tidyr::separate(index_type, - into = c("index_content", "kmer", "sa"), + into = c("index_content", "kmer", "decoy"), extra = "drop") %>% dplyr::mutate(kmer = stringr::str_remove(kmer, "k")) @@ -126,7 +128,7 @@ Plot cell QC gene accumulation curves & mito fraction ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), aes(x = sum, y = detected, color = subsets_mito_percent)) + geom_point() + - facet_grid(sample ~ paste(index_content, sa) ) + + facet_grid(sample ~ paste(index_content, decoy) ) + scale_color_viridis_c() + scale_x_log10() + scale_y_log10() @@ -200,10 +202,10 @@ First, looking at the mean expression of each gene, comparing coding and noncodi ```{r} sce_feature_filtered <- sce_feature_qc %>% - dplyr::filter(kmer == "31", sa == "no") %>% + dplyr::filter(kmer == "31", decoy == "no") %>% dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding", gene_id %in% nc_cdna_genes ~ "noncoding cDNA", - TRUE ~ "ncRNA")) + TRUE ~ "ncRNA")) ggplot(sce_feature_filtered, aes (x = mean, color = class)) + @@ -221,22 +223,24 @@ Just to check, lets look at the correlation of coding genes within a sample: ```{r} # pick a random set of cells to look at -cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100) +cell_sample <- sample(colnames(sces[["905_3-cdna_k31_no_sa"]]), 100) -features <- rownames(sces[["834-cdna_k31_no_sa"]]) +features <- rownames(sces[["905_3-cdna_k31_no_sa"]]) coding_features <- features[features %in% coding_genes] compare_expression <- data.frame( - cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])), - txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample])) + cdna = as.vector(counts(sces[["905_3-cdna_k31_no_sa"]][coding_features, cell_sample])), + txome = as.vector(counts(sces[["905_3-txome_k31_no_sa"]][coding_features, cell_sample])) ) cor(compare_expression, method = "spearman") ``` ```{r} -qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome)) +ggplot(compare_expression, aes(x = log1p(cdna), y = log1p(txome))) + + geom_point() ``` -Correlation is nearly perfect. +Correlation is very good for genes present in both, with expression more often lower in the transcriptome set, which may make sense in the case of multimapping introduced by the larger number of potential targets. +Interesting though that there are some genes which do not appear to be expressed in the cDNA set that have expression in the txome. ### Transcriptome Conclusion @@ -244,6 +248,72 @@ I think there is little cost, and a not insignificant potential gain to includin ## Comparing decoy sequences (TBD) +Get cDNA-only decoy comparison data. +```{r} +sce_feature_filtered <- sce_feature_qc %>% + dplyr::filter(kmer == "31", index_content == "cdna") +``` + +```{r} +decoy_means <- sce_feature_filtered %>% + tidyr::pivot_wider(id_cols = c(gene_id, sample), + names_from = decoy, + values_from = mean) +``` + +First off, how well correlated are the means for various decoy choices? +We expect very good correlations. + +```{r} +cor(decoy_means[,3:5], method = "spearman", use = "complete.obs") +``` + + + +And we have them... interestingly, the best correlation is between no decoys and the partial decoy: the full decoy index is more different by this measure. +This leads me to the preliminary conclusion that the partial index (which notably uses a slightly different set of transcipts at this stage, due to not being built locally) may not be worth pursuing, as it seems to make very little difference. +Don't forget to look at your data! +```{r} +pairs(decoy_means[,3:5]) +``` +Whoa... seems like a lot is being driven by a few points that are very different in the full index data. +What are those genes? + + +```{r} +decoy_means %>% + dplyr::mutate(diff = (no - full)/(full + 1)) %>% + dplyr::arrange(desc(diff)) +``` +ENSG00000269028, which is by far the most different, is the gene *MT-RNR2 like 12* which, aside from being related to at least 12 other genes, does not ring any particular bells. +However, ENSG00000255823 is *MT-RNR2-like 8* + +Other genes at the top of the differential list: +ENSG00000163864: NMNAT3 nicotinamide nucleotide adenylyltransferase 3 +ENSG00000197563: PIGN Phosphatidylinositol Glycan Anchor Biosynthesis Class N +ENSG00000173559: NABP1 Nucleic Acid Binding Protein 1 + +I don't really know what to do with this list... it seems like few genes will have large differences, but a few differences are enormous! + + +Just as a sanity check, lets repeat one of the analyses we did before, comparing the cDNA to the transcriptome for the full decoy index (since we now have both): + +```{r} +# using the same `cell_sample` and `coding_features` as previously + +compare_expression_full <- data.frame( + cdna = as.vector(counts(sces[["905_3-cdna_k31_full_sa"]][coding_features, cell_sample])), + txome = as.vector(counts(sces[["905_3-txome_k31_full_sa"]][coding_features, cell_sample])) +) + +cor(compare_expression_full, method = "spearman") +``` +```{r} +ggplot(compare_expression_full, aes(log1p(cdna), y = log1p(txome))) + + geom_point() +``` +Interestingly, the correlation between cDNA and txome expression values (for coding genes) using the full decoy seems substantially better than with the no decoy sequences! +This seems to me to be a good argument for using the full decoy, if it is less sensitive to the chosen transcript list. diff --git a/workflows/benchmarks/benchmark-analysis.nb.html b/workflows/benchmarks/benchmark-analysis.nb.html index f0b986a..8ececda 100644 --- a/workflows/benchmarks/benchmark-analysis.nb.html +++ b/workflows/benchmarks/benchmark-analysis.nb.html @@ -1756,78 +1756,51 @@

