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Example of results of Integrated Analysis for a school case

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mAGLAVE edited this page Jul 5, 2021 · 37 revisions

Make the integrated analysis

1- Make the Configuration Parameter file:

We will do the integration, normalization, dimension reduction, and evaluate the biais and the clustering. Alignment, quality control and filtering have already been done in the individual sample analyzes (see Example of results of Individual Analysis for a school case for more information). To simplify the explanation, we will focus on an integration by Seurat, but other integration methods are available. For Seurat integration, individual normalization by SCTransform are kept. I advise to put the integration method in the name you give to the integrated object (here: name.int : ["sc5p_v2_hs_PBMC_Int_Seurat"]).

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Params_int.yaml 
Steps: ["Int_Norm_DimRed_Eval_GE"]

Int_Norm_DimRed_Eval_GE :
  name.int : ["sc5p_v2_hs_PBMC_Int_Seurat"]
  input.list.rda : ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_individual_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_1k_5gex_GE/F200_C1000_M0-0.15_R0-1_G5/DOUBLETSFILTER_all/SCTransform/pca/dims35_res1.2/sc5p_v2_hs_PBMC_1k_5gex_GE_SCTransform_pca_35_1.2_ADT_TCR_BCR.rda,/mnt/beegfs/userdata/m_aglave/pipeline/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_10k_5gex_GE/F200_C1000_M0-0.15_R0-1_G5/DOUBLETSFILTER_all/SCTransform/pca/dims33_res0.4/sc5p_v2_hs_PBMC_10k_5gex_GE_SCTransform_pca_33_0.4_ADT_TCR_BCR.rda"]
  output.dir.int : ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/"]
  eval.markers : "GAPDH"
  author.name : "marine aglave"
  author.mail : "[email protected], [email protected]"
  integration.method : "Seurat"

1- Launch of the analysis:

For the traceability of the analysis, I prefer to put the command lines in a script, but it is not mandatory.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/launcher_int.sh 
#!/bin/bash

########################################################################
## Single-cell script to launch single-cell pipeline
##
## using: sbatch /mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/launcher_int.sh
########################################################################
#SBATCH --job-name=pipeline_sc
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=1G
#SBATCH --partition=mediumq

source /mnt/beegfs/software/conda/etc/profile.d/conda.sh
conda activate /mnt/beegfs/userdata/m_aglave/.environnement_conda/single_cell_user
module load singularity

path_to_configfile="/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Params_int.yaml"
path_to_pipeline="/mnt/beegfs/pipelines/single-cell"

snakemake --profile ${path_to_pipeline}/profiles/slurm -s ${path_to_pipeline}/Snakefile --configfile ${path_to_configfile}

conda deactivate

sbatch /mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/launcher_int.sh

1- Interpretation of results:

The Int_Norm_DimRed_Eval_GE step corresponds to the Norm_DimRed_Eval_GE step but with an integration step beforehand. The results, their interpretations, and their conclusions, are similar to those described in the Norm_DimRed_Eval_GE section of the individual sample analysis. Thus, I would develop the analysis succinctly. For more details, please refer to the section Normalization, Dimension Reduction, Biases and Clustering Evaluation of Individual analysis.

Integration, Normalization and Dimension Reduction

Correlation graph:

Provided by the Int_Norm_DimRed_Eval_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat_dims.bias.cor.png

This graph represent the correlation between potential biases and each dimension, after nomalization and dimension reduction. Here, we choose an intergration by Seurat so the individual normalization is kept and we can't correct biases (we will be able to estimate the impact of the biases on the final umap results).

Dimensions and Clustering

UMAPs graph:

Provided by the Int_Norm_DimRed_Eval_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/clustree_integrated_pca_Seurat/uMAPs/
sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat3_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat7_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat9_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat11_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat13_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat15_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat17_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat19_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat21_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat23_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat25_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat27_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat29_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat31_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat33_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat35_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat37_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat39_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat41_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat43_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat45_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat47_ALLres.png

sc5p_v2_hs_PBMC_Int_Seurat_uMAPs_integrated_pca_Seurat49_ALLres.png

To identify the number of dimensions to keep for clustering as well as the adequate resolution, the pipeline has drawn all possible umaps according to these 2 parameters. We have to look at all the umaps and choose the one that seems to be the most "beautiful": cluster well isolated from each other, cells well grouped within its cluster. We know ​​the expected number of clusters because we performed the individual analysis of each sample, so we know the cell types (or cell subtypes) present.

clustree plots

The clutree plot is a tree plot to observe the influence of a parameter on the results. Here we measure the evolution of the membership of cells to a cluster:

  • the resolution is fixed and the number of dimensions to keep evolves:
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/clustree_integrated_pca_Seurat/louvain_resolution

Provided by the Int_Norm_DimRed_Eval_GE step.

