This file details all steps of the analysis.
cd ~/Downloads
mkdir RNAseq
# this will be the project directory
cd RNAseq
mkdir data
cd data
mkdir memonet_data AIBS_data
cd ~/Downloads/RNAseq
mkdir memonet_github_repo
cd memonet_github_repo
git clone [email protected]:zhanglab/memonet.git
You can find scripts here: ~/Downloads/RNAseq/memonet_github_repo/memonet/scripts
For the full list for all dependencies by script, see memonet/scripts/README
Cell Ranger v4.0.0: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/installation
- make note of download location
Seurat v4: https://satijalab.org/seurat/articles/install.html
DESC v2.1.1: https://eleozzr.github.io/desc/installation.html
DESeq2 v1.36.0: https://bioconductor.org/packages/release/bioc/html/DESeq2.html
clusterProfiler v4.2.2: https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
cd ~/Downloads/RNAseq/data/memonet_data
mkdir data_download
MEMONET dataset:
- Location of data: https://docs.google.com/spreadsheets/d/1mU7l8Oj-Fr4FYE6IlmTcX_s2YdCtZXx0GgJI3jBE3J4/edit#gid=0
- Script for downloading: download.sh
- Download to: ~/Downloads/RNAseq/data/memonet_data/data_download
AIBS dataset:
- Location of data: /data/zhanglab/AWS-bkup/MEMONET/data_BICCN/10X-v3_sn_AIBS/
- Download to: ~/Downloads/RNAseq/data/AIBS_data
- Rename the files to: aibs_barcodes.tsv, aibs_genes.tsv, aibs_matrix.mtx
Use Cell Ranger's aggr command to combine the data of all mice:
- Wd: ~/Downloads/RNAseq/data/memonet_data
- Generate a libraries.csv file with the locations of each mouse's data, in the following format:
- the order of rows determines the suffix attached to the barcodes of each mouse (in order to differentiate which mouse is which in the combined file), ie mouse 262 will have -1, 263 -2, etc
- trained mice: 263,277,279 ie -2,-4,-6
- control mice: 262,276,278 ie -1,-3,-5
- Run aggr command:
- First run this line on the terminal so that Cell Ranger package can be found, replacing the path with the location of your Cell Ranger download location:
export PATH=<path to cellranger download>/cellranger-4.0.0:$PATH- Then run aggr. --none turns off depth normalization, due to the requirement of the DESC clustering package needing unnormalized counts as input. --id is the name of the output file
nohup srun -o jobs%j.out -c 20 \cellranger aggr --csv=/data/memonet_data/libraries.csv --none --id=combined_cellranger_no-normalization &- The output directory will be found here: ~/Downloads/RNAseq/data/memonet_data/combined_cellranger_no-normalization/outs/filtered_feature_bc_matrix
cd ~/Downloads/RNAseq
mkdir QC
Script: QCthresholds.r
- Thresholds were chosen based on violin plots of the data before QC
Wd: ~/Downloads/RNAseq/QC
Input:
- Data directory for each mouse, ie ~/Downloads/RNAseq/data/memonet_data/data_download/slPsiwmg_JB_262_1_2_3/filtered_feature_bc_matrix/
Output:
- ctrl_without_cutoff.png: violin plots of the 3 control mice before QC
- QC_ctrl.png: violin plots of the 3 control mice after QC
- train_without_cutoff.png
- QC_train.png
The number of cells removed with each filter step are given with filterSteps.r
Input:
- MEMONET dataset: ~/Downloads/RNAseq/data/memonet_data/combined_cellranger_no-normalization/outs/filtered_feature_bc_matrix/
Output:
- Numbers are output on R console
cd ~/Downloads/RNAseq
mkdir AIBSmapping
cd AIBSmapping
mkdir OA test
# 'OA' refers to Our (MEMONET) data mapped to AIBS data
cd OA
mkdir barcode_files count_matrices
Script: mapping_OA.r
Wd: ~/Downloads/RNAseq/AIBSmapping/OA
Query: MEMONET data
Reference: AIBS data
Input:
- 10X directory of all mice combined: ~/Downloads/RNAseq/data/memonet_data/combined_cellranger_no-normalization/outs/filtered_feature_bc_matrix/
- AIBS dataset. The three files here: ~/Downloads/RNAseq/data/AIBS_data
Output:
- prediction_scores.csv: lists each cell, the predicted cell type label, and prediction scores for each cell type
- umap_AIBS_subclassLabelLegend.png: umap of the reference (AIBS data) with cell type subclass labels
- umap_MEMONET_subclassLabelLegend.png: umap of the query (MEMONET data) projected onto AIBS space, labeled with the predicted labels
1. Generate a test dataset (downsample to 25% of each cell type; remove sample cells from rest of reference) and perform label transfer from the remaining 75% of data. Do this 100 times.