Joshua Shapiro for CCDL

Load Libraries

- + +
Quitting from lines 11-18 (benchmark-analysis.Rmd) 
+ +
library(ggplot2)
-library(magrittr)
-library(SingleCellExperiment)
+library(magrittr) - -
Loading required package: SummarizedExperiment
-Loading required package: GenomicRanges
-Loading required package: stats4
-Loading required package: BiocGenerics
-Loading required package: parallel
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-Attaching package: ‘BiocGenerics’
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-    clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply,
-    parSapply, parSapplyLB
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-    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
-    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order,
-    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
-    rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit,
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-Welcome to Bioconductor
+
+

+Attaching package: 'magrittr'
 
-    Vignettes contain introductory material; view with 'browseVignettes()'.
-    To cite Bioconductor, see 'citation("Biobase")', and for packages
-    'citation("pkgname")'.
+The following object is masked from 'package:AnnotationFilter':
 
+    not
+ + +
library(SingleCellExperiment)
+ + +
Loading required package: SummarizedExperiment
 Loading required package: DelayedArray
 Loading required package: matrixStats
 
-Attaching package: ‘matrixStats’
+Attaching package: 'matrixStats'
 
-The following objects are masked from ‘package:Biobase’:
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     anyMissing, rowMedians
 
 
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     aperm, apply, rowsum
- -
library(tximport)
+ +
library(tximport)
+
+# set seed
+set.seed(2020)
@@ -1850,17 +1823,16 @@

File and directory setup

Sync S3 files

- +
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
-system(sync_call, ignore.stdout = TRUE)
-
+system(sync_call, ignore.stdout = TRUE)

Get the sample list and make a data frame with sample info

- +
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)
 
 # split ids into components for later processing
@@ -1868,7 +1840,7 @@ 

Sync S3 files

tidyr::separate(info, sep = "[-]", into = c("sample", "index_type")) %>% tidyr::separate(index_type, - into = c("index_content", "kmer", "sa"), + into = c("index_content", "kmer", "decoy"), extra = "drop") %>% dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
@@ -1883,732 +1855,6 @@

Get Annotations from AnnotationHub

hub = AnnotationHub::AnnotationHub(ask = FALSE)
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snapshotDate(): 2020-04-27
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# Ensembl v100 Homo Sapiens is AH79689
-ensdb = hub[["AH79689"]]
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- - -
loading from cache
-require(“ensembldb”)
- - -
ensg <- genes(ensdb)
-

Create vectors of mitochondiral genes and coding genes for later.

