added 10xRNA tutorial

Author: vivekbhr

docs/content/images/UMAP_compared_withOrig_10xATAC.png

@@ -10,6 +10,7 @@ Tutorials for using sincei on the command line
     tutorials/sincei_tutorial_sortChIC.rst
     tutorials/sincei_tutorial_10x.rst
     tutorials/sincei_tutorial_10xATAC.rst
+    tutorials/sincei_tutorial_10xRNA.rst
 
 Tutorials for using sincei inside python
 -----------------------------------------

docs/content/images/UMAP_compared_withOrig_10xRNA.png

@@ -95,16 +95,17 @@ of the barcode counts.
 2. scATAC-seq analysis
 ----------------------
 
-Please follow :doc:`this tutorial <sincei_tutorial_10xATAC>` for further analysis of scATAC-seq
-samples from the above data.
+Please follow :doc:`this tutorial <sincei_tutorial_10xATAC>` for further analysis of scATAC-seq samples from the above data.
 
 3. scRNA-seq analysis
 ---------------------
 
-Please follow :doc: `this tutorial <sincei_tutorial_10xATAC>` for further analysis of scRNA-seq
-samples from the above data.
+Please follow :doc: `this tutorial <sincei_tutorial_10xRNA>` for further analysis of scRNA-seq samples from the above data.
 
-4. Joint analysis
------------------
+Notes
+------------
 
-Tutorial in preparation.
+Currently, sincei doesn't provide a method for **doublet estimation and removal**, which is an important step in the analysis of droplet-based data.
+Instead, we use simpler filters of min and max number of detected features per cell, which, to some extent mitigates this issue. However, this could
+lead to some differences in results compared to the published data in used here. Despite this difference, the major published cell types can be separated
+with sincei for both ATAC and RNA fraction of the data, as shown in the 2 tutorials above.

docs/content/images/igv_snapshot_10xRNA.png

@@ -285,7 +285,7 @@ bigwigs with 1kb bins.
    --labels rep1_atac_rep1 rep2_atac_rep2 \
    -i sincei_output/atac/scClusterCells_UMAP.tsv \
    -o sincei_output/atac/sincei_cluster
-   # creates 6 files with names "sincei_cluster_<X>.bw" where X is 0, 1... 9
+   # creates 9 files with names "sincei_cluster_<X>.bw" where X is 0, 1... 9
 
 We can now inspect these bigwigs on IGV. We can clearly see some regions
 with cell-type specific signal, such as the markers described in the