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.1.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.2.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.3.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.4.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.5.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.6.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.7.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.8.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res0.9.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res1.0.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res1.1.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_res1.2.png

  • the number of dimensions to keep is fixed and the resolution evolves:
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/clustree_integrated_pca_Seurat/dimensions/ *Provided by the Int_Norm_DimRed_Eval_GE step.*
sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat3.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat5.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat7.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat9.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat11.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat13.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat15.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat17.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat19.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat21.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat23.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat25.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat27.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat29.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat31.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat33.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat35.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat37.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat39.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat41.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat43.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat45.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat47.png

sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat49.png

The goal is that the membership of the cells in a cluster remains relatively stable.

Here, the umap are very stable across different dimensions. I choose 29 dimensions and a resolution of 0.6. The clusters seem more grouped together and contaminate each other less. In the sample sc5p_v2_hs_PBMC_10k_5gex there are about twenty clusters, but these are mainly CD4 + and CD8 + T lymphocyte subtypes. These subtypes are not present in the sc5p_v2_hs_PBMC_1k_5gex sample.
The integration between the 2 samples seems to have made "disappear" the biological information which made it possible to identify these subtypes.
Therefore:

  • either the information of the subtypes has been relegated to higher dimensions (since we are only testing the first 50 dimensions),
  • or the information of the subtypes has too little impact on the dimensions compared to the information of the cell types. So if we were to get the cell subtypes we would have to redo the analysis up to 100 dimensions (edit: I have tested up to 100 dimensions but the cell subtypes are still not visible, so the second guess should be correct and it will be difficult for us to get them).

2- Make the Configuration Parameter file:

We will do the clustering, find the marker genes, make the annotation, add ADT, add TCR, add BCR and convert main results into a cerebro object. We keep the same Configuration Parameter file, but we add Int_Clust_Markers_Annot_GE, Int_Adding_ADT, Int_Adding_TCR, Int_Adding_BCR and Cerebro steps. Some parameters (as name.int and input.rda.int) will be determined automatically thanks to the Int_Norm_DimRed_Eval_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Params_int.yaml 
Steps: ["Int_Norm_DimRed_Eval_GE","Int_Clust_Markers_Annot_GE","Int_Adding_ADT","Int_Adding_TCR","Int_Adding_BCR","Cerebro"]

Int_Norm_DimRed_Eval_GE :
  name.int : ["sc5p_v2_hs_PBMC_Int_Seurat"]
  input.list.rda : ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_individual_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_1k_5gex_GE/F200_C1000_M0-0.15_R0-1_G5/DOUBLETSFILTER_all/SCTransform/pca/dims35_res1.2/sc5p_v2_hs_PBMC_1k_5gex_GE_SCTransform_pca_35_1.2_ADT_TCR_BCR.rda,/mnt/beegfs/userdata/m_aglave/pipeline/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_10k_5gex_GE/F200_C1000_M0-0.15_R0-1_G5/DOUBLETSFILTER_all/SCTransform/pca/dims33_res0.4/sc5p_v2_hs_PBMC_10k_5gex_GE_SCTransform_pca_33_0.4_ADT_TCR_BCR.rda"]
  output.dir.int : ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/"]
  eval.markers : "GAPDH"
  author.name : "marine aglave"
  author.mail : "[email protected], [email protected]"
  integration.method : "Seurat"

Int_Clust_Markers_Annot_GE:
  markfile : "/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_individual_analysis_example_of_wiki/markfile.xlsx"
  keep.dims : 29
  keep.res : 0.6

