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<h1 class="title toc-ignore">Quality_metrics</h1>
<h4 class="author"><em>Siming Zhao</em></h4>
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<p><strong>Last updated:</strong> 2018-12-14</p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/simingz/cropseq/tree/21193c89db26120ec5344823fb290ff92eedca88" target="_blank">21193c8</a> </summary></p>
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated. <br><br> Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
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<p></details></p>
<hr />
<div id="number-of-guide-rnas-per-cell" class="section level2">
<h2>Number of guide RNAs per cell</h2>
<ul>
<li>number of cells with guide RNA reads =1 From Siwei’s cellranger run:</li>
</ul>
<pre class="r"><code>library(Matrix)
matrix_dir = "/Volumes/CROP-seq/data_from_Siwei/Xin_scRNA_seq_05Nov2018/filtered_gene_bc_matrices/CellRanger_index/"
matrix.path <- paste0(matrix_dir, "matrix.mtx")
dm <- readMM(file = matrix.path)
dm1 <- tail(dm,n=76)
length(colSums(dm1)[colSums(dm1)==1])</code></pre>
<pre><code>[1] 440</code></pre>
<p>From Alan’s cellranger run:</p>
<pre class="r"><code>matrix_dir1 = "/Volumes/CROP-seq/NSC0507_cellranger/outs/filtered_gene_bc_matrices/cellranger_ref/"
matrix.path1 <- paste0(matrix_dir1, "matrix.mtx")
mattemp1 <- readMM(file = matrix.path1)
mattemp11 <- tail(mattemp1,n=76)
length(colSums(mattemp11)[colSums(mattemp11)==1])</code></pre>
<pre><code>[1] 266</code></pre>
<pre class="r"><code>matrix_dir2 = "/Volumes/CROP-seq/NSC08_cellranger/outs/filtered_gene_bc_matrices/cellranger_ref/"
matrix.path2 <- paste0(matrix_dir2, "matrix.mtx")
mattemp2 <- readMM(file = matrix.path2)
mattemp21 <- tail(mattemp2,n=76)
length(colSums(mattemp21)[colSums(mattemp21)==1])</code></pre>
<pre><code>[1] 190</code></pre>
<p>Note: in Alan’s original analysis conversion from h5 to csv step didn’t seem to work properly. if starting from matrix.mtx files. Siwei and Alan’s analyses gave the same results. So from now on, we will always start from Siwei’s matrix.mtx file.</p>
<ul>
<li>distribution of gRNA types per cell</li>
</ul>
<pre class="r"><code>barcode.path <- paste0(matrix_dir, "barcodes.tsv")
features.path <- paste0(matrix_dir, "genes.tsv")
feature.names = read.delim(features.path, header = FALSE,
stringsAsFactors = FALSE)
barcode.names = read.delim(barcode.path, header = FALSE,
stringsAsFactors = FALSE)
colnames(dm) = barcode.names$V1
rownames(dm) = feature.names$V1
dm1 <- tail(dm,n=76)
hist(apply(dm1, 2, function(x) length(x[x>0])),breaks=300,xlim=c(0,15),ylim=c(0,2500), main="Distribution of number of gRNA types per cell", xlab= "# gRNA type per cell")</code></pre>
<p><img src="figure/Quality_metrics.Rmd/gRNAdist-1.png" width="672" style="display: block; margin: auto;" /></p>
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<p></details></p>
</div>
<div id="number-of-cells-targeted-for-each-locus" class="section level2">
<h2>number of cells targeted for each locus</h2>
<pre class="r"><code>library(dplyr)
dm1df <- as.data.frame(as.matrix(dm1))
dm1df$label = sapply(strsplit(rownames(dm1),split = '_'), function(x){x[1]})
dm1dfagg = as.data.frame(dm1df %>% group_by(label) %>% summarise_all(funs(sum)))
row.names(dm1dfagg) =dm1dfagg$label
dm1dfagg$label =NULL</code></pre>
<ul>
<li>number of cells targeted for each locus</li>
</ul>
<pre class="r"><code>ncell <- apply(dm1dfagg,1, function (x) length(x[x>=1]))
barplot(ncell,las=2,cex.lab=1, main= "# cells targted for each locus")</code></pre>
<p><img src="figure/Quality_metrics.Rmd/celldist1-1.png" width="672" style="display: block; margin: auto;" /></p>
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<p></details></p>
<ul>