Query: 25% of AIBS data
Reference: remaining 75% of AIBS data
Script: mapping_test1.r
Wd: ~/Downloads/RNAseq/AIBSmapping/test
Input:
- AIBS dataset. The three files here: RNAseq/data/AIBS_data
Output:
- 100 prediction_scores_*.csv files
Script: mapping_test2.r
Wd: ~/Downloads/RNAseq/AIBSmapping/test
Input:
- 100 prediction_scores_*.csv files
Output:
- prediction_cutoff.csv
3. Summarize results: calculate false classification percentage, generate confusion matrix, compare mean and median scores of the test and OA mapping
Script: mapping_test3.r
Wd: ~/Downloads/RNAseq/AIBSmapping/test
Input:
- ~/Downloads/RNAseq/AIBSmapping/test/prediction_cutoff.csv
- ~/Downloads/RNAseq/AIBSmapping/OA/prediction_scores.csv
Output:
- AIBStest_confusionMtx.png: heatmap of original cell type labels vs predicted labels for AIBS testing
- maxPredictionScores-AIBStest_and_OA.csv: table of mean and median prediction.score.max for AIBS testing and OA mapping; shows calculation for all celltypes and subset for L2/3 cells
Each MEMONET cell gets a prediction score for each cell type in the AIBS dataset; all scores add up to 1. The cell type with the largest score becomes that cell's predicted cell type. To ensure we analyze only L2/3 neurons, we want to remove cells with low reliability for being labeled as L2/3. Reliability is calculated as the sum of prediction scores for L2/3 IT_1, L2/3 IT_2, and L2/3 IT_3.
Script: prediction_score_cutoff_barcodes.r
- This script generates barcode lists for various prediction score cutoff values (0 to 0.5). Wd: ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/
Input:
- ~/Downloads/RNAseq/AIBSmapping/OA/prediction_scores.csv
Output:
- One file for each cutoff, listing the cell barcodes remaining: L23barcodes-fromAIBS_*.csv
Script: DESCnormalization.r
Wd: ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices
Set the 'cutoff' variable to '0' so that all cells predicted to be L2/3 are kept for normalization.
Input:
- Barcode file for all predicted L2/3 cells: ~/Downloads/memonet/prediction_score_cutoff_barcodes/L23barcodes-fromAIBS_0.csv
Output:
- DESCnormalized_counts_L23_0.csv
Script: celltype_contaminants.r
- This script calculates the mean expression of L2/3 marker genes (Cux2, Otof, Rtn4rl1, Slc30a3, Cacna2d3) and glial marker (Mertk) for the cells that are removed at each cutoff. We want to choose a cutoff that removes cells with low values of L2/3 markers but high values of Mertk, to ensure that mostly glial cells are removed.
Wd: ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/
Input:
- Barcode files for each cutoff: ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/L23barcodes-fromAIBS_*.csv
- Normalized expression: ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/DESCnormalized_counts_L23_0.csv
Output:
- markerGeneExp_predictionCutoffs.csv: table listing the average expression of each marker gene for each cutoff.
We chose a cutoff value of 0.3, as it has the highest mean expression of Mertk while having low expression of L2/3 markers. Any cell that was predicted to be L2/3 and has a sum of prediction scores for L2/3 IT_1, L2/3 IT_2, L2/3 IT_3 <= 0.3 will not be included in downstream analysis.