@@ -2705,17 +1951,17 @@

Plotting the QC stats

Plot cell QC gene accumulation curves & mito fraction

- +
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
        aes(x = sum, y = detected, color = subsets_mito_percent)) +
   geom_point() +
-  facet_grid(sample ~ paste(index_content, sa) ) +
+  facet_grid(sample ~ paste(index_content, decoy) ) +
   scale_color_viridis_c() +
   scale_x_log10() +
   scale_y_log10()
- -

+ +

NA
@@ -2781,7 +2027,7 @@

Find features that are reliably detected

nc_feature_comparison - +
+
+ + + +

ENSG00000269028, which is by far the most different, is the gene MT-RNR2 like 12 which, aside from being related to at least 12 other genes, does not ring any particular bells. However, ENSG00000255823 is MT-RNR2-like 8

+

Other genes at the top of the differential list: ENSG00000163864: NMNAT3 nicotinamide nucleotide adenylyltransferase 3 ENSG00000197563: PIGN Phosphatidylinositol Glycan Anchor Biosynthesis Class N ENSG00000173559: NABP1 Nucleic Acid Binding Protein 1

+

I don’t really know what to do with this list… it seems like few genes will have large differences, but a few differences are enormous!

+

Just as a sanity check, lets repeat one of the analyses we did before, comparing the cDNA to the transcriptome for the full decoy index (since we now have both):

+ + + +
# using the same `cell_sample` and `coding_features` as previously
+
+compare_expression_full <- data.frame(
+  cdna = as.vector(counts(sces[["905_3-cdna_k31_full_sa"]][coding_features, cell_sample])),
+  txome = as.vector(counts(sces[["905_3-txome_k31_full_sa"]][coding_features, cell_sample]))
+)
+
+cor(compare_expression_full, method = "spearman")
+ + +
           cdna     txome
+cdna  1.0000000 0.9993992
+txome 0.9993992 1.0000000
+ + + + +
ggplot(compare_expression_full, aes(log1p(cdna), y = log1p(txome))) +
+  geom_point()
+ + +

+ + + +

Interestingly, the correlation between cDNA and txome expression values (for coding genes) using the full decoy seems substantially better than with the no decoy sequences!

+

This seems to me to be a good argument for using the full decoy, if it is less sensitive to the chosen transcript list.

-
---
title: "Alevin Index comparisons"
author: "Joshua Shapiro for CCDL"
output: html_notebook
---

This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates.

### Load Libraries
```{r setup}
library(ggplot2)
library(magrittr)
library(SingleCellExperiment)
library(tximport)
```

### File and directory setup
```{r}
data_dir <- file.path("data", "alevin-quant")
dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)

quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"

```




## Sync S3 files

```{r}
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
system(sync_call, ignore.stdout = TRUE)
```

Get the sample list and make a data frame with sample info

```{r}
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)

# split ids into components for later processing
quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
  tidyr::separate(info, sep = "[-]", 
                  into = c("sample", "index_type")) %>%
  tidyr::separate(index_type, 
                  into = c("index_content", "kmer", "sa"), 
                  extra = "drop") %>%
  dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
  
```

## Get Annotations from AnnotationHub

```{r}
hub = AnnotationHub::AnnotationHub(ask = FALSE)
# Ensembl v100 Homo Sapiens is AH79689
ensdb = hub[["AH79689"]]
ensg <- genes(ensdb)
```

Create vectors of mitochondiral genes and coding genes for later.

```{r}
# create the mitochondrial gene list
mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id

coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id
```



# Process Alevin quantifications

## tximport and make SCEs

```{r}
sces <- quant_dirs %>%
  purrr::map(
    ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"),
               type = "alevin")) %>%
  purrr::map(
    ~ SingleCellExperiment(list(counts = .x$counts))) 

# add names
names(sces) <- quant_dirs
```


Calculate cell and feature QC statistics for each sample.

```{r}
sces <- sces %>% 
  purrr::map(
    ~ scater::addPerCellQC(
      .x,
      subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)],
                     ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] )
    ) 
  ) %>%
  purrr::map(scater::addPerFeatureQC)
```

Combine all cell QC stats into a single data frame
```{r}
sce_cell_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(colData(.x)) %>%
                          tibble::rownames_to_column(var = "cell_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```


Combine all feature QC stats into a single data frame.
```{r}
sce_feature_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(rowData(.x)) %>%
                          tibble::rownames_to_column(var = "gene_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```

## Plotting the QC stats

Plot cell QC gene accumulation curves & mito fraction
```{r}
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
       aes(x = sum, y = detected, color = subsets_mito_percent)) +
  geom_point() +
  facet_grid(sample ~ paste(index_content, sa) ) +
  scale_color_viridis_c() +
  scale_x_log10() +
  scale_y_log10()
  