docs/content/tutorials.rst

@@ -0,0 +1,292 @@
+Analysis of 10x genomics scRNA-seq data using sincei
+====================================================
+
+The data used here is part of our 10x genomics multiome tutorial,
+`described here <sincei_tutorial_10x.rst>`__
+
+We will use the BAM file, barcodes and peaks.bed file obtained using the
+**cellrange-arc** workflow. Alternatively, the BAM file (tagged with
+cell barcode) and peaks.bed can be obtained from a custom mapping/peak
+calling workflow, and the list of filtered cell barcodes can be obtained
+using sincei (see the parent tutorial for explanation).
+
+Define common bash variables:
+
+.. code:: bash
+
+   # create dir
+   mkdir sincei_output/rna
+
+2. Quality control - I (read-level)
+-----------------------------------
+
+In order to define high quality cells, we can use the read-level quality
+statistics from scFilterStats. There could be several indications of low
+quality cells in this data, such as:
+
+-  high PCR duplicates (marked by cellranger workflow, detected using
+   ``--samFlagExclude``)
+-  high fraction of reads aligned to blacklisted regions (filtered using
+   ``--blacklist``)
+-  high fraction of reads with poor mapping quality (filtered using
+   ``--minMappingQuality``)
+-  very high/low GC content of the aligned reads, indicating reads
+   mostly aligned to low-complexity regions (filtered using
+   ``--GCcontentFilter``)
+-  high level of secondary/supplementary alignments (filtered using
+   ``--samFlagExclude/Include``)
+
+.. code:: bash
+
+
+  for r in 1 2
+  do
+      dir=cellranger_output_rep${r}/outs/
+      bamfile=${dir}/gex_possorted_bam.bam
+      barcodes=${dir}/filtered_feature_bc_matrix/barcodes.tsv.gz
+      # from sincei or cellranger output
+      zcat ${barcodes} > ${dir}/filtered_barcodes.txt scFilterStats -p 20
+      –region chr1 \
+      –GCcontentFilter ‘0.2,0.8’ \
+      –minMappingQuality 10 \
+      –samFlagExclude 256 –samFlagExclude 2048 \
+      –barcodes ${dir}/filtered_barcodes.txt \
+      --cellTag CB \
+      --label rna_rep${r}
+      -o sincei_output/rna/scFilterStats_output_rep${r}.txt
+      -b ${bamfile}
+  done
+
+`scFilterStats` summarizes these outputs as a table, which can then be
+visualized using the `MultiQC tool <https://multiqc.info/docs/>`__, to select
+appropriate list of cells to include for counting.
+
+.. code:: bash
+
+   multiqc sincei_output/rna/ # results in multiqc_report.html
+
+3. Signal aggregation (counting reads)
+--------------------------------------
+
+Below we use sincei to aggregate signal from single-cells. For the gene
+expression data, it makes sense to aggregate signal from genes only. For
+this we can use a GTF file, which defines gene annotations.
+
+If needed, we can use the same parameters as in ``scFilterStats`` to
+count only high quality reads from our whitelist of barcodes.
+
+.. code:: bash
+
+  ## Download the GTF file
+  curl -o sincei_output/hg38.gtf.gz \
+  http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/hg38.refGene.gtf.gz && gunzip sincei_output/hg38.gtf.gz
+
+  # count reads on the GTF file
+  for r in 1 2 do dir=cellranger_output_rep${r}/outs/
+      bamfile=${dir}/gex_possorted_bam.bam
+      barcodes=${dir}/filtered_barcodes.txt scCountReads features -p 20
+      –BED sincei_output/hg38.gtf –cellTag CB –minMappingQuality 10 \
+      –samFlagExclude 256 –samFlagExclude 2048  -bc ${barcodes} --cellTag CB \
+      -o sincei_output/rna/scCounts_rna_genes_rep${r}
+      –label rna_rep${r}
+      -b ${bamfile}
+  done
+
+  # Number of bins found: 74538
+
+
+
+4. Quality control - II (count-level)
+-------------------------------------
+
+Even though we already performed read-level QC before, the counts distribution on our specified regions (bins/genes/peaks) could be different from the whole-genome stats.
+
+**scCountQC**, with the `--outMetrics` option, outputs the count statistics at region and cell level (labelled as <prefix>.regions.tsv and <prefix>.cells.tsv).