Script: dataset_descriptions.r, Part A
Functions:
- calculate cell subclass proportion (neurons and glia)
- calculate inhibitory neuron percentage
- calculate neuron subclass proportion (neurons only)
- plot neuron subclass proportion, comparing MEMONET dataset and AIBS
Input:
- AIBS metadata: ~/Downloads/RNAseq/data/AIBS_data/aibs_barcodes.tsv
- cell type predictions for MEMONET data: ~/Downloads/RNAseq/AIBSmapping/OA/prediction_scores.csv
Output:
- plot of neuronal subclass proportions: ~/Downloads/RNAseq/AIBSmapping/OA/CelltypePropPerDataset.png
Script: statistics.r
Wd: ~/Downloads/RNAseq/AIBSmapping/OA/
Input:
- Choose an input file at the beginning of script. For this part calculating L2/3 proportions, use ~/Downloads/RNAseq/AIBSmapping/OA/prediction_scores.csv
- L2/3 barcode list: ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/L23barcodes-fromAIBS_0.3.csv
Output:
- L23subclass_tr_ctrl_prop_hist.png
- 'summary' variable lists p-values
Script: DESCnormalization.r
Set 'cutoff' variable to '0.3'
Input:
- MEMONET data: ~/Downloads/RNAseq/data/memonet_data/combined_cellranger_no-normalization/outs/filtered_feature_bc_matrix
Output:
- ~/Downloads/RNAseq/QC/cells_after_QC.csv: list of cells remaining after QC steps on whole dataset. Needed for DE analysis of clusters
- ~/Downloads/RNAseq/QC/genes_after_QC.csv: list of genes remaining after QC steps. Needed for the background gene list for GO analyses
- ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/unnormalized_counts_L23_0.3.csv: unnormalized counts of L2/3 cells, 0.3 cutoff
- ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/DESCnormalized_counts_L23_0.3.csv: normalized counts of L2/3 cells, 0.3 cutoff. This will be used as input for classifier training
- ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/sampleIDs_0.3.csv: list of cells and mouse ID, for reference during classifier training
- ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/sampleIDs_0.3_transpose.csv: same as above but the transposed version
PlotLogisticFunction.m
- Plot the logistic function
ShuffledLogisticClassifier.m
- Test the classifier accuracy when cell labels ('train' or 'control') have been shuffled 100x
TopGeneOptimization.m
- Makes the prediction accuracy optimization curve by looping over different numbers of top genes (sorted by their logistic regression indices)
- Input: memonet/downloads/LogisticRegressionIndices.xlsx
- This file lists the logistic regression weights for each gene, where indices match the order of genes in ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/DESCnormalized_counts_L23_0.3.csv
Classifier_byAnimal.m
- Calculates the prediction accuracy of the logistic classifier for each animal
cd ~/Downloads/RNAseq/
mkdir cluster_by_genes
cd cluster_by_genes
mkdir 0.3cutoff
Location on github: memonet/downloads/PredictionGenesDescending0.3.csv
- This file contains all genes in the dataset, ranked by their weight in the linear classifier. We will subset to the top 3000 as the EDGs
Download to: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff
Script: DESC_inputFormat.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff
Input:
- Ranked gene list: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/PredictionGenesDescending0.3.csv
- L2/3 normalized counts: ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/DESCnormalized_counts_L23_0.3.csv
Output: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/L23_0.3_EDGmtx.csv
We varied the n_neighbors (n) and louvain_resolution (L) parameters and calculated silhouette scores of the resulting clusters in order to find the best parameters for clustering our data.
cd ~/Downloads/RNAseq/cluster_by_genes/
mkdir DESC_parameter_test
Script: DESC_EDGclustering_loop.py
- This script loops through various parameter combinations
Wd: ~/Downloads/RNAseq/cluster_by_genes/DESC_parameter_test/
Input:
- Normalized L2/3 counts: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/L23_0.3_EDGmtx.csv
Output: n represents the value set for n_neighbors; L represents the value set for louvain_resolution
- cluster_n*.L*.csv
- umap_n*.L*.csv
- sil_scores_n*.L*.csv
Calculate average silhouette score for each clustering result, and visualize scores with respect to the n and L parameters.