  Int_Adding_ADT:
    samples.name.adt: ["sc5p_v2_hs_PBMC_1k_5fb,sc5p_v2_hs_PBMC_10k_5fb"]
    input.dirs.adt: ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_individual_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_1k_5fb_ADT/KALLISTOBUS/,/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_10k_5fb_ADT/KALLISTOBUS/"]
    gene.names: "CD3G,CD19,PTPRC,CD4,CD8A,CD14,FCGR3A,NCAM1,IL2RA,PTPRC,PDCD1,TIGIT,IGHG1,IGHG2,IGHG2,IL7R,FUT4,CCR7,HLA-DRA"

  Int_Adding_TCR:
    vdj.input.files.tcr: ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_individual_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_1k_t_TCR/sc5p_v2_hs_PBMC_1k_t_TCR_CellRanger/outs/filtered_contig_annotations.csv,/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_10k_t_TCR/sc5p_v2_hs_PBMC_10k_t_TCR_CellRanger/outs/filtered_contig_annotations.csv"]

  Int_Adding_BCR:
    vdj.input.files.bcr: ["/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_individual_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_1k_b_BCR/sc5p_v2_hs_PBMC_1k_b_BCR_CellRanger/outs/filtered_contig_annotations.csv,/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/sc5p_v2_hs_PBMC_10k_b_BCR/sc5p_v2_hs_PBMC_10k_b_BCR_CellRanger/outs/filtered_contig_annotations.csv"]

2- Launch of the analysis:

No change in /mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/launcher_int.sh script.

sbatch /mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/launcher_int.sh

2- Interpretation of results:

Clustering:

final umap with clusters:

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat_uMAP_dim29_res0.6.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat_uMAP3d_dim29_res0.6.png

These graphs correspond to the 2D and 3D umaps of the data with the assignment of cells to their cluster. It corresponds to the umap chosen in the previous step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat_uMAP.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/sc5p_v2_hs_PBMC_Int_Seurat_integrated_pca_Seurat_split_uMAP.png

These graphs correspond to the umap of the data with the assignment of cells to their sample (concatenated view, and splited view).

We observe that the cells of the 2 samples are present in all the clusters.

final umap with biases:

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/technical/sc5p_v2_hs_PBMC_Int_Seurat_technical_MULTI_ALL_uMAPs.png

This group of graphs corresponds to the plotting of biases on the umap. The goal of these graphs is to check the correction or not of biases. The cells should not be separated according to biases, but according to the biological processes of interest of the cells.

The interpretation is similar to that of the Clust_Markers_Annot_GE step. Here, there does not seem to be any influence of mitochondrial RNAs, nor of the cell cycle. We can observe a potential effect of the RNAs which code for ribosomal proteins, but we can't correct this effect with this integration method (remember: the correction of this bias depends on the studied biological process). Also, we can observe a cell of cluster 12 which appears to be highly stressed.

Marker genes of clusters

Upsetplot

Provided by the Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/found_markers/sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_upset_all.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/found_markers/sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_upset_top10.png

As in Clust_Markers_Annot_GE step, here you can see the number of marker genes specific to a cluster and the number of marker genes shared between several clusters (the first graph matches all the results and the second graphs matches the 10 marker genes with the highest logFC for each cluster).

Table file

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/found_markers/sc5p_v2_hs_PBMC_Int_Seurat_SCT_pca.29_res.0.6_findmarkers_all.txt

Ten first lines of file:

genes avg_log2FC p_val adj.P.Val pct.1 pct.2 tested_cluster control_cluster min.pct
TCF7 1,42188905020327 0 0 0,927 0,296 0 All 0,75
LEF1 1,34740785673772 0 0 0,929 0,284 0 All 0,75
CCR7 1,1858928614791 0 0 0,906 0,319 0 All 0,75
CD3E 1,09447592276799 0 0 0,975 0,373 0 All 0,75
SARAF 1,03829981137466 0 0 0,942 0,416 0 All 0,75
C12orf57 1,00574585223127 0 0 0,841 0,347 0 All 0,75
NOSIP 0,90714742977204 0 0 0,757 0,297 0 All 0,75
RPS27 0,895377263962504 0 0 0,978 0,485 0 All 0,75
RPS3A 0,880317796206515 0 0 0,973 0,429 0 All 0,75

As in Clust_Markers_Annot_GE step, this table lists all the marker genes for each cluster (comparison one cluster against all the others):

  • adj.P.Val > 5%,
  • log2FC > 0.5 (positif log2FC only),
  • pct.1 or pct.2 > 0.75 (pct.x: percentage of cells in the group x expressing the tested gene (example: 0.75 corresponds to 75% of cells); min.pct: threshold used for pct.1 and pct.2).