<li>number of cells only targeted for that locus</li>
</ul>
<pre class="r"><code># Singletons (cells with only 1 gRNA)
nlocus <- apply(dm1dfagg, 2, function (x) length(x[x>=1]))
hist(nlocus,breaks=100, main="number of targeted locus each cell")</code></pre>
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<p></details></p>
<pre class="r"><code>dm1dfagg.uni= dm1dfagg[,nlocus==1]
ncell.uni <- apply(dm1dfagg.uni,1, function (x) length(x[x>=1]))
barplot(ncell.uni,las=2,cex.lab=1,main= "# cells uniquely targted for each locus")</code></pre>
<p><img src="figure/Quality_metrics.Rmd/unnamed-chunk-1-2.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-1-2.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/simingz/cropseq/blob/8754cad65d01c5766bac60611204c4cdd22bb23a/docs/figure/Quality_metrics.Rmd/unnamed-chunk-1-2.png" target="_blank">8754cad</a>
</td>
<td style="text-align:left;">
szhao06
</td>
<td style="text-align:left;">
2018-12-02
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
<div id="umi-count-distribution-for-cells-with-unique-targeted-locus" class="section level2">
<h2>UMI count distribution for cells with unique targeted locus</h2>
<pre class="r"><code># Singletons (cells with only 1 targeted locus)
dm.uni <- dm[,nlocus==1]
nUMI <- colSums(dm.uni)
hist(nUMI,breaks=100,xlim=c(0,1e5)) </code></pre>
<p><img src="figure/Quality_metrics.Rmd/UMI-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of UMI-1.png:</em></summary>
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<th style="text-align:left;">
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<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/simingz/cropseq/blob/8754cad65d01c5766bac60611204c4cdd22bb23a/docs/figure/Quality_metrics.Rmd/UMI-1.png" target="_blank">8754cad</a>
</td>
<td style="text-align:left;">
szhao06
</td>
<td style="text-align:left;">
2018-12-02
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
<div id="umi-count-distribution-for-grnas-in-cells-with-unique-targeted-locus" class="section level2">
<h2>UMI count distribution for gRNAs in cells with unique targeted locus</h2>
<pre class="r"><code># Singletons (cells with only 1 targeted locus)
nUMIgRNA <- colSums(tail(dm.uni,76))
hist(nUMIgRNA,breaks=500,xlim=c(0,20), main = "Histogram of nUMI for gRNAs") </code></pre>
<p><img src="figure/Quality_metrics.Rmd/UMIgRNA-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="prepare-data-for-differential-gene-expression" class="section level2">
<h2>Prepare data for differential gene expression</h2>
<pre class="r"><code>save(dm,dm1dfagg,nlocus, file="data/DE_input.Rd")</code></pre>
<p>Parameters used:</p>
<ul>
<li><p>for a cell to be considered targeted uniquely at a locus: total read counts for the 3 gRNAs targeting that locus >1, total read counts for gRNA of other locus=0.</p></li>
<li><p>negative control: neg_EGFP and neg_CTRL are pooled together.</p></li>
<li><p>cells to be exluded due to low total UMI count: no filtering</p></li>
</ul>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin11.4.2 (64-bit)
Running under: OS X El Capitan 10.11.6
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_0.7.5 Matrix_1.2-10
loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 knitr_1.20 bindr_0.1.1
[4] whisker_0.3-2 magrittr_1.5 workflowr_1.1.1
[7] tidyselect_0.2.4 lattice_0.20-35 R6_2.2.2
[10] rlang_0.2.1 stringr_1.2.0 tools_3.3.2
[13] grid_3.3.2 R.oo_1.22.0 git2r_0.18.0
[16] htmltools_0.3.6 yaml_2.1.16 rprojroot_1.2
[19] digest_0.6.12 assertthat_0.2.0 tibble_1.4.2
[22] bindrcpp_0.2.2 purrr_0.2.5 R.utils_2.7.0
[25] glue_1.2.0 evaluate_0.10 rmarkdown_1.10
[28] stringi_1.1.5 pillar_1.2.3 backports_1.0.5
[31] R.methodsS3_1.7.1 pkgconfig_2.0.1 </code></pre>
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