Wd: ~/Downloads/RNAseq/cluster_by_genes/DESC_parameter_test/
Script: DESCparameterSummary.r
Input:
- Silhouette scores for each run: ~/Downloads/RNAseq/cluster_by_genes/DESC_parameter_test/sil_scores_n*.L*.csv
Output:
- parameter_silhouette_score_avgs.csv: summary of the silhouette score and number of clusters produced with each parameter setting combination
- heatmap_silScore.png: a heatmap showing the average silhouette score at each combination of n and L parameters
Most scores are very good. A score of -1 indicates wrong clustering, +1 indicates correct clustering, and 0 indicates overlapping clusters.
Script: sil_score_smooth.py
- Courtesy of Dr. EunJung Hwang
Wd: ~/Downloads/RNAseq/cluster_by_genes/DESC_parameter_test/
Input:
- Silhouette score summary: ~/Downloads/RNAseq/cluster_by_genes/DESC_parameter_test/parameter_silhouette_score_avgs.csv
Output:
- Smooth_Silhouette.png: left plot is a heatmap of silhouette scores by n and L parameters (similar to heatmap_silScore.png); right plot is a smoothed version, with red x indicating the peak
Running a smoothing algorithm on the heatmap of silhouette scores by n and L parameters reveals n=25 and L=0.65 to be at the peak of scores. Therefore, we chose n=25 and L=0.65.
cd ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff
mkdir DESC
Script: DESC_EDGclustering.py
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC
Parameters are set to:
- n_neighbors = n = 25
- louvain_resolution = L = 0.65
Input:
- Normalized expression matrix, subset to EDGs: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/L23_0.3_EDGmtx.csv
Output:
- clusters_n25.L0.65.csv: lists each cell barcode and the cluster it is assigned
- umap_n25.L0.65.csv: umap x and y coordinates; row indices correspond to the barcode order in clusters_n25.L0.65.csv
- tsne_n25.L0.65.csv: tsne x and y coordinates
- desc_result.n25.L0.65.h5ad: AnnData object containing the information produced by clustering, including: cluster assignments, umap and tsne coordinates, etc. This will be used for trajectory analysis later
- result_DESC.n25.L0.65/: directory for encoder weights and model info
- figures/
- umap0.65desc.n25.L0.65.png: visual of cluster umap projection
Script: classifier_umap_plot.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/figures
Input:
- Cluster file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
- Umap coordinate file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/umap_n25.L0.65.csv
Output: classifier_umap_tr_ctrl.svg
Script: statistics.r
- Plot train/control proportions across cluster and calculate p-values for significance
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/figures
Input:
- Choose an input file at the beginning of script. For this part calculating cluster train/control proportions, use ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- cluster_tr_ctrl_prop_hist.svg: bar plot of train/control proportion across cluster
- ClusterMouseProp_mouseDots.csv: file containing data for above plot, plus proportion of each mouse across clusters
- 'summary' variable lists p-values
Script: ClusterMouseProp.py
- Plot train/control proportions across cluster and have individual mouse proportions overlaid
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/figures
Input:
- ClusterMouseProp_mouseDots.csv
Output:
- ClusterMouseProp_mouseDots.svg: bar plot of train/control proportion across cluster, with dots for each mouse overlaid
Script: dataset_descriptions.r, Part D
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/figures
Input:
- Clusters file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- cluster_percentage_tbl.csv
What biological processes are supported by the 3000 EDGs?
cd ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff
mkdir GO
cd GO
mkdir EDG clusters
Script: GOvisualization.r, part A; see GoTermAnalysis.m (courtesy of Dr. Nathan Hedrick) for generating manuscript GO term figures
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/EDG
Input:
- Ranked gene list (containing the EDGs): ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/PredictionGenesDescending0.3.csv
- Background gene list: ~/Downloads/RNAseq/QC/genes_after_QC.csv. The background gene list represents all genes in the dataset.