```

# Find features that are reliably detected

To start, will select features that are detected in > 5% of cells in at least one sample/index. 

```{r}
detected_features <- sce_feature_qc %>%
  dplyr::filter(detected >= 5.0) %>%
  dplyr::pull(gene_id) %>%
  unique()
# How many?
length(detected_features)
```

How many of these are not protein coding?

```{r}
detected_nc <- detected_features[!detected_features %in% coding_genes]

length(detected_nc)
```

Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only.
```{r}
# select noncoding detected features
nc_feature_qc <- sce_feature_qc %>% 
  dplyr::filter(gene_id %in% detected_nc) 

# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome 

nc_txome <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-txome_k31_no_sa") %>%
  dplyr::arrange(desc(mean))

nc_cdna <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa")

# Join the tables
nc_feature_comparison <- nc_txome %>%
  dplyr::left_join(nc_cdna, 
                   by = "gene_id", 
                   suffix = c("_txome", "_cdna")) %>%
  dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna")

nc_feature_comparison
```

As expected, most of the features that are not listed as coding do not appear in the cdna only set!
Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file.

Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. 
MALAT1 is potentially interesting, and it does seem that we should probably keep it!

Lets make a vector of the noncoding cDNAs for future work:
```{r}
nc_cdna_genes <- sce_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa",
                ! gene_id %in% coding_genes) %>%
  dplyr::pull(gene_id)
```


## Comparing coding and noncoding txomes

First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding.

```{r}
sce_feature_filtered <- sce_feature_qc %>% 
  dplyr::filter(kmer == "31", sa == "no") %>%
  dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding",
                                         gene_id %in% nc_cdna_genes ~ "noncoding cDNA",
                                          TRUE ~ "ncRNA"))

ggplot(sce_feature_filtered, 
       aes (x = mean, color = class)) +
  geom_density() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Dark2") +
  facet_grid(index_content ~ sample)
```

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. 
If anything, there are ncRNA genes tend to have somewhat higher mean expression than the noncoding cDNAs which are in the cDNA dataset.

Just to check, lets look at the correlation of coding genes within a sample:


```{r}
# pick a random set of cells to look at
cell_sample <- sample(colnames(sces[["834-cdna_k31_no_sa"]]), 100)

features <- rownames(sces[["834-cdna_k31_no_sa"]])
coding_features <- features[features %in% coding_genes]

compare_expression <- data.frame(
  cdna = as.vector(counts(sces[["834-cdna_k31_no_sa"]][coding_features, cell_sample])),
  txome = as.vector(counts(sces[["834-txome_k31_no_sa"]][coding_features, cell_sample]))
)

cor(compare_expression, method = "spearman")
```
```{r}
qqplot(log1p(compare_expression$cdna), log1p(compare_expression$txome))
```
Correlation is nearly perfect.

### Transcriptome Conclusion

I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.

## Comparing decoy sequences (TBD)





+
---
title: "Alevin Index comparisons"
author: "Joshua Shapiro for CCDL"
output: html_notebook
---

This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates.

### Load Libraries
```{r setup}
library(ggplot2)
library(magrittr)
library(SingleCellExperiment)
library(tximport)

# set seed
set.seed(2020)
```



### File and directory setup
```{r}
data_dir <- file.path("data", "alevin-quant")
dir.create(data_dir, recursive = TRUE, showWarnings = FALSE)

quant_s3 <- "s3://nextflow-ccdl-results/scpca-benchmark/alevin-quant"

```

## Sync S3 files

```{r}
sync_call <- paste("aws s3 sync", quant_s3, data_dir)
system(sync_call, ignore.stdout = TRUE)
```

Get the sample list and make a data frame with sample info

```{r}
quant_dirs <- list.dirs(data_dir, full.names = FALSE, recursive = FALSE)

# split ids into components for later processing
quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>%
  tidyr::separate(info, sep = "[-]", 
                  into = c("sample", "index_type")) %>%
  tidyr::separate(index_type, 
                  into = c("index_content", "kmer", "decoy"), 
                  extra = "drop") %>%
  dplyr::mutate(kmer = stringr::str_remove(kmer, "k"))
  