+Just like `scFilterStats`, these outputs can then be visualized using the
+`MultiQC tool <https://multiqc.info/docs/>`__, to select appropriate metrics to
+filter out the unwanted cells/regions.
+
+.. code:: bash
+
+   # list the metrics we can use to filter cells/regions
+   for r in 1 2; do scCountQC -i sincei_output/rna/scCounts_rna_genes_rep${r}.loom --describe; done
+
+   # export the single-cell level metrices
+   for r in 1 2; do scCountQC -i sincei_output/rna/scCounts_rna_genes_rep${r}.loom \
+   -om sincei_output/rna/countqc_rna_genes_rep${r} & done
+
+   # visualize output using multiQC
+   multiqc sincei_output/rna/ # see results in multiqc_report.html
+
+In total >74500 genes have been detected in >13.7K cells here.
+
+Below, we perform a basic filtering using **scCountQC**. We exclude the
+cells with <500 and >10000 detected genes (``--filterRegionArgs``).
+Also, we exclude the genes that are detected in too few cells (<100) or
+too many (approax >90%) of cells (``--filterCellArgs``).
+
+.. code:: bash
+
+  for r in 1 2 do scCountQC -i
+  sincei_output/rna/scCounts_rna_genes_rep\ :math:`{r}.loom \
+  -o sincei_output/rna/scCounts_rna_genes_filtered_rep`\ {r}.loom
+  –filterRegionArgs “n_cells_by_counts: 100, 6000”
+  –filterCellArgs “n_genes_by_counts: 500, 15000” done
+
+  ## rep 1
+  #Applying filters
+  #Remaining cells: 5314
+  #Remaining features: 48219
+
+  ## rep 2
+  #Applying filters
+  #Remaining cells: 4894
+  #Remaining features: 48660
+
+
+5. Combine counts for the 2 replicates
+--------------------------------------
+
+While it's useful to perform count QC seperately for each replicate, the counts can now be combined for downstream analysis.
+
+We provide a tool `scCombineCounts`, which can concatenate counts for cells based on common features. Concatenating the filtered cells for the 2 replicates would result in a total of >12K cells.
+
+.. code:: bash
+
+   scCombineCounts \
+   -i sincei_output/rna/scCounts_rna_genes_filtered_rep1.loom \
+   sincei_output/rna/scCounts_rna_genes_filtered_rep2.loom \
+   -o sincei_output/rna/scCounts_rna_genes_filtered.merged.loom \
+   --method multi-sample --labels rep1 rep2
+   #Combined cells: 10208
+   #Combined features: 48059
+
+5. Dimentionality reduction and clustering
+------------------------------------------
+
+Finally, we will apply glmPCA to this data, assuming the data follows a
+poisson distribution (which is nearly appropritate for count-based data
+such as scRNA-seq), we will reduce the dimentionality of the data to 20
+principle components (the default), followed by a graph-based (louvain)
+clustering of the output.
+
+.. code:: bash
+
+    scClusterCells -i sincei_output/rna/scCounts_rna_genes_filtered.merged.loom \
+    -m glmPCA -gf poisson --clusterResolution 1 \
+    -op sincei_output/rna/scClusterCells_UMAP.png \
+    -o sincei_output/rna/scCounts_rna_genes_clustered.loom
+
+    # Coherence Score:  Coherence Score:  -0.9893882070519782
+    # also produces the tsv file "sincei_output/rna/scClusterCells_UMAP.tsv"
+
+(optional) Confirmation of clustering using metadata
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Below, we will load this data in R and compare it to the cell metadata
+provided with our files to see if our clustering separates celltypes in
+a biologically meaningful way.
+
+We can color our UMAP output from ``scClusterCells`` with the cell-type
+information based on the metadata provided by the original authors.
+
+.. code:: r
+
+    library(dplyr)
+    library(magrittr)
+    library(ggplot2)
+    library(patchwork)
+
+    umap <- read.delim(“sincei_output/rna/scClusterCells_UMAP.tsv”) meta <-
+    read.csv(“metadata_cd34_rna.csv”, row.names = 1)
+    umap$celltype <- meta[gsub("rep1_|rep2_", "", umap`\ Cell_ID),
+    “celltype”]
+
+    # keep only cells with published labels
+