Output:
- EDG_GO.csv: GO results for the 3000 EDGs
- EDG_GO.png: treeplot showing GO results for the 3000 EDGs
What biological processes are supported by the unique EDGs (those that do not appear in the DE results of L2/3 train vs control)?
Script: GOvisualization.r, part A-2
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/EDG
Input:
- Ranked gene list (containing the EDGs): ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/PredictionGenesDescending0.3.csv
- Background gene list: ~/Downloads/RNAseq/QC/genes_after_QC.csv. The background gene list represents all genes in the dataset.
- DEGs from L2/3 tr vs ctrl: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/L23_0.3_tr_vs_ctrl/train_vs_control_sig_genes.csv
Output:
- L2/3 tr vs ctrl DEG results with an extra column specifying whether the DEG is also an EDG: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/L23_0.3_tr_vs_ctrl/train_vs_control_sig_genes_EDGcolumn.csv
- GO results for the unique EDGs: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/EDG/uniqueEDG_GO.csv
Script: dataset_descriptions.r, Part B
Input: data_neurons variable from Part A
Output:
- ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/glut_barcodes.csv
- ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/GABA_barcodes.csv
- ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/L5_barcodes.csv
- ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/pvalb_barcodes.csv
- ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/sst_barcodes.csv
- ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/vip_barcodes.csv
cd ~/Downloads/RNAseq/AIBSmapping/OA
mkdir DESeq2
cd DESeq2
mkdir glut_tr_vs_ctrl GABA_tr_vs_ctrl L5_tr_vs_ctrl pvalb_tr_vs_ctrl sst_tr_vs_ctrl vip_tr_vs_ctrl
Script: DESeq2_tr_vs_ctrl.r
DE analysis: Run the script in each subtype folder
Wd: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/_tr_vs_ctrl
- i.e. L5_tr_vs_ctrl
Input: ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/_barcodes.csv
- i.e. L5_barcodes.csv
Output:
- unnormalized_counts_from_dds.csv: unnormalized gene expression
- normalized_sizeFactors_calculateSumFactors.csv: size factors that generate the normalized data
- normalized_counts_from_dds.csv: normalized gene expression
- train_vs_control_all_genes.csv: DESeq2 results for all genes
- train_vs_control_sig_genes.csv: DESeq2 results for significant genes (padj <0.05)
Rename the train_vs_control_sig_genes.csv file with the neuron subtype appended to the end like so: train_vs_control_sig_genes_pvalb.csv, train_vs_control_sig_genes_sst.csv, etc
cd ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2
mkdir L23_0.3_tr_vs_ctrl
Script: DESeq2_tr_vs_ctrl.r
Wd: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/L23_0.3_tr_vs_ctrl
Input: L2/3 barcode list: ~/Downloads/RNAseq/AIBSmapping/OA/barcode_files/L23barcodes-fromAIBS_0.3.csv
Output:
- unnormalized_counts_from_dds.csv: unnormalized gene expression
- normalized_sizeFactors_calculateSumFactors.csv: size factors that generate the normalized data
- normalized_counts_from_dds.csv: normalized gene expression
- train_vs_control_all_genes.csv: DESeq2 results for all genes
- train_vs_control_sig_genes.csv: DESeq2 results for significant genes (padj <0.05)
Script: IEGcomparison.r
Input:
- DE results for each neuron type -L2/3: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/L23_0.3_tr_vs_ctrl/train_vs_control_sig_genes.csv -L5: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/L5_tr_vs_ctrl/train_vs_control_sig_genes_L5.csv -Pvalb: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/pvalb_tr_vs_ctrl/train_vs_control_sig_genes_pvalb.csv -Sst: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/sst_tr_vs_ctrl/train_vs_control_sig_genes_sst.csv -Vip: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/vip_tr_vs_ctrl/train_vs_control_sig_genes_vip.csv
Output:
- Table listing the IEGs significantly up- or down-regulated in each neuron type: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/IEGcomparison_neuronTypes2.csv
Script: dataset_descriptions.r, part C
Input:
- Ranked gene list: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/PredictionGenesDescending0.3.csv
- L2/3 DEG list: ~/Downloads/RNAseq/AIBSmapping/OA/DESeq2/L23_0.3_tr_vs_ctrl/train_vs_control_sig_genes.csv