```

## Get Annotations from AnnotationHub

```{r}
hub = AnnotationHub::AnnotationHub(ask = FALSE)
# Ensembl v100 Homo Sapiens is AH79689
ensdb = hub[["AH79689"]]
ensg <- genes(ensdb)
```

Create vectors of mitochondiral genes and coding genes for later.

```{r}
# create the mitochondrial gene list
mito_genes <- ensg[seqnames(ensg) == 'MT']$gene_id

coding_genes <- ensg[ensg$gene_biotype == "protein_coding"]$gene_id
```



# Process Alevin quantifications

## tximport and make SCEs

```{r}
sces <- quant_dirs %>%
  purrr::map(
    ~ tximport(file.path(data_dir, .x, "alevin", "quants_mat.gz"),
               type = "alevin")) %>%
  purrr::map(
    ~ SingleCellExperiment(list(counts = .x$counts))) 

# add names
names(sces) <- quant_dirs
```


Calculate cell and feature QC statistics for each sample.

```{r}
sces <- sces %>% 
  purrr::map(
    ~ scater::addPerCellQC(
      .x,
      subsets = list(mito = mito_genes[mito_genes %in% rownames(.x)],
                     ncRNA = rownames(.x)[!rownames(.x) %in% coding_genes] )
    ) 
  ) %>%
  purrr::map(scater::addPerFeatureQC)
```

Combine all cell QC stats into a single data frame
```{r}
sce_cell_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(colData(.x)) %>%
                          tibble::rownames_to_column(var = "cell_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```


Combine all feature QC stats into a single data frame.
```{r}
sce_feature_qc <- purrr::map_df(sces, 
                        ~ as.data.frame(rowData(.x)) %>%
                          tibble::rownames_to_column(var = "gene_id"), 
                        .id = "quant_id") %>%
  dplyr::left_join(quant_info, by = c("quant_id" = "quant_dir"))
```

## Plotting the QC stats

Plot cell QC gene accumulation curves & mito fraction
```{r}
ggplot(sce_cell_qc %>% dplyr::filter(kmer == "31"), 
       aes(x = sum, y = detected, color = subsets_mito_percent)) +
  geom_point() +
  facet_grid(sample ~ paste(index_content, decoy) ) +
  scale_color_viridis_c() +
  scale_x_log10() +
  scale_y_log10()
  
```

# Find features that are reliably detected

To start, will select features that are detected in > 5% of cells in at least one sample/index. 

```{r}
detected_features <- sce_feature_qc %>%
  dplyr::filter(detected >= 5.0) %>%
  dplyr::pull(gene_id) %>%
  unique()
# How many?
length(detected_features)
```

How many of these are not protein coding?

```{r}
detected_nc <- detected_features[!detected_features %in% coding_genes]

length(detected_nc)
```

Lets look at these features in a bit more depth, comparing the same sample with full transcriptome vs cDNA only.
```{r}
# select noncoding detected features
nc_feature_qc <- sce_feature_qc %>% 
  dplyr::filter(gene_id %in% detected_nc) 

# Get detected ncRNAs in 834-txome_k31_no_sa as a transcriptome 

nc_txome <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-txome_k31_no_sa") %>%
  dplyr::arrange(desc(mean))

nc_cdna <-  nc_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa")

# Join the tables
nc_feature_comparison <- nc_txome %>%
  dplyr::left_join(nc_cdna, 
                   by = "gene_id", 
                   suffix = c("_txome", "_cdna")) %>%
  dplyr::select("gene_id", "mean_txome", "mean_cdna", "detected_txome", "detected_cdna")

nc_feature_comparison
```

As expected, most of the features that are not listed as coding do not appear in the cdna only set!
Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file.

Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. 
MALAT1 is potentially interesting, and it does seem that we should probably keep it!

Lets make a vector of the noncoding cDNAs for future work:
```{r}
nc_cdna_genes <- sce_feature_qc %>%
  dplyr::filter(quant_id == "834-cdna_k31_no_sa",
                ! gene_id %in% coding_genes) %>%
  dplyr::pull(gene_id)
```


## Comparing coding and noncoding txomes

First, looking at the mean expression of each gene, comparing coding and noncoding: comparing within samples, coding to noncoding.