+    umap %<>% filter(!is.na(celltype)) # remove clusters with low number of
+    cells cl = umap %>% group_by(cluster) %>%
+    summarise(Cell_ID = dplyr::n()) %>%
+    filter(Cell_ID < 50) %>% .$cluster umap %<>%
+    filter(!(cluster %in% cl))
+
+    # make plots
+    df_center <- group_by(umap, cluster) %>%
+    summarise(UMAP1 = mean(UMAP1),  UMAP2 = mean(UMAP2))
+    df_center2 <- group_by(umap, celltype) %>%
+    summarise(UMAP1 = mean(UMAP1), UMAP2 = mean(UMAP2))
+
+    # colors for metadata (8 celltypes)
+
+    col_pallete <- RColorBrewer::brewer.pal(8, “Paired”)
+    names(col_pallete) <- unique(umap$celltype) # grey is for NA
+
+    # colors for sincei UMAP (10 clusters)
+
+    colors_cluster <- RColorBrewer::brewer.pal(10, “Paired”)
+    names(colors_cluster) <- sort(unique(umap$cluster))
+
+    p1 <- umap %>% ggplot(., aes(UMAP1, UMAP2, color=factor(cluster),
+    label=cluster)) + geom_point() +
+    geom_label(data = df_center, aes(UMAP1, UMAP2)) +
+    scale_color_manual(values = colors_cluster) +
+    theme_void(base_size = 12) + theme(legend.position = “none”) +
+
+    p2 <- umap %>% filter(!is.na(celltype)) %>% ggplot(., aes(UMAP1, UMAP2,
+    color=factor(celltype), label=celltype)) + geom_point() +
+    geom_label(data = df_center2, aes(UMAP1, UMAP2)) +
+    scale_color_manual(values = col_pallete) + labs(color=“Cluster”) +
+    theme_void(base_size = 12) + theme(legend.position = “none”) +
+    ggtitle(“Published Cell Types”)
+
+    pl <- p1 + p2
+    ggsave(plot=pl, “sincei_output/rna/UMAP_compared_withOrig.png”,
+    dpi=300, width = 11, height = 6)
+
+
+.. image:: ./../images/UMAP_compared_withOrig_10xRNA.png
+    :height: 800px
+    :width: 1600 px
+    :scale: 50 %
+
+The figure above shows that we can easily replicate the expected cell-type
+results from the scRNA-seq data using **sincei**. However there are some
+interesting differences, especially, a separation of the CLP cluster into 2 clusters,
+where one of these cluster is similar to the annotated pDC.
+
+This was done using basic pre-processing steps, therefore the better cell/region filtering and optimizing the analysis parameters.
+
+6. Creating bigwigs and visualizing signal on IGV
+-------------------------------------------------
+
+For further exploration of data, It's very useful to create in-silico bulk
+coverage files (bigwigs) that aggregate the signal across cells in our clusters.
+The tool **scBulkCoverage** takes sincei clustered `.tsv` file, along with the
+corresponding BAM files, and aggregate the signal to create these bigwigs.
+
+The parameters here are same as other sincei tools that work on BAM files,
+except that we can ask for a normalized bulk signal (specified using
+`--normalizeUsing` option) . Below, we produce CPM-normalized bigwigs with 100 bp bins.
+
+.. code:: bash
+
+   scBulkCoverage -p 20 --cellTag CB --region chr1 \
+   --normalizeUsing CPM --binSize 100 \
+   --minMappingQuality 10 --samFlagExclude 2048 \
+   -b cellranger_output_rep1/outs/gex_possorted_bam.bam \
+   cellranger_output_rep2/outs/gex_possorted_bam.bam \
+   --labels rep1_rna_rep1 rep2_rna_rep2 \
+   -i sincei_output/rna/scClusterCells_UMAP.tsv \
+   -o sincei_output/rna/sincei_cluster
+   # creates 6 files with names "sincei_cluster_<X>.bw" where X is 0, 1... 9
+
+We can now inspect these bigwigs on IGV. Looking at the region around one of the markers described in the
+original manuscript, **TAL1**, we can see that the CLP (lyphoid) and pDCs lack it's expression, while the
+myloid cells and HSCs have the signal. The neighboring gene STIL, which is involved in cell-cycle regulation
+is not expressed in the highly proliferative HSC/HMP/MEP cell clusters. Overall this confirms that the signal
+coverage extracted from our clusters broadly reflects the biology of the underlying cell types.
+
+.. image:: ./../images/igv_snapshot_10xRNA.png
+   :height: 500px
+   :width: 6000 px
+   :scale: 50 %