Output: The length of DEGoverlap$gene will tell you how many genes are shared between the two gene sets
cd ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC
mkdir DESeq2
cd DESeq2
mkdir n25.L0.65
Script: DESeq2_clusterX_vs_others.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65
Input:
- Cluster file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
- unnormalized counts file: ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/unnormalized_counts_L23_0.3.csv
Output directory: all_cells/
- unnormalized_counts_from_dds.csv: unnormalized gene expression
- normalized_sizeFactors_calculateSumFactors.csv: size factors that generate the normalized data
- normalized_counts_from_dds.csv: normalized gene expression
- *_vs_others_all_genes.csv: DESeq2 results for all genes
- *_vs_others_sig_genes.csv: DESeq2 results for significant genes (padj <0.05)
Combine cluster result files into one file:
Script: DESeq2-table.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells
Input: files ending in *_all_genes.csv
Output:
- DEGstats_allGenes.csv: all genes
- DEGstats_padj0.05.csv: significant genes (padj <0.05)
DEGvisuals.r, Part A
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/
Input:
- DE results: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/DEGstats_allGenes.csv
- DESeq2 normalized counts: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/normalized_counts_from_dds.csv
- Cluster file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- IEG_lineplot_zscore.png: line plot indicating the average z-scored expression value of IEGs across clusters. DE significance is indicated by open or closed dot.
cd ~/Downloads/RNAseq/AIBSmapping
mkdir AO
# 'AO' stands for AIBS onto Our (MEMONET) cells
cd AO
mkdir n25.L0.65
Script: mapping_AO.r
Wd: ~/Downloads/RNAseq/AIBSmapping/AO/n25.L0.65
Input:
- MEMONET data: ~/Downloads/RNAseq/data/memonet_data/combined_cellranger_no-normalization/outs/filtered_feature_bc_matrix/
- AIBS dataset. The three files here: ~/Downloads/RNAseq/data/AIBS_data
- Cluster file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
- Umap coordinate file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/umap_n25.L0.65.csv
Output:
- prediction_scores.csv: prediction score for the AIBS cells onto the clusters
- Images:
- umap_MEMONETL23.png: MEMONET cells visualized in the DESC-generated umap space
- umap_AIBSL23.png: AIBS cells placed in classifier space, colored by their predicted cluster
What proportion of AIBS cells map to each cluster? Script: dataset_description.r, Part E
Wd: ~/Downloads/RNAseq/AIBSmapping/AO/n25.L0.65
Input: ~/Downloads/RNAseq/AIBSmapping/AO/n25.L0.65/prediction_scores.csv
Output:
- AIBSpie.svg: pie chart of proportions
- AIBSmap_table: summary table
- AIBS_L23_mappedToClusters.csv: counts of AIBS L2/3 subtypes mapped to C0-5 (Table 6)
Script: DESeq2_clusterX_vs_clusterY.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/
Input:
- Cluster file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Set 'ref' variable to '0' to indicate cluster 0 as the baseline comparison
Output directory: x_vs_0/all_cells/
- unnormalized_counts_from_dds.csv: unnormalized gene expression
- normalized_sizeFactors_calculateSumFactors.csv: size factors that generate the normalized data
- normalized_counts_from_dds.csv: normalized gene expression
- x_vs_0_all_genes.csv: DESeq2 results for all genes
- x_vs_0_sig_genes.csv: DESeq2 results for significant genes (padj <0.05)
Combine cluster result files into one file: Script: DESeq2-table.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/x_vs_0/all_cells/
Input: files ending in *_all_genes.csv
Output:
- DEGstats_allGenes.csv: all genes
- DEGstats_padj0.05.csv: significant genes (padj <0.05)
DEGvisuals.r, Part B
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/x_vs_0/all_cells/
Input:
- DE results: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/x_vs_0/all_cells/DEGstats_allGenes.csv
- DESeq2 normalized counts: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/x_vs_0/all_cells/normalized_counts_from_dds.csv