```{r}
sce_feature_filtered <- sce_feature_qc %>% 
  dplyr::filter(kmer == "31", decoy == "no") %>%
  dplyr::mutate(class = dplyr::case_when(gene_id %in% coding_genes ~ "coding",
                                         gene_id %in% nc_cdna_genes ~ "noncoding cDNA",
                                         TRUE ~ "ncRNA"))

ggplot(sce_feature_filtered, 
       aes (x = mean, color = class)) +
  geom_density() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Dark2") +
  facet_grid(index_content ~ sample)
```

The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. 
If anything, there are ncRNA genes tend to have somewhat higher mean expression than the noncoding cDNAs which are in the cDNA dataset.

Just to check, lets look at the correlation of coding genes within a sample:


```{r}
# pick a random set of cells to look at
cell_sample <- sample(colnames(sces[["905_3-cdna_k31_no_sa"]]), 100)

features <- rownames(sces[["905_3-cdna_k31_no_sa"]])
coding_features <- features[features %in% coding_genes]

compare_expression <- data.frame(
  cdna = as.vector(counts(sces[["905_3-cdna_k31_no_sa"]][coding_features, cell_sample])),
  txome = as.vector(counts(sces[["905_3-txome_k31_no_sa"]][coding_features, cell_sample]))
)

cor(compare_expression, method = "spearman")
```
```{r}
ggplot(compare_expression, aes(x = log1p(cdna), y = log1p(txome))) +
  geom_point()
```
Correlation is very good for genes present in both, with expression more often lower in the transcriptome set, which may make sense in the case of multimapping introduced by the larger number of potential targets.
Interesting though that there are some genes which do not appear to be expressed in the cDNA set that have expression in the txome.

### Transcriptome Conclusion

I think there is little cost, and a not insignificant potential gain to including noncoding RNA transcripts in the mapping sets for this project.

## Comparing decoy sequences (TBD)

Get cDNA-only decoy comparison data.
```{r}
sce_feature_filtered <- sce_feature_qc %>% 
  dplyr::filter(kmer == "31", index_content == "cdna")
```

```{r}
decoy_means <- sce_feature_filtered %>%
  tidyr::pivot_wider(id_cols = c(gene_id, sample),
                     names_from = decoy,
                     values_from = mean) 
```

First off, how well correlated are the means for various decoy choices? 
We expect very good correlations.

```{r}
cor(decoy_means[,3:5], method = "spearman", use = "complete.obs")
```



And we have them... interestingly, the best correlation is between no decoys and the partial decoy: the full decoy index is more different by this measure.
This leads me to the preliminary conclusion that the partial index (which notably uses a slightly different set of transcipts at this stage, due to not being built locally) may not be worth pursuing, as it seems to make very little difference.

Don't forget to look at your data!
```{r}
pairs(decoy_means[,3:5])
```
Whoa... seems like a lot is being driven by a few points that are very different in the full index data. 
What are those genes?


```{r}
decoy_means %>% 
  dplyr::mutate(diff = (no - full)/(full + 1)) %>%
  dplyr::arrange(desc(diff))
```
ENSG00000269028, which is by far the most different, is the gene *MT-RNR2 like 12* which, aside from being related to at least 12 other genes, does not ring any particular bells.
However, ENSG00000255823 is *MT-RNR2-like 8* 

Other genes at the top of the differential list:
ENSG00000163864: NMNAT3 nicotinamide nucleotide adenylyltransferase 3
ENSG00000197563: PIGN Phosphatidylinositol Glycan Anchor Biosynthesis Class N
ENSG00000173559: NABP1 Nucleic Acid Binding Protein 1

I don't really know what to do with this list... it seems like few genes will have large differences, but a few differences are enormous!


Just as a sanity check, lets repeat one of the analyses we did before, comparing the cDNA to the transcriptome for the full decoy index (since we now have both):