Heatmap

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/found_markers/sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_heatmap.png

The heatmap represents the expression of the 10 best marker genes in logFC for each cluster for all cells group by cluster. The expressions were normalized between the genes in order to allow a visual comparison between these genes. See Clust_Markers_Annot_GE for more details.

Here, we observe that clusters 1 and 2 are very similar, so maybe they are the same cell type, possibly in different activation states. Same thing for clusters 0 and 3. The other clusters seem to be different from each other.

Violinplot

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/found_markers/
sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster0_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster1_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster2_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster3_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster4_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster5_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster6_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster7_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster8_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster9_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster10_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster11_vln.png

sc5p_v2_hs_PBMC_Int_Seurat_findmarkers_top10_cluster12_vln.png

The violinplot represents the expression of the 10 best marker genes in logFC for each cluster by cell for all clusters. The expression of the marker gene of each cell is plotted by cluster. This allows to verify that a marker gene is quite specific for a cluster and is not shared by other clusters.

For example, the TCF7 gene is a marker gene of cluster 0 but it's also highly expressed in clusters 3,4,6 and 10. So it isn't specific of this cluster. The S100AB gene(as a lot of other genes) is a marker gene of clusters 1 and 2, which confirms the similarity of these clusters. The CD8B gene is a marker gene of cluster 4 and it's lowly expressed by other clusters, so it is very specific of this cluster.

Markers plot from the Markfile:

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/markers/sc5p_v2_hs_PBMC_Int_Seurat_markers_ALL_uMAPs.png

This is a representation of all genes from the Markfile. It can help to annotate cell types.

Annotations

Provided by the Int_Clust_Markers_Annot_GE step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/cells_annotation/singler/sc5p_v2_hs_PBMC_Int_Seurat_integrated_uMAP_SR_NovershternHematopoieticData_clust.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/cells_annotation/singler/sc5p_v2_hs_PBMC_Int_Seurat_integrated_uMAP_SR_NovershternHematopoieticData_cells.png

The automatic annotation is done with clusterifyR and singleR as in Clust_Markers_Annot_GE step.

Here, I present you 2 examples (one realized on the clusters, the other realized on each cell). Warning: the references provided with the tools are not necessarily very relevant (from microarray or bulk RNA-seq) but they are the best available at the moment. So, the results should be interpreted with caution.

Note: It is better to group the information to annotate the cells: the automatic annotation, the marker genes and the Markfile genes.

By cross-checking the results with the Markfile we can conclude that:

  • cluster 0 corresponds to Lymphocytes T CD4+,
  • cluster 1 corresponds to Monocytes,
  • cluster 2 corresponds to Monocytes,
  • cluster 3 corresponds to Lymphocytes T CD4+,
  • cluster 4 corresponds to Lymphocytes T CD8+,
  • cluster 5 corresponds to Lymphocytes B,
  • cluster 6 corresponds to Lymphocytes T (CD8+?, Naïve?)
  • cluster 7 corresponds to NK cells,
  • cluster 8 corresponds to Dendritic cells,
  • cluster 9 corresponds to Monocytes,
  • cluster 10 corresponds to Lymphocytes T CD4+?
  • cluster 11 corresponds to Dendritic cells,
  • cluster 12 corresponds to Erythroid cells? Platelet cells?

ADT

Comparison gene expression and protein level plot

Provided by the Int_Adding_ADT step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/ADT_results/ADT_dimplot.png

This plot shows the normalized expression of the genes (left) with the normalized expression of the corresponding proteins (right). Often protein expression is very strong with background noise due to non-specific hybridization of the antibodies. To solve this problem we can modify the cutoff of the legend by quantiles.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/ADT_results/ADT_dimplot_legend_cutoff.png

This plot shows exactly the same things, but with a legend cutoff (default parameters).

TCR

Provided by the Adding_TCR step.

