edit description

Author: lskfs

05.hotspot/README.md

@@ -4,10 +4,10 @@
 **step 1:** extract stereo-seq data as h5ad file named 'Styela_clava.stereo.h5ad'
 > The h5ad data have been deposited at CNGBdb (https://db.cngb.org/) under the accession number CNP0004228
 
-
 **step 2:** run the following code which will read in previous h5ad file
 ```shell
 $ python3 performHotspot.py
 ```
+
 > [!NOTE]
 > Only one slice was calculated in this code.

06.spearman_correlation/README.md

@@ -1,4 +1,5 @@
 
+# How to run
 
 ### step 1. reciprocal blast using S. clava and zebrafish protein sequences
 
@@ -46,6 +47,7 @@ python3 correlation_estimation.py \
     -figname corr.single
 ```
 ![Alt text](./img/corr_single.png)
+
 - for one-to-many version correlation
 ```shell
 python3 correlation_estimation.py \

10.cross-species_comparison/README.md

@@ -1,7 +1,7 @@
 
 # How to run
 
-### Pre-process and integrate zebrafish datasets
+## *Pre-process and integrate zebrafish datasets
 
 > The zebrafish datasets were collectted from [Farnsworth et al.](https://www.sciencedirect.com/science/article/pii/S0012160619304919), [Qian et al.](https://link.springer.com/article/10.1007/s00018-022-04410-2) and [Gillotay et al.](https://www.embopress.org/doi/full/10.15252/embr.202050612).
 
@@ -19,7 +19,8 @@ $ Rscript zebrafish.subset.R
 
 this will generate the final zebrafish dataset which shall be used in our comparison analysis, including files `zebrafish.final.rds` and `zebrafish.final.loom`
 
-### SAMap comparison for thyroid like cell and hair cell-like cell
+
+## *SAMap comparison for thyroid like cell and hair cell-like cell
 
 **step 1:** *pseudo-metacell* aggregation and data subsetting
 
@@ -71,17 +72,19 @@ $ awk -F '\t' 'BEGIN{print "source\ttarget\tValue"}{if(NR==1){source=$2}else{pri
 $ Rscript sankeyPlot.R -i TLC.for_plot.txt
 ```
 ![TLC sankey](./img/TLC.sankey.png)
+
 - for HCLC cells
 ```shell
 $ Rscript sankeyPlot.R -i HCLC.for_plot.txt
 ```
 ![HCLC sankey](./img/HCLC.sankey.png)
-### Additional intepretion
+
+
+## *Additional intepretion
 
 As the result of cross-species comparison largely depend on quality of the dataset we can access, it is necessary to perform with reasonalble filteration process.
 
 In our case, both the zebrafish thyroid data and cochlea data somewhat co-embedded with nervous and immune cells from the zebrafish developmental dataset, as shown in below figure, which might caused by residual sampling.
-
 ![splitted umap for thyroid and cochlea cell types](./img/umap.split.png)
 
 So we use diverse filtering criteria for different purpose to avoid this problem. And the final used cell groups in each task was labeled in their code or description seperately.