```{r}
# using the same `cell_sample` and `coding_features` as previously

compare_expression_full <- data.frame(
  cdna = as.vector(counts(sces[["905_3-cdna_k31_full_sa"]][coding_features, cell_sample])),
  txome = as.vector(counts(sces[["905_3-txome_k31_full_sa"]][coding_features, cell_sample]))
)

cor(compare_expression_full, method = "spearman")
```
```{r}
ggplot(compare_expression_full, aes(log1p(cdna), y = log1p(txome))) +
  geom_point()
```

Interestingly, the correlation between cDNA and txome expression values (for coding genes) using the full decoy seems substantially better than with the no decoy sequences!

This seems to me to be a good argument for using the full decoy, if it is less sensitive to the chosen transcript list.

From d3f39fb20a98e83e2a88d6dc5bc25fb58593c788 Mon Sep 17 00:00:00 2001 From: Joshua Shapiro Date: Thu, 10 Sep 2020 14:50:24 -0400 Subject: [PATCH 10/15] Spellcheck and rebuild with TOC --- workflows/benchmarks/benchmark-analysis.Rmd | 19 +- .../benchmarks/benchmark-analysis.nb.html | 1189 ++++++++++++++++- 2 files changed, 1193 insertions(+), 15 deletions(-) diff --git a/workflows/benchmarks/benchmark-analysis.Rmd b/workflows/benchmarks/benchmark-analysis.Rmd index f428922..a7b3c2a 100644 --- a/workflows/benchmarks/benchmark-analysis.Rmd +++ b/workflows/benchmarks/benchmark-analysis.Rmd @@ -1,10 +1,13 @@ --- title: "Alevin Index comparisons" author: "Joshua Shapiro for CCDL" -output: html_notebook +output: + html_notebook: + toc: true + toc_float: true --- -This notebook contains analysis of some intial benchmark in runs of Alevin usign different index files, looking at mapping rates. +This notebook contains analysis of some initial benchmark in runs of Alevin using different index files, looking at mapping rates. ### Load Libraries ```{r setup} @@ -55,12 +58,12 @@ quant_info <- data.frame (quant_dir = quant_dirs, info = quant_dirs) %>% ```{r} hub = AnnotationHub::AnnotationHub(ask = FALSE) -# Ensembl v100 Homo Sapiens is AH79689 +# Ensembl v100 Homo sapiens is AH79689 ensdb = hub[["AH79689"]] ensg <- genes(ensdb) ``` -Create vectors of mitochondiral genes and coding genes for later. +Create vectors of mitochondrial genes and coding genes for later. ```{r} # create the mitochondrial gene list @@ -181,10 +184,10 @@ nc_feature_comparison <- nc_txome %>% nc_feature_comparison ``` -As expected, most of the features that are not listed as coding do not appear in the cdna only set! +As expected, most of the features that are not listed as coding do not appear in the cDNA only set! Some do, and spot checking indicates that those are listed as pseudogenes, which are for whatever reason still included in the cDNA file. -Of those that do not, the top few genes are MALAT1 and mitchondrial rRNAs. +Of those that do not, the top few genes are MALAT1 and mitochondrial rRNAs. MALAT1 is potentially interesting, and it does seem that we should probably keep it! Lets make a vector of the noncoding cDNAs for future work: @@ -215,7 +218,7 @@ ggplot(sce_feature_filtered, facet_grid(index_content ~ sample) ``` -The noncoding cDNA genes seem to follow a very similar ditribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. +The noncoding cDNA genes seem to follow a very similar distribution to the noncoding cDNA genes (mostly pseudogenes?), but I can't see any particular reason to exclude them. If anything, there are ncRNA genes tend to have somewhat higher mean expression than the noncoding cDNAs which are in the cDNA dataset. Just to check, lets look at the correlation of coding genes within a sample: @@ -271,7 +274,7 @@ cor(decoy_means[,3:5], method = "spearman", use = "complete.obs") And we have them... interestingly, the best correlation is between no decoys and the partial decoy: the full decoy index is more different by this measure. -This leads me to the preliminary conclusion that the partial index (which notably uses a slightly different set of transcipts at this stage, due to not being built locally) may not be worth pursuing, as it seems to make very little difference. +This leads me to the preliminary conclusion that the partial index (which notably uses a slightly different set of transcripts at this stage, due to not being built locally) may not be worth pursuing, as it seems to make very little difference. Don't forget to look at your data! ```{r} diff --git a/workflows/benchmarks/benchmark-analysis.nb.html b/workflows/benchmarks/benchmark-analysis.nb.html index 8ececda..76e6a4c 100644 --- a/workflows/benchmarks/benchmark-analysis.nb.html +++ b/workflows/benchmarks/benchmark-analysis.nb.html @@ -1318,6 +1318,1080 @@ PagedTableDoc.initAll(); }; + + + +