- Cluster file: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- IEG_lineplot_refC0.png: line plot indicating the LFC value of IEGs across clusters. DE significance is indicated by open or closed dot. LFC is a metric used by DESeq2 to assess how much a gene’s expression has changed between the two comparisons. A positive value indicates upregulation in comparison to C0 while negative indicates downregulation
Script: GOvisualization.r, Part B; see GoTermAnalysis.m for generating manuscript GO term figures
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/clusters/n25.L0.65/x_vs_0
Set 'regulation' variable to 'up' in order to analyze the upregulated genes only
Input:
- Background gene list: ~/Downloads/RNAseq/QC/genes_after_QC.csv
- DE results from x vs 0 scenario: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/x_vs_0/all_cells/DEGstats_padj0.05.csv
Output:
- GOresults_n25.L0.65_Bio-upregDEG.csv: csv file of results for all clusters
-
- up_treeBio_n25.L0.65.png: tree plot showing the top 30 most significant GO terms; one tree plot for each cluster
C4's tree plot shows terms characterizing other cell types. Perhaps this cluster contains contaminants that were not filtered out by the L2/3 prediction cutoff threshold.
Script: celltype_contaminants.r, Part B
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/
Input:
- Normalized counts: ~/Downloads/RNAseq/AIBSmapping/OA/count_matrices/DESCnormalized_counts_L23_0.3.csv
- Clusters: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- L23_glial_marker_exp.csv
Script: GOvisualization.r, Part C.1; see GoTermAnalysis.m for generating manuscript GO term figures
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/clusters/n25.L0.65/x_vs_0
Input:
- GO results: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/clusters/n25.L0.65/x_vs_0/GOresults_n25.L0.65_Bio-upregDEG.csv
- DESeq2-normalized counts: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/normalized_counts_from_dds.csv
- Clusters: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- GO_line.png: line plot showing the average z-scored gene expressions in each cluster per GO term
- GO_heatmap.png: heatmap for each GO term showing the z-scored expression value of each gene in the GO term per cluster
Script: GOvisualization.r, Part C.2; see GoTermAnalysis.m for generating manuscript GO term figures
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/clusters/n25.L0.65/x_vs_0
Input:
- GO results: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/GO/clusters/n25.L0.65/x_vs_0/GOresults_n25.L0.65_Bio-upregDEG.csv
- DESeq2-normalized counts: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/normalized_counts_from_dds.csv
- Clusters: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/clusters_n25.L0.65.csv
Output:
- C5_GOtermLinePlot_zscore.png: line plot showing the average z-scored gene expressions in each cluster per GO term
- C5_GOtermHeatmap.png: heatmap for each GO term showing the z-scored expression value of each gene in the GO term per cluster
Note: Glycolysis and NE (norepinephrine) receptors are not GO terms, we just visualize in the same way as GO terms to show the trends.
Location on github: memonet/downloads/DG_React_NotReact.csv
- This file is from Jaeger et al. (2018) Supplementary table 3, tab ‘DG_React_NotReact’
Download to: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/
Script: reactivation_score.r
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/
Input:
- DE results of each cluster vs the others: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/DEGstats_padj0.05.csv
- Reactivation gene list: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/DESeq2/n25.L0.65/all_cells/DG_React_NotReact.csv
Output:
- ReactivationScores.csv
- reactivation_line.png: line plot indicating the reactivation score of each cluster
- reactivation_matches.png: bar plot indicating the number of DEGs in each cluster that either match or don't match the sign of the reference list
Script: Trajectory_Analysis.ipynb
- Courtesy of Dr. Rudiyanto Gunawan
Use this Jupyter Notebook script to run trajectory analysis and visualize plots.
Wd: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/
Input:
- AnnData object containing clustering results: ~/Downloads/RNAseq/cluster_by_genes/0.3cutoff/DESC/desc_result.n25.L0.65.h5ad
Output:
- Plots are visualized within the Jupyter Notebook