There are several ways to define a clonotype:

  • gene: use the genes comprising the TCR.
  • nt: use the nucleotide sequence of the CDR3 region.
  • aa: use the amino acid sequence of the CDR3 region.
  • gene+nt: use the genes comprising the TCR + the nucleotide sequence of the CDR3 region for T cells. This is the proper definition of clonotype.

Global analysis

Unique Contigs Quantification
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/quantUniqueContig.png

This group of graphs represents the number of different (unique) clonotypes in the sample.

Here, we can observe that almost all contigs are present in a single copy (they are unique), regardless of the definition of clonotypes chosen. For clonotypes defined only by their gene, we observe 444 unique contigs out of a total of 451 contigs for sc5p_v2_hs_PBMC_1k_5gex_GE and 4112 unique contigs out of a total of 4443 contigs for sc5p_v2_hs_PBMC_10k_5gex_GE.

Clonotypes Abundance
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/abundanceContig.png

This group of line graphs represents the number of clonotypes depending on the number of cells where the contig is present. The points of the graph are connected.

The interpretation is the same as for the TCR part of the individual analysis of samples, but we have the 2 samples present on the graphs.

Here we have mostly single clonotypes, present in a single cell, which is represented by a dot at over 400 clonotypes with an abundance of 1 for sc5p_v2_hs_PBMC_1k_5gex_GE sample and at over 4000 clonotypes with an abundance of 1 for sc5p_v2_hs_PBMC_10k_5gex_GE sample. It is in agreement with the previous graph of unique contigs.

Clonal Space Homeostasis
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/clhomeo.png

By examining the clonal space, we are effectively looking at the relative space occupied by clones at specific proportions. Another way to think about this would be thinking of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different number of clonal proportions. Clonal space homeostasis is asking what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy).

The interpretation is the same as for the TCR part of the individual analysis of samples, but we have the 2 samples present on the graphs.

Clonal Proportion
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/clprop.png

Like clonal space homeostasis above, clonal proportion acts to place clones into separate bins. The key difference is instead of looking at the relative proportion of the clone to the total, the clonalProportion() function will rank the clones by total number and place them into bins. Example: [1:10] are the top 10 clonotypes in each sample.

The interpretation is the same as for the TCR part of the individual analysis of samples, but we have the 2 samples present on the graphs.

Clonotypes Frequencies
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_top_10_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_top11to20_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_top_10_umapsc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_top11to20_umapsc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_top_10_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_top11to20_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/Frequency_umapsc5p_v2_hs_PBMC_Int_Seurat.png

The frequency represents the number of cells that contain the clonotype based on its amino acid sequence. We have theses results for each sample and the integration.

Here, we can confirm the observations of the individual analysis of each sample. The localization of clonotypes on the umap confirms the T cell annotation of the clusters. We also observe that not all T cells have an identified TCR.

CDR3 Length
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/lengthContig.png

This graph represents the length distribution of the CDR3 sequences (combined or separate chains) for each sample.

The interpretation of this type of graph depends on the biological context.

Clonotypes Decomposition
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/cloneType_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/cloneType_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/cloneType_sc5p_v2_hs_PBMC_Int_Seurat.png

These groups of graphs represents several umap with the location of each part of the TCR and the size of the TRA and TRB, for each sample and the integration.

The interpretation of this type of graph depends on the biological context.

Diversity
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/cldiv.png

This graph represents the measures the diversity of clonotypes within the sample. It is provided by 4 metrics (Shannon, inverse Simpson, Chao1, and Abundance-based Coverage Estimator (ACE)) for each sample.

The interpretation of this type of graph depends on the biological context.

Physicochemical Properties
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Global_analysis/aaProperties.png

This group of graphs represents a list the physicochemical properties of the amino acids that make up the receptors.

The interpretation of this type of graph depends on the biological context.

Clusters analysis

Unique Contigs Quantification
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/clust_quantContig_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/clust_quantContig_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_quantContig_sc5p_v2_hs_PBMC_Int_Seurat.png

This group of graphs are similar to that of the globale analysis, but by clusters, and for each sample and the integration, so the interpretation is similar too.

Clonotypes Abundance
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_abundanceContig.png

This group of graphs are similar to that of the globale analysis, but by clusters for the integration, so the interpretation is similar too.

Clonal Space Homeostasis
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/clust_clhomeo_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/clust_clhomeo_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_clhomeo_sc5p_v2_hs_PBMC_Int_Seurat.png

This group of graphs are similar to that of the globale analysis, but by clusters, and for each sample and the integration, so the interpretation is similar too.

Clonal Proportion
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/clust_clprop_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/clust_clprop_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_clprop_sc5p_v2_hs_PBMC_Int_Seurat.png

This group of graphs are similar to that of the globale analysis, but by clusters, and for each sample and the integration, so the interpretation is similar too.

Clonotypes Frequencies
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/Frequency_top_10_clust0_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/Frequency_top_10_clust3_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/Frequency_top_10_clust4_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/Frequency_top_10_clust6_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/Frequency_top_10_clust10_umapsc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/Frequency_top_10_clust0_umapsc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/Frequency_top_10_clust2_umapsc5p_v2_hs_PBMC_10k_5gex.png

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/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/Frequency_top_10_clust6_umapsc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/Frequency_top_10_clust7_umapsc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/Frequency_top_10_clust10_umapsc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust0_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust2_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust3_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust4_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust5_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust6_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust7_umapsc5p_v2_hs_PBMC_Int_Seurat.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/Frequency_top_10_clust10_umapsc5p_v2_hs_PBMC_Int_Seurat.png

This group of graphs are similar to that of the globale analysis, but by clusters, and for each sample and the integration, so the interpretation is similar too.

Overlap
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/clust_clOverlap_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/clust_clOverlap_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_clOverlap_sc5p_v2_hs_PBMC_Int_Seurat.png

The graph represents the percentages of common clonotypes between 2 clusters (scaled to the number of unique clonotypes in the smaller cluster). The tables which present the number and the sequence of common clonotypes between the clusters are not shown here but are computed too.

The interpretation is exactly the same as individual analysisof sample.

Diversity
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/clust_cldiv_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/clust_cldiv_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_cldiv_sc5p_v2_hs_PBMC_Int_Seurat.png

This group of graphs are similar to that of the globale analysis, but by clusters, and for each sample and the integration, so the interpretation is similar too.

Physicochemical Properties
/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_1k_5gex/clust_aaProperties_sc5p_v2_hs_PBMC_1k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/sc5p_v2_hs_PBMC_10k_5gex/clust_aaProperties_sc5p_v2_hs_PBMC_10k_5gex.png

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/TCR_results/Clusters_analysis/clust_aaProperties_sc5p_v2_hs_PBMC_Int_Seurat.png

This group of graphs are similar to that of the globale analysis, but by clusters, and for each sample and the integration, so the interpretation is similar too.

BCR

Provided by the Adding_BCR step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/BCR_results/

The results provided for the BCRs are the same as for the TCR analysis. So I will not explain again. We observe that all the clonotypes are unique except 3 (1 in sc5p_v2_hs_PBMC_1k_5gex and 2 in sc5p_v2_hs_PBMC_10k_5gex, and isn't the same clonotype). In addition, the clonotypes colocalize well with the cluster of B lymphocytes identified with the annotation. Cluster analysis is not very interesting because we have only one cluster of B lymphocytes in this example. There are one clonotype in cluster 2 and one another in cluster 10, but they are probably artefacts.

Cerebro

Provided by the Cerebro step.

/mnt/beegfs/userdata/m_aglave/pipeline/single-cell/examples/complete_grouped_integrated_analysis_example_of_wiki/Results/GROUPED_ANALYSIS/INTEGRATED/sc5p_v2_hs_PBMC_Int_Seurat/NORMKEPT/pca/dims29_res0.6/sc5p_v2_hs_PBMC_Int_Seurat_SCTransform_pca_26_0.6_ADT_TCR_BCR.crb

Cerebro file can be loaded into CerebroApp R Shiny to exploit the main results.

Notes

  • The cerebro file is not present in the results because its size exceed the threshold of 50 mb of github to store it.

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Resources of the Theory of single cell RNA-seq

v1.3
Pipeline details
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Usage
Configuration
Results help
Complete Examples of school cases
Individual analysis :
1 sample (scRNA-seq + ADT + TCR + BCR)
Grouped/Integrated analysis :
2 samples (scRNA-seq + ADT + TCR + BCR)

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